Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm

Falls in our elder population are a major cause of injury, hospitalization and mortality worldwide, and a major source of medical costs. Current risk screening and assessment methods are often manual, include a limited number of intrinsic factors, exclude environmental factors and/or lack discriminatory capabilities that a robust machine-learned model can provide. See how machine learning can prov

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Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records

Suicide is a leading cause of death in the US, particularly for young people, veterans and Native Americans. An early warning system can be an effective way to identify those in need before a crisis is upon them. See how AI and machine learning on longitudinal health records can be used to identify those at risk – up to 60+ times as likely to attempt a suicide in a future 1 year period.

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EGFR blockade reverts resistance to KRAS G12C inhibition in colorectal cancer

Most KRAS G12C mutant non-small cell lung cancer (NSCLC) patients experience clinical benefit from selective KRAS G12C inhibition, while patients with colorectal cancer (CRC) bearing the same mutation rarely respond. To investigate the cause of the limited efficacy of KRAS G12C inhibitors in CRC, we examined the effects of AMG510 in KRAS G12C CRC cell lines. Unlike NSCLC cell lines, KRAS G12C CRC

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HIMSS 2020

HIMSS18 Presentation: Practical Use Cases for AI & Machine Learning in Healthcare Organization Tuesday, February 12, 2019 at 11:00am | InterSystems Booth #1559 Artificial intelligence (AI) and machine learning (ML) are effective tools to manage healthcare costs...

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