AIMC Topic: Machine Learning

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Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) e...

Automated Machine Learning Tools to Build Regression Models for Schizosaccharomyces pombe Omics Data.

Methods in molecular biology (Clifton, N.J.)
Machine learning is a powerful tool for analyzing biological data and making useful predictions. The surge of biological data from high-throughput omics technologies has raised the need for modeling approaches capable of tackling such amounts of data...

Measuring Cell Dimensions in Fission Yeast Using Machine Learning.

Methods in molecular biology (Clifton, N.J.)
In fission yeast (Schizosaccharomyces pombe), cell length is a crucial indicator of cell cycle progression. Microscopy screens that examine the effect of agents or genotypes suspected of altering genomic or metabolic stability and thus cell size are ...

Mini-mental status examination phenotyping for Alzheimer's disease patients using both structured and narrative electronic health record features.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study aims to automate the prediction of Mini-Mental State Examination (MMSE) scores, a widely adopted standard for cognitive assessment in patients with Alzheimer's disease, using natural language processing (NLP) and machine learnin...

RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes.

Radiology. Artificial intelligence
Purpose To evaluate the performance of the winning machine learning models from the 2023 RSNA Abdominal Trauma Detection AI Challenge. Materials and Methods The competition was hosted on Kaggle and took place between July 26 and October 15, 2023. The...

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning cu...

ML-based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel.

Current topics in medicinal chemistry
BACKGROUND: Epilepsy remains the most common and chronic disorder demanding longterm management. The impact of epilepsy disease is a cause of great concern and has resulted in efforts to develop treatment for epilepsy. It occurs due to an increase in...

Just another tool in their repertoire: uncovering insights into public and patient perspectives on clinicians' use of machine learning in perioperative care.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Successful implementation of machine learning-augmented clinical decision support systems (ML-CDSS) in perioperative care requires the prioritization of patient-centric approaches to ensure alignment with societal expectations. We assesse...

Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

JAMA psychiatry
IMPORTANCE: Cognitive functioning is associated with various factors, such as age, sex, education, and childhood adversity, and is impaired in people with psychosis. In addition to specific effects of the disorder, cognitive impairments may reflect a...

Prediction of vaults in eyes with vertical implantable collamer lens implantation.

Journal of cataract and refractive surgery
PURPOSE: To design formulas for predicting postoperative vaults in vertical implantable collamer lens (ICL) implantation and to achieve more precise predictions using machine learning models.