We developed and validated models to predict 1- and 2-week risk of non-elective hospitalization (NEH) and sepsis hospitalization following outpatient clinic, emergency department treat and release (EDTR), or hospitalization encounters. We employed data from…
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The Promise of EHDS for European Healthcare
The European Health Data Space (EHDS) represents a groundbreaking initiative launched by the European Commission in 2022 to revolutionize healthcare across the continent. At its core, EHDS aims…
Background: Consumer-grade wearables allow researchers to capture a representative picture of human behavior in the real world over extended periods. However, maintaining users’ engagement remains a challenge and can lead to a decrease in…
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Alarming Gaps in Healthcare AI Detection
New research published in Nature Communications Medicine has exposed critical flaws in machine learning mortality prediction models currently used in healthcare settings. These AI systems failed to recognize approximately…
How Machine Learning Addresses Ancestral Bias
The risk of bias in artificial intelligence has been extensively documented, but a groundbreaking new tool is poised to transform genetic research by addressing critical ancestral bias in datasets….
What You Should Know:
– Nimblemind.ai, an innovative AI-powered data infrastructure platform secures $2.5M in funding led by Bread & Butter Ventures, with participation from prominent investors including Great Oaks Venture Capital, SpringTime Ventures, Stone…
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Academic health system Emory Healthcare is partnering with Atropos Health, a company that translates real-world clinical data into real-world evidence, to advance medication formulary decisions and protocols. Emory is piloting a program that aims to…
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The study introduces a clinical decision support system (CDSS) developed at a single academic cancer center, integrating real-time clinical, genomic, and imaging data for over 170,000 patients across 11 cancer types. We have developed…
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Distributed learning enables collaborative machine learning model training without requiring cross-institutional data sharing, thereby addressing privacy concerns. However, local quality control variability can negatively impact model performance while systematic human visual inspection is time-consuming…
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Applying machine learning to clinical outcome prediction is challenging due to imbalanced datasets and sensitive tasks that contain rare yet critical outcomes and where equitable treatment across diverse patient groups is essential. Despite attempts,…
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