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Adebanji AO, Asare C, Gyamerah SA. Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective. Int J Med Inform 2024; 189:105529. [PMID: 38905958 DOI: 10.1016/j.ijmedinf.2024.105529] [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: 02/13/2024] [Revised: 06/11/2024] [Accepted: 06/18/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Recent studies reveal that around 1.9 million stillbirths occur annually worldwide, with Sub-Saharan Africa having among the highest cases. Some Sub-Saharan African countries, including Ghana, failed to meet Millennium Development Goal 5 (MDG5) by 2015 and may struggle to meet Sustainable Development Goal 3 (SDG3) despite maternal healthcare interventions. Concerns arise about Ghana's ability to achieve the World Health Organization's neonatal mortality goal of 12 per 1000 live births by 2030. This study aims to identify key factors influencing childbirth outcomes and create a predictive method for high-risk pregnancies. METHODS We compared four machine learning classifiers (Extreme Gradient Boosting, Random Forest, Logistic Regression, and Artificial Neural Network) in predicting childbirth outcomes using data from a tertiary health facility in Ghana. To address class imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE). RESULTS Our findings show that fetal heartbeat, gestation age at birth are the most influential factors on birth outcome (stillbirth or live birth), while there is no significant association with maternal age, number of babies, and type of delivery method. Among the machine learning models considered, Random Forest emerged as the optimal model achieving an accuracy, F1-score, and AUC values of approximately 0.98, 0.99, and 0.90 respectively. CONCLUSION Our study identifies key factors affecting childbirth outcomes and highlights the potential of machine learning for early high-risk pregnancy detection in clinical settings. These findings are crucial for Ghana and other Sub-Saharan African countries striving to meet maternal and neonatal healthcare goals. Further research and policy initiatives can use these results to improve healthcare in the region and work toward the World Health Organization's objectives by 2030.
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Affiliation(s)
- Atinuke Olusola Adebanji
- Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Clement Asare
- Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Samuel Asante Gyamerah
- Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.
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Li Q, Li P, Chen J, Ren R, Ren N, Xia Y. Machine Learning for Predicting Stillbirth: A Systematic Review. Reprod Sci 2024:10.1007/s43032-024-01655-z. [PMID: 39078567 DOI: 10.1007/s43032-024-01655-z] [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: 01/04/2024] [Accepted: 07/11/2024] [Indexed: 07/31/2024]
Abstract
Stillbirth is a major global issue, with over 5 million cases each year. The multifactorial nature of stillbirth makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study reviewed the literature on predictive ML models for stillbirth highlighting input characteristics, performance metrics, and validation. The PubMed, Cochrane, and Web of Science databases were searched for studies using AI to develop predictive models for stillbirth. Findings were analyzed qualitatively using narrative synthesis and graphics. Risk of bias and the applicability of the studies were assessed using PROBAST. Model design and performance were discussed. Eight studies involving 14,840,654 women with gestational ages ranging from 20 weeks to full term were included in the qualitative analysis. Most studies used neural networks, random forests, and logistic regression algorithms. The number of predictive features varied from 14 to 53. Only 50% of studies validated the models. Cross-validation was commonly employed, and only 25% of studies performed external validation. All studies reported area under the curve as a performance metric (range 0.54-0.9), and five studies reported sensitivity (range, 60- 90%) and specificity (range, 64 - 93.3%). A stacked ensemble model that analyzed 53 features performed better than other models (AUC = 0.9; sensitivity and specificity > 85%). Available ML models can attain a considerable degree of accuracy for prediction of stillbirth; however, these models require further development before they can be applied in a clinical setting.
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Affiliation(s)
- Qingyuan Li
- Department of Clinical Medicine, International Medical College of Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China
| | - Pan Li
- Department of Clinical Medicine, Southwest Medical University, Zhongshan Road, No.319 Section 3, Luzhou, 646000, China
| | - Junyu Chen
- School of Public Health, Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China
| | - Ruyu Ren
- School of Public Health, Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China
| | - Ni Ren
- School of Public Health, Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China
| | - Yinyin Xia
- School of Public Health, Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China.
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Robles Espinoza K, López Uriarte GA, García Castañeda GB, Torres Muñoz I, Lugo Trampe JDJ, Elizondo Riojas G, Barboza Quintana O, Ponce Camacho M, Guzmán López A, Martínez de Villareal L. Multidisciplinary Workup for Stillbirth at a Tertiary-Care Hospital in Northeast Mexico: Findings, Challenges and Perspectives. Matern Child Health J 2024; 28:1072-1079. [PMID: 38184497 DOI: 10.1007/s10995-023-03874-3] [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] [Accepted: 12/17/2023] [Indexed: 01/08/2024]
Abstract
OBJECTIVES Stillbirth is an important health problem, and in Mexico, only half of the stillbirths have an explainable cause. The aim of this study was to implement a multidisciplinary workup to identify the etiology and potential risk factors for stillbirth at the Hospital Universitario "Dr. José Eleuterio González". METHODS This is a prospective, descriptive, observational study that included stillbirths from the Obstetrics Service from October 1st, 2019 to May 25, 2020. Evaluation strategies included a complete maternal medical history, physical examination of the fetus, and a photographic medical record. For every stillbirth either a prenatal ultrasound, a postnatal x-ray, or a fetal autopsy, were needed. Multiplex Ligation Probe Amplification (MLPA) was performed with an umbilical cord sample. RESULTS Thirty-three stillbirths were reported; 21 were included in the analysis. Eleven women (52.3%) had known risk factors for stillbirth, mainly elevated body mass index and diabetes. On physical examination, external birth defects were found in 8 fetuses (38%). X-ray was performed in 14 cases (66%), alterations were detected as a probable etiologic cause just in one. All cases underwent MLPA, which were reported negative. Three cases had criteria for autopsy. Findings were inconclusive to determine etiology. CONCLUSIONS The best tools for evaluation of stillbirth were the elaboration of clinical history, physical examination, and prenatal ultrasound. Diabetes and obesity were the most frequent risk factors found in our population. These factors are preventable by implementing strategies that lead to better prenatal care.
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Affiliation(s)
- Kiabeth Robles Espinoza
- Department of Genetics, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico.
| | - Graciela Arelí López Uriarte
- Department of Genetics, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico.
| | - Gloria Beatriz García Castañeda
- Department of Genetics, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico
| | - Iris Torres Muñoz
- Department of Genetics, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico
| | - José de Jesús Lugo Trampe
- Department of Genetics, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico
| | - Guillermo Elizondo Riojas
- Department of Radiology and Imaging, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico
| | - Oralia Barboza Quintana
- Department of Pathologic Anatomy, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico
| | - Marco Ponce Camacho
- Department of Pathologic Anatomy, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico
| | - Abel Guzmán López
- Gynecology and Obstetrics Service, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico
| | - Laura Martínez de Villareal
- Department of Genetics, Universidad Autónoma de Nuevo León, Facultad de Medicina y Hospital Universitario "Dr. José Eleuterio González", Monterrey, Mexico
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Feduniw S, Krupa I, Łagowska K, Laudański P, Tabarkiewicz J, Stawarz B, Raba G. Placental Cannabinoid Receptor Expression in Preterm Birth. J Pregnancy 2024; 2024:6620156. [PMID: 38745869 PMCID: PMC11093692 DOI: 10.1155/2024/6620156] [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: 10/12/2023] [Revised: 03/04/2024] [Accepted: 03/21/2024] [Indexed: 05/16/2024] Open
Abstract
Background: The cannabinoid receptor (CBR) plays a significant role in oogenesis, pregnancy, and childbirth. It might also play a significant role in preterm birth (PTB). The aim of the study was to investigate the association between the expression of the CBR in the placenta and the incidence of PTB. Methods: This prospective, observational, multicentre preliminary study was conducted on placental samples obtained from 109 women. The study included 95 patients hospitalized due to the high risk of PTB. They were divided into two groups: Group 1, where the expression of the CBR1 and CBR1a was analyzed, and Group 2, in which we examined CBR2 expression. The control group, that is, Group 3, consisted of 14 women who delivered at term, and their placentas were tested for the presence of all three receptor types (CBR1, CBR1a, and CBR2). Results: The study used reverse transcription and real-time PCR methods to assess the expression of CBRs in the placental tissues. The expression of the CBR2, CBR1, and CBR1a receptors was significantly lower in the placentas of women after PTB compared to those after term births, p = 0.038, 0.033, and 0.034, respectively. Conclusions: The presence of CBR mRNA in the human placental tissue was confirmed. The decreased expression of CBRs could serve as an indicator in predicting PTB.
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MESH Headings
- Humans
- Female
- Pregnancy
- Placenta/metabolism
- Premature Birth/metabolism
- Prospective Studies
- Adult
- Receptor, Cannabinoid, CB2/metabolism
- Receptor, Cannabinoid, CB2/genetics
- Receptor, Cannabinoid, CB1/metabolism
- Receptor, Cannabinoid, CB1/genetics
- Case-Control Studies
- RNA, Messenger/metabolism
- Receptors, Cannabinoid/metabolism
- Receptors, Cannabinoid/genetics
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Affiliation(s)
- Stepan Feduniw
- Department of Gynecology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Izabela Krupa
- Laboratory for Translational Research in Medicine, Centre for Innovative Research in Medical and Natural Sciences, Faculty of Medicine, University of Rzeszów, 35-310 Rzeszów, Poland
| | - Katarzyna Łagowska
- Laboratory for Translational Research in Medicine, Centre for Innovative Research in Medical and Natural Sciences, Faculty of Medicine, University of Rzeszów, 35-310 Rzeszów, Poland
| | - Piotr Laudański
- Chair and Department of Obstetrics, Gynecology and Gynecological Oncology, Medical University of Warsaw, Warsaw, Poland
- Women's Health Research Institute, Calisia University, 62-800 Kalisz, Poland
- OVIklinika Infertility Center, 01-377 Warsaw, Poland
| | - Jacek Tabarkiewicz
- Laboratory for Translational Research in Medicine, Centre for Innovative Research in Medical and Natural Sciences, Faculty of Medicine, University of Rzeszów, 35-310 Rzeszów, Poland
- Department of Human Immunology, Institute of Medical Sciences, Medical College of Rzeszów University, University of Rzeszów, 35-959 Rzeszów, Poland
| | | | - Grzegorz Raba
- Medical College of Rzeszów University, University of Rzeszów, 35-315 Rzeszów, Poland
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Wu Y, Yu X, Li M, Zhu J, Yue J, Wang Y, Man Y, Zhou C, Tong R, Wu X. Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities. Front Pharmacol 2024; 15:1366529. [PMID: 38711993 PMCID: PMC11070771 DOI: 10.3389/fphar.2024.1366529] [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: 01/06/2024] [Accepted: 04/03/2024] [Indexed: 05/08/2024] Open
Abstract
Introduction: It is known that patients with immune-abnormal co-pregnancies are at a higher risk of adverse pregnancy outcomes. Traditional pregnancy risk management systems have poor prediction abilities for adverse pregnancy outcomes in such patients, with many limitations in clinical application. In this study, we will use machine learning to screen high-risk factors for miscarriage and develop a miscarriage risk prediction model for patients with immune-abnormal pregnancies. This model aims to provide an adjunctive tool for the clinical identification of patients at high risk of miscarriage and to allow for active intervention to reduce adverse pregnancy outcomes. Methods: Patients with immune-abnormal pregnancies attending Sichuan Provincial People's Hospital were collected through electronic medical records (EMR). The data were divided into a training set and a test set in an 8:2 ratio. Comparisons were made to evaluate the performance of traditional pregnancy risk assessment tools for clinical applications. This analysis involved assessing the cost-benefit of clinical treatment, evaluating the model's performance, and determining its economic value. Data sampling methods, feature screening, and machine learning algorithms were utilized to develop predictive models. These models were internally validated using 10-fold cross-validation for the training set and externally validated using bootstrapping for the test set. Model performance was assessed by the area under the characteristic curve (AUC). Based on the best parameters, a predictive model for miscarriage risk was developed, and the SHapley additive expansion (SHAP) method was used to assess the best model feature contribution. Results: A total of 565 patients were included in this study on machine learning-based models for predicting the risk of miscarriage in patients with immune-abnormal pregnancies. Twenty-eight risk warning models were developed, and the predictive model constructed using XGBoost demonstrated the best performance with an AUC of 0.9209. The SHAP analysis of the best model highlighted the total number of medications, as well as the use of aspirin and low molecular weight heparin, as significant influencing factors. The implementation of the pregnancy risk scoring rules resulted in accuracy, precision, and F1 scores of 0.3009, 0.1663, and 0.2852, respectively. The economic evaluation showed a saving of ¥7,485,865.7 due to the model. Conclusion: The predictive model developed in this study performed well in estimating the risk of miscarriage in patients with immune-abnormal pregnancies. The findings of the model interpretation identified the total number of medications and the use of other medications during pregnancy as key factors in the early warning model for miscarriage risk. This provides an important basis for early risk assessment and intervention in immune-abnormal pregnancies. The predictive model developed in this study demonstrated better risk prediction performance than the Pregnancy Risk Management System (PRMS) and also demonstrated economic value. Therefore, miscarriage risk prediction in patients with immune-abnormal pregnancies may be the most cost-effective management method.
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Affiliation(s)
- Yue Wu
- Department of Pharmacy, Personalised Drug Therapy Key Laboratory of Sichuan Province, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xixuan Yu
- School of Pharmacy, Chengdu Medical College, Chengdu, China
| | - Mengting Li
- Department of Pharmacy, Personalised Drug Therapy Key Laboratory of Sichuan Province, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Zhu
- Department of Rheumatology and Immunology, Sichuan Provincial People’s Hospital, Chengdu, China
| | - Jun Yue
- Department of Gynaecology and Obstetrics, Sichuan Provincial People’s Hospital, Chengdu, China
| | - Yan Wang
- Department of Gynaecology and Obstetrics, Sichuan Provincial People’s Hospital, Chengdu, China
| | - Yicun Man
- Department of Gynaecology and Obstetrics, Sichuan Provincial People’s Hospital, Chengdu, China
| | - Chao Zhou
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, Chengdu, China
| | - Rongsheng Tong
- Department of Pharmacy, Personalised Drug Therapy Key Laboratory of Sichuan Province, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xingwei Wu
- Department of Pharmacy, Personalised Drug Therapy Key Laboratory of Sichuan Province, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Bosschieter TM, Xu Z, Lan H, Lengerich BJ, Nori H, Painter I, Souter V, Caruana R. Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2024; 8:65-87. [PMID: 38273984 PMCID: PMC10805688 DOI: 10.1007/s41666-023-00151-4] [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: 02/01/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 01/27/2024]
Abstract
Although most pregnancies result in a good outcome, complications are not uncommon and can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through a better understanding of risk factors, heightened surveillance for high-risk patients, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. We identify and study the most important risk factors for four types of pregnancy complications: (i) severe maternal morbidity, (ii) shoulder dystocia, (iii) preterm preeclampsia, and (iv) antepartum stillbirth. We use an Explainable Boosting Machine (EBM), a high-accuracy glass-box learning method, for the prediction and identification of important risk factors. We undertake external validation and perform an extensive robustness analysis of the EBM models. EBMs match the accuracy of other black-box ML methods, such as deep neural networks and random forests, and outperform logistic regression, while being more interpretable. EBMs prove to be robust. The interpretability of the EBM models reveal surprising insights into the features contributing to risk (e.g., maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.
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Affiliation(s)
| | - Zifei Xu
- Stanford University, Stanford, CA USA
| | - Hui Lan
- Stanford University, Stanford, CA USA
| | | | | | - Ian Painter
- Foundation for Healthcare Quality, Seattle, WA USA
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Ersak DT, Tanacan A, Laleli Koç B, Sınacı S, Kara Ö, Şahin D. The utility of complete blood parameter indices to predict stillbirths. J Matern Fetal Neonatal Med 2023; 36:2183747. [PMID: 36859825 DOI: 10.1080/14767058.2023.2183747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
OBJECTIVE In this study, we aimed to investigate the relationship between unexplained stillbirth (SB) cases and the complete blood parameter indices and we compared them with uncomplicated healthy cases. METHODS Patients diagnosed with unexplained SB cases in a tertiary center between 2019-2022 were included in this retrospective case-control study. The gestational age threshold for SBs was accepted as births after the 20th week of pregnancy. Consecutive patients with no adverse obstetric outcomes were accepted as the control group. Patients' complete blood parameter results at the time of the first admission to the hospital until 14 weeks were labeled as "1'' and at the time of delivery were labeled as "2'' and recorded. As inflammatory parameters, neutrophile-lymphocyte ratio, derivated neutrophile-lymphocyte ratio, platelet-lymphocyte ratio, lymphocyte-monocyte ratio (LMR), and hemoglobin-lymphocyte ratio (HLR) were calculated from complete blood results and recorded. RESULTS There were statistically significant differences between the groups' LMR1 (p = .040). Additionally, whereas HLR1 of the study group was 0.693 (0.38-2.72), it was 0.645 (0.15-1.82) in the control group (p = .026). However, the HLR2 of the study group was significantly lower than the control group (p = .021). CONCLUSION Necessary precautions such as fetal biophysical profile examination can be taken more frequently in the antenatal follow-up in patients considered to be at high risk of SB by using HLR. It is a novel marker that can be easily accessible and calculated from the complete blood parameters.
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Affiliation(s)
- Duygu Tugrul Ersak
- Department of Obstetrics and Gynecology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Atakan Tanacan
- Department of Obstetrics and Gynecology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Bergen Laleli Koç
- Department of Obstetrics and Gynecology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Selcan Sınacı
- Department of Obstetrics and Gynecology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Özgür Kara
- Department of Obstetrics and Gynecology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Dilek Şahin
- Department of Obstetrics and Gynecology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
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8
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Pereira G. Prediction Models for Adverse Pregnancy Outcomes in India: Methodological Considerations for an Emerging Topic. J Obstet Gynaecol India 2023; 73:461-463. [PMID: 37916050 PMCID: PMC10615984 DOI: 10.1007/s13224-021-01617-4] [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: 10/03/2021] [Accepted: 12/27/2021] [Indexed: 11/29/2022] Open
Abstract
Stillbirth is over-represented in lower and lower-middle-income countries and understandably this has motivated greater research investment in the development of prediction models. Prediction is particularly challenging for pregnancy outcomes because only part of the population is represented in observational research. Notably, unrecognised pregnancies and miscarriages are typically excluded from the development of prediction models and the consequences of such selection are not well understood. Other methodological challenges in developing stillbirth prediction models are within the control of the researcher. Identifying whether the intended model is for aetiological explanation versus prediction, attainment of a sufficiently large representative sample, and internal and external validation are among such methodological considerations. These considerations are discussed in relation to a recently published study on prediction of stillbirth after 28 weeks of pregnancy for women with hypertensive disorders of pregnancy in India. The predictive ability of this model amounts to the flip of a coin. Future screening based on such a model may be expensive, increase psychological distress among patients and introduce additional iatrogenic perinatal morbidities from over-treatment. Future research should address the methodological considerations described in this article.
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Affiliation(s)
- Gavin Pereira
- Curtin School of Population Health, Curtin University, Perth, WA 6102 Australia
- enAble Institute, Curtin University, Perth, WA Australia
- Centre for Fertility and Health (CeFH), Norwegian Institute of Public Health, Oslo, Norway
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9
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Cersonsky TEK, Ayala NK, Pinar H, Dudley DJ, Saade GR, Silver RM, Lewkowitz AK. Identifying risk of stillbirth using machine learning. Am J Obstet Gynecol 2023; 229:327.e1-327.e16. [PMID: 37315754 PMCID: PMC10527568 DOI: 10.1016/j.ajog.2023.06.017] [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: 03/06/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/16/2023]
Abstract
BACKGROUND Previous predictive models using logistic regression for stillbirth do not leverage the advanced and nuanced techniques involved in sophisticated machine learning methods, such as modeling nonlinear relationships between outcomes. OBJECTIVE This study aimed to create and refine machine learning models for predicting stillbirth using data available before viability (22-24 weeks) and throughout pregnancy, as well as demographic, medical, and prenatal visit data, including ultrasound and fetal genetics. STUDY DESIGN This is a secondary analysis of the Stillbirth Collaborative Research Network, which included data from pregnancies resulting in stillborn and live-born infants delivered at 59 hospitals in 5 diverse regions across the United States from 2006 to 2009. The primary aim was the creation of a model for predicting stillbirth using data available before viability. Secondary aims included refining models with variables available throughout pregnancy and determining variable importance. RESULTS Among 3000 live births and 982 stillbirths, 101 variables of interest were identified. Of the models incorporating data available before viability, the random forests model had 85.1% accuracy (area under the curve) and high sensitivity (88.6%), specificity (85.3%), positive predictive value (85.3%), and negative predictive value (84.8%). A random forests model using data collected throughout pregnancy resulted in accuracy of 85.0%; this model had 92.2% sensitivity, 77.9% specificity, 84.7% positive predictive value, and 88.3% negative predictive value. Important variables in the previability model included previous stillbirth, minority race, gestational age at the earliest prenatal visit and ultrasound, and second-trimester serum screening. CONCLUSION Applying advanced machine learning techniques to a comprehensive database of stillbirths and live births with unique and clinically relevant variables resulted in an algorithm that could accurately identify 85% of pregnancies that would result in stillbirth, before they reached viability. Once validated in representative databases reflective of the US birthing population and then prospectively, these models may provide effective risk stratification and clinical decision-making support to better identify and monitor those at risk of stillbirth.
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Affiliation(s)
- Tess E K Cersonsky
- Department of Obstetrics & Gynecology, Women & Infants Hospital of Rhode Island, Warren Alpert Medical School of Brown University, Providence, RI.
| | - Nina K Ayala
- Department of Obstetrics & Gynecology, Women & Infants Hospital of Rhode Island, Warren Alpert Medical School of Brown University, Providence, RI
| | - Halit Pinar
- Department of Pathology, Women & Infants Hospital of Rhode Island, Warren Alpert Medical School of Brown University, Providence, RI
| | - Donald J Dudley
- Department of Obstetrics & Gynecology, University of Virginia, Charlottesville, VA
| | - George R Saade
- Department of Obstetrics & Gynecology, Eastern Virginia Medical School, Norfolk, VA
| | - Robert M Silver
- Department of Obstetrics & Gynecology, University of Utah Health, Salt Lake City, UT
| | - Adam K Lewkowitz
- Department of Obstetrics & Gynecology, Women & Infants Hospital of Rhode Island, Warren Alpert Medical School of Brown University, Providence, RI
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Patterson JK, Thorsten VR, Eggleston B, Nolen T, Lokangaka A, Tshefu A, Goudar SS, Derman RJ, Chomba E, Carlo WA, Mazariegos M, Krebs NF, Saleem S, Goldenberg RL, Patel A, Hibberd PL, Esamai F, Liechty EA, Haque R, Petri B, Koso-Thomas M, McClure EM, Bose CL, Bauserman M. Building a predictive model of low birth weight in low- and middle-income countries: a prospective cohort study. BMC Pregnancy Childbirth 2023; 23:600. [PMID: 37608358 PMCID: PMC10464177 DOI: 10.1186/s12884-023-05866-1] [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: 02/13/2023] [Accepted: 07/21/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Low birth weight (LBW, < 2500 g) infants are at significant risk for death and disability. Improving outcomes for LBW infants requires access to advanced neonatal care, which is a limited resource in low- and middle-income countries (LMICs). Predictive modeling might be useful in LMICs to identify mothers at high-risk of delivering a LBW infant to facilitate referral to centers capable of treating these infants. METHODS We developed predictive models for LBW using the NICHD Global Network for Women's and Children's Health Research Maternal and Newborn Health Registry. This registry enrolled pregnant women from research sites in the Democratic Republic of the Congo, Zambia, Kenya, Guatemala, India (2 sites: Belagavi, Nagpur), Pakistan, and Bangladesh between January 2017 - December 2020. We tested five predictive models: decision tree, random forest, logistic regression, K-nearest neighbor and support vector machine. RESULTS We report a rate of LBW of 13.8% among the eight Global Network sites from 2017-2020, with a range of 3.8% (Kenya) and approximately 20% (in each Asian site). Of the five models tested, the logistic regression model performed best with an area under the curve of 0.72, an accuracy of 61% and a recall of 72%. All of the top performing models identified clinical site, maternal weight, hypertensive disorders, severe antepartum hemorrhage and antenatal care as key variables in predicting LBW. CONCLUSIONS Predictive modeling can identify women at high risk for delivering a LBW infant with good sensitivity using clinical variables available prior to delivery in LMICs. Such modeling is the first step in the development of a clinical decision support tool to assist providers in decision-making regarding referral of these women prior to delivery. Consistent referral of women at high-risk for delivering a LBW infant could have extensive public health consequences in LMICs by directing limited resources for advanced neonatal care to the infants at highest risk.
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Affiliation(s)
- Jackie K Patterson
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, 101 Manning Dr, Chapel Hill, NC, 27514, USA.
| | | | | | - Tracy Nolen
- RTI International, Research Triangle Park, Durham, NC, USA
| | - Adrien Lokangaka
- Kinshasa School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Antoinette Tshefu
- Kinshasa School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | | | - Richard J Derman
- Department of Obstetrics and Gynecology, Thomas Jefferson University, Philadelphia, PA, USA
| | | | | | - Manolo Mazariegos
- Institute of Nutrition of Central America and Panama, Guatemala City, Guatemala
| | - Nancy F Krebs
- School of Medicine, University of Colorado, Aurora, CO, USA
| | - Sarah Saleem
- Department of Community Health Sciences, Aga Khan University, Karachi, Pakistan
| | - Robert L Goldenberg
- Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Archana Patel
- Lata Medical Research Foundation, Nagpur & Datta Meghe Institute of Medical Sciences, Sawangi, India
| | | | - Fabian Esamai
- Department of Child Health and Paediatrics, School of Medicine, Moi University, Eldoret, Kenya
| | | | - Rashidul Haque
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Bill Petri
- Division of Infectious Diseases, University of Virginia, Charlottesville, VA, USA
| | - Marion Koso-Thomas
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | | | - Carl L Bose
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, 101 Manning Dr, Chapel Hill, NC, 27514, USA
| | - Melissa Bauserman
- Department of Pediatrics, University of North Carolina at Chapel Hill School of Medicine, 101 Manning Dr, Chapel Hill, NC, 27514, USA
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11
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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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12
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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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Kaliappan J, Bagepalli AR, Almal S, Mishra R, Hu YC, Srinivasan K. Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise. Diagnostics (Basel) 2023; 13:diagnostics13101692. [PMID: 37238178 DOI: 10.3390/diagnostics13101692] [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: 04/12/2023] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise. Machine learning models such as Decision Trees, Random Forest, SVM Classifier, KNN, Gaussian Naïve Bayes, Adaboost, Gradient Boosting, Voting Classifier, and Neural Networks are trained to determine whether the fetal health is Normal, Suspect, or Pathological. This work uses 22 features related to fetal heart rate obtained from the Cardiotocogram (CTG) clinical procedure for 2126 patients. Our paper focuses on applying various cross-validation techniques, namely, K-Fold, Hold-Out, Leave-One-Out, Leave-P-Out, Monte Carlo, Stratified K-fold, and Repeated K-fold, on the above ML algorithms to enhance them and determine the best performing algorithm. We conducted exploratory data analysis to obtain detailed inferences on the features. Gradient Boosting and Voting Classifier achieved 99% accuracy after applying cross-validation techniques. The dataset used has the dimension of 2126 × 22, and the label is multiclass classified as Normal, Suspect, and Pathological condition. Apart from incorporating cross-validation strategies on several machine learning algorithms, the research paper focuses on Blackbox evaluation, which is an Interpretable Machine Learning Technique used to understand the underlying working mechanism of each model and the means by which it picks features to train and predict values.
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Affiliation(s)
- Jayakumar Kaliappan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Apoorva Reddy Bagepalli
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Shubh Almal
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Rishabh Mishra
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Yuh-Chung Hu
- Department of Mechanical and Electromechanical Engineering, National ILan University, Yilan 26047, Taiwan
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
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Woolner AM, Bhattacharya S. Intergenerational trends in reproduction: Infertility and pregnancy loss. Best Pract Res Clin Obstet Gynaecol 2023; 86:102305. [PMID: 36639284 DOI: 10.1016/j.bpobgyn.2022.102305] [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: 08/31/2022] [Revised: 11/25/2022] [Accepted: 12/19/2022] [Indexed: 12/29/2022]
Abstract
This review article summarises the evidence for intergenerational trends observed to date within infertility and pregnancy loss. There appears to be evidence of intergenerational trends between mothers and daughters for the age at menopause, endometriosis, polycystic ovarian syndrome (PCOS), male factor infertility and miscarriage. At present, there is no evidence for a predisposition to stillbirth between mothers and daughters. One study found an association with familial predisposition for ectopic pregnancy. Very few studies have considered the potential for paternal transmission of risk of infertility or pregnancy loss. The majority of studies to date have significant limitations because of their observational design, risk of recall bias and risk of confounding. Therefore, high-quality well-designed research, with multi-centre collaboration and utilisation of registry-based data sources and individual patient data, is needed to understand whether infertility and pregnancy loss may have heritable factors. Epidemiological findings need to be followed up and investigated with translational research to determine the possible causalities as well as any implications for clinical practice.
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Affiliation(s)
- Andrea Mf Woolner
- Aberdeen Centre for Women's Health Research, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Scotland, United Kingdom.
| | - Siladitya Bhattacharya
- Aberdeen Centre for Women's Health Research, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Scotland, United Kingdom.
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15
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Kharb S, Joshi A. Multi-omics and machine learning for the prevention and management of female reproductive health. Front Endocrinol (Lausanne) 2023; 14:1081667. [PMID: 36909346 PMCID: PMC9996332 DOI: 10.3389/fendo.2023.1081667] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.
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Affiliation(s)
- Simmi Kharb
- Department of Biochemistry, Postgraduate Institute of Medical Sciences, Rohtak, Haryana, India
- *Correspondence: Simmi Kharb, ; Anagha Joshi,
| | - Anagha Joshi
- Computational Biology Unit (CBU), Department of Clinical Science, University of Bergen, Bergen, Norway
- *Correspondence: Simmi Kharb, ; Anagha Joshi,
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16
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Silva Rocha ED, de Morais Melo FL, de Mello MEF, Figueiroa B, Sampaio V, Endo PT. On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature. BMC Med Inform Decis Mak 2022; 22:334. [PMID: 36536413 PMCID: PMC9764498 DOI: 10.1186/s12911-022-02082-3] [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: 08/13/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models. METHODS We conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source. RESULTS From 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother's age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother's quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios. CONCLUSION Based on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community.
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Affiliation(s)
- Elisson da Silva Rocha
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
| | - Flavio Leandro de Morais Melo
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
| | | | - Barbara Figueiroa
- Programa Mãe Coruja Pernambucana, Secretaria de Saúde do Estado de Pernambuco, Recife, Brazil
| | | | - Patricia Takako Endo
- grid.26141.300000 0000 9011 5442Programa de Pós-Graduação em Engenharia da Computação, Universidade de Pernambuco, Recife, Brazil
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Awor S, Byanyima R, Abola B, Kiondo P, Orach CG, Ogwal-Okeng J, Kaye D, Nakimuli A. Prediction of stillbirth low resource setting in Northern Uganda. BMC Pregnancy Childbirth 2022; 22:855. [DOI: 10.1186/s12884-022-05198-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 11/08/2022] [Indexed: 11/20/2022] Open
Abstract
Abstract
Background
Women of Afro-Caribbean and Asian origin are more at risk of stillbirths. However, there are limited tools built for risk-prediction models for stillbirth within sub-Saharan Africa. Therefore, we examined the predictors for stillbirth in low resource setting in Northern Uganda.
Methods
Prospective cohort study at St. Mary’s hospital Lacor in Northern Uganda. Using Yamane’s 1967 formula for calculating sample size for cohort studies using finite population size, the required sample size was 379 mothers. We doubled the number (to > 758) to cater for loss to follow up, miscarriages, and clients opting out of the study during the follow-up period. Recruited 1,285 pregnant mothers at 16–24 weeks, excluded those with lethal congenital anomalies diagnosed on ultrasound. Their history, physical findings, blood tests and uterine artery Doppler indices were taken, and the mothers were encouraged to continue with routine prenatal care until the time for delivery. While in the delivery ward, they were followed up in labour until delivery by the research team. The primary outcome was stillbirth 24 + weeks with no signs of life. Built models in RStudio. Since the data was imbalanced with low stillbirth rate, used ROSE package to over-sample stillbirths and under-sample live-births to balance the data. We cross-validated the models with the ROSE-derived data using K (10)-fold cross-validation and obtained the area under curve (AUC) with accuracy, sensitivity and specificity.
Results
The incidence of stillbirth was 2.5%. Predictors of stillbirth were history of abortion (aOR = 3.07, 95% CI 1.11—8.05, p = 0.0243), bilateral end-diastolic notch (aOR = 3.51, 95% CI 1.13—9.92, p = 0.0209), personal history of preeclampsia (aOR = 5.18, 95% CI 0.60—30.66, p = 0.0916), and haemoglobin 9.5 – 12.1 g/dL (aOR = 0.33, 95% CI 0.11—0.93, p = 0.0375). The models’ AUC was 75.0% with 68.1% accuracy, 69.1% sensitivity and 67.1% specificity.
Conclusion
Risk factors for stillbirth include history of abortion and bilateral end-diastolic notch, while haemoglobin of 9.5—12.1 g/dL is protective.
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Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015. Sci Rep 2022; 12:19153. [PMID: 36352095 PMCID: PMC9646808 DOI: 10.1038/s41598-022-23782-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022] Open
Abstract
Preterm birth is a global public health problem with a significant burden on the individuals affected. The study aimed to extend current research on preterm birth prognostic model development by developing and internally validating models using machine learning classification algorithms and population-based routinely collected data in Western Australia. The longitudinal retrospective cohort study involved all births in Western Australia between 1980 and 2015, and the analytic sample contains 81,974 (8.6%) preterm births (< 37 weeks of gestation). Prediction models for preterm birth were developed using regularised logistic regression, decision trees, Random Forests, extreme gradient boosting, and multi-layer perceptron (MLP). Predictors included maternal socio-demographics and medical conditions, current and past pregnancy complications, and family history. Class weight was applied to handle imbalanced outcomes and stratified tenfold cross-validation was used to reduce overfitting. Close to half of the preterm births (49.1% at 5% FPR, 95% CI 48.9%,49.5%) were correctly classified by the best performing classifier (MLP) for all women when current pregnancy information was available. The sensitivity was boosted to 52.7% (95% CI 52.1%,53.3%) after including past obstetric history in a sub-population of births from multiparous women. Around half of the preterm birth can be identified antenatally at high specificity using population-based routinely collected maternal and pregnancy data. The performance of the prediction models depends on the available predictor pool that is individual and time specific.
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Khan M, Khurshid M, Vatsa M, Singh R, Duggal M, Singh K. On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review. Front Public Health 2022; 10:880034. [PMID: 36249249 PMCID: PMC9562034 DOI: 10.3389/fpubh.2022.880034] [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: 02/20/2022] [Accepted: 05/30/2022] [Indexed: 01/21/2023] Open
Abstract
A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.
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Affiliation(s)
- Misaal Khan
- Department of Smart Healthcare, Indian Institute of Technology Jodhpur, Karwar, India,All India Institute of Medical Sciences Jodhpur, Jodhpur, India
| | - Mahapara Khurshid
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India
| | - Mayank Vatsa
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India,*Correspondence: Mayank Vatsa
| | - Richa Singh
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India
| | - Mona Duggal
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Kuldeep Singh
- Department of Pediatrics, All India Institute of Medical Sciences Jodhpur, Jodhpur, India
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Shukla VV, Carlo WA. Risk Prediction for Stillbirth and Neonatal Mortality in Low-resource Settings. NEWBORN (CLARKSVILLE, MD.) 2022; 1:215-218. [PMID: 36540873 PMCID: PMC9762612 DOI: 10.5005/jp-journals-11002-0034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
High stillbirth and neonatal mortality are major public health problems, particularly in low-resource settings in low- and middle-income countries (LMIC). Despite sustained efforts by national and international organizations over the last several decades, quality intrapartum and neonatal care is not universally available, especially in these low-resource settings. A few studies identify risk factors for adverse perinatal outcomes in low-resource settings in LMICs. This review highlights the evidence of risk prediction for stillbirth and neonatal death. Evidence using advanced machine-learning statistical models built on data from low-resource settings in LMICs suggests that the predictive accuracy for intrapartum stillbirth and neonatal mortality using prenatal and pre-delivery data is low. Models with delivery and post-delivery data have good predictive accuracy of the risk for neonatal mortality. Birth weight is the most important predictor of neonatal mortality. Further validation and testing of the models in other low-resource settings and subsequent development and testing of possible interventions could advance the field.
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Affiliation(s)
- Vivek V Shukla
- Division of Neonatology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Waldemar A Carlo
- Division of Neonatology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
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Bertini A, Salas R, Chabert S, Sobrevia L, Pardo F. Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review. Front Bioeng Biotechnol 2022; 9:780389. [PMID: 35127665 PMCID: PMC8807522 DOI: 10.3389/fbioe.2021.780389] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/10/2021] [Indexed: 12/11/2022] Open
Abstract
Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications.Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications.Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method.Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy.Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.
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Affiliation(s)
- Ayleen Bertini
- Metabolic Diseases Research Laboratory (MDRL), Interdisciplinary Center for Research in Territorial Health of the Aconcagua Valley (CIISTe Aconcagua), Center for Biomedical Research (CIB), Universidad de Valparaíso, Valparaiso, Chile
- PhD Program Doctorado en Ciencias e Ingeniería para La Salud, Faculty of Medicine, Universidad de Valparaíso, Valparaiso, Chile
| | - Rodrigo Salas
- School of Biomedical Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaiso, Chile
- Centro de Investigación y Desarrollo en INGeniería en Salud – CINGS, Universidad de Valparaíso, Valparaiso, Chile
- Instituto Milenio Intelligent Healthcare Engineering, Valparaíso, Chile
| | - Steren Chabert
- School of Biomedical Engineering, Faculty of Engineering, Universidad de Valparaíso, Valparaiso, Chile
- Centro de Investigación y Desarrollo en INGeniería en Salud – CINGS, Universidad de Valparaíso, Valparaiso, Chile
- Instituto Milenio Intelligent Healthcare Engineering, Valparaíso, Chile
| | - Luis Sobrevia
- Cellular and Molecular Physiology Laboratory (CMPL), Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Physiology, Faculty of Pharmacy, Universidad de Sevilla, Seville, Spain
- University of Queensland Centre for Clinical Research (UQCCR), Faculty of Medicine and Biomedical Sciences, University of Queensland, Herston, QLD, Australia
- Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Medical School (Faculty of Medicine), São Paulo State University (UNESP), São Paulo, Brazil
- Tecnologico de Monterrey, Eutra, The Institute for Obesity Research, School of Medicine and Health Sciences, Monterrey, Mexico
| | - Fabián Pardo
- Metabolic Diseases Research Laboratory (MDRL), Interdisciplinary Center for Research in Territorial Health of the Aconcagua Valley (CIISTe Aconcagua), Center for Biomedical Research (CIB), Universidad de Valparaíso, Valparaiso, Chile
- Cellular and Molecular Physiology Laboratory (CMPL), Division of Obstetrics and Gynaecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- School of Medicine, Campus San Felipe, Faculty of Medicine, Universidad de Valparaíso, San Felipe, Chile
- *Correspondence: Fabián Pardo,
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Velichko A. A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare. SENSORS (BASEL, SWITZERLAND) 2021; 21:6209. [PMID: 34577414 PMCID: PMC8473446 DOI: 10.3390/s21186209] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/10/2021] [Accepted: 09/13/2021] [Indexed: 11/17/2022]
Abstract
Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3-10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.
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Affiliation(s)
- Andrei Velichko
- Institute of Physics and Technology, Petrozavodsk State University, 31 Lenina Str., 185910 Petrozavodsk, Russia
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23
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Gestational age as a predictor for subsequent preterm birth in New South Wales, Australia. BMC Pregnancy Childbirth 2021; 21:607. [PMID: 34488655 PMCID: PMC8422620 DOI: 10.1186/s12884-021-04084-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 08/29/2021] [Indexed: 11/10/2022] Open
Abstract
Background There is no validated evidence base on predictive ability and absolute risk of preterm birth by gestational age of the previous pregnancy. Methods We conducted a retrospective cohort study of mothers who gave birth to their first two children in New South Wales, 1994–2016 (N = 517,558 mothers). For each week of final gestational age of the first birth, we calculated relative and absolute risks of subsequent preterm birth. Results For mothers whose first birth had a gestational age of 22 to 30 weeks the absolute risks of clinically significant preterm second birth (before 28, 32, and 34 weeks) were all less than 14%. For all gestational ages of the first child the median gestational ages of the second child were all at least 38 weeks. Sensitivity and positive predictive values were all below 30%. Conclusion Previous gestational age alone is a poor predictor of subsequent risk of preterm birth.
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Khatibi T, Hanifi E, Sepehri MM, Allahqoli L. Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study. BMC Pregnancy Childbirth 2021; 21:202. [PMID: 33706701 PMCID: PMC7953639 DOI: 10.1186/s12884-021-03658-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 02/22/2021] [Indexed: 11/10/2022] Open
Abstract
Background Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features. Method A two-step stack ensemble classifier is proposed for classifying the instances into stillbirth and livebirth at the first step and then, classifying stillbirth before delivery from stillbirth during the labor at the second step. The proposed SE has two consecutive layers including the same classifiers. The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated based on Vote boosting method. Moreover, a new feature ranking method is proposed in this study based on mean decrease accuracy, Gini Index and model coefficients to find high-ranked features. Results IMAN registry dataset is used in this study considering all births at or beyond 28th gestational week from 2016/04/01 to 2017/01/01 including 1,415,623 live birth and 5502 stillbirth cases. A combination of maternal demographic features, clinical history, fetal properties, delivery descriptors, environmental features, healthcare service provider descriptors and socio-demographic features are considered. The experimental results show that our proposed SE outperforms the compared classifiers with the average accuracy of 90%, sensitivity of 91%, specificity of 88%. The discrimination of the proposed SE is assessed and the average AUC of ±95%, CI of 90.51% ±1.08 and 90% ±1.12 is obtained on training dataset for model development and test dataset for external validation, respectively. The proposed SE is calibrated using isotopic nonparametric calibration method with the score of 0.07. The process is repeated 10,000 times and AUC of SE classifiers using random different training datasets as null distribution. The obtained p-value to assess the specificity of the proposed SE is 0.0126 which shows the significance of the proposed SE. Conclusions Gestational age and fetal height are two most important features for discriminating livebirth from stillbirth. Moreover, hospital, province, delivery main cause, perinatal abnormality, miscarriage number and maternal age are the most important features for classifying stillbirth before and during delivery. Supplementary Information The online version contains supplementary material available at 10.1186/s12884-021-03658-z.
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Affiliation(s)
- Toktam Khatibi
- School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, 14117-13114, Iran.
| | - Elham Hanifi
- School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, 14117-13114, Iran
| | - Mohammad Mehdi Sepehri
- School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, 14117-13114, Iran
| | - Leila Allahqoli
- Endometriosis Research Center, Iran University of Medical Sciences (IUMS), Tehran, Iran
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25
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Espinosa C, Becker M, Marić I, Wong RJ, Shaw GM, Gaudilliere B, Aghaeepour N, Stevenson DK. Data-Driven Modeling of Pregnancy-Related Complications. Trends Mol Med 2021; 27:762-776. [PMID: 33573911 DOI: 10.1016/j.molmed.2021.01.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/01/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022]
Abstract
A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
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Affiliation(s)
- Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Ivana Marić
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald J Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA; Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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Pereira E, Tessema G, Gissler M, Regan AK, Pereira G. Re-evaluation of gestational age as a predictor for subsequent preterm birth. PLoS One 2021; 16:e0245935. [PMID: 33481959 PMCID: PMC7822520 DOI: 10.1371/journal.pone.0245935] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 01/10/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND To evaluate gestational age as a predictor of subsequent preterm birth. MATERIALS AND METHODS This was a retrospective birth cohort study to evaluate gestational age as a predictor of subsequent preterm birth. Participants were mothers who gave birth to their first two children in Western Australia, 1980-2015 (N = 255,151 mothers). For each week of final gestational age of the first birth, we calculated relative risks (RR) and absolute risks (AR) of subsequent preterm birth defined as final gestational age before 28, 32, 34 and <37 weeks. Risks were unadjusted to preserve risk factor profiles at each week of gestation. RESULTS The relative risks of second birth before 28, 32, and 34 weeks' gestation were all approximately twenty times higher for mothers whose first birth had a gestational age of 22 to 30 weeks compared to those whose first birth was at 40 weeks' gestation. The absolute risks of second birth before 28, 32, and 34 weeks' gestation for these mothers had upper confidence limits that were all less than 16.74%. The absolute risk of second birth before 37 weeks was highest at 32.11% (95% CI: 30.27, 34.02) for mothers whose first birth was 22 to 30 weeks' gestation. For all gestational ages of the first child, the lowest quartile and median gestational age of the second birth were at least 36 weeks and at least 38 weeks, respectively. Sensitivity and positive predictive values were all below 35%. CONCLUSION Relative risks of early subsequent birth increased markedly with decreasing gestational age of the first birth. However, absolute risks of clinically significant preterm birth (<28 weeks, <32 weeks, <34 weeks), sensitivity and positive predictive values remained low. Early gestational age is a strong risk factor but a poor predictor of subsequent preterm birth.
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Affiliation(s)
- Elizabeth Pereira
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
| | - Gizachew Tessema
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
| | - Mika Gissler
- Information Services Department, THL National Institute for Health and Welfare, Helsinki, Finland
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Annette K. Regan
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- School of Nursing and Health Professions, University of San Francisco, San Francisco, CA, United States of America
| | - Gavin Pereira
- Curtin School of Population Health, Curtin University, Perth, WA, Australia
- Centre for Fertility and Health (CeFH), Norwegian Institute of Public Health, Oslo, Norway
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Chiera M, Cerritelli F, Casini A, Barsotti N, Boschiero D, Cavigioli F, Corti CG, Manzotti A. Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review. Front Neurosci 2020; 14:561186. [PMID: 33071738 PMCID: PMC7544983 DOI: 10.3389/fnins.2020.561186] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/28/2020] [Indexed: 12/18/2022] Open
Abstract
Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often time-consuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a non-invasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby’s overall wellbeing and neurological development to detect stress-/pain-related behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby’s stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality. Based on literature and previous experiences, the first step to efficiently introduce HRV in the NICUs could consist in a monitoring system that uses photoplethysmography, which is low-cost and non-invasive, and displays one or a few metrics with good clinical utility. However, to fully harness HRV clinical potential and to greatly improve neonatal care, the monitoring systems will have to rely on modern bioinformatics (machine learning and artificial intelligence algorithms), which could easily integrate infant’s HRV metrics, vital signs, and especially past history, thus elaborating models capable to efficiently monitor and predict the infant’s clinical conditions. For this reason, hospitals and institutions will have to establish tight collaborations between the obstetric, neonatal, and pediatric departments: this way, healthcare would truly improve in every stage of the perinatal period (from conception to the first years of life), since information about patients’ health would flow freely among different professionals, and high-quality research could be performed integrating the data recorded in those departments.
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Affiliation(s)
- Marco Chiera
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy.,Research Commission on Manual Therapies and Mind-Body Disciplines, Societ Italiana di Psico Neuro Endocrino Immunologia, Rome, Italy
| | - Francesco Cerritelli
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy
| | - Alessandro Casini
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy
| | - Nicola Barsotti
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy.,Research Commission on Manual Therapies and Mind-Body Disciplines, Societ Italiana di Psico Neuro Endocrino Immunologia, Rome, Italy
| | | | - Francesco Cavigioli
- Neonatal Intensive Care Unit, "V. Buzzi" Children's Hospital, Azienda Socio Sanitaria Territoriale Fatebenefratelli-Sacco, Milan, Italy
| | - Carla G Corti
- Pediatric Cardiology Unit-Pediatric Department, Azienda Socio Sanitaria Territoriale Fatebenefratelli-Sacco, Milan, Italy
| | - Andrea Manzotti
- Research and Assistance for Infants to Support Experience Lab, Foundation Center for Osteopathic Medicine Collaboration, Pescara, Italy.,Neonatal Intensive Care Unit, "V. Buzzi" Children's Hospital, Azienda Socio Sanitaria Territoriale Fatebenefratelli-Sacco, Milan, Italy.,Research Department, SOMA, Istituto Osteopatia Milano, Milan, Italy
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