AIMC Topic: Machine Learning

Clear Filters Showing 1051 to 1060 of 34417 articles

On the Influence of Apologies on the Likelihood of Lawsuits in Cases of Perceived Medical Negligence: Analysis of Archival and Experimental Data.

Journal of medical Internet research
BACKGROUND: Disappointing medical care (DMC) encompasses cases of medical failures, malpractice, or errors. Literature suggests that individuals' perceptions of harm resulting from medical procedures influence their intention to seek legal recourse a...

A prognostic model for gastric cancer constructed by multiple machine learning algorithms.

Journal of molecular histology
Gastric cancer (GC) is a highly heterogeneous disease that requires highly accurate prognostic models. Machine learning is a powerful tool for identifying predictive biomarkers and developing prognostic models. Here, we aim to integrate bioinformatic...

Surface water quality evaluation impacting drinking water sources and sanitation using water quality index, multivariate techniques, and interpretable machine learning models in Mahanadi River, Odisha (India).

Environmental geochemistry and health
Water quality and quantity affect crop productivity, with surface water quality having a significant impact. The amount of surface water being used for drinking is gradually rising. Thus, assessing surface water quality and related hydro-chemical cha...

Predicting All-Cause Mortality in Diabetic Patients 2 Years in Advance Using Aggregated EHR Data and Machine Learning.

Journal of medical systems
This study presents a machine learning-driven model predicting all-cause mortality two years in advance using administrative health data focused on diabetic patients. Integrating hospitalization records, emergency department data, demographics, and c...

Explainable machine learning algorithm predicting working memory performance in Parkinson's disease using task-fMRI.

Journal of neurology
BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions, particularly working memory (WM). Machine learning offers an advantage for decoding complex brain activity patterns, but its applica...

Correspondence regarding "Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients".

World journal of urology
Zhao et al. present machine-learning models to predict intraoperative hemodynamic instability in hypertensive pheochromocytoma and paraganglioma surgery. The clinical motivation is sound and the reported discrimination and decision-curve metrics indi...

Explainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study.

JMIR formative research
BACKGROUND: Artificial intelligence (AI) is increasingly being integrated into clinical diagnostics; yet, its lack of transparency hinders trust and adoption among health care professionals. The explainable artificial intelligence (XAI) has the poten...

Mechanistic Basis for GPCR Phosphorylation-Dependent Allosteric Signaling Specificity of β-Arrestin 1 and 2.

Journal of chemical information and modeling
β-Arrestins (βarr1 and βarr2) are key transducers of G protein-coupled receptor (GPCR) signaling, orchestrating both shared and isoform-specific intracellular pathways. Phosphorylation of the receptor C-terminal tail by GPCR kinases encodes regulator...

Ligand Dissociation Pathways from Membrane Receptors Revealed by Weighted Ensemble Simulations.

The journal of physical chemistry. B
G-protein-coupled receptors (GPCRs) are pivotal in cellular signal transduction and serve as key drug targets. Among them, the β-adrenergic receptors (βAR and βAR) regulate cardiovascular function and are activated by endogenous catecholamines, norep...

GPT-4o and the quest for machine learning interpretability in ICU risk of death prediction.

BMC medical informatics and decision making
BACKGROUND: Clinical utilization of machine learning is hampered by the lack of interpretability inherent in most non-linear black box modeling approaches, reducing trust among clinicians and regulators. Advanced large language models offer a potenti...