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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 PDFscHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.
Bioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.
Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant.
View Article and Find Full Text PDFElderly Patients With Aplastic Anemia: Treatment Patterns and Outcomes in the Real World.
Am J Hematol
January 2025
Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
We retrospectively analyzed a large international cohort of 1113 patients with aplastic anemia to evaluate treatment choice and outcome in elderly patients as compared with a younger population. Overall, 319 (29%) patients were > 60 years old at diagnosis (60-64 years (n = 85), 106 65-69 years (n = 106), and 128 > 70 years (n = 128)). Elderly patients showed a more severe thrombocytopenia at onset and a significantly lower overall response (complete plus partial) to first-line therapy at 6 months as compared to younger patients (47% vs.
View Article and Find Full Text PDFDiscussion on the optimization of personalized medication using information systems based on pharmacogenomics: an example using colorectal cancer.
Front Pharmacol
January 2025
Department of Pharmacy, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Pharmacogenomics (PGx) is a powerful tool for clinical optimization of drug efficacy and safety. However, due to many factors affecting drugs in the real world, PGx still accounts for a small proportion of actual clinical application scenarios. Therefore, based on the information software, pharmacists use their professional advantages to integrate PGx into all aspects of pharmaceutical care, which is conducive to promoting the development of personalized medicine.
View Article and Find Full Text PDFOptimizing Point-in-Space Continuous Monitoring System Sensor Placement on Oil and Gas Sites.
ACS Sustain Resour Manag
January 2025
Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, Colorado 80401, United States.
We propose a generic, modular framework to optimize the placement of point-in-space continuous monitoring system sensors on oil and gas sites aiming to maximize the methane emission detection efficiency. Our proposed framework substantially expands the problem scale compared to previous related studies and can be adapted for different objectives in sensor placement. This optimization framework is comprised of five steps: (1) simulate emission scenarios using site-specific wind and emission information; (2) set possible sensor locations under consideration of the site layout and any site-specific constraints; (3) simulate methane concentrations for each pair of emission scenario and possible sensor location; (4) determine emissions detection based on the site-specific simulated concentrations; and (5) select the best subset of sensor locations, under a given number of sensors to place, using genetic algorithms combined with Pareto optimization.
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