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

Machine Learning (1501) Visualization in eHealth (60)

Published on in Vol 8, No 6 (2020): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15791, first published .
Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

Authors of this article:

Shannon Wongvibulsin1 Author Orcid Image ;   Katherine C Wu2 Author Orcid Image ;   Scott L Zeger3 Author Orcid Image
Article Authors Cited by (16) Tweetations (4) Metrics

Journals

  1. Wu K, Wongvibulsin S, Tao S, Ashikaga H, Stillabower M, Dickfeld T, Marine J, Weiss R, Tomaselli G, Zeger S. Baseline and Dynamic Risk Predictors of Appropriate Implantable Cardioverter Defibrillator Therapy. Journal of the American Heart Association 2020;9(20) View
  2. Flaus A, Amat J, Prevot N, Olagne L, Descamps L, Bouvet C, Barres B, Valla C, Mathieu S, Andre M, Soubrier M, Merlin C, Kelly A, Chanchou M, Cachin F. Decision Tree With Only Two Musculoskeletal Sites to Diagnose Polymyalgia Rheumatica Using [18F]FDG PET-CT. Frontiers in Medicine 2021;8 View
  3. Wongvibulsin S, Garibaldi B, Antar A, Wen J, Wang M, Gupta A, Bollinger R, Xu Y, Wang K, Betz J, Muschelli J, Bandeen-Roche K, Zeger S, Robinson M. Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19. Annals of Internal Medicine 2021;174(6):777 View
  4. Loch A, Lopes-Rocha A, Ara A, Gondim J, Cecchi G, Corcoran C, Mota N, Argolo F. Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders. JMIR Mental Health 2022;9(11):e41014 View
  5. Constantinescu G, Schulze M, Peitzsch M, Hofmockel T, Scholl U, Williams T, Lenders J, Eisenhofer G. Integration of artificial intelligence and plasma steroidomics with laboratory information management systems: application to primary aldosteronism. Clinical Chemistry and Laboratory Medicine (CCLM) 2022;60(12):1929 View
  6. Ibrahim Z, Bean D, Searle T, Qian L, Wu H, Shek A, Kraljevic Z, Galloway J, Norton S, Teo J, Dobson R. A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes From Electronic Health Records Data. IEEE Journal of Biomedical and Health Informatics 2022;26(1):423 View
  7. Chen R, Fu Y, Yi X, Pei Q, Zai H, Chen B, Du X. Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges. Journal of Oncology 2022;2022:1 View
  8. Chen H, Gomez C, Huang C, Unberath M. Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review. npj Digital Medicine 2022;5(1) View
  9. Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review. Journal of Medical Internet Research 2022;24(1):e32215 View
  10. Young D, Engels R, Colantuoni E, Friedman L, Hoyer E. Machine learning prediction of hospital patient need for post-acute care using an admission mobility measure is robust across patient diagnoses. Health Policy and Technology 2023;12(2):100754 View
  11. Gonzalez-Ibañez A, Rojas-Salinas P, Frodden E, Jaureguiberry-Bravo M, Jara M. Development of an Interpretable, Multivariable, Machine Learning Model for Clinical Decision Support on Mortality Prediction of People Admitted to Intensive Care Units. SSRN Electronic Journal 2022 View
  12. Wongvibulsin S, Adamson A. Deep learning for Mpox: Advances, challenges, and opportunities. Med 2023;4(5):283 View
  13. Ambala S, Agarkar A, Raskar P, Gundu V, Mageswari N, Geetha T. Advanced machine learning for real-time tibial bone force monitoring in runners using wearable sensors. Measurement: Sensors 2024;32:101058 View
  14. Young D, Hannum S, Engels R, Colantuoni E, Friedman L, Hoyer E. Dynamic Prediction of Post-Acute Care Needs for Hospitalized Medicine Patients. Journal of the American Medical Directors Association 2024;25(7):104939 View
  15. Salih A, Galazzo I, Gkontra P, Rauseo E, Lee A, Lekadir K, Radeva P, Petersen S, Menegaz G. A review of evaluation approaches for explainable AI with applications in cardiology. Artificial Intelligence Review 2024;57(9) View
  16. Crump R, Mohammed E, Biglarbeiki M, Eshragh M, Shakeri E, Siljedal G, Far B, Weis E. Artificial intelligence in the classification and segmentation of fundus images with choroidal nevi. Canadian Journal of Ophthalmology 2024 View