AIMC Topic: Machine Learning

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Personalising Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and big Data: The PETRUSHKA Tool: Personnalisation du traitement antidépresseur de la dépression unipolaire associant choix individuels, risques et mégadonnées: l'outil PETRUSHKA.

Canadian journal of psychiatry. Revue canadienne de psychiatrie
OBJECTIVE: We summarize the key steps to develop and assess an innovative online, evidence-based tool that supports shared decision-making in routine care to personalize antidepressant treatment in adults with depression. This PETRUSHKA tool is part ...

A machine learning tool for identifying metastatic colorectal cancer in primary care.

Scandinavian journal of primary health care
BACKGROUND: Detection of colorectal cancer (CRC) is mainly achieved by clinical assessment. As new treatments become available for metastatic CRC (MCRC), it is important to accurately identify these patients.

Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation.

Water research
The extensive use of per- and polyfluoroalkyl substances (PFAS) in industrial and consumer products poses health risks due to their toxicity. Computational toxicology approaches, particularly quantitative structure-activity relationship (QSAR) models...

Operational safety risk modeling in a naval organization.

Journal of safety research
INTRODUCTION: Following numerous mishaps and near-misses, the U.S. Naval Surface Force established the Operational Surface Risk Indicators (OSRI) project to explore a robust proactive risk analysis and reduction capability. The OSRI model leverages m...

Multi-omics integration and machine learning identify and validate neutrophil extracellular trap-associated gene signatures in chronic rhinosinusitis with nasal polyps.

Clinical immunology (Orlando, Fla.)
This study aimed to explore the molecular characteristics of neutrophil extracellular traps (NETs) in chronic rhinosinusitis with nasal polyps (CRSwNP). Differentially expressed gene analysis, weighted gene co-expression network analysis, and machine...

Risk of bias assessment of post-stroke mortality machine learning predictive models: Systematic review.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: Stroke is a major cause of mortality and permanent disability worldwide. Precise prediction of post-stroke mortality is essential for guiding treatment decisions and rehabilitation planning. The ability of Machine learning models to proce...

Similar failures of consideration arise in human and machine planning.

Cognition
Humans are remarkably efficient at decision making, even in "open-ended" problems where the set of possible actions is too large for exhaustive evaluation. Our success relies, in part, on processes for calling to mind the right candidate actions. Whe...

Radiomics and machine learning models for diagnosing microvascular invasion in cholangiocarcinoma: a systematic review and meta-analysis of diagnostic test accuracy studies.

Clinical imaging
PURPOSE: We aimed to systematically assess the value of radiomics/machine learning (ML) models for diagnosing microvascular invasion (MVI) in patients with cholangiocarcinoma (CCA) using various radiologic modalities.

Machine Learning-Based localization of the epileptogenic zone using High-Frequency oscillations from SEEG: A Real-World approach.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
INTRODUCTION: Localizing the epileptogenic zone (EZ) using Stereo EEG (SEEG) is often challenging through manual analysis. Even methods based on signal analysis have limitations in identifying the EZ, particularly in patients with neocortical epileps...