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Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning.

Journal of neuroscience methods
BACKGROUND AND PURPOSE: Mechanical Thrombectomy (MT) has recently become the standard of care for anterior circulation stroke with large vessel occlusion, but predictive factors of successful MT are still not clearly defined. To tailor treatment indi...

Identification of a gene signature and prediction of overall survival of patients with stage IV colorectal cancer using a novel machine learning approach.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
OBJECTIVE: We sought to characterize unique gene signature patterns associated with worse overall survival (OS) among patients with stage IV colorectal cancer (CRC) using a machine learning (ML) approach.

New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding.

Scientific reports
Rapid and accurate identification of high-risk acute gastrointestinal bleeding (GIB) patients is essential. We developed two machine-learning (ML) models to calculate the risk of in-hospital mortality in patients admitted due to overt GIB. We analyze...

Efficacy and effectiveness of therapist-guided internet versus face-to-face cognitive behavioural therapy for depression via counterfactual inference using naturalistic registers and machine learning in Finland: a retrospective cohort study.

The lancet. Psychiatry
BACKGROUND: According to meta-analyses of randomised controlled trials (RCTs), therapist-guided internet-delivered cognitive behavioural therapy (iCBT) is as effective a treatment for depression as traditional face-to-face CBT (fCBT), despite its sub...

Prevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands: development and external validation of machine learning models.

Journal of Crohn's & colitis
BACKGROUND AND AIMS: Large registries are promising tools to study the epidemiology of inflammatory bowel disease (IBD). We aimed to develop and validate machine learning models to identify IBD cases in administrative data, aiming to determine the pr...

Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may recur after acutely successful ablation, AF is unusual in that patients may have long-term arrhythmia ...

An ontology-based rare disease common data model harmonising international registries, FHIR, and Phenopackets.

Scientific data
Although rare diseases (RDs) affect over 260 million individuals worldwide, low data quality and scarcity challenge effective care and research. This work aims to harmonise the Common Data Set by European Rare Disease Registry Infrastructure, Health ...

Scale to predict risk for refractory septic shock based on a hybrid approach using machine learning and regression modeling.

Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias
OBJECTIVE: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.

Utilization of non-invasive ventilation before prehospital emergency anesthesia in trauma - a cohort analysis with machine learning.

Scandinavian journal of trauma, resuscitation and emergency medicine
BACKGROUND: For preoxygenation, German guidelines consider non-invasive ventilation (NIV) as a possible method in prehospital trauma care in the absence of aspiration, severe head or face injuries, unconsciousness, or patient non-compliance. As data ...

Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalou...