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

Formative Evaluation of Digital Health Interventions (2193) Artificial Intelligence (1188) Machine Learning (1373) Clinical Information and Decision Making (1315) Electronic Health Records (961) Glucose Tracking and Self-Monitoring of Blood Glucose (150)

Published on in Vol 6, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36176, first published .
Machine Learning Prediction of Hypoglycemia and Hyperglycemia From Electronic Health Records: Algorithm Development and Validation

Machine Learning Prediction of Hypoglycemia and Hyperglycemia From Electronic Health Records: Algorithm Development and Validation

Machine Learning Prediction of Hypoglycemia and Hyperglycemia From Electronic Health Records: Algorithm Development and Validation

Authors of this article:

Harald Witte1 Author Orcid Image ;   Christos Nakas1, 2 Author Orcid Image ;   Lia Bally3 Author Orcid Image ;   Alexander Benedikt Leichtle1, 4 Author Orcid Image
Article Authors Cited by (11) Tweetations (5) Metrics

Journals

  1. Blatter T, Witte H, Nakas C, Leichtle A. Big Data in Laboratory Medicine—FAIR Quality for AI?. Diagnostics 2022;12(8):1923 View
  2. Huang J, Yeung A, Armstrong D, Battarbee A, Cuadros J, Espinoza J, Kleinberg S, Mathioudakis N, Swerdlow M, Klonoff D. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. Journal of Diabetes Science and Technology 2023;17(1):224 View
  3. Zale A, Abusamaan M, McGready J, Mathioudakis N. Prediction of Next Glucose Measurement in Hospitalized Patients by Comparing Various Regression Methods: Retrospective Cohort Study. JMIR Formative Research 2023;7:e41577 View
  4. Zhang L, Yang L, Zhou Z. Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice. Frontiers in Public Health 2023;11 View
  5. Huang J, Yeung A, Nguyen K, Xu N, Preiser J, Rushakoff R, Seley J, Umpierrez G, Wallia A, Drincic A, Gianchandani R, Lansang M, Masharani U, Mathioudakis N, Pasquel F, Schmidt S, Shah V, Spanakis E, Stuhr A, Treiber G, Klonoff D. Hospital Diabetes Meeting 2022. Journal of Diabetes Science and Technology 2022;16(5):1309 View
  6. Witte H, Blatter T, Nagabhushana P, Schär D, Ackermann J, Cadamuro J, Leichtle A. Statistical learning and big data applications. Journal of Laboratory Medicine 2023;47(4):181 View
  7. Nauck M, Holdenrieder S, Klein H, Findeisen P, Winter C, Ceglarek U, Petersmann A, Klouche M, Lichtinghagen R, Biemann R, Adler J, Streichert T, von Meyer A, Wieland E, Hofmann W, Aufenanger J, Orth M, Shipkova M, Bidlingmaier M, Birschmann I, Blüthner M, Conrad K, Luppa P, Kiehntopf M, Bietenbeck A, Baum H, Renz H. German Society for Clinical Chemistry and Laboratory Medicine – areas of expertise: Division reports from the German Congress of Laboratory Medicine 2022 in Mannheim, 13–14 October 2022. Journal of Laboratory Medicine 2024;48(1):3 View
  8. Khalilnejad A, Sun R, Kompala T, Painter S, James R, Wang Y. Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning. JMIR Formative Research 2024;8:e54373 View

Books/Policy Documents

  1. Sly B, Shrapnel S, Sullivan C. Diabetes Digital Health, Telehealth, and Artificial Intelligence. View
  2. Favor C, Ramadhan S, Kisangiri M, Mugenyi L, Musinguzi F, Balaba M, Owarwo N, Laker E, Obaikol R, Kiraga A, Castelnuovo B, Parkes-Ratanshi R. Artificial Intelligence Tools and Applications in Embedded and Mobile Systems. View