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

Viewpoints and Perspectives (514) Artificial Intelligence (1371) Machine Learning (1465) Clinical Informatics (1022) Secondary Use of Clinical Data for Research and Surveillance (366) Health Care Quality and Health Services Research (271) Health Services Research (482)

Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29812, first published .
Analyzing Patient Trajectories With Artificial Intelligence

Analyzing Patient Trajectories With Artificial Intelligence

Analyzing Patient Trajectories With Artificial Intelligence

Authors of this article:

Ahmed Allam1, 2 Author Orcid Image ;   Stefan Feuerriegel3, 4, 5 Author Orcid Image ;   Michael Rebhan3 Author Orcid Image ;   Michael Krauthammer1, 2, 6 Author Orcid Image
Article Authors Cited by (24) Tweetations (27) Metrics

Journals

  1. Schallmoser S, Zueger T, Kraus M, Saar-Tsechansky M, Stettler C, Feuerriegel S. Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study. Journal of Medical Internet Research 2023;25:e42181 View
  2. Naumzik C, Feuerriegel S, Nielsen A. Data-driven dynamic treatment planning for chronic diseases. European Journal of Operational Research 2023;305(2):853 View
  3. Kovács A, Tokodi M. Refining Echocardiographic Surveillance of Aortic Stenosis Using Machine Learning. JACC: Cardiovascular Imaging 2023;16(6):745 View
  4. Alkhodari M, Xiong Z, Khandoker A, Hadjileontiadis L, Leeson P, Lapidaire W. The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare. Expert Review of Cardiovascular Therapy 2023;21(7):531 View
  5. Denck J, Ozkirimli E, Wang K. Machine-learning-based adverse drug event prediction from observational health data: A review. Drug Discovery Today 2023;28(9):103715 View
  6. Annareddy S, Ghewade B, Jadhav U, Wagh P. Unraveling the Predictive Potential of Rapid Scoring in Pleural Infection: A Critical Review. Cureus 2023 View
  7. Carrasco-Ribelles L, Llanes-Jurado J, Gallego-Moll C, Cabrera-Bean M, Monteagudo-Zaragoza M, Violán C, Zabaleta-del-Olmo E. Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review. Journal of the American Medical Informatics Association 2023;30(12):2072 View
  8. Kraus M, Feuerriegel S, Saar-Tsechansky M. Data-Driven Allocation of Preventive Care with Application to Diabetes Mellitus Type II. Manufacturing & Service Operations Management 2024;26(1):137 View
  9. Shi J, Bendig D, Vollmar H, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. Journal of Medical Internet Research 2023;25:e45815 View
  10. Pingi S, Zhang D, Bashar M, Nayak R. Joint Representation Learning with Generative Adversarial Imputation Network for Improved Classification of Longitudinal Data. Data Science and Engineering 2024;9(1):5 View
  11. Wang Y, Li N, Chen L, Wu M, Meng S, Dai Z, Zhang Y, Clarke M. Guidelines, Consensus Statements, and Standards for the Use of Artificial Intelligence in Medicine: Systematic Review. Journal of Medical Internet Research 2023;25:e46089 View
  12. Qian Y, Alhaskawi A, Dong Y, Ni J, Abdalbary S, Lu H. Transforming medicine: artificial intelligence integration in the peripheral nervous system. Frontiers in Neurology 2024;15 View
  13. Palmowski L, Nowak H, Witowski A, Koos B, Wolf A, Weber M, Kleefisch D, Unterberg M, Haberl H, von Busch A, Ertmer C, Zarbock A, Bode C, Putensen C, Limper U, Wappler F, Köhler T, Henzler D, Oswald D, Ellger B, Ehrentraut S, Bergmann L, Rump K, Ziehe D, Babel N, Sitek B, Marcus K, Frey U, Thoral P, Adamzik M, Eisenacher M, Rahmel T, Lazzeri C. Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction. PLOS ONE 2024;19(3):e0300739 View
  14. Wang K, Barton D, McQuillan L, Kobeissy F, Cai G, Xu H, Yang Z, Trifilio E, Williamson J, Rubenstein R, Robertson C, Wagner A. Parallel Cerebrospinal Fluid and Serum Temporal Profile Assessment of Axonal Injury Biomarkers Neurofilament-Light Chain and Phosphorylated Neurofilament-Heavy Chain: Associations With Patient Outcome in Moderate-Severe Traumatic Brain Injury. Journal of Neurotrauma 2024;41(13-14):1609 View
  15. Zwaag S, Hunault C, de Lange D. Predicting the outcome in poisoned patients: look at the past!. Clinical Toxicology 2024;62(3):139 View
  16. Kurasawa H, Waki K, Seki T, Chiba A, Fujino A, Hayashi K, Nakahara E, Haga T, Noguchi T, Ohe K. Enhancing Type 2 Diabetes Treatment Decisions With Interpretable Machine Learning Models for Predicting Hemoglobin A1c Changes: Machine Learning Model Development. JMIR AI 2024;3:e56700 View
  17. Jørgensen I, Haue A, Placido D, Hjaltelin J, Brunak S. Disease Trajectories from Healthcare Data: Methodologies, Key Results, and Future Perspectives. Annual Review of Biomedical Data Science 2024;7(1):251 View
  18. Lisik D, Milani G, Salisu M, Özuygur Ermis S, Goksör E, Basna R, Wennergren G, Kankaanranta H, Nwaru B. Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review. BMJ Open 2024;14(8):e080263 View
  19. SENMAN B, SINGH A, KADOSH B, KATZ J. How Steep is Your Slide? I Really Mean to Learn. Journal of Cardiac Failure 2024;30(10):1208 View
  20. Patharkar A, Cai F, Al-Hindawi F, Wu T. Predictive modeling of biomedical temporal data in healthcare applications: review and future directions. Frontiers in Physiology 2024;15 View
  21. Muyama L, Neuraz A, Coulet A. Machine learning approaches for the discovery of clinical pathways from patient data: A systematic review. Journal of Biomedical Informatics 2024;160:104746 View

Books/Policy Documents

  1. D’hondt R, Moylett S, Goris A, Vens C. Artificial Intelligence in Medicine. View