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Bertini A, Varela MJ, Holz A, Gonzalez P, Bastias D, Giovanetti M, Salas R, Pardo F. Impact of pregestational obesity on perinatal complications: update in a Latin American cohort. Public Health 2024; 233:170-176. [PMID: 38905746 DOI: 10.1016/j.puhe.2024.05.019] [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: 10/11/2023] [Revised: 04/22/2024] [Accepted: 05/16/2024] [Indexed: 06/23/2024]
Abstract
OBJECTIVES While the association between pregestational obesity and perinatal complications has been established, it is necessary to update the current understanding of its impact on maternal and foetal health due to its growing prevalence. Thus, this study aimed to investigate the association between pregestational obesity with the leading perinatal complications during the last 6 years. STUDY DESIGN A cross-sectional study was performed in San Felipe, Chile. Anonymised data of 11,197 deliveries that occurred between 2015 and 2021 were included. METHODS Pregestational body mass index was defined according to the World Health Organisation during the first trimester of pregnancy. The association between pregestational obesity and perinatal complications was analysed by calculating the odds ratio (OR), which was adjusted for confounding variables. Statistical differences were considered with a P-value of <0.05. RESULTS The prevalence of pregestational obesity was 30.1%. Pregestational obesity was related to a high incidence of perinatal complications (≥3 complications; P < 0.0001). The main perinatal complications were caesarean section, large for gestational age (LGA), gestational diabetes (GD), macrosomia, hypertensive disorders of pregnancy (HDP), premature rupture of membranes (PROM), intrauterine growth restriction, and failed induction. Pregestational obesity was shown to be a risk factor for macrosomia (OR: 2.3 [95% confidence interval {95% CI}: 2.0-2.8]), GD (OR: 1.9 [95% CI: 1.6-2.1]), HDP (OR: 1.8 [95% CI: 1.5-2.1]), LGA (OR: 1.6 [95% CI: 1.5-1.8]), failed induction (OR: 1.4 [95% CI: 1.0-1.8]), PROM (OR: 1.3 [95% CI: 1.1-1.6]), and caesarean section (OR: 1.3 [95% CI: 1.2-1.4]). CONCLUSIONS Pregestational obesity has been shown to be a critical risk factor for the main perinatal complications in the study population. Pregestational advice is imperative not only in preventing pregestational obesity but also in the mitigation of critical perinatal complications once they arise.
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Affiliation(s)
- A Bertini
- Metabolic Diseases Research Laboratory (MDRL), Center of Interdisciplinary Biomedical and Engineering Research for Health, School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile; Programa de Doctorado en Ciencias e Ingeniería para la Salud, Universidad de Valparaíso, Chile; Center of Interdisciplinary Biomedical and Engineering Research for Health, School of Biomedical Engineering, Faculty of Engineering, Universidad de Valparaíso, Chile; Millennium Institute for Intelligent Healthcare Engineering, Valparaíso, Chile
| | - M J Varela
- Metabolic Diseases Research Laboratory (MDRL), Center of Interdisciplinary Biomedical and Engineering Research for Health, School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile; School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile
| | - A Holz
- Metabolic Diseases Research Laboratory (MDRL), Center of Interdisciplinary Biomedical and Engineering Research for Health, School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile; School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile
| | - P Gonzalez
- Metabolic Diseases Research Laboratory (MDRL), Center of Interdisciplinary Biomedical and Engineering Research for Health, School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile; School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile
| | - D Bastias
- School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile
| | - M Giovanetti
- Metabolic Diseases Research Laboratory (MDRL), Center of Interdisciplinary Biomedical and Engineering Research for Health, School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile
| | - R Salas
- Center of Interdisciplinary Biomedical and Engineering Research for Health, School of Biomedical Engineering, Faculty of Engineering, Universidad de Valparaíso, Chile; Millennium Institute for Intelligent Healthcare Engineering, Valparaíso, Chile
| | - F Pardo
- Metabolic Diseases Research Laboratory (MDRL), Center of Interdisciplinary Biomedical and Engineering Research for Health, School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile; School of Medicine, Faculty of Medicine, Campus San Felipe, Universidad de Valparaíso, Chile.
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Isleem UN, Zaidat B, Ren R, Geng EA, Burapachaisri A, Tang JE, Kim JS, Cho SK. Can generative artificial intelligence pass the orthopaedic board examination? J Orthop 2024; 53:27-33. [PMID: 38450060 PMCID: PMC10912220 DOI: 10.1016/j.jor.2023.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 10/24/2023] [Accepted: 10/26/2023] [Indexed: 03/08/2024] Open
Abstract
Background Resident training programs in the US use the Orthopaedic In-Training Examination (OITE) developed by the American Academy of Orthopaedic Surgeons (AAOS) to assess the current knowledge of their residents and to identify the residents at risk of failing the Amerian Board of Orthopaedic Surgery (ABOS) examination. Optimal strategies for OITE preparation are constantly being explored. There may be a role for Large Language Models (LLMs) in orthopaedic resident education. ChatGPT, an LLM launched in late 2022 has demonstrated the ability to produce accurate, detailed answers, potentially enabling it to aid in medical education and clinical decision-making. The purpose of this study is to evaluate the performance of ChatGPT on Orthopaedic In-Training Examinations using Self-Assessment Exams from the AAOS database and approved literature as a proxy for the Orthopaedic Board Examination. Methods 301 SAE questions from the AAOS database and associated AAOS literature were input into ChatGPT's interface in a question and multiple-choice format and the answers were then analyzed to determine which answer choice was selected. A new chat was used for every question. All answers were recorded, categorized, and compared to the answer given by the OITE and SAE exams, noting whether the answer was right or wrong. Results Of the 301 questions asked, ChatGPT was able to correctly answer 183 (60.8%) of them. The subjects with the highest percentage of correct questions were basic science (81%), oncology (72.7%, shoulder and elbow (71.9%), and sports (71.4%). The questions were further subdivided into 3 groups: those about management, diagnosis, or knowledge recall. There were 86 management questions and 47 were correct (54.7%), 45 diagnosis questions with 32 correct (71.7%), and 168 knowledge recall questions with 102 correct (60.7%). Conclusions ChatGPT has the potential to provide orthopedic educators and trainees with accurate clinical conclusions for the majority of board-style questions, although its reasoning should be carefully analyzed for accuracy and clinical validity. As such, its usefulness in a clinical educational context is currently limited but rapidly evolving. Clinical relevance ChatGPT can access a multitude of medical data and may help provide accurate answers to clinical questions.
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Affiliation(s)
- Ula N. Isleem
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bashar Zaidat
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Renee Ren
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric A. Geng
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aonnicha Burapachaisri
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Justin E. Tang
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jun S. Kim
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samuel K. Cho
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Khan I, Khare BK. Exploring the potential of machine learning in gynecological care: a review. Arch Gynecol Obstet 2024; 309:2347-2365. [PMID: 38625543 DOI: 10.1007/s00404-024-07479-1] [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: 01/06/2024] [Accepted: 03/10/2024] [Indexed: 04/17/2024]
Abstract
Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester.This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology.
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Affiliation(s)
- Imran Khan
- Harcourt Butler Technical University, Kanpur, India.
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Kuziemsky CE, Chrimes D, Minshall S, Mannerow M, Lau F. AI Quality Standards in Health Care: Rapid Umbrella Review. J Med Internet Res 2024; 26:e54705. [PMID: 38776538 PMCID: PMC11153979 DOI: 10.2196/54705] [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: 11/19/2023] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies. OBJECTIVE This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period. METHODS We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies. RESULTS We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications. CONCLUSIONS Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.
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Affiliation(s)
| | - Dillon Chrimes
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | - Simon Minshall
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | | | - Francis Lau
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
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Patil N, Jain S. Rhinomanometry: A Comprehensive Review of Its Applications and Advancements in Rhinology Practice. Cureus 2024; 16:e61370. [PMID: 38947630 PMCID: PMC11214531 DOI: 10.7759/cureus.61370] [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: 05/22/2024] [Accepted: 05/30/2024] [Indexed: 07/02/2024] Open
Abstract
Rhinomanometry is a pivotal diagnostic technique in rhinology, providing a quantitative assessment of nasal airflow and resistance. This review comprehensively examines the historical development, principles and clinical applications of rhinomanometry, emphasising its role in diagnosing nasal obstructions, preoperative evaluations and monitoring therapeutic outcomes. Recent advancements, including the integration with imaging technologies and the application of artificial intelligence (AI), have significantly enhanced the accuracy and utility of rhinomanometry. Despite facing challenges such as technical limitations and the need for standardisation, rhinomanometry remains an invaluable tool in both clinical and research settings. The review also explores future directions, highlighting the potential for device miniaturisation, telemedicine integration, personalised protocols and collaborative research efforts. These advancements will likely expand the accessibility, accuracy and clinical relevance of rhinomanometry, solidifying its importance in the ongoing evolution of rhinology practice.
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Affiliation(s)
- Nimisha Patil
- Otolaryngology-Head and Neck Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Shraddha Jain
- Otolaryngology-Head and Neck Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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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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Watanabe K. Current status of the position on labor progress prediction for contemporary pregnant women using Friedman curves: An updated review. J Obstet Gynaecol Res 2024; 50:313-321. [PMID: 38037733 DOI: 10.1111/jog.15842] [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: 09/03/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023]
Abstract
AIM Prediction of labor progression is important for maternal and fetal health, as improved accuracy can lead to more timely intervention and improved outcomes. This review aims to outline the importance of predicting the progression of spontaneous parturition, detail the various methods employed to enhance this prediction and provide recommendations for future research. METHODS We searched articles relating to labor progression and systematic review articles on Artificial Inteligence (AI) in childbirth management using PubMed. To supplement, Google Scholar was used to find recent guidelines and related documents. RESULTS Traditional methods like vaginal examinations, criticized for subjectivity and inaccuracy, are gradually being replaced by ultrasound, considered a more objective and accurate approach. Further advancements have been observed with machine learning and artificial intelligence techniques, which promise to surpass the accuracies of conventional methods. The Friedman curve, developed in 1954, is the standard for assessing labor progress, but its application to Asian women, in particular, remains controversial, and various studies have reported that the actual rate of labor was slower than that indicated by the Friedman curve. CONCLUSION There is a need to innovate methodologies for predicting delivery tailored to modern pregnant women, especially when they have different genetic and cultural backgrounds than their Western counterparts, such as Asians. Future research should develop predictive models of labor progression that aim to enhance medical intervention and improve the safety and well-being of both mother and child.
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Affiliation(s)
- Kaori Watanabe
- National Center for Global Health and Medicine, National College of Nursing, Tokyo, Japan
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Houri O, Gil Y, Krispin E, Amitai-Komem D, Chen R, Hochberg A, Wiznitzer A, Hadar E. Predicting adverse perinatal outcomes among gestational diabetes complicated pregnancies using neural network algorithm. J Matern Fetal Neonatal Med 2023; 36:2286928. [PMID: 38044265 DOI: 10.1080/14767058.2023.2286928] [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: 08/19/2023] [Accepted: 11/19/2023] [Indexed: 12/05/2023]
Abstract
OBJECTIVE The primary aim of this study is to utilize a neural network model to predict adverse neonatal outcomes in pregnancies complicated by gestational diabetes (GDM). DESIGN Our model, based on XGBoost, was implemented using Python 3.6 with the Keras framework built on TensorFlow by Google. We sourced data from medical records of GDM-diagnosed individuals who delivered at our tertiary medical center between 2012 and 2016. The model included simple pregnancy parameters, maternal age, body mass index (BMI), parity, gravity, results of oral glucose tests, treatment modality, and glycemic control. The composite neonatal adverse outcomes defined as one of the following: large or small for gestational age, shoulder dystocia, fetal umbilical pH less than 7.2, neonatal intensive care unit (NICU) admission, respiratory distress syndrome (RDS), hyperbilirubinemia, or polycythemia. For the machine training phase, 70% of the cohort was randomly chosen. Each sample in this set consisted of baseline parameters and the composite outcome. The remaining samples were then employed to assess the accuracy of our model. RESULTS The study encompassed a total of 452 participants. The composite adverse outcome occurred in 29% of cases. Our model exhibited prediction accuracies of 82% at the time of GDM diagnosis and 91% at delivery. The factors most contributing to the prediction model were maternal age, pre-pregnancy BMI, and the results of the single 3-h 100 g oral glucose tolerance test. CONCLUSION Our advanced neural network algorithm has significant potential in predicting adverse neonatal outcomes in GDM-diagnosed individuals.
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Affiliation(s)
- Ohad Houri
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yotam Gil
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Eyal Krispin
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Daphna Amitai-Komem
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Obstetrics and Gynecology, Sheba Medical Center, Tel-Hashomer, Israel
| | - Rony Chen
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Alyssa Hochberg
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Arnon Wiznitzer
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eran Hadar
- Helen Schneider Hospital for Women, Rabin Medical Center, Petach-Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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Wang Y, Hou R, Ni B, Jiang Y, Zhang Y. Development and validation of a prediction model based on machine learning algorithms for predicting the risk of heart failure in middle-aged and older US people with prediabetes or diabetes. Clin Cardiol 2023; 46:1234-1243. [PMID: 37519220 PMCID: PMC10577538 DOI: 10.1002/clc.24104] [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: 04/18/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 08/01/2023] Open
Abstract
BACKGROUND The purpose of this study was to develop and validate a machine learning (ML) based prediction model for the risk of heart failure (HF) in patients with prediabetes or diabetes. METHODS We used 3527 subjects aged 40 years and older with a prior diagnosis of prediabetes or diabetes from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. The search for independent risk variables linked to HF was conducted using univariate and multivariable logistic regression analysis. The 3527 subjects were randomly divided into training set and validation set in a 7:3 ratio. Five ML models were built on the training set using five ML algorithms, including random forest (RF), and then validated on the validation set. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis and Bootstrap resampling method were used to measure the predictive performance of the five ML models. RESULTS Multivariate logistic regression analysis showed that age, poverty-to-income ratio, myocardial infarction condition, coronary heart disease condition, chest pain condition, and glucose-lowering medication use were independent predictors of HF. By comparing the performance of the five ML models, the RF model (AUC = 0.978) was the best prediction model. CONCLUSIONS The risk of HF in middle-aged and elderly patients with prediabetes or diabetes can be accurately predicted using ML models. The best prediction performance is presented by RF model, which can assist doctors in making clinical decisions.
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Affiliation(s)
- Yicheng Wang
- Department of Cardiovascular medicineAffiliated Fuzhou First Hospital of Fujian Medical UniversityFuzhouFujianChina
- The Third Clinical Medical CollegeFujian Medical UniversityFuzhouFujianChina
- Cardiovascular Disease Research Institute of Fuzhou CityFuzhouFujianChina
| | - Riting Hou
- Department of Cardiovascular medicineAffiliated Fuzhou First Hospital of Fujian Medical UniversityFuzhouFujianChina
- The Third Clinical Medical CollegeFujian Medical UniversityFuzhouFujianChina
- Cardiovascular Disease Research Institute of Fuzhou CityFuzhouFujianChina
| | - Binghang Ni
- Department of Cardiovascular medicineAffiliated Fuzhou First Hospital of Fujian Medical UniversityFuzhouFujianChina
- The Third Clinical Medical CollegeFujian Medical UniversityFuzhouFujianChina
- Cardiovascular Disease Research Institute of Fuzhou CityFuzhouFujianChina
| | - Yu Jiang
- Department of Cardiovascular medicineAffiliated Fuzhou First Hospital of Fujian Medical UniversityFuzhouFujianChina
- The Third Clinical Medical CollegeFujian Medical UniversityFuzhouFujianChina
- Cardiovascular Disease Research Institute of Fuzhou CityFuzhouFujianChina
| | - Yan Zhang
- Department of Cardiovascular medicineAffiliated Fuzhou First Hospital of Fujian Medical UniversityFuzhouFujianChina
- The Third Clinical Medical CollegeFujian Medical UniversityFuzhouFujianChina
- Cardiovascular Disease Research Institute of Fuzhou CityFuzhouFujianChina
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Lee SJ, Garcia GGP, Stanhope KK, Platner MH, Boulet SL. Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy. Am J Obstet Gynecol MFM 2023; 5:101096. [PMID: 37454734 DOI: 10.1016/j.ajogmf.2023.101096] [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: 03/28/2023] [Revised: 06/13/2023] [Accepted: 07/13/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND The timely identification of nulliparas at high risk of adverse fetal and neonatal outcomes during pregnancy is crucial for initiating clinical interventions to prevent perinatal complications. Although machine learning methods have been applied to predict preterm birth and other pregnancy complications, many models do not provide explanations of their predictions, limiting the clinical use of the model. OBJECTIVE This study aimed to develop interpretable prediction models for a composite adverse perinatal outcome (stillbirth, neonatal death, estimated Combined Apgar score of <10, or preterm birth) at different points in time during the pregnancy and to evaluate the marginal predictive value of individual predictors in the context of a machine learning model. STUDY DESIGN This was a secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be data, a prospective cohort study in which 10,038 nulliparous pregnant individuals with singleton pregnancies were enrolled. Here, interpretable prediction models were developed using L1-regularized logistic regression for adverse perinatal outcomes using data available at 3 study visits during the pregnancy (visit 1: 6 0/7 to 13 6/7 weeks of gestation; visit 2: 16 0/7 to 21 6/7 weeks of gestation; visit 3: 22 0/7 to 29 6/7 weeks of gestation). We identified the important predictors for each model using SHapley Additive exPlanations, a model-agnostic method of computing explanations of model predictions, and evaluated the marginal predictive value of each predictor using the DeLong test. RESULTS Our interpretable machine learning model had an area under the receiver operating characteristic curves of 0.617 (95% confidence interval, 0.595-0.639; all predictor variables at visit 1), 0.652 (95% confidence interval, 0.631-0.673; all predictor variables at visit 2), and 0.673 (95% confidence interval, 0.651-0.694; all predictor variables at visit 3). For all visits, the placental biomarker inhibin A was a valuable predictor, as including inhibin A resulted in better performance in predicting adverse perinatal outcomes (P<.001, all visits). At visit 1, endoglin was also a valuable predictor (P<.001). At visit 2, free beta human chorionic gonadotropin (P=.001) and uterine artery pulsatility index (P=.023) were also valuable predictors. At visit 3, cervical length was also a valuable predictor (P<.001). CONCLUSION Despite various advances in predictive modeling in obstetrics, the accurate prediction of adverse perinatal outcomes remains difficult. Interpretable machine learning can help clinicians understand how predictions are made, but barriers exist to the widespread clinical adoption of machine learning models for adverse perinatal outcomes. A better understanding of the evolution of risk factors for adverse perinatal outcomes throughout pregnancy is necessary for the development of effective interventions.
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Affiliation(s)
- Sun Ju Lee
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA (Ms Lee and Dr Garcia).
| | - Gian-Gabriel P Garcia
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA (Ms Lee and Dr Garcia)
| | - Kaitlyn K Stanhope
- Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA (Drs Stanhope, Platner, and Boulet)
| | - Marissa H Platner
- Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA (Drs Stanhope, Platner, and Boulet)
| | - Sheree L Boulet
- Department of Gynecology and Obstetrics, Emory University School of Medicine, Atlanta, GA (Drs Stanhope, Platner, and Boulet)
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Borboa-Olivares H, Rodríguez-Sibaja MJ, Espejel-Nuñez A, Flores-Pliego A, Mendoza-Ortega J, Camacho-Arroyo I, Gonzalez-Camarena R, Echeverria-Arjonilla JC, Estrada-Gutierrez G. A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth. Int J Mol Sci 2023; 24:13851. [PMID: 37762154 PMCID: PMC10530929 DOI: 10.3390/ijms241813851] [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: 08/23/2023] [Revised: 09/06/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
Preterm birth (PB) is a leading cause of perinatal morbidity and mortality. PB prediction is performed by measuring cervical length, with a detection rate of around 70%. Although it is known that a cytokine-mediated inflammatory process is involved in the pathophysiology of PB, none screening method implemented in clinical practice includes cytokine levels as a predictor variable. Here, we quantified cytokines in cervical-vaginal mucus of pregnant women (18-23.6 weeks of gestation) with high or low risk for PB determined by cervical length, also collecting relevant obstetric information. IL-2, IL-6, IFN-γ, IL-4, and IL-10 were significantly higher in the high-risk group, while IL-1ra was lower. Two different models for PB prediction were created using the Random Forest machine-learning algorithm: a full model with 12 clinical variables and cytokine values and the adjusted model, including the most relevant variables-maternal age, IL-2, and cervical length- (detection rate 66 vs. 87%, false positive rate 12 vs. 3.33%, false negative rate 28 vs. 6.66%, and area under the curve 0.722 vs. 0.875, respectively). The adjusted model that incorporate cytokines showed a detection rate eight points higher than the gold standard calculator, which may allow us to identify the risk PB risk more accurately and implement strategies for preventive interventions.
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Affiliation(s)
- Hector Borboa-Olivares
- Community Interventions Research Branch, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
- PhD Program in Biological and Health Sciences, Universidad Autónoma Metropolitana, Mexico City 09310, Mexico
| | - Maria Jose Rodríguez-Sibaja
- Department of Maternal-Fetal Medicine, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico;
| | - Aurora Espejel-Nuñez
- Department of Immunobiochemistry, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico; (A.E.-N.); (A.F.-P.)
| | - Arturo Flores-Pliego
- Department of Immunobiochemistry, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico; (A.E.-N.); (A.F.-P.)
| | - Jonatan Mendoza-Ortega
- Department of Bioinformatics and Statistical Analysis, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico;
| | - Ignacio Camacho-Arroyo
- Unidad de Investigación en Reproducción Humana, Instituto Nacional de Perinatología, Facultad de Química, Universidad Nacional Autónoma de Mexico, Mexico City 11000, Mexico;
| | - Ramón Gonzalez-Camarena
- Department of Health Sciences, Universidad Autónoma Metropolitana, Unidad Iztapalapa, Mexico City 09310, Mexico;
| | | | - Guadalupe Estrada-Gutierrez
- Research Division, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
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Edwards TL, Greene CA, Piekos JA, Hellwege JN, Hampton G, Jasper EA, Velez Edwards DR. Challenges and Opportunities for Data Science in Women's Health. Annu Rev Biomed Data Sci 2023; 6:23-45. [PMID: 37040736 PMCID: PMC10877578 DOI: 10.1146/annurev-biodatasci-020722-105958] [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] [Indexed: 04/13/2023]
Abstract
The intersection of women's health and data science is a field of research that has historically trailed other fields, but more recently it has gained momentum. This growth is being driven not only by new investigators who are moving into this area but also by the significant opportunities that have emerged in new methodologies, resources, and technologies in data science. Here, we describe some of the resources and methods being used by women's health researchers today to meet challenges in biomedical data science. We also describe the opportunities and limitations of applying these approaches to advance women's health outcomes and the future of the field, with emphasis on repurposing existing methodologies for women's health.
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Affiliation(s)
- Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Catherine A Greene
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jacqueline A Piekos
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jacklyn N Hellwege
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Gabrielle Hampton
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Elizabeth A Jasper
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Center for Precision Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Digna R Velez Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Rescinito R, Ratti M, Payedimarri AB, Panella M. Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis. Healthcare (Basel) 2023; 11:healthcare11111617. [PMID: 37297757 DOI: 10.3390/healthcare11111617] [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: 04/07/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. METHODS We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. RESULTS We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. CONCLUSIONS our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.
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Affiliation(s)
- Riccardo Rescinito
- Department of Translational Medicine (DiMeT), University of Eastern Piedmont/Piemonte Orientale (UPO), 28100 Novara, Italy
| | - Matteo Ratti
- Department of Translational Medicine (DiMeT), University of Eastern Piedmont/Piemonte Orientale (UPO), 28100 Novara, Italy
| | - Anil Babu Payedimarri
- Department of Translational Medicine (DiMeT), University of Eastern Piedmont/Piemonte Orientale (UPO), 28100 Novara, Italy
| | - Massimiliano Panella
- Department of Translational Medicine (DiMeT), University of Eastern Piedmont/Piemonte Orientale (UPO), 28100 Novara, Italy
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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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Edmonds JK. Use of Artificial Intelligence to Improve Women's Health and Enhance Nursing Care. J Obstet Gynecol Neonatal Nurs 2023; 52:169-171. [PMID: 37054751 DOI: 10.1016/j.jogn.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023] Open
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Wang S, Zhou Y, You X, Wang B, Du L. Quantification of the antagonistic and synergistic effects of Pb 2+, Cu 2+, and Zn 2+ bioaccumulation by living Bacillus subtilis biomass using XGBoost and SHAP. JOURNAL OF HAZARDOUS MATERIALS 2023; 446:130635. [PMID: 36584648 DOI: 10.1016/j.jhazmat.2022.130635] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/25/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
Bioaccumulation and adsorption are efficient methods for removing heavy metal ions (HMIs) from aqueous environments. However, methods to quantifiably characterize the removal selectivity for co-existing HMIs are limited. In this study, we applied Shapley additive explanations (SHAP) following extreme gradient boosting (XGBoost) modeling, to generate SHAP values. We used these values to create an affinity interference index (AII) that quantitatively represented the interference between metal ions in a multi-metal bioaccumulation system. The selectivity for simultaneous bioaccumulation of Pb2+, Cu2+, and Zn2+ by living Bacillus subtilis biomass was then characterized as a proof of concept. The AII indicated that the bioaccumulation of Zn2+ was more strongly inhibited by Pb2+/Cu2+ (AII = 1) than that of Pb2+/Cu2+ by Zn2+. Moreover, the presence of Zn2+ promoted the bioaccumulation of Pb2+ (AII = 0.39), which was confirmed in further experiments where the bioaccumulation of Pb2+ (300 μM) was increased by 38% with Zn2+ (300 μM). This study demonstrated that the combination of XGBoost and SHAP is effective in the quantifiable characterization of the antagonistic and synergistic effects in a multi-metal simultaneous bioaccumulation system. This method could also be generalized to similar tasks for analyzing the selectivity effects in a multi-component system.
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Affiliation(s)
- Sheng Wang
- Institute of Eco-Environmental Sciences, Wenzhou Academy of Agricultural Sciences, Wenzhou 325006, Zhejiang, PR China.
| | - Ying Zhou
- Institute of Eco-Environmental Sciences, Wenzhou Academy of Agricultural Sciences, Wenzhou 325006, Zhejiang, PR China
| | - Xinxin You
- Institute of Eco-Environmental Sciences, Wenzhou Academy of Agricultural Sciences, Wenzhou 325006, Zhejiang, PR China
| | - Bing Wang
- Hangzhou Center for Disease Control and Prevention, Hangzhou 310021, Zhejiang, PR China
| | - Linna Du
- College of Advanced Materials Engineering, Jiaxing Nanhu Univerisity, Jiaxing 314001, Zhejiang, PR China.
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Ratnik K, Rull K, Aasmets O, Kikas T, Hanson E, Kisand K, Fischer K, Laan M. Novel Early Pregnancy Multimarker Screening Test for Preeclampsia Risk Prediction. Front Cardiovasc Med 2022; 9:932480. [PMID: 35966513 PMCID: PMC9363612 DOI: 10.3389/fcvm.2022.932480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/17/2022] [Indexed: 11/22/2022] Open
Abstract
Preeclampsia (PE) is a common pregnancy-linked disease, causing preterm births, complicated deliveries, and health consequences for mothers and offspring. We have previously developed 6PLEX, a multiplex assay that measures PE-related maternal serum biomarkers ADAM12, sENG, leptin, PlGF, sFlt-1, and PTX3 in a single test tube. This study investigated the potential of 6PLEX to develop novel PE prediction models for early pregnancy. We analyzed 132 serum samples drawn at 70–275 gestational days (g days) from 53 pregnant women (PE, n = 22; controls, n = 31). PE prediction models were developed using a machine learning strategy based on the stepwise selection of the most significant models and incorporating parameters with optimal resampling. Alternative models included also placental FLT1 rs4769613 T/C genotypes, a high-confidence risk factor for PE. The best performing PE prediction model using samples collected at 70–98 g days comprised of PTX3, sFlt-1, and ADAM12, the subject's parity and gestational age at sampling (AUC 0.94 [95%CI 0.84–0.99]). All cases, that developed PE several months later (onset 257.4 ± 15.2 g days), were correctly identified. The model's specificity was 80% [95%CI 65–100] and the overall accuracy was 88% [95%CI 73–95]. Incorporating additionally the placental FLT1 rs4769613 T/C genotype data increased the prediction accuracy to 93.5% [AUC = 0.97 (95%CI 0.89–1.00)]. However, 6PLEX measurements of samples collected at 100–182 g days were insufficiently informative to develop reliable PE prediction models for mid-pregnancy (accuracy <75%). In summary, the developed model opens new horizons for first-trimester PE screening, combining the easily standardizable 6PLEX assay with routinely collected antenatal care data and resulting in high sensitivity and specificity.
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Affiliation(s)
- Kaspar Ratnik
- Department of Biomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
- SYNLAB Eesti OÜ, Tallinn, Estonia
| | - Kristiina Rull
- Department of Biomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
- Women's Clinic, Tartu University Hospital, Tartu, Estonia
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - Oliver Aasmets
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Triin Kikas
- Department of Biomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Ele Hanson
- Women's Clinic, Tartu University Hospital, Tartu, Estonia
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - Kalle Kisand
- Department of Internal Medicine, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Maris Laan
- Department of Biomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia
- *Correspondence: Maris Laan
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Identifying the Early Signs of Preterm Birth from U.S. Birth Records Using Machine Learning Techniques. INFORMATION 2022. [DOI: 10.3390/info13070310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning (ML) techniques on big data. The 2016 U.S. birth records were obtained and combined with two other area-level datasets, the Area Health Resources File and the County Health Ranking. Then, we applied logistic regression with elastic net regularization, random forest, and gradient boosting machines to study a cohort of 3.6 million singleton deliveries to identify generalizable PTB risk factors. The response variable is preterm birth, which includes spontaneous and indicated PTB, and we performed a binary classification. Our results show that the most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth, which explains 10.92, 5.98, and 5.63% of the predictive power, respectively. Parents' education is one of the influential variables in predicting PTB, explaining 7.89% of the predictive power. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed with an AUC of 0.75 (sensitivity: 0.64, specificity: 0.73) for the validation dataset. In this study, we compare our results with seminal and most related studies to demonstrate the superiority of our results. The application of ML techniques improved the performance measures in the prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of preterm birth. More research is needed on these mechanisms through which socioeconomic factors affect biological responses.
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Perichart-Perera O, Avila-Sosa V, Solis-Paredes JM, Montoya-Estrada A, Reyes-Muñoz E, Rodríguez-Cano AM, González-Leyva CP, Sánchez-Martínez M, Estrada-Gutierrez G, Irles C. Vitamin D Deficiency, Excessive Gestational Weight Gain, and Oxidative Stress Predict Small for Gestational Age Newborns Using an Artificial Neural Network Model. Antioxidants (Basel) 2022; 11:574. [PMID: 35326224 PMCID: PMC8944993 DOI: 10.3390/antiox11030574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 03/10/2022] [Accepted: 03/14/2022] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Size at birth is an important early determinant of health later in life. The prevalence of small for gestational age (SGA) newborns is high worldwide and may be associated with maternal nutritional and metabolic factors. Thus, estimation of fetal growth is warranted. (2) Methods: In this work, we developed an artificial neural network (ANN) model based on first-trimester maternal body fat composition, biochemical and oxidative stress biomarkers, and gestational weight gain (GWG) to predict an SGA newborn in pregnancies with or without obesity. A sensibility analysis to classify maternal features was conducted, and a simulator based on the ANN algorithm was constructed to predict the SGA outcome. Several predictions were performed by varying the most critical maternal features attained by the model to obtain different scenarios leading to SGA. (3) Results: The ANN model showed good performance between the actual and simulated data (R2 = 0.938) and an AUROC of 0.8 on an independent dataset. The top-five maternal predictors in the first trimester were protein and lipid oxidation biomarkers (carbonylated proteins and malondialdehyde), GWG, vitamin D, and total antioxidant capacity. Finally, excessive GWG and redox imbalance predicted SGA newborns in the implemented simulator. Significantly, vitamin D deficiency also predicted simulated SGA independently of GWG or redox status. (4) Conclusions: The study provided a computational model for the early prediction of SGA, in addition to a promising simulator that facilitates hypothesis-driven constructions, to be further validated as an application.
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Affiliation(s)
- Otilia Perichart-Perera
- Nutrition and Bioprogramming Coordination, Instituto Nacional de Perinatologia, Mexico City 11000, Mexico; (O.P.-P.); (A.M.R.-C.); (C.P.G.-L.)
| | - Valeria Avila-Sosa
- Department of Physiology and Cellular Development, Instituto Nacional de Perinatologia, Mexico City 11000, Mexico;
| | - Juan Mario Solis-Paredes
- Department of Human Genetics and Genomics, Instituto Nacional de Perinatologia, Mexico City 11000, Mexico;
| | - Araceli Montoya-Estrada
- Coordination of Gynecological and Perinatal Endocrinology, Instituto Nacional de Perinatologia, Mexico City 11000, Mexico; (A.M.-E.); (E.R.-M.)
| | - Enrique Reyes-Muñoz
- Coordination of Gynecological and Perinatal Endocrinology, Instituto Nacional de Perinatologia, Mexico City 11000, Mexico; (A.M.-E.); (E.R.-M.)
| | - Ameyalli M. Rodríguez-Cano
- Nutrition and Bioprogramming Coordination, Instituto Nacional de Perinatologia, Mexico City 11000, Mexico; (O.P.-P.); (A.M.R.-C.); (C.P.G.-L.)
| | - Carla P. González-Leyva
- Nutrition and Bioprogramming Coordination, Instituto Nacional de Perinatologia, Mexico City 11000, Mexico; (O.P.-P.); (A.M.R.-C.); (C.P.G.-L.)
| | - Maribel Sánchez-Martínez
- Department of Immunobiochemistry, Instituto Nacional de Perinatologia, Mexico City 11000, Mexico;
| | | | - Claudine Irles
- Department of Physiology and Cellular Development, Instituto Nacional de Perinatologia, Mexico City 11000, Mexico;
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Deng Y, Zhou Y, Shi J, Yang J, Huang H, Zhang M, Wang S, Ma Q, Liu Y, Li B, Yan J, Yang H. Potential genetic biomarkers predict adverse pregnancy outcome during early and mid-pregnancy in women with systemic lupus erythematosus. Front Endocrinol (Lausanne) 2022; 13:957010. [PMID: 36465614 PMCID: PMC9708709 DOI: 10.3389/fendo.2022.957010] [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: 05/30/2022] [Accepted: 11/01/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Effectively predicting the risk of adverse pregnancy outcome (APO) in women with systemic lupus erythematosus (SLE) during early and mid-pregnancy is a challenge. This study was aimed to identify potential markers for early prediction of APO risk in women with SLE. METHODS The GSE108497 gene expression dataset containing 120 samples (36 patients, 84 controls) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed, and differentially expressed genes (DEGs) were screened to define candidate APO marker genes. Next, three individual machine learning methods, random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator, were combined to identify feature genes from the APO candidate set. The predictive performance of feature genes for APO risk was assessed using area under the receiver operating characteristic curve (AUC) and calibration curves. The potential functions of these feature genes were finally analyzed by conventional gene set enrichment analysis and CIBERSORT algorithm analysis. RESULTS We identified 321 significantly up-regulated genes and 307 down-regulated genes between patients and controls, along with 181 potential functionally associated genes in the WGCNA analysis. By integrating these results, we revealed 70 APO candidate genes. Three feature genes, SEZ6, NRAD1, and LPAR4, were identified by machine learning methods. Of these, SEZ6 (AUC = 0.753) showed the highest in-sample predictive performance for APO risk in pregnant women with SLE, followed by NRAD1 (AUC = 0.694) and LPAR4 (AUC = 0.654). After performing leave-one-out cross validation, corresponding AUCs for SEZ6, NRAD1, and LPAR4 were 0.731, 0.668, and 0.626, respectively. Moreover, CIBERSORT analysis showed a positive correlation between regulatory T cell levels and SEZ6 expression (P < 0.01), along with a negative correlation between M2 macrophages levels and LPAR4 expression (P < 0.01). CONCLUSIONS Our preliminary findings suggested that SEZ6, NRAD1, and LPAR4 might represent the useful genetic biomarkers for predicting APO risk during early and mid-pregnancy in women with SLE, and enhanced our understanding of the origins of pregnancy complications in pregnant women with SLE. However, further validation was required.
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Affiliation(s)
- Yu Deng
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
- Beijing Key Laboratory of Maternal Fetal Medicine of Gestational Diabetes Mellitus, Beijing, China
| | - Yiran Zhou
- Department of Medicinal Chemistry, School of Pharmaceutical Sciences, Peking University, Beijing, China
| | - Jiangcheng Shi
- School of Life Sciences, Tiangong University, Tianjin, China
| | - Junting Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Hong Huang
- Department of Rheumatology and Clinical Immunology, Peking University First Hospital, Beijing, China
| | - Muqiu Zhang
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
- Beijing Key Laboratory of Maternal Fetal Medicine of Gestational Diabetes Mellitus, Beijing, China
| | - Shuxian Wang
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
- Beijing Key Laboratory of Maternal Fetal Medicine of Gestational Diabetes Mellitus, Beijing, China
| | - Qian Ma
- Department of Clinical Laboratory, Peking University First Hospital, Beijing, China
| | - Yingnan Liu
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
- Beijing Key Laboratory of Maternal Fetal Medicine of Gestational Diabetes Mellitus, Beijing, China
| | - Boya Li
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
- Beijing Key Laboratory of Maternal Fetal Medicine of Gestational Diabetes Mellitus, Beijing, China
| | - Jie Yan
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
- Beijing Key Laboratory of Maternal Fetal Medicine of Gestational Diabetes Mellitus, Beijing, China
| | - Huixia Yang
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
- Beijing Key Laboratory of Maternal Fetal Medicine of Gestational Diabetes Mellitus, Beijing, China
- *Correspondence: Huixia Yang,
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