Evaluation of practice change following SAFE obstetric courses in Tanzania: : a prospective cohort study

Funding Information: The study was funded by the Laerdal Foundation. ML and AZ are joint first authors. We would like to thank the World Federation of Societies of Anaesthesiologists and the Association of Anaesthetists, UK for operational and administrative support. We would also like to express our deepest gratitude to the faculty and research assistants: B. Asnake, A. Chamwanzi, A. Cheng, T. Kasole, K. Khalid, L. Frostan Komba, C. L. S. Kwan, A. F. Lwiza, P. Massawe, B. McKenna, S. S. Mohamed, C. Msadabwe, P. Murambi, A. Musgrave, M. C. Mutagwaba, G. Mwakisambwe, A. S. Ndebeya, S. G. Ndezi, H. Phiri, P. Ponsian, R. Samwel, E. Shang'a and R. Swai. This paper is dedicated to the memory of our dear friend and colleague, Soloman Gerald Ndezi (1984–2022), who was a dedicated teacher and compassionate doctor. No competing interests declared. Publisher Copyright: © 2023 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists.Peer reviewedPublisher PD

Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (Nature Medicine, (2022), 28, 5, (924-933), 10.1038/s41591-022-01772-9)

In the version of this article initially published, a list of the DECIDE-AI expert group members and their affiliations was omitted and has now been included in the HTML and PDF versions of the article. *A list of authors and their affiliations appears online

Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems

Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.Comment: Final manuscript (46 pages), published in Artificial Intelligence Journal. The arXiv version also contains a table of contents after the abstract, but is otherwise identical to the AIJ version. Keywords: autonomous agents, multiagent systems, modelling other agents, opponent modellin

A framework for individualizing predictions of disease trajectories by exploiting multi-resolution structure.

Abstract For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an individual's disease trajectory as a continuous-valued continuous-time function describing the severity of the disease over time. We propose a hierarchical latent variable model that individualizes predictions of disease trajectories. This model shares statistical strength across observations at different resolutions-the population, subpopulation and the individual level. We describe an algorithm for learning population and subpopulation parameters offline, and an online procedure for dynamically learning individual-specific parameters. Finally, we validate our model on the task of predicting the course of interstitial lung disease, a leading cause of death among patients with the autoimmune disease scleroderma. We compare our approach against state-of-the-art and demonstrate significant improvements in predictive accuracy

Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery

Diseases such as autism, cardiovascular disease, and the autoimmune disorders are difficult to treat because of the remarkable degree of variation among affected individuals. Subtyping research seeks to refine the definition of such complex, multi-organ diseases by identifying homogeneous patient subgroups. In this paper, we propose the Probabilistic Subtyping Model (PSM) to identify subgroups based on clustering individual clinical severity markers. This task is challenging due to the presence of nuisance variability — variations in measurements that are not due to disease subtype — which, if not accounted for, generate biased estimates for the group-level trajectories. Measurement sparsity and irregular sampling patterns pose additional challenges in clustering such data. PSM uses a hierarchical model to account for these different sources of variability. Our experiments demonstrate that by accounting for nuisance variability, PSM is able to more accurately model the marker data. We also discuss novel subtypes discovered using PSM and the resulting clinical hypotheses that are now the subject of follow up clinical experiments