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Urgency Prediction for Medical Laboratory Tests Through Optimal Sparse Decision Tree: Case Study With Echocardiograms.
JMIR AI
January 2025
Department of Information Systems and Business Analytics, Iowa State University, Ames, IA, United States.
Background: In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care.
View Article and Find Full Text PDFDiagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.
JMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
Prediction of gene expression using histone modification patterns extracted by Particle Swarm Optimization.
Bioinformatics
January 2025
Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, 37099, Germany.
Motivation: Histone modifications play an important role in transcription regulation. Although the general importance of some histone modifications for transcription regulation has been previously established, the relevance of others and their interaction is subject to ongoing research. By training Machine Learning models to predict a gene's expression and explaining their decision making process, we can get hints on how histone modifications affect transcription.
View Article and Find Full Text PDFPostoperative symptom changes following uterine artery embolization for uterine fibroid based on FIGO classification.
CVIR Endovasc
January 2025
Department of Radiology, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo Bunkyo-Ku, Tokyo, 113-8421, Japan.
Background: Classifying uterine fibroid using the International Federation of Gynecology and Obstetrics (FIGO) classification system assists treatment decision-making and planning. This study aimed to study whether different fibroid locations influence clinical outcomes following uterine artery embolization (UAE).
Methods: This is a retrospective cohort study of patients who underwent UAE for symptomatic uterine fibroid between December 2016 and January 2023 at our hospital.
Developing a novel model for predicting overall survival in late-onset colon adenocarcinoma patients based on LODDS: a study based on the SEER database and external validation.
Discov Oncol
January 2025
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230000, China.
Aim: To construct a predictive model based on the LODDS stage established for patients with late-onset colon adenocarcinoma to enhance survival stratification.
Methods: Late-onset colon adenocarcinoma data were obtained from the public database. After determining the optimal LODDS truncation value for the training set via X-tile software, we created a new staging system by integrating the T stage and M stage.
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