The study advanced beyond a one-size-fits-all model by developing both a global XGBoost classifier and cancer-type-specific ...

To build a model that used routine clinical information to screen for anxiety and depression, Patten's group studied 828 MS ...

A study by the World Bank Group and MIT GOV/LAB finds that donor project ratings rarely reflect real development impact, ...

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, despite significant advancements in diagnosis and treatment. However, the integration of artificial ...

Researchers say findings provide compelling evidence underscoring the potential of electric-field molecular fingerprinting for minimally invasive disease detection.

A new study identifies a blood-based biomarker that detects pre-symptomatic Parkinson’s disease by measuring the ratio of ...

Statistical and machine learning models were developed to predict ... with areas under the receiver operating curve (AUC) ranging from 0.81 to 0.95. The model that incorporated age at diagnosis ...

CONTINUOUS monitoring of heart rate recovery (HRR) using wearable ECG technology can identify individuals at higher risk of ...

While machine learning has shown promise ... By day five of ICU admission, it achieved an impressive AUC of 0.92, significantly outperforming traditional scoring systems like APACHE-II.

The Cerebral Palsy market has demonstrated substantial growth in recent years, driven by increasing awareness, improved ...

By leveraging multi-omics analysis and machine learning techniques, the research team led by Professor Shuo Wang (Institute ...