Published on in Vol 6, No 4 (2020): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22400, first published .
A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

Journals

  1. Givens R. Are the current evaluation tools for advanced therapies biased?. Current Opinion in Cardiology 2021;36(3):340 View
  2. Pang K, Li L, Ouyang W, Liu X, Tang Y. Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database. Diagnostics 2022;12(5):1068 View
  3. Yan Y, Chen C, Liu Y, Zhang Z, Xu L, Pu K. Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin. Frontiers in Public Health 2021;9 View
  4. Bitew F, Sparks C, Nyarko S. Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia. Public Health Nutrition 2021:1 View
  5. Celi L. PLOS Digital Health, a new journal driving transformation in the delivery of equitable and unbiased healthcare. PLOS Digital Health 2022;1(1):e0000009 View
  6. Chi S, Guo A, Heard K, Kim S, Foraker R, White P, Moore N. Development and Structure of an Accurate Machine Learning Algorithm to Predict Inpatient Mortality and Hospice Outcomes in the Coronavirus Disease 2019 Era. Medical Care 2022;60(5):381 View
  7. Shanklin R, Samorani M, Harris S, Santoro M. Ethical Redress of Racial Inequities in AI: Lessons from Decoupling Machine Learning from Optimization in Medical Appointment Scheduling. Philosophy & Technology 2022;35(4) View
  8. Hamilton A, Strauss A, Martinez D, Hinson J, Levin S, Lin G, Klein E. Machine learning and artificial intelligence: applications in healthcare epidemiology. Antimicrobial Stewardship & Healthcare Epidemiology 2021;1(1) View
  9. Vesoulis Z, Husain A, Cole F. Improving child health through Big Data and data science. Pediatric Research 2023;93(2):342 View
  10. Park J, Arunachalam R, Silenzio V, Singh V. Fairness in Mobile Phone–Based Mental Health Assessment Algorithms: Exploratory Study. JMIR Formative Research 2022;6(6):e34366 View
  11. Thapa R, Garikipati A, Shokouhi S, Hurtado M, Barnes G, Hoffman J, Calvert J, Katzmann L, Mao Q, Das R. Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging 2022;5(2):e35373 View
  12. Huang J, Galal G, Etemadi M, Vaidyanathan M. Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review. JMIR Medical Informatics 2022;10(5):e36388 View
  13. Le J, Shashikumar S, Malhotra A, Nemati S, Wardi G. Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape. Critical Care Clinics 2023;39(4):751 View
  14. Kohn R, Weissman G, Wang W, Ingraham N, Scott S, Bayes B, Anesi G, Halpern S, Kipnis P, Liu V, Dudley R, Kerlin M. Prediction of In-hospital Mortality Among Intensive Care Unit Patients Using Modified Daily Laboratory-based Acute Physiology Score, Version 2. Medical Care 2023;61(8):562 View
  15. Cary M, Zink A, Wei S, Olson A, Yan M, Senior R, Bessias S, Gadhoumi K, Jean-Pierre G, Wang D, Ledbetter L, Economou-Zavlanos N, Obermeyer Z, Pencina M. Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review. Health Affairs 2023;42(10):1359 View
  16. Bagheri A, Rouzi M, Koohbanani N, Mahoor M, Finco M, Lee M, Najafi B, Chung J. Potential applications of artificial intelligence and machine learning on diagnosis, treatment, and outcome prediction to address health care disparities of chronic limb-threatening ischemia. Seminars in Vascular Surgery 2023;36(3):454 View
  17. Defilippo A, Bertucci G, Zurzolo C, Veltri P, Guzzi P. On the computational approaches for supporting triage systems. Interdisciplinary Medicine 2023;1(3) View
  18. El-Azab S, Nong P. Clinical algorithms, racism, and “fairness” in healthcare: A case of bounded justice. Big Data & Society 2023;10(2) View
  19. Srinivasan Y, Liu A, Rameau A. Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient. Current Opinion in Otolaryngology & Head & Neck Surgery 2024;32(2):105 View
  20. Kaur D, Hughes J, Rogers A, Kang G, Narayan S, Ashley E, Perez M. Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure. Circulation: Heart Failure 2024;17(1) View
  21. Wang H, Weiner J, Saria S, Kharrazi H. Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis. Journal of Medical Internet Research 2024;26:e47125 View
  22. Yang P, Gregory I, Robichaux C, Holder A, Martin G, Esper A, Kamaleswaran R, Gichoya J, Bhavani S. Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19. Critical Care Explorations 2024;6(3):e1059 View
  23. Chen F, Wang L, Hong J, Jiang J, Zhou L. Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models. Journal of the American Medical Informatics Association 2024;31(5):1172 View
  24. Rusinovich Y, Rusinovich V. Confounders in Predictive Medical Models: Roles of Nationality and Immigrant Status. Web3 Journal: ML in Health Science 2024;1(1):d070224 View
  25. Rusinovich Y, Vareiko A, Shestak N. Human-centered Evaluation of AI and ML Projects. Web3 Journal: ML in Health Science 2024;1(2) View
  26. Defilippo A, Veltri P, Lió P, Guzzi P. Leveraging graph neural networks for supporting automatic triage of patients. Scientific Reports 2024;14(1) View
  27. Rotenstein L, Wang L, Zupanc S, Penumarthy A, Laurentiev J, Lamey J, Farah S, Lipsitz S, Jain N, Bates D, Zhou L, Lakin J. Looking Beyond Mortality Prediction: Primary Care Physician Views of Patients' Palliative Care Needs Predicted by a Machine Learning Tool. Applied Clinical Informatics 2024;15(03):460 View
  28. Wang Y, Wang L, Zhou Z, Laurentiev J, Lakin J, Zhou L, Hong P. Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases. Journal of Biomedical Informatics 2024;156:104677 View
  29. Straw I, Rees G, Nachev P. Sex-Based Performance Disparities in Machine Learning Algorithms for Cardiac Disease Prediction: Exploratory Study. Journal of Medical Internet Research 2024;26:e46936 View
  30. Ismail S, Wardah Z, Wibowo A. Use of Artificial Intelligence in Early Warning Score in Critical ill Patients: Scoping Review. JURNAL INFO KESEHATAN 2023;21(4):652 View
  31. Colacci M, Huang Y, Postill G, Zhelnov P, Fennelly O, Verma A, Straus S, Tricco A. Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms. Journal of Clinical Epidemiology 2025;178:111606 View