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

Data Science (276) Electronic Health Records (1065) Machine Learning (1488) Natural Language Processing (703) Secondary Use of Clinical Data for Research and Surveillance (373) Big Data (241) Clinical Informatics (1042)

Published on in Vol 24, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30720, first published .
Sequential Data–Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development

Sequential Data–Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development

Sequential Data–Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development

Authors of this article:

Ni Wang1, 2 Author Orcid Image ;   Muyu Wang1, 2 Author Orcid Image ;   Yang Zhou3 Author Orcid Image ;   Honglei Liu1, 2 Author Orcid Image ;   Lan Wei4 Author Orcid Image ;   Xiaolu Fei4 Author Orcid Image ;   Hui Chen1, 2 Author Orcid Image
Article Authors Cited by (7) Tweetations (5) Metrics

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  1. Huang Y, Zheng Z, Ma M, Xin X, Liu H, Fei X, Wei L, Chen H. Improving the Performance of Outcome Prediction for Inpatients With Acute Myocardial Infarction Based on Embedding Representation Learned From Electronic Medical Records: Development and Validation Study. Journal of Medical Internet Research 2022;24(8):e37486 View
  2. Zhang X, Wang X, Xu L, Liu J, Ren P, Wu H. The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis. European Journal of Medical Research 2023;28(1) View
  3. Liu Q, Ostinelli E, De Crescenzo F, Li Z, Tomlinson A, Salanti G, Cipriani A, Efthimiou O. Predicting outcomes at the individual patient level: what is the best method?. BMJ Mental Health 2023;26(1):e300701 View
  4. Huang Y, Wang M, Zheng Z, Ma M, Fei X, Wei L, Chen H. Representation of time-varying and time-invariant EMR data and its application in modeling outcome prediction for heart failure patients. Journal of Biomedical Informatics 2023;143:104427 View
  5. Li H, Zhou M, Sun Y, Yang J, Zeng X, Qiu Y, Xia Y, Zheng Z, Yu J, Feng Y, Shi Z, Huang T, Tan L, Lin R, Li J, Fan X, Ye J, Duan H, Shi S, Shu Q. A Patient Similarity Network (CHDmap) to Predict Outcomes After Congenital Heart Surgery: Development and Validation Study. JMIR Medical Informatics 2024;12:e49138 View
  6. Chatton A, Bally M, Lévesque R, Malenica I, Platt R, Schnitzer M. Personalized dynamic super learning: an application in predicting hemodiafiltration convection volumes. Journal of the Royal Statistical Society Series C: Applied Statistics 2024 View
  7. Nie S, Zhang S, Zhao Y, Li X, Xu H, Wang Y, Wang X, Zhu M. Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management. Advances in Therapy 2024 View