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Torrik A, Zarif M. Machine learning assisted sorting of active microswimmers. J Chem Phys 2024; 161:094907. [PMID: 39225539 DOI: 10.1063/5.0216862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
Active matter systems, being in a non-equilibrium state, exhibit complex behaviors, such as self-organization, giving rise to emergent phenomena. There are many examples of active particles with biological origins, including bacteria and spermatozoa, or with artificial origins, such as self-propelled swimmers and Janus particles. The ability to manipulate active particles is vital for their effective application, e.g., separating motile spermatozoa from nonmotile and dead ones, to increase fertilization chance. In this study, we proposed a mechanism-an apparatus-to sort and demix active particles based on their motility values (Péclet number). Initially, using Brownian simulations, we demonstrated the feasibility of sorting self-propelled particles. Following this, we employed machine learning methods, supplemented with data from comprehensive simulations that we conducted for this study, to model the complex behavior of active particles. This enabled us to sort them based on their Péclet number. Finally, we evaluated the performance of the developed models and showed their effectiveness in demixing and sorting the active particles. Our findings can find applications in various fields, including physics, biology, and biomedical science, where the sorting and manipulation of active particles play a pivotal role.
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Affiliation(s)
- Abdolhalim Torrik
- Department of Physical and Computational Chemistry, Shahid Beheshti University, Tehran 19839-9411, Iran
| | - Mahdi Zarif
- Department of Physical and Computational Chemistry, Shahid Beheshti University, Tehran 19839-9411, Iran
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Dong W, Wan EYF, Fong DYT, Tan KCB, Tsui WWS, Hui EMT, Chan KH, Fung CSC, Lam CLK. Development and validation of 10-year risk prediction models of cardiovascular disease in Chinese type 2 diabetes mellitus patients in primary care using interpretable machine learning-based methods. Diabetes Obes Metab 2024; 26:3969-3987. [PMID: 39010291 DOI: 10.1111/dom.15745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 06/03/2024] [Accepted: 06/11/2024] [Indexed: 07/17/2024]
Abstract
AIM To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods. METHODS In this 10-year population-based retrospective cohort study, 141 516 Chinese T2DM patients aged 18 years or above, without history of CVD or end-stage renal disease and managed in public primary care clinics in 2008, were included and followed up until December 2017. Two-thirds of the patients were randomly selected to develop sex-specific CVD risk prediction models. The remaining one-third of patients were used as the validation sample to evaluate the discrimination and calibration of the models. ML-based methods were applied to missing data imputation, predictor selection, risk prediction modelling, model interpretation, and model evaluation. Cox regression was used to develop the statistical models in parallel for comparison. RESULTS During a median follow-up of 9.75 years, 32 445 patients (22.9%) developed CVD. Age, T2DM duration, urine albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), systolic blood pressure variability and glycated haemoglobin (HbA1c) variability were the most important predictors. ML models also identified nonlinear effects of several predictors, particularly the U-shaped effects of eGFR and body mass index. The ML models showed a Harrell's C statistic of >0.80 and good calibration. The ML models performed significantly better than the Cox regression models in CVD risk prediction and achieved better risk stratification for individual patients. CONCLUSION Using routinely available predictors and ML-based algorithms, this study established 10-year CVD risk prediction models for Chinese T2DM patients in primary care. The findings highlight the importance of renal function indicators, and variability in both blood pressure and HbA1c as CVD predictors, which deserve more clinical attention. The derived risk prediction tools have the potential to support clinical decision making and encourage patients towards self-care, subject to further research confirming the models' feasibility, acceptability and applicability at the point of care.
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Affiliation(s)
- Weinan Dong
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China
| | - Eric Yuk Fai Wan
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China
- Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong, China
| | | | | | - Wendy Wing-Sze Tsui
- Department of Family Medicine & Primary Healthcare, Hong Kong West Cluster, Hosptial Authority, Hong Kong, China
| | - Eric Ming-Tung Hui
- Department of Family Medicine, New Territories East Cluster, Hospital Authority, Hong Kong, China
| | - King Hong Chan
- Department of Family Medicine & General Out-patient Clinics, Kowloon Central Cluster, Hospital Authority, Hong Kong, China
| | - Colman Siu Cheung Fung
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China
| | - Cindy Lo Kuen Lam
- Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China
- Department of Family Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen, China
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Kaya Y, Bütün Z, Çelik Ö, Salik EA, Tahta T, Yavuz AA. The early prediction of gestational diabetes mellitus by machine learning models. BMC Pregnancy Childbirth 2024; 24:574. [PMID: 39217284 PMCID: PMC11365266 DOI: 10.1186/s12884-024-06783-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period. METHODS We collected the data of pregnant women who were admitted to the obstetric clinic in the first trimester. The maternal age, body mass index, gravida, parity, previous birth weight, smoking status, the first-visit venous plasma glucose level, the family history of diabetes mellitus, and the results of an oral glucose tolerance test of the patients were evaluated. The women were categorized into groups based on having and not having a GDM diagnosis and also as being nulliparous or primiparous. 7 common ML algorithms were employed to construct the predictive model. RESULTS 97 mothers were included in the study. 19 and 26 nulliparous were with and without GDM, respectively. 29 and 23 primiparous were with and without GDM, respectively. It was found that the greatest feature importance variables were the venous plasma glucose level, maternal BMI, and the family history of diabetes mellitus. The eXtreme Gradient Boosting (XGB) Classifier had the best predictive value for the two models with the accuracy of 66.7% and 72.7%, respectively. DISCUSSION The XGB classifier model constructed with maternal sociodemographic findings and the obstetric history could be used as an early prediction model for GDM especially in low-income countries.
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Affiliation(s)
- Yeliz Kaya
- Faculty of Health Sciences, Department of Gynecology and Obstetrics Nursing, Eskişehir Osmangazi University, Eskişehir, Turkey.
| | - Zafer Bütün
- Hoşnudiye Mah. Ayşen Sokak Dorya Rezidans, A Blok no:28/77, Eskişehir, Turkey
| | - Özer Çelik
- Faculty of Science, Department of Mathematics-Computer Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Ece Akça Salik
- Department of Gynecology and Obstetrics, Eskisehir City Hospital, Eskişehir, Turkey
| | - Tuğba Tahta
- Ankara Medipol Üniversity, Health Services Vocational School, Ankara, Turkey
| | - Arzu Altun Yavuz
- Faculty of Science, Department of Statistics, Eskişehir Osmangazi University, Eskisehir, Turkey
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Wang J, Huang P, Hou F, Hao D, Li W, Jin H. Predicting gestational diabetes mellitus risk at 11-13 weeks' gestation: the role of extrachromosomal circular DNA. Cardiovasc Diabetol 2024; 23:289. [PMID: 39113025 PMCID: PMC11304788 DOI: 10.1186/s12933-024-02381-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Accepted: 07/30/2024] [Indexed: 08/11/2024] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) significantly impacts maternal and infant health both immediately and over the long term, yet effective early diagnostic biomarkers are currently lacking. Thus, it is essential to identify early diagnostic biomarkers for GDM risk screening. Extrachromosomal circular DNA (eccDNA), being more stable than linear DNA and involved in disease pathologies, is a viable biomarker candidate for diverse conditions. In this study, eccDNA biomarkers identified for early diagnosis and assessment of GDM risk were explored. METHODS Using Circle-seq, we identified plasma eccDNA profiles in five pregnant women who later developed GDM and five matched healthy controls at 11-13 weeks of gestation. These profiles were subsequently analyzed through bioinformatics and validated through outward PCR combined with Sanger sequencing. Furthermore, candidate eccDNA was validated by quantitative PCR (qPCR) in a larger cohort of 70 women who developed GDM and 70 normal glucose-tolerant (NGT) subjects. A ROC curve assessed the eccDNA's diagnostic potential for GDM. RESULTS 2217 eccDNAs were differentially detected between future GDM patients and controls, with 1289 increased and 928 decreased in abundance. KEGG analysis linked eccDNA genes mainly to GDM-related pathways such as Rap1, MAPK, and PI3K-Akt, and Insulin resistance, among others. Validation confirmed a significant decrease in eccDNA PRDM16circle in the plasma of 70 women who developed GDM compared to 70 NGT women, consistent with the eccDNA-seq results. PRDM16circle showed significant diagnostic value in 11-13 weeks of gestation (AUC = 0.941, p < 0.001). CONCLUSIONS Our study first demonstrats that eccDNAs are aberrantly produced in women who develop GDM, including PRDM16circle, which can predict GDM at an early stage of pregnancy, indicating its potential as a biomarker. TRIAL REGISTRATION ChiCTR2300075971, http://www.chictr.org.cn . Registered 20 September 2023.
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Affiliation(s)
- Jin Wang
- Prenatal Diagnosis Center, Jinan Maternal and Child Health Care Hospital, No.2, Jianguo Xiaojing Roud, Jinan, 250002, Shandong Province, People's Republic of China
| | - Pengyu Huang
- Fujian Provincial Sperm Bank, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350005, Fujian Province, People's Republic of China
| | - Fei Hou
- Prenatal Diagnosis Center, Jinan Maternal and Child Health Care Hospital, No.2, Jianguo Xiaojing Roud, Jinan, 250002, Shandong Province, People's Republic of China
| | - Dongdong Hao
- Department of Family Planning, Jinan Maternal and Child Health Care Hospital, Jinan, Shandong Province, People's Republic of China
| | - Wushan Li
- Department of Obstetrics, Jinan Maternal and Child Health Care Hospital, Jinan, Shandong Province, People's Republic of China
| | - Hua Jin
- Prenatal Diagnosis Center, Jinan Maternal and Child Health Care Hospital, No.2, Jianguo Xiaojing Roud, Jinan, 250002, Shandong Province, People's Republic of China.
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Zhou F, Ran X, Song F, Wu Q, Jia Y, Liang Y, Chen S, Zhang G, Dong J, Wang Y. A stepwise prediction and interpretation of gestational diabetes mellitus: Foster the practical application of machine learning in clinical decision. Heliyon 2024; 10:e32709. [PMID: 38975148 PMCID: PMC11225730 DOI: 10.1016/j.heliyon.2024.e32709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 04/22/2024] [Accepted: 06/07/2024] [Indexed: 07/09/2024] Open
Abstract
Background Machine learning has shown to be an effective method for early prediction and intervention of Gestational diabetes mellitus (GDM), which greatly decreases GDM incidence, reduces maternal and infant complications and improves the prognosis. However, there is still much room for improvement in data quality, feature dimension, and accuracy. The contributions and mechanism explanations of clinical data at different pregnancy stages to the prediction accuracy are still lacking. More importantly, current models still face notable obstacles in practical applications due to the complex and diverse input features and difficulties in redeployment. As a result, a simple, practical but accurate enough model is urgently needed. Design and methods In this study, 2309 samples from two public hospitals in Shenzhen, China were collected for analysis. Different algorithms were systematically compared to build a robust and stepwise prediction system (level A to C) based on advanced machine learning, and models under different levels were interpreted. Results XGBoost reported the best performance with ACC of 0.922, 0.859 and 0.850, AUC of 0.974, 0.924 and 0.913 for the selected level A to C models in the test set, respectively. Tree-based feature importance and SHAP method successfully identified the commonly recognized risk factors, while indicated new inconsistent impact trends for GDM in different stages of pregnancy. Conclusion A stepwise prediction system was successfully established. A practical tool that enables a quick prediction of GDM was released at https://github.com/ifyoungnet/MedGDM.This study is expected to provide a more detailed profiling of GDM risk and lay the foundation for the application of the model in practice.
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Affiliation(s)
- Fang Zhou
- Department of Pharmacy, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, 518055, PR China
| | - Xiao Ran
- School of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China
- SINOCARE Inc., Changsha, 410004, PR China
| | - Fangliang Song
- School of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China
| | - Qinglan Wu
- Department of Pharmacy, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, 518055, PR China
| | - Yuan Jia
- Department of Pharmacy, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, 518055, PR China
| | - Ying Liang
- School of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China
| | - Suichen Chen
- Department of Pharmacy, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, 518055, PR China
| | - Guojun Zhang
- Department of Pharmacy, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, 518055, PR China
| | - Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410083, PR China
| | - Yukun Wang
- Department of Pharmacy, Southern University of Science and Technology Hospital, Shenzhen, Guangdong, 518055, PR China
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, PR China
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Rönn T, Perfilyev A, Oskolkov N, Ling C. Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets. Sci Rep 2024; 14:14637. [PMID: 38918439 PMCID: PMC11199577 DOI: 10.1038/s41598-024-64846-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 06/13/2024] [Indexed: 06/27/2024] Open
Abstract
Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.
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Affiliation(s)
- Tina Rönn
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Lund University, 205 02, Malmö, Sweden
| | - Alexander Perfilyev
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Lund University, 205 02, Malmö, Sweden
| | - Nikolay Oskolkov
- Science for Life Laboratory, Department of Biology, National Bioinformatics Infrastructure Sweden, Lund University, Sölvegatan 35, 223 62, Lund, Sweden
| | - Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Scania University Hospital, Lund University, 205 02, Malmö, Sweden.
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Design, rationale and protocol for Glycemic Observation and Metabolic Outcomes in Mothers and Offspring (GO MOMs): an observational cohort study. BMJ Open 2024; 14:e084216. [PMID: 38851233 PMCID: PMC11163666 DOI: 10.1136/bmjopen-2024-084216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 04/09/2024] [Indexed: 06/10/2024] Open
Abstract
INTRODUCTION Given the increasing prevalence of both obesity and pre-diabetes in pregnant adults, there is growing interest in identifying hyperglycaemia in early pregnancy to optimise maternal and perinatal outcomes. Multiple organisations recommend first-trimester diabetes screening for individuals with risk factors; however, the benefits and drawbacks of detecting glucose abnormalities more mild than overt diabetes in early gestation and the best screening method to detect such abnormalities remain unclear. METHODS AND ANALYSIS The goal of the Glycemic Observation and Metabolic Outcomes in Mothers and Offspring study (GO MOMs) is to evaluate how early pregnancy glycaemia, measured using continuous glucose monitoring and oral glucose tolerance testing, relates to the diagnosis of gestational diabetes (GDM) at 24-28 weeks' gestation (maternal primary outcome) and large-for-gestational-age birth weight (newborn primary outcome). Secondary objectives include relating early pregnancy glycaemia to other adverse pregnancy outcomes and comprehensively detailing longitudinal changes in glucose over the course of pregnancy. GO MOMs enrolment began in April 2021 and will continue for 3.5 years with a target sample size of 2150 participants. ETHICS AND DISSEMINATION GO MOMs is centrally overseen by Vanderbilt University's Institutional Review Board and an Observational Study Monitoring Board appointed by National Institute of Diabetes and Digestive and Kidney Diseases. GO MOMs has potential to yield data that will improve understanding of hyperglycaemia in pregnancy, elucidate better approaches for early pregnancy GDM screening, and inform future clinical trials of early GDM treatment. TRIAL REGISTRATION NUMBER NCT04860336.
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Yaseen I, Rather RA. A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery. Int J Womens Health 2024; 16:903-915. [PMID: 38800118 PMCID: PMC11128252 DOI: 10.2147/ijwh.s454127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 04/29/2024] [Indexed: 05/29/2024] Open
Abstract
The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.
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Affiliation(s)
- Ishfaq Yaseen
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Riyaz Ahmad Rather
- Department of Biotechnology, College of Natural and Computational Science, Wachemo University, Hossana, Ethiopia
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Leiherer A, Muendlein A, Mink S, Mader A, Saely CH, Festa A, Fraunberger P, Drexel H. Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence. Int J Mol Sci 2024; 25:5331. [PMID: 38791370 PMCID: PMC11120685 DOI: 10.3390/ijms25105331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/30/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography and who were initially free of T2DM according to American Diabetes Association (ADA) criteria was analyzed at baseline, including anthropometric data and targeted metabolomics, using liquid chromatography (LC)-mass spectroscopy (MS) and flow injection analysis (FIA)-MS, respectively. All patients were followed for four years. During this time, 11.5% of the patients developed T2DM. After data preprocessing, 362 variables were used for ML, employing the Caret package in R. The dataset was divided into training and test sets (75:25 ratio) and we used an oversampling approach to address the classifier imbalance of T2DM incidence. After an additional recursive feature elimination step, identifying a set of 77 variables that were the most valuable for model generation, a Support Vector Machine (SVM) model with a linear kernel demonstrated the most promising predictive capabilities, exhibiting an F1 score of 50%, a specificity of 93%, and balanced and unbalanced accuracies of 72% and 88%, respectively. The top-ranked features were bile acids, ceramides, amino acids, and hexoses, whereas anthropometric features such as age, sex, waist circumference, or body mass index had no contribution. In conclusion, ML analysis of metabolomics data is a promising tool for identifying individuals at risk of developing T2DM and opens avenues for personalized and early intervention strategies.
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Affiliation(s)
- Andreas Leiherer
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
- Central Medical Laboratories, A-6800 Feldkirch, Austria; (S.M.); (P.F.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
| | - Axel Muendlein
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
| | - Sylvia Mink
- Central Medical Laboratories, A-6800 Feldkirch, Austria; (S.M.); (P.F.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
| | - Arthur Mader
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
- Department of Internal Medicine III, Academic Teaching Hospital Feldkirch, A-6800 Feldkirch, Austria
| | - Christoph H. Saely
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
- Department of Internal Medicine III, Academic Teaching Hospital Feldkirch, A-6800 Feldkirch, Austria
| | - Andreas Festa
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
| | - Peter Fraunberger
- Central Medical Laboratories, A-6800 Feldkirch, Austria; (S.M.); (P.F.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
| | - Heinz Drexel
- Vorarlberg Institute for Vascular Investigation and Treatment (VIVIT), A-6800 Feldkirch, Austria; (A.M.); (A.M.); (C.H.S.); (A.F.); (H.D.)
- Faculty of Medical Sciences, Private University of the Principality of Liechtenstein, FL-9495 Triesen, Liechtenstein
- Vorarlberger Landeskrankenhausbetriebsgesellschaft, Academic Teaching Hospital Feldkirch, A-6800 Feldkirch, Austria
- Drexel University College of Medicine, Philadelphia, PA 19129, USA
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Yang X, Han R, Song Y, Zhang J, Huang H, Zhang J, Wang Y, Gao L. The Mediating Role of Physical Activity Self-Efficacy in Predicting Moderate-Intensity Physical Activity in Pregnant People at High Risk for Gestational Diabetes. J Midwifery Womens Health 2024; 69:403-413. [PMID: 38069454 DOI: 10.1111/jmwh.13589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 10/09/2023] [Indexed: 06/05/2024]
Abstract
INTRODUCTION Gestational diabetes mellitus (GDM) is a common medical complication in pregnancy. Moderate-intensity physical activity during pregnancy can lower the risk of GDM. However, the relationship between moderate-intensity physical activity and correlated factors among pregnant people at high risk for GDM remains unknown. METHODS A cross-sectional study was conducted in China. Two hundred fifty-two participants completed the Pregnancy Physical Activity Questionnaire, Pregnancy Physical Activity Self-Efficacy Scale, Physical Activity Knowledge Questionnaire, Physical Activity Social Support Scale, 7-item Generalized Anxiety Disorder Scale, Edinburgh Postnatal Depression Scale, and a sociodemographic data sheet. Structural equation modeling was used to explore the direct and indirect associations between the study variables. RESULTS A total of 51.6% of the participants did not meet the current physical activity guidelines. Only physical activity self-efficacy was significantly correlated with moderate-intensity physical activity. Physical activity self-efficacy mediated the relationship between moderate-intensity physical activity and knowledge of physical activity, social support for physical activity, and anxiety symptoms. Furthermore, knowledge of physical activity was also associated with improved moderate-intensity physical activity mediated by reduced anxiety symptoms and increased physical activity self-efficacy. CONCLUSION Our study revealed a high prevalence of not meeting current physical activity guidelines among pregnant people at high risk for GDM. Physical activity self-efficacy played an important mediating role in predicting moderate-intensity physical activity. Future studies should focus on enhancing self-efficacy to improve moderate-intensity physical activity for pregnant people at high risk for GDM.
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Affiliation(s)
- Xiao Yang
- School of Nursing, Sun Yat-Sen University, Guangzhou, China
| | - Rongrong Han
- School of Nursing, Sun Yat-Sen University, Guangzhou, China
| | - Yingli Song
- Zhengzhou Maternal and Child Health Hospital, Zhengzhou, China
| | - Ji Zhang
- Zhengzhou Maternal and Child Health Hospital, Zhengzhou, China
| | - Hui Huang
- Zhengzhou Maternal and Child Health Hospital, Zhengzhou, China
| | - Jing Zhang
- Zhengzhou Maternal and Child Health Hospital, Zhengzhou, China
| | - Yan Wang
- Zhengzhou Maternal and Child Health Hospital, Zhengzhou, China
| | - Lingling Gao
- School of Nursing, Sun Yat-Sen University, Guangzhou, China
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Wu Y, Hamelmann P, van der Ven M, Asvadi S, van der Hout-van der Jagt MB, Oei SG, Mischi M, Bergmans J, Long X. Early prediction of gestational diabetes mellitus using maternal demographic and clinical risk factors. BMC Res Notes 2024; 17:105. [PMID: 38622619 PMCID: PMC11021008 DOI: 10.1186/s13104-024-06758-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/27/2024] [Indexed: 04/17/2024] Open
Abstract
OBJECTIVE To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors. METHODS To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset. RESULTS An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).
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Affiliation(s)
- Yanqi Wu
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Eindhoven, The Netherlands
| | | | - Myrthe van der Ven
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Obstetrics and Gynaecology, Máxima Medical Center, Veldhoven, The Netherlands
| | - Sima Asvadi
- Philips Research, Eindhoven, The Netherlands
| | - M Beatrijs van der Hout-van der Jagt
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Obstetrics and Gynaecology, Máxima Medical Center, Veldhoven, The Netherlands
| | - S Guid Oei
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Obstetrics and Gynaecology, Máxima Medical Center, Veldhoven, The Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Jan Bergmans
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
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12
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Jones CH, Dolsten M. Healthcare on the brink: navigating the challenges of an aging society in the United States. NPJ AGING 2024; 10:22. [PMID: 38582901 PMCID: PMC10998868 DOI: 10.1038/s41514-024-00148-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 03/21/2024] [Indexed: 04/08/2024]
Abstract
The US healthcare system is at a crossroads. With an aging population requiring more care and a strained system facing workforce shortages, capacity issues, and fragmentation, innovative solutions and policy reforms are needed. This paper aims to spark dialogue and collaboration among healthcare stakeholders and inspire action to meet the needs of the aging population. Through a comprehensive analysis of the impact of an aging society, this work highlights the urgency of addressing this issue and the importance of restructuring the healthcare system to be more efficient, equitable, and responsive.
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Affiliation(s)
- Charles H Jones
- Pfizer, 66 Hudson Boulevard, New York, New York, 10018, USA.
| | - Mikael Dolsten
- Pfizer, 66 Hudson Boulevard, New York, New York, 10018, USA.
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13
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Cerono G, Chicco D. Ensemble machine learning reveals key features for diabetes duration from electronic health records. PeerJ Comput Sci 2024; 10:e1896. [PMID: 38435625 PMCID: PMC10909161 DOI: 10.7717/peerj-cs.1896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024]
Abstract
Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R2. Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort.
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Affiliation(s)
- Gabriel Cerono
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Davide Chicco
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada
- Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Italy
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14
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Zhang H, Zeng T, Zhang J, Zheng J, Min J, Peng M, Liu G, Zhong X, Wang Y, Qiu K, Tian S, Liu X, Huang H, Surmach M, Wang P, Hu X, Chen L. Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China. Front Endocrinol (Lausanne) 2024; 15:1292346. [PMID: 38332892 PMCID: PMC10850228 DOI: 10.3389/fendo.2024.1292346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/11/2024] [Indexed: 02/10/2024] Open
Abstract
Objective Insulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the "common soil" of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings. Methods We analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models. Results The LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc. Conclusion The ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.
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Affiliation(s)
- Hao Zhang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Tianshu Zeng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Jiaoyue Zhang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Juan Zheng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Jie Min
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Miaomiao Peng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Geng Liu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Xueyu Zhong
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Ying Wang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Kangli Qiu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Shenghua Tian
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Xiaohuan Liu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Hantao Huang
- Department of Emergency Medicine, Yichang Yiling Hospital, Yichang, China
| | - Marina Surmach
- Department of Public Health and Health Services, Grodno State Medical University, Grodno, Belarus
| | - Ping Wang
- Precision Health Program, Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, United States
| | - Xiang Hu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Lulu Chen
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
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Zheng X, Pan F, Naumovski N, Wei Y, Wu L, Peng W, Wang K. Precise prediction of metabolites patterns using machine learning approaches in distinguishing honey and sugar diets fed to mice. Food Chem 2024; 430:136915. [PMID: 37515908 DOI: 10.1016/j.foodchem.2023.136915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 07/13/2023] [Accepted: 07/15/2023] [Indexed: 07/31/2023]
Abstract
As a natural sweetener produced by honey bees, honey was recognized as being healthier for consumption than table sugar. Our previous study also indicated thatmetaboliteprofiles in mice fed honey and mixedsugardiets aredifferent. However, it is still noteworthy about the batch-to-batch consistency of the metabolic differences between two diet types. Here, the machine learning (ML) algorithms were applied to complement and calibrate HPLC-QTOF/MS-based untargeted metabolomics data. Data were generated from three batches of mice that had the same treatment, which can further mine the metabolite biomarkers. Random Forest and Extra-Trees models could better discriminate between honey and mixed sugar dietary patterns under five-fold cross-validation. Finally, SHapley Additive exPlanations tool identified phosphatidylethanolamine and phosphatidylcholine as reliable metabolic biomarkers to discriminate the honey diet from the mixed sugar diet. This study provides us new ideas for metabolomic analysis of larger data sets.
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Affiliation(s)
- Xing Zheng
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
| | - Fei Pan
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
| | - Nenad Naumovski
- University of Canberra Health Research Institute (UCHRI), University of Canberra, Locked Bag 1, Bruce, Canberra, ACT 2601, Australia
| | - Yue Wei
- College of Science & Technology, Hebei Agricultural University, Huanghua, Hebei 061100, China
| | - Liming Wu
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
| | - Wenjun Peng
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China.
| | - Kai Wang
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China.
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16
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Jacob R, Curelaru S, Chayen G, Samuel N. Reply to the Letter to the Editor Regarding Outcomes of Febrile Infants Aged 29-90 Days Discharged from the Emergency Department. J Pediatr 2024; 264:113793. [PMID: 37865181 DOI: 10.1016/j.jpeds.2023.113793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 10/16/2023] [Indexed: 10/23/2023]
Affiliation(s)
- Ron Jacob
- Pediatric Emergency Department, Ha'Emek Medical Center, Afula, Israel; Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Shiri Curelaru
- Pediatric Department, Ha'Emek Medical Center, Afula, Israel
| | - Gilad Chayen
- Pediatric Emergency Department, Ha'Emek Medical Center, Afula, Israel
| | - Nir Samuel
- Emergency Department, Schneider Children's Medical Center, Petakh Tikva, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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17
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Yimit Y, Yasin P, Tuersun A, Abulizi A, Jia W, Wang Y, Nijiati M. Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach. Eur J Med Res 2023; 28:577. [PMID: 38071384 PMCID: PMC10709961 DOI: 10.1186/s40001-023-01550-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Cerebral alveolar echinococcosis (CAE) and brain metastases (BM) share similar in locations and imaging appearance. However, they require distinct treatment approaches, with CAE typically treated with chemotherapy and surgery, while BM is managed with radiotherapy and targeted therapy for the primary malignancy. Accurate diagnosis is crucial due to the divergent treatment strategies. PURPOSE This study aims to evaluate the effectiveness of radiomics and machine learning techniques based on magnetic resonance imaging (MRI) to differentiate between CAE and BM. METHODS We retrospectively analyzed MRI images of 130 patients (30 CAE and 100 BM) from Xinjiang Medical University First Affiliated Hospital and The First People's Hospital of Kashi Prefecture, between January 2014 and December 2022. The dataset was divided into training (91 cases) and testing (39 cases) sets. Three dimensional tumors were segmented by radiologists from contrast-enhanced T1WI images on open resources software 3D Slicer. Features were extracted on Pyradiomics, further feature reduction was carried out using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator (LASSO). Finally, we built five machine learning models, support vector machine, logistic regression, linear discrimination analysis, k-nearest neighbors classifier, and Gaussian naïve bias and evaluated their performance via several metrics including sensitivity (recall), specificity, positive predictive value (precision), negative predictive value, accuracy and the area under the curve (AUC). RESULTS The area under curve (AUC) of support vector classifier (SVC), linear discrimination analysis (LDA), k-nearest neighbors (KNN), and gaussian naïve bias (NB) algorithms in training (testing) sets are 0.99 (0.94), 1.00 (0.87), 0.98 (0.92), 0.97 (0.97), and 0.98 (0.93), respectively. Nested cross-validation demonstrated the robustness and generalizability of the models. Additionally, the calibration plot and decision curve analysis demonstrated the practical usefulness of these models in clinical practice, with lower bias toward different subgroups during decision-making. CONCLUSION The combination of radiomics and machine learning approach based on contrast enhanced T1WI images could well distinguish CAE and BM. This approach holds promise in assisting doctors with accurate diagnosis and clinical decision-making.
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Affiliation(s)
- Yasen Yimit
- Medical Imaging Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, 844000, People's Republic of China
| | - Parhat Yasin
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China
| | - Abuduresuli Tuersun
- Medical Imaging Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, 844000, People's Republic of China
| | - Abudoukeyoumujiang Abulizi
- Medical Imaging Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, 844000, People's Republic of China
| | - Wenxiao Jia
- Medical Imaging Center, Xinjiang Medical University Affiliated First Hospital, Urumqi, 830054, People's Republic of China
| | - Yunling Wang
- Medical Imaging Center, Xinjiang Medical University Affiliated First Hospital, Urumqi, 830054, People's Republic of China
| | - Mayidili Nijiati
- Medical Imaging Center, The First People's Hospital of Kashi (Kashgar) Prefecture, Kashi, 844000, People's Republic of China.
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18
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Cui S, Zhu X, Li S, Zhang C. Study on the predictive value of serum hypersensitive C-reactive protein, homocysteine, fibrinogen, and omentin-1 levels with gestational diabetes mellitus. Gynecol Endocrinol 2023; 39:2183046. [PMID: 36996863 DOI: 10.1080/09513590.2023.2183046] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/01/2023] Open
Abstract
Objective: To investigate whether hypersensitive C-reactive protein (Hs-CRP), homocysteine, fibrinogen, and omentin-1 could predict gestational diabetes mellitus (GDM) risk. Methods: Case-control study was conducted at Hengshui People's Hospital. The GDM group included data about 150 patients aged between 22 and 35 years in 24-28 weeks. An equivalent comparative control group without GDM was composed of the same pool of patients. Body mass index (BMI), total cholesterol (TC), triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), oral glucose tolerance test (OGTT) 0-2h, hs-CRP, homocysteine, fibrinogen, and omentin-1 levels were studied in the serum samples of research groups. Univariate logistic regression analysis was used to explore the risk factors of GDM. The area under the curve (AUC) was calculated by the receiver operating characteristic curve (ROC) to analyze the predictive values. Results: Hs-CRP, homocysteine, and fibrinogen in GDM group were significantly higher than those in non-GDM group. Omentin-1 were significantly lower than those in non-GDM group. Logistic regression showed that hs-CRP, homocysteine, fibrinogen, and omentin-1 were risk factors for GDM. The AUC of the established GDM risk prediction model was 0.977, and the sensitivity and specificity were 92.10% and 98.70%, respectively; which were greater than that of hs-CRP, homocysteine, fibrinogen, and omentin-1 alone. Conclusions: Hs-CRP, homocysteine, fibrinogen, and omentin-1 in pregnancy have important clinical value for the prediction of GDM. We used these laboratory indications to establish a GDM risk prediction model that allows for early detection and treatment of GDM, lowering the morbidity of maternal and infant complications.
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Affiliation(s)
- Shaoyong Cui
- Department of Clinical Laboratory, Hengshui People's Hospital, Hengshui, China
| | - Xiaocui Zhu
- Department of Clinical Laboratory, Hengshui People's Hospital, Hengshui, China
| | - Sen Li
- Department of Clinical Laboratory, Hengshui People's Hospital, Hengshui, China
| | - Changgeng Zhang
- Department of Clinical Laboratory, Hengshui People's Hospital, Hengshui, China
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Curelaru S, Samuel N, Chayen G, Jacob R. Outcomes of Infants Who Are Febrile Aged 29-90 Days Discharged from the Emergency Department. J Pediatr 2023; 263:113714. [PMID: 37659589 DOI: 10.1016/j.jpeds.2023.113714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/22/2023] [Accepted: 08/29/2023] [Indexed: 09/04/2023]
Abstract
OBJECTIVE To evaluate the characteristics and outcomes of infants aged 29-90 days who are febrile discharged from the pediatric emergency department (ED). STUDY DESIGN This was a multicenter, retrospective cohort study of infants aged 29-90 days who visited any of the 7 Clalit Health Services pediatric EDs in Israel between January 1, 2019, and March 31, 2022. Infants who were febrile discharged from the ED after having blood and urine cultures taken were included. The primary outcome measure was the incidence of return visit (RV) to an ED. Secondary outcome measures were the incidence of invasive bacterial infection, urinary tract infection, pediatric intensive care unit admissions, and deaths. We assessed variables associated with the primary outcomes. RESULTS A total of 1647 infants were included. Their median (IQR) age at ED visit was 58.5 (47.7, 72.7) days, 53.1% were male. A total of 329 patients (20%) returned to the ED within 120 hours. Overall, 7.8% of discharged infants had a positive urine culture, 4 (0.2%) had a positive blood culture, and none had meningitis. One patient was admitted to the pediatric intensive care unit, and there was no death. Abnormal C-reactive protein was associated with RV among 61- to 90-day-old infants. CONCLUSIONS Infants aged 29-90 days who were febrile and discharged following a protocol-driven pathway from the pediatric ED had a relatively high RV rate. However, the rate of urinary tract infection was relatively low, and rate of invasive bacterial infection was extremely low. There were no deaths or serious sequelae.
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Affiliation(s)
- Shiri Curelaru
- Pediatric Department, Ha'Emek Medical Center, Afula, Israel
| | - Nir Samuel
- Emergency Department, Schneider Children's Medical Center, Petakh Tikva, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Gilad Chayen
- Pediatric Emergency Department, Ha'Emek Medical Center, Afula, Israel
| | - Ron Jacob
- Pediatric Emergency Department, Ha'Emek Medical Center, Afula, Israel; Rappaport Faculty of Medicine, Technion-Institute of Technology, Haifa, Israel.
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Watanabe M, Eguchi A, Sakurai K, Yamamoto M, Mori C. Prediction of gestational diabetes mellitus using machine learning from birth cohort data of the Japan Environment and Children's Study. Sci Rep 2023; 13:17419. [PMID: 37833313 PMCID: PMC10575866 DOI: 10.1038/s41598-023-44313-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 10/06/2023] [Indexed: 10/15/2023] Open
Abstract
Recently, prediction of gestational diabetes mellitus (GDM) using artificial intelligence (AI) from medical records has been reported. We aimed to evaluate GDM-predictive AI-based models using birth cohort data with a wide range of information and to explore factors contributing to GDM development. This investigation was conducted as a part of the Japan Environment and Children's Study. In total, 82,698 pregnant mothers who provided data on lifestyle, anthropometry, and socioeconomic status before pregnancy and the first trimester were included in the study. We employed machine learning methods as AI algorithms, such as random forest (RF), gradient boosting decision tree (GBDT), and support vector machine (SVM), along with logistic regression (LR) as a reference. GBDT displayed the highest accuracy, followed by LR, RF, and SVM. Exploratory analysis of the JECS data revealed that health-related quality of life in early pregnancy and maternal birthweight, which were rarely reported to be associated with GDM, were found along with variables that were reported to be associated with GDM. The results of decision tree-based algorithms, such as GBDT, have shown high accuracy, interpretability, and superiority for predicting GDM using birth cohort data.
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Affiliation(s)
- Masahiro Watanabe
- Department of Sustainable Health Science, Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba, 263-8522, Japan.
| | - Akifumi Eguchi
- Department of Sustainable Health Science, Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba, 263-8522, Japan
| | - Kenichi Sakurai
- Department of Nutrition and Metabolic Medicine, Center for Preventive Medical Sciences, Chiba University, Chiba, Japan
| | - Midori Yamamoto
- Department of Sustainable Health Science, Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba, 263-8522, Japan
| | - Chisato Mori
- Department of Sustainable Health Science, Center for Preventive Medical Sciences, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba, 263-8522, Japan
- Department of Bioenvironmental Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
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Zhang H, Dai J, Zhang W, Sun X, Sun Y, Wang L, Li H, Zhang J. Integration of clinical demographics and routine laboratory analysis parameters for early prediction of gestational diabetes mellitus in the Chinese population. Front Endocrinol (Lausanne) 2023; 14:1216832. [PMID: 37900122 PMCID: PMC10613106 DOI: 10.3389/fendo.2023.1216832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 09/19/2023] [Indexed: 10/31/2023] Open
Abstract
Gestational diabetes mellitus (GDM) is one of the most common complications in pregnancy, impairing both maternal and fetal health in short and long term. As early interventions are considered desirable to prevent GDM, this study aims to develop a simple-to-use nomogram based on multiple common risk factors from electronic medical health records (EMHRs). A total of 924 pregnant women whose EMHRs were available at Peking University International Hospital from January 2022 to October 2022 were included. Clinical demographics and routine laboratory analysis parameters at 8-12 weeks of gestation were collected. A novel nomogram was established based on the outcomes of multivariate logistic regression. The nomogram demonstrated powerful discrimination (the area under the receiver operating characteristic curve = 0.7542), acceptable agreement (Hosmer-Lemeshow test, P = 0.3214) and favorable clinical utility. The C-statistics of 10-Fold cross validation, Leave one out cross validation and Bootstrap were 0.7411, 0.7357 and 0.7318, respectively, indicating the stability of the nomogram. A novel nomogram based on easily-accessible parameters was developed to predict GDM in early pregnancy, which may provide a paradigm for repurposing clinical data and benefit the clinical management of GDM. There is a need for prospective multi-center studies to validate the nomogram before employing the nomogram in real-world clinical practice.
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Affiliation(s)
- Hesong Zhang
- Department of Clinical Laboratory, Peking University International Hospital, Beijing, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Juhua Dai
- Department of Clinical Laboratory, Peking University International Hospital, Beijing, China
| | - Wei Zhang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Xinping Sun
- Department of Clinical Laboratory, Peking University International Hospital, Beijing, China
| | - Yujing Sun
- Department of Clinical Laboratory, Peking University International Hospital, Beijing, China
| | - Lu Wang
- Department of Clinical Laboratory, Peking University International Hospital, Beijing, China
| | - Hongwei Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Jie Zhang
- Department of Clinical Laboratory, Peking University International Hospital, Beijing, China
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22
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Xu Z, Yang HS, Liu L, Meng L, Lu Y, Han L, Tang G, Wang J, Chen L, Zhang Y, Zhai Y, Su S, Cao Z. Elevated levels of renal function tests conferred increased risks of developing various pregnancy complications and adverse perinatal outcomes: insights from a population-based cohort study. Clin Chem Lab Med 2023; 61:1760-1769. [PMID: 37015065 DOI: 10.1515/cclm-2023-0104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 03/27/2023] [Indexed: 04/06/2023]
Abstract
OBJECTIVES Physiological changes during pregnancy can affect the results of renal function tests (RFTs). In this population-based cohort study, we aimed to establish trimester-specific reference intervals (RIs) of RFTs in singleton and twin pregnancies and systematically investigate the relationship between RFTs and adverse pregnancy outcomes. METHODS The laboratory results of the first- and third-trimester RFTs, including blood urea nitrogen (BUN), serum uric acid (UA), creatinine (Crea) and cystatin C (Cys C), and the relevant medical records, were retrieved from 29,328 singleton and 840 twin pregnant women who underwent antenatal examinations from November 20, 2017 to January 31, 2021. The trimester-specific RIs of RFTs were estimated with both of the direct observational and the indirect Hoffmann methods. The associations between RTFs and pregnancy complications as well as perinatal outcomes were assessed by logistic regression analysis. RESULTS Maternal RFTs showed no significant difference between the direct RIs established with healthy pregnant women and the calculated RIs derived from the Hoffmann method. In addition, elevated levels of RFTs were associated with increased risks of developing various pregnancy complications and adverse perinatal outcomes. Notably, elevated third-trimester RFTs posed strong risks of preterm birth (PTB) and fetal growth restriction (FGR). CONCLUSIONS We established the trimester-specific RIs of RFTs in both singleton and twin pregnancies. Our risk analysis findings underscored the importance of RFTs in identifying women at high risks of developing adverse complications or outcomes during pregnancy.
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Affiliation(s)
- Zhengwen Xu
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - He S Yang
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, USA
| | - Lin Liu
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Lanlan Meng
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Yifan Lu
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Lican Han
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Guodong Tang
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Jing Wang
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Lu Chen
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Yue Zhang
- Information Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Yanhong Zhai
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Shaofei Su
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
| | - Zheng Cao
- Department of Laboratory Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
- Center of Clinical Mass Spectrometry, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, P.R. China
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23
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Shamshuzzoha M, Islam MM. Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support. Diagnostics (Basel) 2023; 13:2754. [PMID: 37685292 PMCID: PMC10487237 DOI: 10.3390/diagnostics13172754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 09/10/2023] Open
Abstract
The condition of fetal overgrowth, also known as macrosomia, can cause serious health complications for both the mother and the infant. It is crucial to identify high-risk macrosomia-relevant pregnancies and intervene appropriately. Despite this need, there are several gaps in research related to macrosomia, including limited predictive models, insufficient machine learning applications, ineffective interventions, and inadequate understanding of how to integrate machine learning models into clinical decision-making. To address these gaps, we developed a machine learning-based model that uses maternal characteristics and medical history to predict macrosomia. Three different algorithms, namely logistic regression, support vector machine, and random forest, were used to develop the model. Based on the evaluation metrics, the logistic regression algorithm provided the best results among the three. The logistic regression algorithm was chosen as the final algorithm to predict macrosomia. The hyper parameters of the logistic regression model were tuned using cross-validation to achieve the best possible performance. Our results indicate that machine learning-based models have the potential to improve macrosomia prediction and enable appropriate interventions for high-risk pregnancies, leading to better health outcomes for both mother and fetus. By leveraging machine learning algorithms and addressing research gaps related to macrosomia, we can potentially reduce the health risks associated with this condition and make informed decisions about high-risk pregnancies.
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Affiliation(s)
| | - Md. Motaharul Islam
- Department of CSE, United International University, Madani Avenue, Dhaka 1212, Bangladesh;
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24
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Kang BS, Lee SU, Hong S, Choi SK, Shin JE, Wie JH, Jo YS, Kim YH, Kil K, Chung YH, Jung K, Hong H, Park IY, Ko HS. Prediction of gestational diabetes mellitus in Asian women using machine learning algorithms. Sci Rep 2023; 13:13356. [PMID: 37587201 PMCID: PMC10432552 DOI: 10.1038/s41598-023-39680-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/28/2023] [Indexed: 08/18/2023] Open
Abstract
This study developed a machine learning algorithm to predict gestational diabetes mellitus (GDM) using retrospective data from 34,387 pregnancies in multi-centers of South Korea. Variables were collected at baseline, E0 (until 10 weeks' gestation), E1 (11-13 weeks' gestation) and M1 (14-24 weeks' gestation). The data set was randomly divided into training and test sets (7:3 ratio) to compare the performances of light gradient boosting machine (LGBM) and extreme gradient boosting (XGBoost) algorithms, with a full set of variables (original). A prediction model with the whole cohort achieved area under the receiver operating characteristics curve (AUC) and area under the precision-recall curve (AUPR) values of 0.711 and 0.246 at baseline, 0.720 and 0.256 at E0, 0.721 and 0.262 at E1, and 0.804 and 0.442 at M1, respectively. Then comparison of three models with different variable sets were performed: [a] variables from clinical guidelines; [b] selected variables from Shapley additive explanations (SHAP) values; and [c] Boruta algorithms. Based on model [c] with the least variables and similar or better performance than the other models, simple questionnaires were developed. The combined use of maternal factors and laboratory data could effectively predict individual risk of GDM using a machine learning model.
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Affiliation(s)
- Byung Soo Kang
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seon Ui Lee
- Department of Obstetrics and Gynecology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Subeen Hong
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sae Kyung Choi
- Department of Obstetrics and Gynecology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jae Eun Shin
- Department of Obstetrics and Gynecology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Jeong Ha Wie
- Department of Obstetrics and Gynecology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yun Sung Jo
- Department of Obstetrics and Gynecology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yeon Hee Kim
- Department of Obstetrics and Gynecology, Uijeongbu St. Mary's Hospital,, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Kicheol Kil
- Department of Obstetrics and Gynecology, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yoo Hyun Chung
- Department of Obstetrics and Gynecology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | | | - In Yang Park
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hyun Sun Ko
- Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
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25
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Huang Y, Alvernaz S, Kim SJ, Maki P, Dai Y, Bernabé BP. Predicting prenatal depression and assessing model bias using machine learning models. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.17.23292587. [PMID: 37503225 PMCID: PMC10371186 DOI: 10.1101/2023.07.17.23292587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Perinatal depression (PND) is one of the most common medical complications during pregnancy and postpartum period, affecting 10-20% of pregnant individuals. Black and Latina women have higher rates of PND, yet they are less likely to be diagnosed and receive treatment. Machine learning (ML) models based on Electronic Medical Records (EMRs) have been effective in predicting postpartum depression in middle-class White women but have rarely included sufficient proportions of racial and ethnic minorities, which contributed to biases in ML models for minority women. Our goal is to determine whether ML models could serve to predict depression in early pregnancy in racial/ethnic minority women by leveraging EMR data. We extracted EMRs from a hospital in a large urban city that mostly served low-income Black and Hispanic women (N=5,875) in the U.S. Depressive symptom severity was assessed from a self-reported questionnaire, PHQ-9. We investigated multiple ML classifiers, used Shapley Additive Explanations (SHAP) for model interpretation, and determined model prediction bias with two metrics, Disparate Impact, and Equal Opportunity Difference. While ML model (Elastic Net) performance was low (ROCAUC=0.67), we identified well-known factors associated with PND, such as unplanned pregnancy and being single, as well as underexplored factors, such as self-report pain levels, lower levels of prenatal vitamin supplement intake, asthma, carrying a male fetus, and lower platelet levels blood. Our findings showed that despite being based on a sample mostly composed of 75% low-income minority women (54% Black and 27% Latina), the model performance was lower for these communities. In conclusion, ML models based on EMRs could moderately predict depression in early pregnancy, but their performance is biased against low-income minority women.
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Affiliation(s)
- Yongchao Huang
- Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, IL, USA
| | - Suzanne Alvernaz
- Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, IL, USA
| | - Sage J Kim
- Division of Health Policy and Administration, School of Public Health, University of Illinois, Chicago, IL, USA
| | - Pauline Maki
- Department of Psychiatry, College of Medicine, University of Illinois, Chicago, IL, USA
- Department of Psychology, College of Medicine, University of Illinois, Chicago, IL, USA
- Department of Obstetrics and Gynecology, College of Medicine, University of Illinois, Chicago, IL, USA
| | - Yang Dai
- Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, IL, USA
- Center of Bioinformatics and Quantitative Biology, University of Illinois, Chicago, IL, USA
| | - Beatriz Penñalver Bernabé
- Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, IL, USA
- Center of Bioinformatics and Quantitative Biology, University of Illinois, Chicago, IL, USA
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26
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Hall JA, Barrett G, Stephenson JM, Edelman NL, Rocca C. Desire to Avoid Pregnancy scale: clinical considerations and comparison with other questions about pregnancy preferences. BMJ SEXUAL & REPRODUCTIVE HEALTH 2023; 49:167-175. [PMID: 36717217 PMCID: PMC10359540 DOI: 10.1136/bmjsrh-2022-201750] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Clinicians and women of reproductive age would benefit from a reliable way to identify who is likely to become pregnant in the next year, in order to direct health advice. The 14-item Desire to Avoid Pregnancy (DAP) scale is predictive of pregnancy; this paper compares it with other ways of assessing pregnancy preferences to shortlist options for clinical implementation. METHODS A cohort of 994 UK women of reproductive age completed the DAP and other questions about pregnancy preferences, including the Attitude towards Potential Pregnancy Scale (APPS), at baseline and reported on pregnancies quarterly for a year. For each question, DAP item and combinations of DAP items, we examined the predictive ability, sensitivity, specificity, area under the receiver operating curve (AUROC), and positive and negative predictive values. RESULTS The AUROCs and predictive ability of the APPS and DAP single items were weaker than the full DAP, though all except one had acceptable AUROCs (>0.7). The most predictive individual DAP item was 'It would be a good thing for me if I became pregnant in the next 3 months', where women who strongly agreed had a 66.7% chance of pregnancy within 12 months and the AUROC was acceptable (0.77). CONCLUSION We recommend exploring the acceptability to women and healthcare professionals of asking a single DAP item ('It would be a good thing for me if I became pregnant in the next 3 months'), possibly in combination with additional DAP items. This will help to guide service provision to support reproductive preferences.
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Affiliation(s)
- Jennifer Anne Hall
- Research Department of Reproductive Health, UCL Institute for Women's Health, London, UK
| | - Geraldine Barrett
- Research Department of Reproductive Health, UCL Institute for Women's Health, London, UK
| | - Judith M Stephenson
- Research Department of Reproductive Health, UCL Institute for Women's Health, London, UK
| | - Natalie Lois Edelman
- School of Sport & Health Sciences, University of Brighton, Brighton, UK
- Primary Care & Public Health, Brighton and Sussex Medical School, Brighton, UK
| | - Corinne Rocca
- Advancing New Standards in Reproductive Health (ANSIRH), Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco (UCSF) School of Medicine, Oakland, San Francisco, California, USA
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27
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Kurt B, Gürlek B, Keskin S, Özdemir S, Karadeniz Ö, Kırkbir İB, Kurt T, Ünsal S, Kart C, Baki N, Turhan K. Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques. Med Biol Eng Comput 2023; 61:1649-1660. [PMID: 36848010 PMCID: PMC9969040 DOI: 10.1007/s11517-023-02800-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 01/31/2023] [Indexed: 03/01/2023]
Abstract
The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using deep learning algorithms. With this aim, a prospective study was designed and the data was taken from 489 patients between the years 2019 and 2021, and informed consent was obtained. The clinical decision support system for the diagnosis of GD was developed using the generated dataset with deep learning algorithms and Bayesian optimization. As a result, a novel successful decision support model was developed using RNN-LSTM with Bayesian optimization that gave 95% sensitivity and 99% specificity on the dataset for the diagnosis of patients in the GD risk group by obtaining 98% AUC (95% CI (0.95-1.00) and p < 0.001). Thus, with the clinical diagnosis system developed to assist physicians, it is planned to save both cost and time, and reduce possible adverse effects by preventing unnecessary OGTT for patients who are not in the GD risk group.
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Affiliation(s)
- Burçin Kurt
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Beril Gürlek
- Faculty of Medicine, Department of Gynecology and Obstetrics, Recep Tayyip Erdoğan University, Rize, Turkey
| | - Seda Keskin
- Faculty of Medicine, Department of Gynecology and Obstetrics, Ordu University, Ordu, Turkey
| | - Sinem Özdemir
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Özlem Karadeniz
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - İlknur Buçan Kırkbir
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Tuğba Kurt
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Serbülent Ünsal
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Cavit Kart
- Faculty of Medicine, Department of Gynecology and Obstetrics, Karadeniz Technical University, Trabzon, Turkey
| | - Neslihan Baki
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
| | - Kemal Turhan
- Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey
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28
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Cubillos G, Monckeberg M, Plaza A, Morgan M, Estevez PA, Choolani M, Kemp MW, Illanes SE, Perez CA. Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy. BMC Pregnancy Childbirth 2023; 23:469. [PMID: 37353749 DOI: 10.1186/s12884-023-05766-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/08/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Early prediction of Gestational Diabetes Mellitus (GDM) risk is of particular importance as it may enable more efficacious interventions and reduce cumulative injury to mother and fetus. The aim of this study is to develop machine learning (ML) models, for the early prediction of GDM using widely available variables, facilitating early intervention, and making possible to apply the prediction models in places where there is no access to more complex examinations. METHODS The dataset used in this study includes registries from 1,611 pregnancies. Twelve different ML models and their hyperparameters were optimized to achieve early and high prediction performance of GDM. A data augmentation method was used in training to improve prediction results. Three methods were used to select the most relevant variables for GDM prediction. After training, the models ranked with the highest Area under the Receiver Operating Characteristic Curve (AUCROC), were assessed on the validation set. Models with the best results were assessed in the test set as a measure of generalization performance. RESULTS Our method allows identifying many possible models for various levels of sensitivity and specificity. Four models achieved a high sensitivity of 0.82, a specificity in the range 0.72-0.74, accuracy between 0.73-0.75, and AUCROC of 0.81. These models required between 7 and 12 input variables. Another possible choice could be a model with sensitivity of 0.89 that requires just 5 variables reaching an accuracy of 0.65, a specificity of 0.62, and AUCROC of 0.82. CONCLUSIONS The principal findings of our study are: Early prediction of GDM within early stages of pregnancy using regular examinations/exams; the development and optimization of twelve different ML models and their hyperparameters to achieve the highest prediction performance; a novel data augmentation method is proposed to allow reaching excellent GDM prediction results with various models.
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Grants
- Basal funding for Scientific and Technological Center of Excellence, IMPACT, #FB210024, FONDECYT 1231675 Agencia Nacional de Investigación y Desarrollo
- Basal funding for Scientific and Technological Center of Excellence, IMPACT, #FB210024, FONDECYT 1231675 Agencia Nacional de Investigación y Desarrollo
- Basal funding for Scientific and Technological Center of Excellence, IMPACT, #FB210024, FONDECYT 1231675 Agencia Nacional de Investigación y Desarrollo
- Basal funding for Scientific and Technological Center of Excellence, IMPACT, #FB210024, FONDECYT 1231675 Agencia Nacional de Investigación y Desarrollo
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Affiliation(s)
- Gabriel Cubillos
- Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, 8370451, Santiago, Chile
- IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile
| | - Max Monckeberg
- Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes, 7620001, Santiago, Chile
| | - Alejandra Plaza
- Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes, 7620001, Santiago, Chile
| | - Maria Morgan
- Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes, 7620001, Santiago, Chile
| | - Pablo A Estevez
- Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, 8370451, Santiago, Chile
- IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile
| | - Mahesh Choolani
- Department of Obstetrics and Gynaecology, NUS Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 12, Singapore, 119228, Singapore
| | - Matthew W Kemp
- Department of Obstetrics and Gynaecology, NUS Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 12, Singapore, 119228, Singapore
| | - Sebastian E Illanes
- IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile.
- Department of Obstetrics and Gynecology and Laboratory of Reproductive Biology, Faculty of Medicine, Universidad de los Andes, 7620001, Santiago, Chile.
| | - Claudio A Perez
- Department of Electrical Engineering, Universidad de Chile, Av. Tupper 2007, 8370451, Santiago, Chile.
- IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile.
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29
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Li J, Cairns BJ, Li J, Zhu T. Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications. NPJ Digit Med 2023; 6:98. [PMID: 37244963 DOI: 10.1038/s41746-023-00834-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/05/2023] [Indexed: 05/29/2023] Open
Abstract
The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate single type of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN that simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy.
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Affiliation(s)
- Jin Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Benjamin J Cairns
- Clinical Trial Service Unit and Epidemiological Studies, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, UK.
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Arain Z, Iliodromiti S, Slabaugh G, David AL, Chowdhury TT. Machine learning and disease prediction in obstetrics. Curr Res Physiol 2023; 6:100099. [PMID: 37324652 PMCID: PMC10265477 DOI: 10.1016/j.crphys.2023.100099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/09/2023] [Indexed: 06/17/2023] Open
Abstract
Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice.
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Affiliation(s)
- Zara Arain
- Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Stamatina Iliodromiti
- Women's Health Research Unit, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK
| | - Gregory Slabaugh
- Digital Environment Research Institute, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 1HH, UK
| | - Anna L. David
- Elizabeth Garrett Anderson Institute for Women's Health, University College London, Medical School Building, Huntley Street, London, WC1E 6AU, UK
| | - Tina T. Chowdhury
- Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
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Mennickent D, Rodríguez A, Opazo MC, Riedel CA, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano AE, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front Endocrinol (Lausanne) 2023; 14:1130139. [PMID: 37274341 PMCID: PMC10235786 DOI: 10.3389/fendo.2023.1130139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology. Aim To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Current state ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used. Future challenges To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models. Conclusion The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.
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Affiliation(s)
- Daniela Mennickent
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Andrés Rodríguez
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad del Bío-Bío, Chillán, Chile
| | - Ma. Cecilia Opazo
- Instituto de Ciencias Naturales, Facultad de Medicina Veterinaria y Agronomía, Universidad de Las Américas, Santiago, Chile
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
| | - Claudia A. Riedel
- Millennium Institute on Immunology and Immunotherapy, Santiago, Chile
- Departamento de Ciencias Biológicas, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Santiago, Chile
| | - Erica Castro
- Departamento de Obstetricia y Puericultura, Facultad de Ciencias de la Salud, Universidad de Atacama, Copiapó, Chile
| | - Alma Eriz-Salinas
- Departamento de Obstetricia y Puericultura, Facultad de Medicina, Universidad de Concepción, Concepción, Chile
| | - Javiera Appel-Rubio
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Claudio Aguayo
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
| | - Alicia E. Damiano
- Cátedra de Biología Celular y Molecular, Departamento de Ciencias Biológicas, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
- Laboratorio de Biología de la Reproducción, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO-Houssay)- CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Enrique Guzmán-Gutiérrez
- Departamento de Bioquímica Clínica e Inmunología, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
| | - Juan Araya
- Departamento de Análisis Instrumental, Facultad de Farmacia, Universidad de Concepción, Concepción, Chile
- Machine Learning Applied in Biomedicine (MLAB), Concepción, Chile
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Pinto Y, Frishman S, Turjeman S, Eshel A, Nuriel-Ohayon M, Shrossel O, Ziv O, Walters W, Parsonnet J, Ley C, Johnson EL, Kumar K, Schweitzer R, Khatib S, Magzal F, Muller E, Tamir S, Tenenbaum-Gavish K, Rautava S, Salminen S, Isolauri E, Yariv O, Peled Y, Poran E, Pardo J, Chen R, Hod M, Borenstein E, Ley RE, Schwartz B, Louzoun Y, Hadar E, Koren O. Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis. Gut 2023; 72:918-928. [PMID: 36627187 PMCID: PMC10086485 DOI: 10.1136/gutjnl-2022-328406] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 12/26/2022] [Indexed: 01/12/2023]
Abstract
OBJECTIVE Gestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities. DESIGN We comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts. RESULTS We found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy. CONCLUSION GDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention.
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Affiliation(s)
- Yishay Pinto
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Sigal Frishman
- Helen Schneider Hospital for Women, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Institute of Biochemistry, School of Nutritional Sciences Food Science and Nutrition, The School of Nutritional Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Sondra Turjeman
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - Adi Eshel
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | | | - Oshrit Shrossel
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Oren Ziv
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
| | - William Walters
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tubingen, Germany
| | - Julie Parsonnet
- Department of Medicine, Stanford University, Stanford, California, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA
| | - Catherine Ley
- Department of Medicine, Stanford University, Stanford, California, USA
| | | | - Krithika Kumar
- Division of Nutritional Sciences, Cornell University, Ithaca, New York, USA
| | - Ron Schweitzer
- Department of Natural Compounds and Analytical Chemistry, Migal-Galilee Research Institute, Kiryat Shmona, Israel
- Analytical Chemistry Laboratory, Tel-Hai College, Upper Galilee, Israel
| | - Soliman Khatib
- Department of Natural Compounds and Analytical Chemistry, Migal-Galilee Research Institute, Kiryat Shmona, Israel
- Analytical Chemistry Laboratory, Tel-Hai College, Upper Galilee, Israel
| | - Faiga Magzal
- Laboratory of Human Health and Nutrition Sciences, Migal-Galilee Technology Center, Kiryat Shmona, Israel
- Nutritional Science Department, Tel Hai College, Upper Galilee, Israel
| | - Efrat Muller
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Snait Tamir
- Laboratory of Human Health and Nutrition Sciences, Migal-Galilee Technology Center, Kiryat Shmona, Israel
- Nutritional Science Department, Tel Hai College, Upper Galilee, Israel
| | - Kinneret Tenenbaum-Gavish
- Helen Schneider Hospital for Women, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Samuli Rautava
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
- University of Helsinki & Helsinki University Hospital, New Children's Hospital, Pediatric Research Center, Helsinki, Finland
| | - Seppo Salminen
- Functional Foods Forum, University of Turku, Turku, Finland
| | - Erika Isolauri
- Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
| | - Or Yariv
- Clalit Health Services, Tel Aviv, Israel
| | - Yoav Peled
- Helen Schneider Hospital for Women, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Clalit Health Services, Tel Aviv, Israel
| | - Eran Poran
- Clalit Health Services, Tel Aviv, Israel
| | - Joseph Pardo
- Helen Schneider Hospital for Women, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Clalit Health Services, Tel Aviv, Israel
| | - Rony Chen
- Helen Schneider Hospital for Women, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Moshe Hod
- Helen Schneider Hospital for Women, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Elhanan Borenstein
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Ruth E Ley
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tubingen, Germany
| | - Betty Schwartz
- Institute of Biochemistry, School of Nutritional Sciences Food Science and Nutrition, The School of Nutritional Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Eran Hadar
- Helen Schneider Hospital for Women, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Omry Koren
- Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
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Geyer K, Raab R, Hoffmann J, Hauner H. Development and validation of a screening questionnaire for early identification of pregnant women at risk for excessive gestational weight gain. BMC Pregnancy Childbirth 2023; 23:249. [PMID: 37055730 PMCID: PMC10100402 DOI: 10.1186/s12884-023-05569-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 04/01/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND Excessive weight gain during pregnancy is associated with adverse health outcomes for mother and child. Intervention strategies to prevent excessive gestational weight gain (GWG) should consider women's individual risk profile, however, no tool exists for identifying women at risk at an early stage. The aim of the present study was to develop and validate a screening questionnaire based on early risk factors for excessive GWG. METHODS The cohort from the German "Gesund leben in der Schwangerschaft"/ "healthy living in pregnancy" (GeliS) trial was used to derive a risk score predicting excessive GWG. Sociodemographics, anthropometrics, smoking behaviour and mental health status were collected before week 12th of gestation. GWG was calculated using the last and the first weight measured during routine antenatal care. The data were randomly split into development and validation datasets with an 80:20 ratio. Using the development dataset, a multivariate logistic regression model with stepwise backward elimination was performed to identify salient risk factors associated with excessive GWG. The β coefficients of the variables were translated into a score. The risk score was validated by an internal cross-validation and externally with data from the FeLIPO study (GeliS pilot study). The area under the receiver operating characteristic curve (AUC ROC) was used to estimate the predictive power of the score. RESULTS 1790 women were included in the analysis, of whom 45.6% showed excessive GWG. High pre-pregnancy body mass index, intermediate educational level, being born in a foreign country, primiparity, smoking, and signs of depressive disorder were associated with the risk of excessive GWG and included in the screening questionnaire. The developed score varied from 0-15 and divided the women´s risk for excessive GWG into low (0-5), moderate (6-10) and high (11-15). The cross-validation and the external validation yielded a moderate predictive power with an AUC of 0.709 and 0.738, respectively. CONCLUSIONS Our screening questionnaire is a simple and valid tool to identify pregnant women at risk for excessive GWG at an early stage. It could be used in routine care to provide targeted primary prevention measures to women at particular risk to gain excessive gestational weight. TRIAL REGISTRATION NCT01958307, ClinicalTrials.gov, retrospectively registered 9 October 2013.
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Affiliation(s)
- Kristina Geyer
- Institute of Nutritional Medicine, Else Kröner-Fresenius-Centre for Nutritional Medicine, School of Medicine, Technical University of Munich, Georg-Brauchle-Ring 62, 80992, Munich, Germany
| | - Roxana Raab
- Institute of Nutritional Medicine, Else Kröner-Fresenius-Centre for Nutritional Medicine, School of Medicine, Technical University of Munich, Georg-Brauchle-Ring 62, 80992, Munich, Germany
| | - Julia Hoffmann
- Institute of Nutritional Medicine, Else Kröner-Fresenius-Centre for Nutritional Medicine, School of Medicine, Technical University of Munich, Georg-Brauchle-Ring 62, 80992, Munich, Germany
- European Foundation for the Care of Newborn Infants, Hofmannstrasse 7a, 81379, Munich, Germany
| | - Hans Hauner
- Institute of Nutritional Medicine, Else Kröner-Fresenius-Centre for Nutritional Medicine, School of Medicine, Technical University of Munich, Georg-Brauchle-Ring 62, 80992, Munich, Germany.
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Chan YN, Wang P, Chun KH, Lum JTS, Wang H, Zhang Y, Leung KSY. A machine learning approach for early prediction of gestational diabetes mellitus using elemental contents in fingernails. Sci Rep 2023; 13:4184. [PMID: 36918683 PMCID: PMC10015050 DOI: 10.1038/s41598-023-31270-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
The aim of this pilot study was to predict the risk of gestational diabetes mellitus (GDM) by the elemental content in fingernails and urine with machine learning analysis. Sixty seven pregnant women (34 control and 33 GDM patient) were included. Fingernails and urine were collected in the first and second trimesters, respectively. The concentrations of elements were determined by inductively coupled plasma-mass spectrometry. Logistic regression model was applied to estimate the adjusted odd ratios and 95% confidence intervals. The predictive performances of multiple machine learning algorithms were evaluated, and an ensemble model was built to predict the risk for GDM based on the elemental contents in the fingernails. Beryllium, selenium, tin and copper were positively associated with the risk of GDM while nickel and mercury showed opposite result. The trained ensemble model showed larger area under curve (AUC) of receiver operating characteristic curve (0.81) using fingernail Ni, Cu and Se concentrations. The model was validated by external data set with AUC = 0.71. In summary, the results of the present study highlight the potential of fingernails, as an alternative sample, together with machine learning in human biomonitoring studies.
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Affiliation(s)
- Yun-Nam Chan
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR
| | - Pengpeng Wang
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China (Fudan University), Shanghai, China
| | - Ka-Him Chun
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR
| | - Judy Tsz-Shan Lum
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR
| | - Hang Wang
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China (Fudan University), Shanghai, China
| | - Yunhui Zhang
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China (Fudan University), Shanghai, China
| | - Kelvin Sze-Yin Leung
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR.
- HKBU Institute of Research and Continuing Education, Shenzhen Virtual University Park, Shenzhen, China.
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Wang Z, Zhang L, Chao Y, Xu M, Geng X, Hu X. DEVELOPMENT OF A MACHINE LEARNING MODEL FOR PREDICTING 28-DAY MORTALITY OF SEPTIC PATIENTS WITH ATRIAL FIBRILLATION. Shock 2023; 59:400-408. [PMID: 36597764 DOI: 10.1097/shk.0000000000002078] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
ABSTRACT Introduction: Septic patients with atrial fibrillation (AF) are common in the intensive care unit accompanied by high mortality. The early prediction of prognosis of these patients is critical for clinical intervention. This study aimed to develop a model by using machine learning (ML) algorithms to predict the risk of 28-day mortality in septic patients with AF. Methods: In this retrospective cohort study, we extracted septic patients with AF from the Medical Information Mart for Intensive Care III (MIMIC-III) and IV database. Afterward, only MIMIC-IV cohort was randomly divided into training or internal validation set. External validation set was mainly extracted from MIMIC-III database. Propensity score matching was used to reduce the imbalance between the external validation and internal validation data sets. The predictive factors for 28-day mortality were determined by using multivariate logistic regression. Then, we constructed models by using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve, sensitivity, specificity, recall, and accuracy. Results: A total of 5,317 septic patients with AF were enrolled, with 3,845 in the training set, 960 in the internal testing set, and 512 in the external testing set, respectively. Then, we established four prediction models by using ML algorithms. AdaBoost showed moderate performance and had a higher accuracy than the other three models. Compared with other severity scores, the AdaBoost obtained more net benefit. Conclusion: We established the first ML model for predicting the 28-day mortality of septic patients with AF. Compared with conventional scoring systems, the AdaBoost model performed moderately. The model established will have the potential to improve the level of clinical practice.
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Affiliation(s)
- Ziwen Wang
- Department of Intensive Care Unit, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, People's Republic of China
| | - Linna Zhang
- Department of Intensive Care Unit, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, People's Republic of China
| | - Yali Chao
- Department of Intensive Care Unit, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, People's Republic of China
| | - Meng Xu
- Department of Intensive Care Unit, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, People's Republic of China
| | - Xiaojuan Geng
- Department of Intensive Care Unit, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, People's Republic of China
| | - Xiaoyi Hu
- Department of Anesthesiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui Province, People's Republic of China
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Amylidi‐Mohr S, Lang C, Mosimann B, Fiedler GM, Stettler C, Surbek D, Raio L. First-trimester glycosylated hemoglobin (HbA1c) and maternal characteristics in the prediction of gestational diabetes: An observational cohort study. Acta Obstet Gynecol Scand 2023; 102:294-300. [PMID: 36524557 PMCID: PMC9951355 DOI: 10.1111/aogs.14495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/30/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022]
Abstract
INTRODUCTION This study aimed to investigate the extent to which gestational diabetes mellitus (GDM) can be predicted in the first trimester by combining a marker of growing interest, glycosylated hemoglobin A1c (HbA1c), and maternal characteristics. MATERIAL AND METHODS This observational study was conducted in the outpatient obstetric department of our institution. The values of HbA1c and venous random plasma glucose were prospectively assessed in the first trimester of pregnancy. We determined maternal characteristics that were independent predictors from the regression analysis and calculated areas under the receiver-operating curves by combining the maternal age, body mass index, previous history of GDM, and first-degree family history for diabetes mellitus. Moreover we investigated the predictive capability of HbA1c to exclude GDM. Patients with a first-trimester HbA1c level of 6.5% (48 mmol/mol) or more were excluded. The study was registered at ClinicalTrials.gov ID: NCT02139254. RESULTS We included 785 cases with complete dataset. The prevalence of GDM was 14.7% (115/785). Those who developed GDM had significantly higher HbA1c and random plasma glucose values (p < 0.0001 and p = 0.0002, respectively). In addition, they had a higher body mass index, were more likely to have a history of GDM and/or a first-degree family history of diabetes. When these maternal characteristics were combined with the first-trimester HbA1c and random plasma glucose the combined area under the receiver operating characteristics curve was 0.76 (95% CI 0.70-0.81). CONCLUSIONS Our results indicate that HbA1c and random plasma glucose values combined with age, body mass index, and personal and family history, allow the identification of women in the first trimester who are at increased risk of developing GDM.
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Affiliation(s)
- Sofia Amylidi‐Mohr
- Department of Gynecology and ObstetricsUniversity Institute of Clinical Chemistry, University of BernBernSwitzerland
| | - Cheryl Lang
- Department of Gynecology and ObstetricsUniversity Institute of Clinical Chemistry, University of BernBernSwitzerland
| | - Beatrice Mosimann
- Department of Gynecology and ObstetricsUniversity Institute of Clinical Chemistry, University of BernBernSwitzerland
| | - Georg M. Fiedler
- Laboratory of MedicineUniversity Institute of Clinical Chemistry, University of BernBernSwitzerland
| | - Christoph Stettler
- Department of Diabetology and EndocrinologyUniversity Hospital Inselspital Bern, University of BernBernSwitzerland
| | - Daniel Surbek
- Department of Gynecology and ObstetricsUniversity Institute of Clinical Chemistry, University of BernBernSwitzerland
| | - Luigi Raio
- Department of Gynecology and ObstetricsUniversity Institute of Clinical Chemistry, University of BernBernSwitzerland
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Sufriyana H, Wu YW, Su ECY. Human-guided deep learning with ante-hoc explainability by convolutional network from non-image data for pregnancy prognostication. Neural Netw 2023; 162:99-116. [PMID: 36898257 DOI: 10.1016/j.neunet.2023.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 01/30/2023] [Accepted: 02/14/2023] [Indexed: 02/26/2023]
Abstract
BACKGROUND AND OBJECTIVE Deep learning is applied in medicine mostly due to its state-of-the-art performance for diagnostic imaging. Supervisory authorities also require the model to be explainable, but most explain the model after development (post hoc) instead of incorporating explanation into the design (ante hoc). This study aimed to demonstrate a human-guided deep learning with ante-hoc explainability by convolutional network from non-image data to develop, validate, and deploy a prognostic prediction model for PROM and an estimator of time of delivery using a nationwide health insurance database. METHODS To guide modeling, we constructed and verified association diagrams respectively from literatures and electronic health records. Non-image data were transformed into meaningful images utilizing predictor-to-predictor similarities, harnessing the power of convolutional neural network mostly used for diagnostic imaging. The network architecture was also inferred from the similarities. RESULTS This resulted the best model for prelabor rupture of membranes (n=883, 376) with the area under curves 0.73 (95% CI 0.72 to 0.75) and 0.70 (95% CI 0.69 to 0.71) respectively by internal and external validations, and outperformed previous models found by systematic review. It was explainable by knowledge-based diagrams and model representation. CONCLUSIONS This allows prognostication with actionable insights for preventive medicine.
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Affiliation(s)
- Herdiantri Sufriyana
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan; Department of Medical Physiology, Faculty of Medicine, Universitas Nahdlatul Ulama Surabaya, 57 Raya Jemursari Road, Surabaya 60237, Indonesia
| | - Yu-Wei Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, 250 Wu-Xing Street, Taipei 11031, Taiwan
| | - Emily Chia-Yu Su
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, 250 Wu-Xing Street, Taipei 11031, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wu-Xing Street, Taipei 11031, Taiwan.
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Kim JH, Kim Y, Yoo K, Kim M, Kang SS, Kwon YS, Lee JJ. Prediction of Postoperative Pulmonary Edema Risk Using Machine Learning. J Clin Med 2023; 12:jcm12051804. [PMID: 36902590 PMCID: PMC10003313 DOI: 10.3390/jcm12051804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 02/16/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023] Open
Abstract
Postoperative pulmonary edema (PPE) is a well-known postoperative complication. We hypothesized that a machine learning model could predict PPE risk using pre- and intraoperative data, thereby improving postoperative management. This retrospective study analyzed the medical records of patients aged > 18 years who underwent surgery between January 2011 and November 2021 at five South Korean hospitals. Data from four hospitals (n = 221,908) were used as the training dataset, whereas data from the remaining hospital (n = 34,991) were used as the test dataset. The machine learning algorithms used were extreme gradient boosting, light-gradient boosting machine, multilayer perceptron, logistic regression, and balanced random forest (BRF). The prediction abilities of the machine learning models were assessed using the area under the receiver operating characteristic curve, feature importance, and average precisions of precision-recall curve, precision, recall, f1 score, and accuracy. PPE occurred in 3584 (1.6%) and 1896 (5.4%) patients in the training and test sets, respectively. The BRF model exhibited the best performance (area under the receiver operating characteristic curve: 0.91, 95% confidence interval: 0.84-0.98). However, its precision and f1 score metrics were not good. The five major features included arterial line monitoring, American Society of Anesthesiologists physical status, urine output, age, and Foley catheter status. Machine learning models (e.g., BRF) could predict PPE risk and improve clinical decision-making, thereby enhancing postoperative management.
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Affiliation(s)
- Jong Ho Kim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon-si 24252, Republic of Korea
| | - Youngmi Kim
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon-si 24252, Republic of Korea
| | - Kookhyun Yoo
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
| | - Minguan Kim
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
| | - Seong Sik Kang
- Department of Anesthesiology and Pain Medicine, College of Medicine, Kangwon National University, Chuncheon-si 24341, Republic of Korea
| | - Young-Suk Kwon
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon-si 24252, Republic of Korea
- Correspondence: (Y.-S.K.); (J.J.L.); Tel.: +82-33-240-5271 (Y.-S.K. & J.J.L.)
| | - Jae Jun Lee
- Department of Anesthesiology and Pain Medicine, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon-si 24253, Republic of Korea
- Institute of New Frontier Research Team, Hallym University College of Medicine, Chuncheon-si 24252, Republic of Korea
- Correspondence: (Y.-S.K.); (J.J.L.); Tel.: +82-33-240-5271 (Y.-S.K. & J.J.L.)
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Wang J, Qiu J, Zhu T, Zeng Y, Yang H, Shang Y, Yin J, Sun Y, Qu Y, Valdimarsdóttir UA, Song H. Prediction of Suicidal Behaviors in the Middle-aged Population: Machine Learning Analyses of UK Biobank. JMIR Public Health Surveill 2023; 9:e43419. [PMID: 36805366 PMCID: PMC9989910 DOI: 10.2196/43419] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/21/2022] [Accepted: 01/12/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Suicidal behaviors, including suicide deaths and attempts, are major public health concerns. However, previous suicide models required a huge amount of input features, resulting in limited applicability in clinical practice. OBJECTIVE We aimed to construct applicable models (ie, with limited features) for short- and long-term suicidal behavior prediction. We further validated these models among individuals with different genetic risks of suicide. METHODS Based on the prospective cohort of UK Biobank, we included 223 (0.06%) eligible cases of suicide attempts or deaths, according to hospital inpatient or death register data within 1 year from baseline and randomly selected 4460 (1.18%) controls (1:20) without such records. We similarly identified 833 (0.22%) cases of suicidal behaviors 1 to 6 years from baseline and 16,660 (4.42%) corresponding controls. Based on 143 input features, mainly including sociodemographic, environmental, and psychosocial factors; medical history; and polygenic risk scores (PRS) for suicidality, we applied a bagged balanced light gradient-boosting machine (LightGBM) with stratified 10-fold cross-validation and grid-search to construct the full prediction models for suicide attempts or deaths within 1 year or between 1 and 6 years. The Shapley Additive Explanations (SHAP) approach was used to quantify the importance of input features, and the top 20 features with the highest SHAP values were selected to train the applicable models. The external validity of the established models was assessed among 50,310 individuals who participated in UK Biobank repeated assessments both overall and by the level of PRS for suicidality. RESULTS Individuals with suicidal behaviors were on average 56 years old, with equal sex distribution. The application of these full models in the external validation data set demonstrated good model performance, with the area under the receiver operating characteristic (AUROC) curves of 0.919 and 0.892 within 1 year and between 1 and 6 years, respectively. Importantly, the applicable models with the top 20 most important features showed comparable external-validated performance (AUROC curves of 0.901 and 0.885) as the full models, based on which we found that individuals in the top quintile of predicted risk accounted for 91.7% (n=11) and 80.7% (n=25) of all suicidality cases within 1 year and during 1 to 6 years, respectively. We further obtained comparable prediction accuracy when applying these models to subpopulations with different genetic susceptibilities to suicidality. For example, for the 1-year risk prediction, the AUROC curves were 0.907 and 0.885 for the high (>2nd tertile of PRS) and low (<1st) genetic susceptibilities groups, respectively. CONCLUSIONS We established applicable machine learning-based models for predicting both the short- and long-term risk of suicidality with high accuracy across populations of varying genetic risk for suicide, highlighting a cost-effective method of identifying individuals with a high risk of suicidality.
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Affiliation(s)
- Junren Wang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jiajun Qiu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Ting Zhu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yu Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Huazhen Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yanan Shang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jin Yin
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yajing Sun
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Yuanyuan Qu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Unnur A Valdimarsdóttir
- Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland.,Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.,Department of Epidemiology, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China.,Center of Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavík, Iceland
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Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia. PLoS One 2023; 18:e0280513. [PMID: 36638142 PMCID: PMC9838876 DOI: 10.1371/journal.pone.0280513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/02/2023] [Indexed: 01/14/2023] Open
Abstract
Maternal thyroid alterations have been widely associated with the risk of gestational diabetes mellitus (GDM). This study aims to 1) test the first and the second trimester full maternal thyroid profile on the prediction of GDM, both alone and combined with non-thyroid data; and 2) make that prediction independent of the diagnostic criteria, by evaluating the effectiveness of the different maternal variables on the prediction of oral glucose tolerance test (OGTT) post load glycemia. Pregnant women were recruited in Concepción, Chile. GDM diagnosis was performed at 24-28 weeks of pregnancy by an OGTT (n = 54 for normal glucose tolerance, n = 12 for GDM). 75 maternal thyroid and non-thyroid parameters were recorded in the first and the second trimester of pregnancy. Various combinations of variables were assessed for GDM and post load glycemia prediction through different classification and regression machine learning techniques. The best predictive models were simplified by variable selection. Every model was subjected to leave-one-out cross-validation. Our results indicate that thyroid markers are useful for the prediction of GDM and post load glycemia, especially at the second trimester of pregnancy. Thus, they could be used as an alternative screening tool for GDM, independently of the diagnostic criteria used. The final classification models predict GDM with cross-validation areas under the receiver operating characteristic curve of 0.867 (p<0.001) and 0.920 (p<0.001) in the first and the second trimester of pregnancy, respectively. The final regression models predict post load glycemia with cross-validation Spearman r correlation coefficients of 0.259 (p = 0.036) and 0.457 (p<0.001) in the first and the second trimester of pregnancy, respectively. This investigation constitutes the first attempt to test the performance of the whole maternal thyroid profile on GDM and OGTT post load glycemia prediction. Future external validation studies are needed to confirm these findings in larger cohorts and different populations.
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Venkaiahppalaswamy B, Prasad Reddy PVGD, Batha S. Hybrid deep learning approaches for the detection of diabetic retinopathy using optimized wavelet based model. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Huang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, Kleinberg S, Mathioudakis N, Swerdlow MA, Klonoff DC. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. J Diabetes Sci Technol 2023; 17:224-238. [PMID: 36121302 PMCID: PMC9846408 DOI: 10.1177/19322968221124583] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.
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Affiliation(s)
| | | | - David G. Armstrong
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - Ashley N. Battarbee
- Center for Women’s Reproductive Health,
The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jorge Cuadros
- Meredith Morgan Optometric Eye Center,
University of California, Berkeley, Berkeley, CA, USA
| | - Juan C. Espinoza
- Children’s Hospital Los Angeles,
University of Southern California, Los Angeles, CA, USA
| | | | | | - Mark A. Swerdlow
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - David C. Klonoff
- Diabetes Technology Society,
Burlingame, CA, USA
- Diabetes Research Institute,
Mills-Peninsula Medical Center, San Mateo, CA, USA
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Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi MZ. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetol Metab Syndr 2022; 14:196. [PMID: 36572938 PMCID: PMC9793536 DOI: 10.1186/s13098-022-00969-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022] Open
Abstract
Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.
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Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08007-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractGestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22–26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IOT sensors to aggregate vital sings from pregnancies using invasive and noninvasive sensors. Then the vital signs transmitted to fog nodes to processed and finally stored in the cloud layer. The main contribution in this paper is located in the fog layer producing GDM module to implement two influential tasks which are: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free the cache space for the new incoming data items. The cache replacement is very important in the case of healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the incidence of GDM that may occur in the second trimester of the pregnancy. To evaluate our model, we extract data of 16,354 pregnancy women from medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data and laboratory tests was aggregated. The results of the prediction model superior the state of the art (ACC = 0.957, AUC = 0.942). Regarding to explainability, we utilized Shapley additive explanation framework to provide local and global explanation for the developed models. Overall, the proposed framework is medically intuitive, allow the early prediction of GDM with cost effective solution.
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Yan C, Yan Y, Wan Z, Zhang Z, Omberg L, Guinney J, Mooney SD, Malin BA. A Multifaceted benchmarking of synthetic electronic health record generation models. Nat Commun 2022; 13:7609. [PMID: 36494374 PMCID: PMC9734113 DOI: 10.1038/s41467-022-35295-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
Synthetic health data have the potential to mitigate privacy concerns in supporting biomedical research and healthcare applications. Modern approaches for data generation continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a systematic benchmarking framework to appraise key characteristics with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic health data and further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context.
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Affiliation(s)
- Chao Yan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yao Yan
- Sage Bionetworks, Seattle, WA, USA
| | - Zhiyu Wan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ziqi Zhang
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | - Justin Guinney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
- Tempus Labs, Chicago, IL, USA
| | - Sean D Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.
| | - Bradley A Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
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Polycystic ovary syndrome (PCOS) increases the risk of subsequent gestational diabetes mellitus (GDM): A novel therapeutic perspective. Life Sci 2022; 310:121069. [DOI: 10.1016/j.lfs.2022.121069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/03/2022] [Accepted: 10/07/2022] [Indexed: 11/09/2022]
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Wei Y, He A, Tang C, Liu H, Li L, Yang X, Wang X, Shen F, Liu J, Li J, Li R. Risk prediction models of gestational diabetes mellitus before 16 gestational weeks. BMC Pregnancy Childbirth 2022; 22:889. [PMID: 36456970 PMCID: PMC9714187 DOI: 10.1186/s12884-022-05219-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 11/15/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) can lead to adverse maternal and fetal outcomes, and early prevention is particularly important for their health, but there is no widely accepted approach to predict it in the early pregnancy. The aim of the present study is to build and evaluate predictive models for GDM using routine indexes, including maternal clinical characteristics and laboratory biomarkers, before 16 gestational weeks. METHODS A total of 2895 pregnant women were recruited and maternal clinical characteristics and laboratory biomarkers before 16 weeks of gestation were collected from two hospitals. All participants were randomly stratified into the training cohort and the internal validation cohort by the ratio of 7:3. Using multivariable logistic regression analysis, two nomogram models, including a basic model and an extended model, were built. The discrimination, calibration, and clinical validity were used to evaluate the models in the internal validation cohort. RESULTS The area under the receiver operating characteristic curve of the basic and the extended model was 0.736 and 0.756 in the training cohort, and was 0.736 and 0.763 in the validation cohort, respectively. The calibration curve analysis showed that the predicted values of the two models were not significantly different from the actual observations (p = 0.289 and 0.636 in the training cohort, p = 0.684 and 0.635 in the internal validation cohort, respectively). The decision-curve analysis showed a good clinical application value of the models. CONCLUSIONS The present study built simple and effective models, indicating that routine clinical and laboratory parameters can be used to predict the risk of GDM in the early pregnancy, and providing a novel reference for studying the prediction of GDM.
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Affiliation(s)
- Yiling Wei
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Andong He
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Chaoping Tang
- Department of Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510150, China
| | - Haixia Liu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Ling Li
- Department of Obstetrics and Gynecology, Jiangmen Maternity and Child Health Care Hospital, Jiangmen, 529000, China
| | - Xiaofeng Yang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Xiufang Wang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Fei Shen
- Department of Obstetrics and Gynecology, Jiangmen Maternity and Child Health Care Hospital, Jiangmen, 529000, China
| | - Jia Liu
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Jing Li
- Department of Obstetrics and Gynecology, Jiangmen Maternity and Child Health Care Hospital, Jiangmen, 529000, China
| | - Ruiman Li
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
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Wang N, Guo H, Jing Y, Song L, Chen H, Wang M, Gao L, Huang L, Song Y, Sun B, Cui W, Xu J. Development and Validation of Risk Prediction Models for Gestational Diabetes Mellitus Using Four Different Methods. Metabolites 2022; 12:1040. [PMID: 36355123 PMCID: PMC9697464 DOI: 10.3390/metabo12111040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/26/2022] [Accepted: 10/25/2022] [Indexed: 09/21/2023] Open
Abstract
Gestational diabetes mellitus (GDM), a common perinatal disease, is related to increased risks of maternal and neonatal adverse perinatal outcomes. We aimed to establish GDM risk prediction models that can be widely used in the first trimester using four different methods, including a score-scaled model derived from a meta-analysis using 42 studies, a logistic regression model, and two machine learning models (decision tree and random forest algorithms). The score-scaled model (seven variables) was established via a meta-analysis and a stratified cohort of 1075 Chinese pregnant women from the Northwest Women's and Children's Hospital (NWCH) and showed an area under the curve (AUC) of 0.772. The logistic regression model (seven variables) was established and validated using the above cohort and showed AUCs of 0.799 and 0.834 for the training and validation sets, respectively. Another two models were established using the decision tree (DT) and random forest (RF) algorithms and showed corresponding AUCs of 0.825 and 0.823 for the training set, and 0.816 and 0.827 for the validation set. The validation of the developed models suggested good performance in a cohort derived from another period. The score-scaled GDM prediction model, the logistic regression GDM prediction model, and the two machine learning GDM prediction models could be employed to identify pregnant women with a high risk of GDM using common clinical indicators, and interventions can be sought promptly.
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Affiliation(s)
- Ning Wang
- Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
- International Center for Obesity and Metabolic Disease Research of Xi’an Jiaotong University, Xi’an 710061, China
| | - Haonan Guo
- Department of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Yingyu Jing
- Department of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Lin Song
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
| | - Huan Chen
- Department of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Mengjun Wang
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
- Department of Endocrinology, 521 Hospital of Norinco Group, Xi’an 710065, China
| | - Lei Gao
- Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
| | - Lili Huang
- Department of Medical Ultrasound, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
| | - Yanan Song
- Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
| | - Bo Sun
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China
| | - Wei Cui
- International Center for Obesity and Metabolic Disease Research of Xi’an Jiaotong University, Xi’an 710061, China
- Department of Endocrinology and Second Department of Geriatrics, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Jing Xu
- Department of Endocrinology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, China
- International Center for Obesity and Metabolic Disease Research of Xi’an Jiaotong University, Xi’an 710061, China
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Sana SRGL, Chen GM, Lv Y, Guo L, Li EY. Metabonomics fingerprint of volatile organic compounds in serum and urine of pregnant women with gestational diabetes mellitus. World J Diabetes 2022; 13:888-899. [PMID: 36312001 PMCID: PMC9606790 DOI: 10.4239/wjd.v13.i10.888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/23/2022] [Accepted: 09/12/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is a metabolic disease with an increasing annual incidence rate. Our previous observational study found that pregnant women with GDM had mild cognitive decline.
AIM To analyze the changes in metabonomics in pregnant women with GDM and explore the mechanism of cognitive function decline.
METHODS Thirty GDM patients and 30 healthy pregnant women were analyzed. Solid-phase microextraction gas chromatography/mass spectrometry was used to detect organic matter in plasma and urine samples. Statistical analyses were conducted using principal component analysis and partial least squares discriminant analysis.
RESULTS Differential volatile metabolites in the serum of pregnant women with GDM included hexanal, 2-octen-1-ol, and 2-propanol. Differential volatile metabolites in the urine of these women included benzene, cyclohexanone, 1-hexanol, and phenol. Among the differential metabolites, the conversion of 2-propanol to acetone may further produce methylglyoxal. Therefore, 2-propanol may be a potential marker for serum methylglyoxal.
CONCLUSION 2-propanol may be a potential volatile marker to evaluate cognitive impairment in pregnant women with GDM.
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Affiliation(s)
- Si-Ri-Gu-Leng Sana
- Department of Anesthesiology, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
| | - Guang-Min Chen
- Department of Anesthesiology, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
| | - Yang Lv
- Department of Anesthesiology, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
| | - Lei Guo
- Department of Anesthesiology, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
| | - En-You Li
- Department of Anesthesiology, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
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Rahman ASMZ, Liu C, Sturm H, Hogan AM, Davis R, Hu P, Cardona ST. A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery. PLoS Comput Biol 2022; 18:e1010613. [PMID: 36228001 PMCID: PMC9624395 DOI: 10.1371/journal.pcbi.1010613] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 11/01/2022] [Accepted: 09/26/2022] [Indexed: 01/24/2023] Open
Abstract
Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their growth inhibitory activity (hit rate 0.87%) against the antibiotic-resistant bacterium Burkholderia cenocepacia and described their molecular features with a directed-message passing neural network (D-MPNN). Then, we used the data to train an ML model that achieved a receiver operating characteristic (ROC) score of 0.823 on the test set. Finally, we predicted antibacterial activity in virtual libraries corresponding to 1,614 compounds from the Food and Drug Administration (FDA)-approved list and 224,205 natural products. Hit rates of 26% and 12%, respectively, were obtained when we tested the top-ranked predicted compounds for growth inhibitory activity against B. cenocepacia, which represents at least a 14-fold increase from the previous hit rate. In addition, more than 51% of the predicted antibacterial natural compounds inhibited ESKAPE pathogens showing that predictions expand beyond the organism-specific dataset to a broad range of bacteria. Overall, the developed ML approach can be used for compound prioritization before screening, increasing the typical hit rate of drug discovery.
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Affiliation(s)
| | - Chengyou Liu
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Hunter Sturm
- Department of Chemistry, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Andrew M. Hogan
- Department of Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Rebecca Davis
- Department of Chemistry, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Pingzhao Hu
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Silvia T. Cardona
- Department of Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Medical Microbiology & Infectious Diseases, University of Manitoba, Winnipeg, Canada
- * E-mail:
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