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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

Abstract 150: Machine Learning Methodology Predicts Comorbidities are Associated With Increased Total Healthcare Costs Among Patients With Severe Peripheral Artery Disease Session Title: Poster Session II
Conclusion: In this study, the presence of chronic ulcers in the lower extremities and CKD were two factors most predictive of increased all-cause total HC in a geographically diverse population of severe PAD patients.
Source: Circulation: Cardiovascular Quality and Outcomes - March 31, 2017 Category: Cardiology Authors: Berger, J. S., Haskell, L., Ting, W., Lurie, F., Eapen, Z., Valko, M., Alas, V., Rich, K., Crivera, C., Schein, J. Tags: Session Title: Poster Session II Source Type: research

Psychosis Polyrisk Score (PPS) for the Detection of Individuals At-Risk and the Prediction of Their Outcomes
Conclusions The combination of risk/protective factors encompassing genetic (PRS) and non-genetic information (PPS) holds promise for overcoming the epidemiological weakness of the CHR-P paradigm. The PPS conceptually and empirically developed here will facilitate future research in this field and hopefully advance our ability to detect individuals at-risk for psychosis and forecast their clinical outcomes. Ethics Statement This study was supported by the King's College London Confidence in Concept award from the Medical Research Council (MRC) (MC_PC_16048) to PF-P. This study also represents independent researc...
Source: Frontiers in Psychiatry - April 16, 2019 Category: Psychiatry Source Type: research