Predicting mortality risk in intensive care unit (ICU) patients is a crucial part of medical treatment, and recent research ...

ROC curves illustrating the discriminative ability of the VR-specific 30-day mortality prediction models. (A–C) Performance of the high VR model in the ARDSnet training cohort (A), internal validation ...

Extreme heat is a leading cause of climate-related mortality, responsible for over five million deaths globally each year. As ...

In a bid to improve end-of-life care for incarcerated individuals in the California prison system, UC San Francisco researchers have found a way to predict who is most likely to die within two years ...

Patients with myelodysplastic syndromes (MDS) exhibit diverse disease trajectories necessitating different clinical approaches ranging from watch-and-wait strategies to hematopoietic stem cell ...

Among critically ill patients with AECOPD, machine learning models using ICU data could help predict their 28-day mortality risk.

This study applied three models—random forest (RF), gradient boosting regression (GBR), and linear regression (LR)—to predict county-level LC mortality rates across the United States. Model ...

Biological age acceleration was linked to higher mortality risk in adults with CKM syndrome, with PAA showing stronger prognostic value. Read more.

Researchers developed and externally validated a machine learning model to predict the 28-day mortality risk in ICU patients with sepsis complicated by acute respiratory failure. Using routinely ...

Training on raw EEG, ECG, airflow, oximetry, and related channels generated high-dimensional embeddings that clustered into five reproducible risk groups across 9608 polysomnograms. Fully adjusted ...