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Machine 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 PDFBackground And Aims: The lack of therapeutic response characterizes treatment-resistant depression despite undergoing at least two adequate monotherapy trials with medications from distinct pharmacologic classes. The inability to attain remission in patients diagnosed with major depressive disorder (MDD) is a significant issue of concern within public health. Therefore, the management of treatment-resistant depression (TRD) poses significant obstacles for both patients and healthcare professionals.
View Article and Find Full Text PDFArtificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare.
Health Sci Rep
January 2025
Department of Research The Medical Research Circle (MedReC) Goma Democratic Republic of the Congo.
Background And Aim: Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress and quality of life in affected children. With the advent of artificial intelligence (AI), there's a growing interest in leveraging its capabilities to improve the diagnosis and management of pediatric epilepsy.
View Article and Find Full Text PDFMachine learning applications in placenta accreta spectrum disorders.
Eur J Obstet Gynecol Reprod Biol X
March 2025
Mother and Newborn Health Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
This review examines the emerging applications of machine learning (ML) and radiomics in the diagnosis and prediction of placenta accreta spectrum (PAS) disorders, addressing a significant challenge in obstetric care. It highlights recent advancements in ML algorithms and radiomic techniques that utilize medical imaging modalities like magnetic resonance imaging (MRI) and ultrasound for effective classification and risk stratification of PAS. The review discusses the efficacy of various deep learning models, such as nnU-Net and DenseNet-PAS, which have demonstrated superior performance over traditional diagnostic methods through high AUC scores.
View Article and Find Full Text PDFObjectives: The pairing of immunotherapy and radiotherapy in the treatment of locally advanced nonsmall cell lung cancer (NSCLC) has shown promise. By combining radiotherapy with immunotherapy, the synergistic effects of these modalities not only bolster antitumor efficacy but also exacerbate lung injury. Consequently, developing a model capable of accurately predicting radiotherapy- and immunotherapy-related pneumonitis in lung cancer patients is a pressing need.
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