AIMC Topic: Machine Learning

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Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.

Translational vision science & technology
PURPOSE: Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia a...

Novel machine-learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis.

Cancer medicine
BACKGROUND: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple progno...

A Novel Machine-Learning Algorithm to Predict Stone Recurrence with 24-Hour Urine Data.

Journal of endourology
The absence of predictive markers for kidney stone recurrence poses a challenge for the clinical management of stone disease. The unpredictability of stone events is also a significant limitation for clinical trials, where many patients must be enro...

Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.

Translational vision science & technology
PURPOSE: To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data.

Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.

Clinical cardiology
BACKGROUND: Stent malapposition (SM) following percutaneous coronary intervention (PCI) for myocardial infarction continues to present significant clinical challenges. In recent years, machine learning (ML) models have demonstrated potential in disea...

Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.

JCO clinical cancer informatics
PURPOSE: Identifying cancer symptoms in electronic health record (EHR) narratives is feasible with natural language processing (NLP). However, more efficient NLP systems are needed to detect various symptoms and distinguish observed symptoms from neg...

Genetic Analysis of Potential Biomarkers and Therapeutic Targets in Ferroptosis from Steroid-Induced Osteonecrosis of the Femoral Head Based on Machine Learning.

Journal of the College of Physicians and Surgeons--Pakistan : JCPSP
OBJECTIVE: To locate the candidate therapeutic target genes involved in ferroptosis in steroid-induced osteonecrosis of the femoral head (SONFH).

Machine learning and deep learning tools for the automated capture of cancer surveillance data.

Journal of the National Cancer Institute. Monographs
The National Cancer Institute and the Department of Energy strategic partnership applies advanced computing and predictive machine learning and deep learning models to automate the capture of information from unstructured clinical text for inclusion ...