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

Foundations of AI (56) Research Methodology - AI (23)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40755, first published .
Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework

Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework

Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework

Authors of this article:

Edgar Steiger1 Author Orcid Image ;   Lars Eric Kroll1 Author Orcid Image
Article Authors Cited by (5) Tweetations (7) Metrics

Journals

  1. Steiger E, Kroll L. Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework. JMIR AI 2023;2:e40755 View
  2. El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View
  3. Tansitpong P. Probabilistic Model of Patient Classification Using Bayesian Model. International Journal of Reliable and Quality E-Healthcare 2024;13(1):1 View

Conference Proceedings

  1. Guyomard M, Bouhnik A, Tassy L, Urena R. 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS). Encoding breast cancer patients’ medical pathways from reimbursement data using representation learning: a benchmark for clustering tasks View
  2. Kandasamy N, Basukoski A, Chaussalet T. 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS). A Comparative Analysis of Clustering Methods for Identifying Patient Subgroups in Chronic Kidney Disease Using Feature Engineering View