predictive-modeling

BackgroundAcute branch atheromatous disease (BAD) is one of the leading contributors to morbidity and disability in Asia, and early neurological deterioration (END) is common in affected patients. This study aimed to establish machine learning models to predict the risk of END in patients without reperfusion therapy.MethodsPatients with acute BAD who did not receive reperfusion therapy were retro…

BackgroundPostoperative delirium (POD) is a severe complication in elderly hypertensive patients, associated with poor long-term outcomes. Existing models often rely on intraoperative data, limiting preoperative risk stratification. This study aimed to develop a non-invasive machine learning model to predict POD and investigate its preoperative markers’ impact on three-year mortality.MethodsPreop…

BackgroundFertility intentions in patients with Crohn's disease (CD) are shaped by complex interactions between biological and psychosocial factors. However, predictive tools that integrate these dimensions remain underdeveloped.ObjectiveThis study aimed to construct and validate interpretable machine learning (ML) models to predict fertility intentions among reproductive-age patients with CD, an…

BackgroundThis study aims to develop machine learning (ML) models to predict 90-day functional outcomes for acute ischemic stroke (AIS) patients receiving thrombolysis with low-dose alteplase at 0.6 mg/kg between 4.5 and 9 h after symptom onset.MethodsWe conducted a retrospective analysis of AIS patients receiving thrombolysis between August 1, 2019 and August 31, 2023. Eligible patients were ran…

A new algorithm can predict type 2 diabetes years before it is normally detected by analysing younger patients’ health data. If the tool is put into use, doctors could use already collected health data to identify disease risk several years earlier in patients under the age of 40. The study illustrates a growing field of research in which artificial intelligence (AI) analyses health records for e…

An artificial intelligence (AI) model developed by researchers at The University of Texas MD Anderson Cancer Center demonstrated the ability to accurately predict responses to immunotherapy for patients with metastatic non-small cell lung cancer (NSCLC).

Scientific Reports, Published online: 15 April 2026; doi:10.1038/s41598-026-47814-x Predicting post-stroke functional outcome using explainable machine learning and integrated data

A newly developed artificial intelligence (AI) model is highly accurate in predicting blood loss in patients undergoing high-volume liposuction, reports a study in the January issue of Plastic and Reconstructive Surgery®, the official medical journal of the American Society of Plastic Surgeons (ASPS).

A study published in Scientific Reports looks at AI and predicting chemicals formed in vapes.    Prof Jacob George, Professor of Cardiovascular … read more

ShiNung Ching, collaborators seek to develop predictive model for secondary brain injuries