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Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time-To-Event Outcomes.
Stat Med
March 2025
Faculté de Pharmacie, Université de Montréal, Québec, Canada.
Targeted maximum likelihood estimation (TMLE) is an increasingly popular framework for the estimation of causal effects. It requires modeling both the exposure and outcome but is doubly robust in the sense that it is valid if at least one of these models is correctly specified. In addition, TMLE allows for flexible modeling of both the exposure and outcome with machine learning methods.
View Article and Find Full Text PDFChallenges and opportunities in uncertainty quantification for healthcare and biological systems.
Philos Trans A Math Phys Eng Sci
March 2025
Department of Mathematics and Statistics, University of Exeter, Exeter, UK.
Uncertainty quantification (UQ) is an essential aspect of computational modelling and statistical prediction. Multiple applications, including geophysics, climate science and aerospace engineering, incorporate UQ in the development and translation of new technologies. In contrast, the application of UQ to biological and healthcare models is understudied and suffers from several critical knowledge gaps.
View Article and Find Full Text PDFHighly Optimized Simulation of Atomic Resolution Cell-Like Protein Environment.
J Phys Chem B
March 2025
Computational Biology Program, The University of Kansas, Lawrence, Kansas 66045, United States.
Computational approaches can provide details of molecular mechanisms in a crowded environment inside cells. Protein docking predicts stable configurations of molecular complexes, which correspond to deep energy minima. Systematic docking approaches, such as those based on fast Fourier transform (FFT), also map the entire intermolecular energy landscape by determining the position and depth of the full spectrum of the energy minima.
View Article and Find Full Text PDFExploring the Role of Artificial Intelligence (AI)-Driven Training in Laparoscopic Suturing: A Systematic Review of Skills Mastery, Retention, and Clinical Performance in Surgical Education.
Healthcare (Basel)
March 2025
Surgical Skills Centre, Dundee Institute for Healthcare Simulation, Respiratory Medicine and Gastroenterology, School of Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK.
Artificial Intelligence (AI)-driven training systems are becoming increasingly important in surgical education, particularly in the context of laparoscopic suturing. This systematic review aims to assess the impact of AI on skill acquisition, long-term retention, and clinical performance, with a specific focus on the types of machine learning (ML) techniques applied to laparoscopic suturing training and their associated advantages and limitations. A comprehensive search was conducted across multiple databases, including PubMed, IEEE Xplore, Cochrane Library, and ScienceDirect, for studies published between 2005 and 2024.
View Article and Find Full Text PDFTheoretical background of the game design element "chatbot" in serious games for medical education.
Adv Simul (Lond)
March 2025
Institute of Medical Education, University Hospital Bonn, Bonn, Germany.
Background: The use of virtual patients enables learning medical history taking in a safe environment without endangering patients' safety. The use of a chatbot embedded in serious games provides one way to interact with virtual patients. In this sense, the chatbot can be understood as a game design element, whose implementation should be theory driven and evidence based.
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