predictive-modeling

Scientific Reports, Published online: 11 September 2026; doi:10.1038/s41598-026-67437-6 Machine learning to predict moderate-to-severe AKI in ICU acute pancreatitis patients

BackgroundHypokalemia is a common adverse event during piperacillin/tazobactam (TZP) therapy in critically ill patients. This study developed and externally validated machine learning models to predict hypokalemia using baseline variables.MethodsData were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database and an external cohort from the Third Affiliated Hospital of …

Prospective prediction systems should not only generate individual-level predictions but also indicate when event-level conditions suggest high upset risk or difficult-to-act-on cases. This study developed and evaluated a leakage-aware race-level upset-risk diagnostic for prospective horse-race prediction under temporal validation. Japanese flat-racing data were analyzed using a fixed pre-event e…

BackgroundEarly identification of delayed hematoma progression (DHP) in patients with frontal lobe contusion remains challenging in emergency settings. This study aimed to develop and externally validate an interpretable multimodal machine-learning model integrating routinely available clinical, laboratory, and CT imaging features to predict DHP.MethodsThis retrospective multicenter study include…

BackgroundTuberculous meningitis (TBM) is a severe central nervous system infection with high disability and mortality rates. However, during TBM treatment, anti-tuberculosis drug-induced liver injury (ATB-DILI) often precipitates treatment interruption and contributes to poor clinical outcomes. This study aims to develop an automatic machine learning (AutoML) model for predicting the risk of ATB…

James McDonagh discusses how AI and machine learning are transforming drug discovery and safety assessment, enhancing toxicology and decision-making processes.

Researchers developed ALADYNOULLI, a Bayesian generative model that combines longitudinal health records, age, and polygenic risk to identify reproducible disease signatures across more than 683,000 participants. In UK Biobank testing, the framework achieved stronger short- and long-term risk discrimination than established clinical scores while revealing disease subgroups and genetic association…

BackgroundPostpartum depression (PPD) affects nearly 20% of women globally. Conventional regression models often have limited predictive accuracy.ObjectiveThis study aimed to create and test a machine learning model for predicting PPD using comprehensive infant and maternal health indicators.MethodsIn this prospective study, 273 postpartum women were enrolled, and data on 44 demographic, obstetri…

A new AI model called COMPASS uses tumor gene expression data to predict which patients will respond to cancer immunotherapy drugs called immune checkpoint inhibitors. Analyzing past data from 16 clinical cohorts, COMPASS predicted ICI response 8.5 percent more accurately than the best existing approach.

Cedars-Sinai Health Sciences University investigators developed an AI-based model that can identify hospitalized patients at risk of low blood sugar up to 24 hours before the condition occurs. The long short-term memory (LSTM) model, described in npj Digital Medicine, could help clinicians intervene earlier and prevent complications, including, in severe cases, seizures, coma and long-term [&#823…