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Derivation and validation of a clinical predictive model for longer duration diarrhea among pediatric patients in Kenya using machine learning algorithms.
BMC Med Inform Decis Mak
January 2025
Kenya Medical Research Institute- Center for Global Health Research (KEMRI-CGHR), P.O Box 1578-40100, Kisumu, Kenya.
Background: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities.
Methods: LDD was defined as a diarrhea episode lasting ≥ 7 days.
MultiChem: predicting chemical properties using multi-view graph attention network.
BioData Min
January 2025
Department of Computer Science, Hanyang University, Seoul, Republic of Korea.
Background: Understanding the molecular properties of chemical compounds is essential for identifying potential candidates or ensuring safety in drug discovery. However, exploring the vast chemical space is time-consuming and costly, necessitating the development of time-efficient and cost-effective computational methods. Recent advances in deep learning approaches have offered deeper insights into molecular structures.
View Article and Find Full Text PDFHigh-reward, high-risk technologies? An ethical and legal account of AI development in healthcare.
BMC Med Ethics
January 2025
Faculty of Law, University of Montreal, Ch de la Tour, Montreal, QC, H3T 1J7, Canada.
Background: Considering the disruptive potential of AI technology, its current and future impact in healthcare, as well as healthcare professionals' lack of training in how to use it, the paper summarizes how to approach the challenges of AI from an ethical and legal perspective. It concludes with suggestions for improvements to help healthcare professionals better navigate the AI wave.
Methods: We analyzed the literature that specifically discusses ethics and law related to the development and implementation of AI in healthcare as well as relevant normative documents that pertain to both ethical and legal issues.
A radiomics and deep learning nomogram developed and validated for predicting no-collapse survival in patients with osteonecrosis after multiple drilling.
BMC Med Inform Decis Mak
January 2025
Department of Orthopedics, the First Hospital of Jilin University, Changchun, Jilin Province, 130021, China.
Purpose: Identifying patients who may benefit from multiple drilling are crucial. Hence, the purpose of the study is to utilize radiomics and deep learning for predicting no-collapse survival in patients with femoral head osteonecrosis.
Methods: Patients who underwent multiple drilling were enrolled.
Identification of EXPA4 as a key gene in cotton salt stress adaptation through transcriptomic and coexpression network analysis of root tip protoplasts.
BMC Plant Biol
January 2025
National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, Henan, 455000, China.
Background: Salinity stress impairs cotton growth and fiber quality. Protoplasts enable elucidation of early salt-responsive signaling. Elucidating crop tolerance mechanisms that ameliorate these diverse salinity-induced stresses is key for improving agricultural productivity under saline conditions.
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