ArticleBMC medical informatics and decision making2019
Implementation of machine learning algorithms to create diabetic patient re-admission profiles.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review.Diabetic medicine : a journal of the British Diabetic Association · 2026Article
- An Intelligent Method for Predicting Pacific Oyster (Foods (Basel, Switzerland) · 2023Article
- The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.Diseases (Basel, Switzerland) · 2023Article
- Predicting Intraoperative Hypothermia Burden during Non-Cardiac Surgery: A Retrospective Study Comparing Regression to Six Machine Learning Algorithms.Journal of clinical medicine · 2023Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- AI-enhanced solutions during COVID-19: Current trends and future innovations.Annals of medicine and surgery (2012) · 2022Article
- Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes.Current diabetes reports · 2021Review
- Benchmarking Analysis of the Accuracy of Classification Methods Related to Entropy.Entropy (Basel, Switzerland) · 2021Article
- Correction to: Implementation of machine learning algorithms to create diabetic patient re-admission profiles.BMC medical informatics and decision making · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today's computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business.
methodsIn this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k-Nearest Neighbor, Naïve Bayes, J48 and Support vector machine.
resultsOf the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c.
conclusionDiabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.
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