AIMC Topic: Data Science

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Data Science Education for Residents, Researchers, and Students in Psychiatry and Psychology: Program Development and Evaluation Study.

JMIR medical education
BACKGROUND: The use of artificial intelligence (AI) to analyze health care data has become common in behavioral health sciences. However, the lack of training opportunities for mental health professionals limits clinicians' ability to adopt AI in cli...

Evaluating large language models in biomedical data science challenges through a classroom experiment.

Proceedings of the National Academy of Sciences of the United States of America
Large language models (LLMs) have shown remarkable capabilities in algorithm design, but their effectiveness in solving data science challenges in real-world settings remains poorly understood. We conducted a classroom experiment in which graduate st...

Leveraging Artificial Intelligence for Clinical Study Matching: Key Threads for Interweaving Data Science and Implementation Science.

JMIR formative research
Artificial intelligence holds the potential to enhance the efficiency of clinical research. Yet, like all innovations, its impact is dependent upon target user uptake and adoption. As efforts to leverage artificial intelligence for clinical trial scr...

Using AI and big data analytics to support entrepreneurial decisions in the digital economy.

Scientific reports
Despite extensive research on AI's theoretical benefits in entrepreneurship, few studies compare machine learning models' effectiveness using real-world data or address challenges like model interpretability and overfitting. This study investigates h...

Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol.

BMJ health & care informatics
INTRODUCTION: Multimorbidity (MM), defined as two or more chronic diseases in an individual, is linked to adverse outcomes. MM is increasing in sub-Saharan Africa due to rapidly advancing epidemiological and social transitions. The Research Hub (MAD...

Artificial Intelligence in Clinical Nutrition: Bridging Data Analytics and Nutritional Care.

Current nutrition reports
PURPOSE OF REVIEW: This review explores how artificial intelligence can help advance clinical nutrition and address nutrition education and practice challenges. It highlights the role of AI, mainly through advanced clinical decision-making using gene...

Synthesizing Research in Sport and Exercise: Transitioning to Real-World Data and Data Science.

Journal of applied biomechanics
The American Society of Biomechanics (ASB) Jim Hay Memorial Award recognizes individuals for their original long-term contributions to the field of biomechanics. The recipient's work is highlighted at the Jim Hay Symposium held during the ASB annual ...

Advancing Clinical Information Systems: Harnessing Telemedicine, Data Science, and AI for Enhanced and More Precise Healthcare Delivery.

Yearbook of medical informatics
OBJECTIVE: In this synopsis, the editors of the Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics overview recent research and propose a selection of best papers published in 2023 in the CIS field.

Artificial intelligence for modelling infectious disease epidemics.

Nature
Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social sci...

Health Care Professionals and Data Scientists' Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study.

Journal of medical Internet research
BACKGROUND: Heart failure (HF) is a significant global health problem, affecting approximately 64.34 million people worldwide. The worsening of HF, also known as HF decompensation, is a major factor behind hospitalizations, contributing to substantia...