Prediction of Cervical Cancer Patients' Survival Period with Machine Learning Techniques
CONCLUSIONS: Machine learning demonstrated the ability to provide high-accuracy predictions of survival periods in both classification and regression problems. This suggests its potential use as a decision-support tool in the process of treatment planning and resource allocation for each patient.PMID:38359850 | PMC:PMC10879821 | DOI:10.4258/hir.2024.30.1.60 (Source: Healthcare Informatics Research)
Source: Healthcare Informatics Research - February 15, 2024 Category: Information Technology Authors: Intorn Chanudom Ekkasit Tharavichitkul Wimalin Laosiritaworn Source Type: research
Prediction of Diabetes Using Data Mining and Machine Learning Algorithms: A Cross-Sectional Study
CONCLUSIONS: A gradient boosted decision tree model accurately identified the most important risk factors related to diabetes. Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the most important risk factors for diabetes, respectively. This model can support planning for diabetes management and prevention.PMID:38359851 | PMC:PMC10879823 | DOI:10.4258/hir.2024.30.1.73 (Source: Healthcare Informatics Research)
Source: Healthcare Informatics Research - February 15, 2024 Category: Information Technology Authors: Hassan Shojaee-Mend Farnia Velayati Batool Tayefi Ebrahim Babaee Source Type: research
Fostering Digital Health in Universities: An Experience of the First Junior Scientific Committee of the Brazilian Congress of Health Informatics
CONCLUSIONS: Forming a JSC proved to be a valuable tool to foster DH, particularly due to the valuable interactions it facilitated between esteemed professionals and students. It also supports the cultivation of leadership skills in DH, a field that has not yet received the recognition it deserves.PMID:38359852 | PMC:PMC10879825 | DOI:10.4258/hir.2024.30.1.83 (Source: Healthcare Informatics Research)
Source: Healthcare Informatics Research - February 15, 2024 Category: Information Technology Authors: Alexandre Negrao Pantaleao Anna Lu ísa Mennitti Felipe Baptista Brunheroto Vit ória Stavis Laura Teresa Ricoboni Victor Augusto Fonseca de Castro Ollivia Frederigue Ferreira Eura Martins Lage Deborah Ribeiro Carvalho Anita Maria da Rocha Fernandes Julia Source Type: research
Beyond Data: Actionable AI - Review of the 2023 Fall Conference of the Korean Society of Medical Informatics
Healthc Inform Res. 2024 Jan;30(1):1-2. doi: 10.4258/hir.2024.30.1.1. Epub 2024 Jan 31.NO ABSTRACTPMID:38359844 | PMC:PMC10879822 | DOI:10.4258/hir.2024.30.1.1 (Source: Healthcare Informatics Research)
Source: Healthcare Informatics Research - February 15, 2024 Category: Information Technology Authors: Younghee Lee Taehoon Ko Kwangmo Yang Source Type: research
Survey of Medical Applications of Federated Learning
CONCLUSIONS: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.PMID:38359845 | PMC:PMC10879826 | DOI:10.4258/hir.2024.30.1.3 (Source: Healthcare Informatics Research)
Source: Healthcare Informatics Research - February 15, 2024 Category: Information Technology Authors: Geunho Choi Won Chul Cha Se Uk Lee Soo-Yong Shin Source Type: research
