Scientific Research Publishing

A Multimodal Machine-Learning Framework for Predicting Lithium-Associated Renal Dysfunction from Synthetic Biomarker Trajectories ()

Long-term lithium therapy remains the most effective maintenance treatment for bipolar disorder, yet it poses a significant risk of progressive renal impairment in a subset of patients. Early ...
Analytics Insight

10 Must-Know Machine Learning Algorithms for Data Science in 2026

Machine learning algorithms help computers analyse large datasets and make accurate predictions automatically.Classic models like regression, dec ...
Scientific Research Publishing

A Lightweight Self-Supervised Representation Learning Framework for Depression Risk Profiling from Synthetic Daily Behavioural Trajectories ()

Detecting behavioural signatures of depression from everyday digital traces is a central challenge in computational psychiatry. Real-world datasets from smartphones and wearables often suffer from ...
IEEE

AI-Driven Classification of Tsunami-Generating Earthquakes: Harnessing Random Forest, SVM, and Logistic Regression for Early Detection

Abstract: Predicting whether an earthquake will generate a tsunami is critical for early warning systems and disaster mitigation. In this study, we present an AI-driven approach to classify ...
BMJ Open

A multicentre, prospective, observational study on detecting medication discrepancies using medication reconciliation as a tool at the emergency department among geriatric ...

Objective Geriatric patients often face issues related to polypharmacy and adverse drug events. Re-evaluating prescribed medications and considering deprescribing is critical. Medication discrepancies ...
Frontiers

A clinical prediction model for delirium tremens: development and validation in alcohol-dependent patients using multivariable logistic regression

Delirium tremens (DT) is a severe complication of alcohol withdrawal. This study aimed to develop and validate a prediction model for DT risk in hospitalized patients with alcohol dependence, using ...
IEEE

Principal Component and Physical Quantities Enhanced Logistic Regression for Electric Load Classification

Abstract: The classification and recognition of household appliance loads is of great significance for understanding user electricity consumption behavior, optimizing electricity planning, and ...