This paper is in the following e-collection/theme issue:

Machine Learning (1370) Clinical Information and Decision Making (1314) Digital Health Reviews (1174) Decision Support for Health Professionals (1127)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16503, first published .
Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

Authors of this article:

Herdiantri Sufriyana1, 2 Author Orcid Image ;   Atina Husnayain1, 3 Author Orcid Image ;   Ya-Lin Chen1, 4 Author Orcid Image ;   Chao-Yang Kuo1 Author Orcid Image ;   Onkar Singh5, 6 Author Orcid Image ;   Tso-Yang Yeh7 Author Orcid Image ;   Yu-Wei Wu1, 8 Author Orcid Image ;   Emily Chia-Yu Su1, 8 Author Orcid Image
Article Authors Cited by (50) Tweetations (4) Metrics

Journals

  1. Jia G, Song Z, Xu Z, Tao Y, Wu Y, Wan X. Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis. BMC Medical Genomics 2021;14(1) View
  2. Nagayasu Y, Fujita D, Ohmichi M, Hayashi Y. Use of an artificial intelligence‐based rule extraction approach to predict an emergency cesarean section. International Journal of Gynecology & Obstetrics 2022;157(3):654 View
  3. Hanhart J, Weill Y, Wasser L, Zadok D, Glick A, Farkash R, Grisaro-Granovsky S, Sela H, Avitan T. Thinning of specific retinal layers as a novel biomarker for adverse outcomes in high-risk pregnancy. Journal Français d'Ophtalmologie 2022;45(10):1171 View
  4. Wu C, Shen H, Lu C, Chen S, Chen H. Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT. Diagnostics 2021;11(9):1718 View
  5. Zhao L, Zheng Y, Zhao J, Li G, Compton B, Zhang R, Fang F, Heyman G, Lee K. Cheating among elementary school children: A machine learning approach. Child Development 2023;94(4):922 View
  6. Zarkowsky D, Stonko D. Artificial intelligence's role in vascular surgery decision-making. Seminars in Vascular Surgery 2021;34(4):260 View
  7. Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. International Journal of Medical Informatics 2022;166:104855 View
  8. Susanty S, Sufriyana H, Su E, Chuang Y, Rashid T. Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults. PLOS ONE 2023;18(1):e0280330 View
  9. Ryu L, Han K. Machine Learning vs. Statistical Model for Prediction Modelling: Application in Medical Imaging Research. Journal of the Korean Society of Radiology 2022;83(6):1219 View
  10. Sharifi-Heris Z, Laitala J, Airola A, Rahmani A, Bender M. Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review. JMIR Medical Informatics 2022;10(4):e33875 View
  11. Tang R, Luo R, Tang S, Song H, Chen X. Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis. International Journal of Antimicrobial Agents 2022;60(5-6):106684 View
  12. Barboi C, Tzavelis A, Muhammad L. Comparison of Severity of Illness Scores and Artificial Intelligence Models That Are Predictive of Intensive Care Unit Mortality: Meta-analysis and Review of the Literature. JMIR Medical Informatics 2022;10(5):e35293 View
  13. Perrone S, Negro S, Laschi E, Calderisi M, Giordano M, De Bernardo G, Parigi G, Toni A, Esposito S, Buonocore G. Metabolomic Profile of Young Adults Born Preterm. Metabolites 2021;11(10):697 View
  14. Sufriyana H, Wu Y, Su E. Human-guided deep learning with ante-hoc explainability by convolutional network from non-image data for pregnancy prognostication. Neural Networks 2023;162:99 View
  15. Melinte-Popescu A, Vasilache I, Socolov D, Melinte-Popescu M. Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study. Journal of Clinical Medicine 2023;12(2):418 View
  16. Bertini A, Salas R, Chabert S, Sobrevia L, Pardo F. Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review. Frontiers in Bioengineering and Biotechnology 2022;9 View
  17. Dhiman P, Ma J, Andaur Navarro C, Speich B, Bullock G, Damen J, Hooft L, Kirtley S, Riley R, Van Calster B, Moons K, Collins G. Risk of bias of prognostic models developed using machine learning: a systematic review in oncology. Diagnostic and Prognostic Research 2022;6(1) View
  18. Kim J, Gwak D, Kim S, Gang M. Identifying the suicidal ideation risk group among older adults in rural areas: Developing a predictive model using machine learning methods. Journal of Advanced Nursing 2023;79(2):641 View
  19. Brankovic A, Rolls D, Boyle J, Niven P, Khanna S. Identifying patients at risk of unplanned re-hospitalisation using statewide electronic health records. Scientific Reports 2022;12(1) View
  20. Melinte-Popescu M, Vasilache I, Socolov D, Melinte-Popescu A. Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study. Diagnostics 2023;13(2):287 View
  21. Liu Q, Salanti G, De Crescenzo F, Ostinelli E, Li Z, Tomlinson A, Cipriani A, Efthimiou O. Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression. BMC Psychiatry 2022;22(1) View
  22. Liu Z, Han N, Su T, Ji Y, Bao H, Zhou S, Luo S, Wang H, Liu J, Wang H. Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study. Frontiers in Pediatrics 2022;10 View
  23. Langenberger B, Schulte T, Groene O, Huk M. The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data. PLOS ONE 2023;18(1):e0279540 View
  24. Vieira S, Liang X, Guiomar R, Mechelli A. Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies. Clinical Psychology Review 2022;97:102193 View
  25. Chiu Y, Jhou M, Lee T, Lu C, Chen M. Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease. Risk Management and Healthcare Policy 2021;Volume 14:4401 View
  26. Zhang Z, Yang L, Han W, Wu Y, Zhang L, Gao C, Jiang K, Liu Y, Wu H. Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis. Journal of Medical Internet Research 2022;24(3):e26634 View
  27. Mennickent D, Rodríguez A, Opazo M, Riedel C, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano A, Guzmán-Gutiérrez E, Araya J. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Frontiers in Endocrinology 2023;14 View
  28. Tabatabaian F, Vora S, Mirabbasi S. Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review. Journal of Esthetic and Restorative Dentistry 2023;35(6):842 View
  29. Langenhuijsen L, Janse R, Venema E, Kent D, van Diepen M, Dekker F, Steyerberg E, de Jong Y. Systematic metareview of prediction studies demonstrates stable trends in bias and low PROBAST inter-rater agreement. Journal of Clinical Epidemiology 2023;159:159 View
  30. Abdulazeem H, Whitelaw S, Schauberger G, Klug S, Vathy-Fogarassy Á. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLOS ONE 2023;18(9):e0274276 View
  31. Belsti Y, Moran L, Du L, Mousa A, De Silva K, Enticott J, Teede H. Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model. International Journal of Medical Informatics 2023;179:105228 View
  32. Mahmud S, Mohsin M, Muyeed A, Nazneen S, Abu Sayed M, Murshed N, Tonmon T, Islam A. Machine learning approaches for predicting suicidal behaviors among university students in Bangladesh during the COVID-19 pandemic: A cross-sectional study. Medicine 2023;102(28):e34285 View
  33. Edwards T, Greene C, Piekos J, Hellwege J, Hampton G, Jasper E, Velez Edwards D. Challenges and Opportunities for Data Science in Women's Health. Annual Review of Biomedical Data Science 2023;6(1):23 View
  34. Deng X, Cao P, Nan S, Pan Y, Yu C, Pan T, Dai G. Differentiation Between Phyllodes Tumors and Fibroadenomas of Breast Using Mammography-based Machine Learning Methods: A Preliminary Study. Clinical Breast Cancer 2023;23(7):729 View
  35. Huang Y, Li J, Li M, Aparasu R. Application of machine learning in predicting survival outcomes involving real-world data: a scoping review. BMC Medical Research Methodology 2023;23(1) View
  36. Chen H, Pan Y, Chen J, Chen J, Liu L, Yang Y, Li K, Ma Q, Shi L, Yu R, Shao G. Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors. Academic Radiology 2024;31(5):1898 View
  37. Tan J, Liu C, Yang M, Xiong Y, Huang S, Qi Y, Chen M, Thabane L, Liu X, He L, Sun X. Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year‐span cross‐sectional study. Acta Obstetricia et Gynecologica Scandinavica 2024;103(3):611 View
  38. Cheng L, Li L, Zhang Y, Deng F, Lan T. Clinical nursing value of predictive nursing in reducing complications of pregnant women undergoing short-term massive blood transfusion during cesarean section. World Journal of Clinical Cases 2024;12(1):51 View
  39. Yoo D, Divard G, Raynaud M, Cohen A, Mone T, Rosenthal J, Bentall A, Stegall M, Naesens M, Zhang H, Wang C, Gueguen J, Kamar N, Bouquegneau A, Batal I, Coley S, Gill J, Oppenheimer F, De Sousa-Amorim E, Kuypers D, Durrbach A, Seron D, Rabant M, Van Huyen J, Campbell P, Shojai S, Mengel M, Bestard O, Basic-Jukic N, Jurić I, Boor P, Cornell L, Alexander M, Toby Coates P, Legendre C, Reese P, Lefaucheur C, Aubert O, Loupy A. A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients. Nature Communications 2024;15(1) View
  40. Lee C, Zhang K, Li W, Tang K, Ling Y, Walji M, Jiang X. Identifying predictors of the tooth loss phenotype in a large periodontitis patient cohort using a machine learning approach. Journal of Dentistry 2024;144:104921 View
  41. Mirzaei A, Hiller B, Stelzer I, Thiele K, Tan Y, Becker M. Computational Approaches for Connecting Maternal Stress to Preterm Birth. Clinics in Perinatology 2024;51(2):345 View
  42. Didier A, Nigro A, Noori Z, Omballi M, Pappada S, Hamouda D. Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis. Frontiers in Artificial Intelligence 2024;7 View
  43. Yao J, Du Z, Yang F, Duan R, Feng T. The relationship between heavy metals and metabolic syndrome using machine learning. Frontiers in Public Health 2024;12 View
  44. Duan R, Li Q, Yuan Q, Hu J, Feng T, Ren T. Predictive model for assessing malnutrition in elderly hospitalized cancer patients: A machine learning approach. Geriatric Nursing 2024;58:388 View

Books/Policy Documents

  1. Xanthis C, Filos D, Chouvarda I. Comprehensive Clinical Approach to Diabetes During Pregnancy. View
  2. Priyanka , Goyal S, Bhatia R. Communication and Intelligent Systems. View
  3. Shanthalakshmi Revathy J, Mangaiyarkkarasi J. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning. View
  4. Aliferis C, Simon G. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences. View