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To train and test a model predicting chronic kidney disease (CKD) using the Generalized Additive2 Model (GA2M), and compare it with other models being obtained with traditional or machine learning approaches.

To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications.

To derive a comprehensive implementation framework for clinical AI models within hospitals informed by existing AI frameworks and integrated with reporting standards for clinical AI research.

To compare the effectiveness of 2 clinical decision support (CDS) tools to avoid prescription of nonsteroidal anti-inflammatory drugs (NSAIDs) in patients with heart failure (HF): a "commercial" and a locally "customized" alert.

We aimed to assess ChatGPT's performance on the Clinical Informatics Board Examination and to discuss the implications of large language models (LLMs) for board certification and maintenance. We tested ChatGPT using 260 multiple-choice questions from Mankowitz's Clinical Informatics Board Review book, omitting 6 image-dependent questions. ChatGPT answered 190 (74%) of 254 eligible questions correctly. While performance varied across the Clinical Informatics Core Content Areas, differences were not statistically significant. ChatGPT's [...]

Successful model development requires both an accurate a priori understanding of future performance and high performance on deployment. Optimistic estimations of model performance that are unrealized in real-world clinical settings can contribute to nonuse of predictive models. This study used 2 tasks, predicting ICU mortality and Bi-Level Positive Airway Pressure failure, to quantify: (1) how well internal test performances derived from different methods of partitioning data into development and test [...]

The design, development, implementation, use, and evaluation of high-quality, patient-centered clinical decision support (PC CDS) is necessary if we are to achieve the quintuple aim in healthcare. We developed a PC CDS lifecycle framework to promote a common understanding and language for communication among researchers, patients, clinicians, and policymakers. The framework puts the patient, and/or their caregiver at the center and illustrates how they are involved in all the following [...]

Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the [...]

Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a "sick care" system into a "health-promoting" system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and [...]

Artificial intelligence (AI) has the potential to be a disruptive technology in healthcare. Recently, there is increased speculation that AI may be used to replace healthcare providers in the future. To answer this question, we reviewed over 21 000 articles published in medical specialty journals between 2019 and 2021 to evaluate whether these AI models were intended to assist or replace healthcare providers. We also evaluated whether all Food and Drug [...]