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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

Real-time reviews of research findings will help policymakers address global crises such as COVID-19
Real-time reviews of research findings could help policymakers address global crises such as COVID-19, saysthis   articlepublished   inNature. Living evidence was first developed by Cochrane and an important recommendation for future health emergencies that came out of the recent Cochrane Convenes meetings. According to scientists writing in the peer-reviewed journal  Nature, policy missteps will continue to overshadow the global response to COVID-19 because policymakers are overwhelmed with rapidly shifting research evidence. Faced with new challenges such as the Omicron variant, decision-makers can ’t keep up wi...
Source: Cochrane News and Events - December 15, 2021 Category: Information Technology Authors: Lydia Parsonson Source Type: news

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing
CONCLUSION: PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.PMID:35144291 | DOI:10.1055/s-0042-1742671
Source: Methods of Information in Medicine - February 10, 2022 Category: Information Technology Authors: Alexander Gerharz Carmen Ruff Lucas Wirbka Felicitas Stoll Walter E Haefeli Andreas Groll Andreas D Meid Source Type: research

Pilot Report for Intracranial Hemorrhage Detection with Deep Learning Implanted Head Computed Tomography Images at Emergency Department
AbstractHemorrhagic stroke is a serious clinical condition that requires timely diagnosis. An artificial intelligence algorithm system called DeepCT can identify hemorrhagic lesions rapidly from non-contrast head computed tomography (NCCT) images and has received regulatory clearance.  A non-controlled retrospective pilot clinical trial was conducted. Patients who received NCCT at the emergency department (ED) of Kaohsiung Veteran General Hospital were collected. From 2020 January-1st to April-30th, the physicians read NCCT images without DeepCT. From 2020May-1st to August-31st, the physicians were assisted by DeepCT. The...
Source: Journal of Medical Systems - June 8, 2022 Category: Information Technology Source Type: research

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