AIMC Topic: Machine Learning

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A Scoping Review of AI/ML Algorithm Updating Practices for Model Continuity and Patient Safety Using a Simplified Checklist.

Studies in health technology and informatics
The ubiquity of clinical artificial intelligence (AI) and machine learning (ML) models necessitates measures to ensure the reliability of model output over time. Previous reviews have highlighted the lack of external validation for most clinical mode...

Beyond GPT-NER: ChatGPT as Ensemble Arbitrator for Discontinuous Named Entity Recognition in Health Corpora.

Studies in health technology and informatics
In medicine and healthcare, NER (Named Entity Recognition) involves identifying clinically relevant entities such as medications, symptoms, and adverse drug events (ADEs). This task is particularly challenging due to discontinuous NER (DNER), fragmen...

EvidenceOutcomes: A Dataset of Clinical Trial Publications with Clinically Meaningful Outcomes.

Studies in health technology and informatics
The fundamental process of evidence extraction in evidence-based medicine relies on identifying PICO elements, with Outcomes being the most complex and often overlooked. To address this, we introduce EvidenceOutcomes, a large annotated corpus of clin...

Natural Language Processing-Based Approach to Detect Common Adverse Events of Anticancer Agents from Unstructured Clinical Notes: A Time-to-Event Analysis.

Studies in health technology and informatics
This study assessed the effectiveness of natural language processing (NLP) in detecting adverse events (AEs) from anticancer agents by analyzing data from over 39,000 cancer patients. A specialized machine learning model identified known AEs from ant...

An Ensemble Approach Integrating Retrieval-Augmented Large Language Models and Boosting Algorithms for Enhanced Catatonia Phenotyping.

Studies in health technology and informatics
A critical first step in using large-scale data to study catatonia is the development of precise phenotyping algorithms that can identify instances of the condition. In this work, we present an ensemble approach that combines retrieval-augmented gene...

Integrating Large Language Models and Machine Learning for Enhanced Catatonia Phenotyping: A Study on Clinical Data from Electronic Health Records.

Studies in health technology and informatics
Catatonia, a complex syndrome with diagnostic challenges, was studied using a novel approach combining LightGBM and GPT-4 to enhance phenotyping from electronic health record (EHR) data. LightGBM, trained on structured data, achieved superior perform...

Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens.

Microbiology spectrum
Mycobacteria, including (MTB) and non-tuberculosis mycobacteria (NTM), are important causes of infectious disease and cause significant mortality and morbidity globally. Fast detection is extremely important to reduce transmission and mortality asso...

Genomic and machine learning approaches to predict antimicrobial resistance in .

Microbiology spectrum
UNLABELLED: is a multidrug-resistant pathogen, which poses a major challenge to clinical management due to its increasing resistance to common antibiotics, such as levofloxacin (LEV) and trimethoprim-sulfamethoxazole (SXT), and poor clinical respons...

Single-cell omics: moving towards a new era in ischemic stroke research.

European journal of pharmacology
Ischemic stroke (IS) is a highly complex and heterogeneous disease involving multiple pathophysiological events. A better understanding of the pathophysiology of IS will enhance preventive, diagnostic and therapeutic strategies. Despite significant a...

Vibrational spectroscopy of body fluids combined with machine learning for the early diagnosis of cystic echinococcosis.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Cystic echinococcosis (CE) is a globally prevalent zoonotic parasitic disease. Due to the covert symptoms and the inadequacies of screening technologies, accurate early diagnosis is crucial. This study explores the feasibility of employing body fluid...