Real-time prediction of short-term mortality risk in the intensive care unit (ICU) is often hampered by missing medical data. To address this, we developed RealMIP, an end-to-end framework leveraging generative model for the dynamic…
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Clinical decision-making often simplifies continuous risk data into discrete levels using round-number thresholds. These simplifications can distort risk assessments. To systematically uncover these distortions, we develop an interpretable machine learning model that identifies anomalies…
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Colorectal cancer recurrence remains a major challenge after curative resection, and accurate tools for early risk assessment are essential to stratify patients and guide personalized therapeutic planning. We developed MPMRecNet, a dual-stream deep learning…
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PreOpNet is a novel deep-learning algorithm using 12-lead digital electrocardiogram (ECG) for preoperative risk assessment of all-cause death and major adverse cardiac events (MACE) within 30 days. Its performance in European high-risk patients undergoing…
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Accurate ovarian cancer screening and diagnosis are critical for patient survival. We present UMORSS, an AI-assisted diagnostic system integrating ultrasound (US) imaging and clinical data with uncertainty quantification for precise ovarian cancer risk assessment….
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Predicting multiple postoperative complications remains challenging in perioperative care. Current approaches often address complications individually, limiting the potential for integrated risk assessment. We developed and externally validated a scalable, interpretable, tree-based multitask learning model…
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Funding Round Overview
Optain Health has successfully raised $26 million in a Series A funding round, marking a significant milestone in AI-powered healthcare diagnostics. The oversubscribed investment round demonstrates strong market confidence in artificial intelligence…
Background: Assessing fall risk, especially in individuals with neurological disorders, is essential to prevent hospitalization, hypomobility, and reduced functional independence. Wearable sensors are increasingly used in neurorehabilitation, as they enable unsupervised fall risk assessment…
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Artificial intelligence (AI) is increasingly applied to clinical trial risk assessment, aiming to improve safety and efficiency. This scoping review analyzed 142 studies published between 2013 and 2024, focusing on safety (n = 55), efficacy (n = 46),…
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Cognitive impairment is a frequent complication of Parkinson’s disease (PD), affecting up to half of newly diagnosed patients. To improve early detection and risk assessment, we developed machine learning models using clinical data from…
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