AIMC Topic: Machine Learning

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Artificial intelligence-driven approaches in antibiotic stewardship programs and optimizing prescription practices: A systematic review.

Artificial intelligence in medicine
Antimicrobial stewardship programs (ASPs) are essential in optimizing the use of antibiotics to address the global concern of antimicrobial resistance (AMR). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for e...

Neuradicon: Operational representation learning of neuroimaging reports.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Radiological reports typically summarize the content and interpretation of imaging studies in unstructured form that precludes quantitative analysis. This limits the monitoring of radiological services to throughput undiffer...

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...

Comparative analysis of machine learning models for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: An MRI radiomics approach.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: The aim of this work is to compare different machine learning models for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer using radiomics features from dynamic contrast-enhanced magnetic reso...

metaCDA: A Novel Framework for CircRNA-Driven Drug Discovery Utilizing Adaptive Aggregation and Meta-Knowledge Learning.

Journal of chemical information and modeling
In the emerging field of RNA drugs, circular RNA (circRNA) has attracted much attention as a novel multifunctional therapeutic target. Delving deeper into the intricate interactions between circRNA and disease is critical for driving drug discovery e...

Rapidly self-healing electronic skin for machine learning-assisted physiological and movement evaluation.

Science advances
Emerging electronic skins (E-Skins) offer continuous, real-time electrophysiological monitoring. However, daily mechanical scratches compromise their functionality, underscoring urgent need for self-healing E-Skins resistant to mechanical damage. Cur...

Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Science advances
Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardiz...

Discrimination of the Lame Limb in Horses Using a Machine Learning Method (Support Vector Machine) Based on Asymmetry Indices Measured by the EQUISYM System.

Sensors (Basel, Switzerland)
Lameness detection in horses is a critical challenge in equine veterinary practice, particularly when symptoms are mild. This study aimed to develop a predictive system using a support vector machine (SVM) to identify the affected limb in horses trot...

A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms.

Journal of ovarian research
Ovarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for OC diagnosis. ...

Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.

BMC medical informatics and decision making
BACKGROUND: Despite substantial progress in maternal and neonatal health, Rwanda's mortality rates remain high, necessitating innovative approaches to meet health related Sustainable Development Goals (SDGs). By leveraging data collected from Electro...