Advance care planning (ACP) is generally considered as valuable in guiding treatments that are aligned with patients' preferences. Despite its benefits, there are some practical and legal difficulties in its implementation. Predictive modelling is increasingly used in clinical decision-making, for example, in predicting patients' life expectancy, thus enabling clinicians to initiate timely ACP conversations. This development could transform the way end-of-life conversations are implemented. In this article we advocate for the use of predictive modelling in assisting clinicians to initiate ACP conversations provided several safeguards are in place to address ethical concerns that arise. Predictive modelling applications resolve several practical and legal difficulties in conducting end-of-life conversations. Ethical concerns such as explicability, accountability, trustworthiness and reliability of these models in clinical settings are important considerations. However, safeguards are needed to address these ethical concerns to ensure the models are appropriately supportive of patient needs and interests.
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J Med Internet Res
March 2025
Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
Background: Hypertension is a major global health issue and a significant modifiable risk factor for cardiovascular diseases, contributing to a substantial socioeconomic burden due to its high prevalence. In China, particularly among populations living near desert regions, hypertension is even more prevalent due to unique environmental and lifestyle conditions, exacerbating the disease burden in these areas, underscoring the urgent need for effective early detection and intervention strategies.
Objective: This study aims to develop, calibrate, and prospectively validate a 2-year hypertension risk prediction model by using large-scale health examination data collected from populations residing in 4 regions surrounding the Taklamakan Desert of northwest China.
J Biomol Struct Dyn
March 2025
School of Mechatronic Engineering and automation, Shanghai University, Shanghai, China.
Prediction of protein-ligand interactions is critical for drug discovery and repositioning. Traditional prediction methods are computationally intensive and limited in modeling structural changes. In contrast, data-driven deep learning methods significantly reduce computational costs and offer a more efficient approach for drug discovery.
View Article and Find Full Text PDFJAMA Dermatol
March 2025
Department of Surgery, Arthur J.E. Child Comprehensive Cancer Centre, University of Calgary, Calgary, Alberta, Canada.
Importance: There is a need to identify the best performing risk prediction model for sentinel lymph node biopsy (SLNB) positivity in melanoma.
Objective: To comprehensively review the characteristics and discriminative performance of existing risk prediction models for SLNB positivity in melanoma.
Data Sources: Embase and MEDLINE were searched from inception to May 1, 2024, for English language articles.
Addict Biol
March 2025
Department of Psychiatry and Psychotherapy, Neurophysiology and Interventional Neuropsychiatry, University of Tübingen, Tübingen, Germany.
Addictive behaviour is shaped by the dynamic interaction of implicit, bottom-up and explicit, top-down cognitive processes. In alcohol use disorder (AUD), implicit alcohol-related associations have been shown to predict increased subsequent alcohol consumption and are linked to the risk of relapse. Explicit cognitive processes, exerting prefrontal top-down control, are particularly significant during the critical period following the decision to abstain.
View Article and Find Full Text PDFEpilepsia
March 2025
Department of Neurosciences, University of Montreal, Montreal, Quebec, Canada.
Objective: To determine whether interictal epileptiform discharges (IEDs) on routine electroencephalography (EEG) predict seizure recurrence in adults with established epilepsy.
Methods: We conducted a retrospective survival analysis of consecutive adults with epilepsy undergoing routine EEG at a tertiary center between 2018 and 2019. Using multivariate Cox proportional hazards models guided by a directed acyclic graph and adjusted for confounders including past seizure frequency and duration of epilepsy, we estimated the association between the presence of IEDs and time to next seizure, stratified by epilepsy type.
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