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MTIOT: Identifying HPV subtypes from multiple infection data.
Comput Struct Biotechnol J
December 2024
Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.
Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis.
View Article and Find Full Text PDFClinical benefits and risks of remote patient monitoring: an overview and assessment of methodological rigour of systematic reviews for selected patient groups.
BMC Health Serv Res
January 2025
Department of Planning and Development, Region Västra Götaland, NU Hospital Group, Trollhättan, Sweden.
Background: Remote patient monitoring implies continuous follow-up of health-related parameters of patients outside healthcare facilities. Patients share health-related data with their healthcare unit and obtain feedback (which may be automatically generated if data are within a predefined range). The goals of remote patient monitoring are improvements for patients and reduced healthcare costs.
View Article and Find Full Text PDFMachine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.
Eur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
View Article and Find Full Text PDFMental and Oral Health: A Dual Frontier in Healthcare Integration and Prevention.
Cureus
December 2024
Public Health Dentistry, Sri Ramaswamy Memorial (SRM) Kattankulathur Dental College and Hospital, Sri Ramaswamy Memorial Institute of Science and Technology (SRMIST), Chennai, IND.
Mental and oral health are interrelated, and problems in one area usually affect the other. This review discusses the complex relationships between oral and mental health, particularly the psychosocial challenges faced by individuals with mental health disorders in maintaining oral hygiene, including stigma, lack of access to care, and financial barriers. It also discusses how psychiatric conditions influence oral health, with regard to issues such as dry mouth, gum disease, and tooth decay, and how poor oral health can aggravate mental well-being.
View Article and Find Full Text PDFA Comprehensive Review on the Application of Artificial Intelligence for Predicting Postsurgical Recurrence Risk in Early-Stage Non-Small Cell Lung Cancer Using Computed Tomography, Positron Emission Tomography, and Clinical Data.
J Med Radiat Sci
January 2025
Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Introduction: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Despite advancements in early detection and treatment, postsurgical recurrence remains a significant challenge, occurring in 30%-55% of patients within 5 years after surgery. This review analysed existing studies on the utilisation of artificial intelligence (AI), incorporating CT, PET, and clinical data, for predicting recurrence risk in early-stage NSCLCs.
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