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Management: Electronic Medical Records (EMR)

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Total 24 results found since Jan 2013.

Machine Learning-Based Prediction of Atrial Fibrillation Risk Using Electronic Medical Records in Older Aged Patients
Atrial fibrillation (AF) is an independent risk factor that increases the risk of stroke 5-fold. The purpose of our study was to develop a 1-year new-onset AF predictive model by machine learning based on 3-year medical information without electrocardiograms in our database to identify AF risk in older aged patients. We developed the predictive model according to the Taipei Medical University clinical research database electronic medical records, including diagnostic codes, medications, and laboratory data.
Source: The American Journal of Cardiology - May 18, 2023 Category: Cardiology Authors: Yung-Ta Kao, Chun-Yao Huang, Yu-Ann Fang, Ju-Chi Liu, Tzu-Hao Chang Source Type: research

Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events
CONCLUSION: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.PMID:33965930 | DOI:10.3233/SHTI210100
Source: Studies in Health Technology and Informatics - May 9, 2021 Category: Information Technology Authors: Michael Schrempf Diether Kramer Stefanie Jauk Sai P K Veeranki Werner Leodolter Peter P Rainer Source Type: research

Preliminary development of a prediction model for daily stroke occurrences based on meteorological and calendar information using deep learning framework (Prediction One; Sony Network Communications Inc., Japan).
Conclusion: Our preliminary results suggested a probability of the DL-based prediction models for stroke occurrence only by meteorological and calendar factors. In the future, by synchronizing a variety of medical information among the electronic medical records and personal smartphones as well as integrating the physical activities or meteorological conditions in real time, the prediction of stroke occurrence could be performed with high accuracy, to save medical resources, to have patients care for themselves, and to perform efficient medicine. PMID: 33598347 [PubMed]
Source: Surgical Neurology International - February 21, 2021 Category: Neurosurgery Tags: Surg Neurol Int Source Type: research