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Estimating readmission risk for intensive care unit (ICU) patients is critical for clinicians to optimize resource allocation and prevent premature discharges. Machine learning models currently applied to this task either lack interpretability or cannot identify patient subgroups with distinctive readmission risks and characteristics. We addressed this gap by introducing a cutting-edge rule-based model, namely truly unordered rule sets (TURS), to reveal heterogeneous readmission risks and subgroup-level patient characteristics.

The National Cancer Institute (NCI), part of the National Institutes of Health (NIH) supports efforts to address critical challenges in advancing cancer research. As part of this effort, NCI sponsored the development of a privacy-preserving record linkage (PPRL) software that transforms identifying patient information into multiple tokens through a set of cryptographically secure keyed hash functions. This project aims to evaluate the PPRL software in the perspective of re-identification risks [...]

To develop a transfer-learning Bayesian sparse logistic regression model that transfers information learned from one dataset to another by using an informed prior to facilitate model fitting in small-sample clinical patient-level prediction problems that suffer from a lack of available information.

This content analysis study investigates potential biases in image generation by 2 artificial intelligence (AI) tools, DALL-E 3 and Midjourney, in portraying older adults and individuals living with dementia. Despite widespread use of generative AI in various sectors, there is limited research on how these models might perpetuate stereotypes and stigmatization through their images.

Large language models' (LLMs') performance in high-stakes, compliance-driven settings such as drafting clinical research documents remains underexplored. This study aims to build a benchmark and an evaluation framework for assessing LLMs' compliance and factuality in generating informed consent forms (ICFs) from clinical trial protocols.

To inform initiatives to improve the interoperability of healthcare data, we described the experience of distinct phenotypes of physicians when obtaining information from outside sources.

To support ambulatory care innovation, we created Observer, a multimodal dataset comprising videotaped outpatient visits, electronic health record (EHR) data, and structured surveys. This paper describes the data collection procedures and summarizes the clinical and contextual features of the dataset.

The objectives of this study were to: (1) explore and analyze the structure and content of 19 SNOMED Clinical Terms (SNOMED CT) national editions; (2) identify overlaps and types of quality issues; and (3) offer recommendations to improve the quality of these national editions.

To construct a data quality (DQ) system that incorporates combinations of methods to evaluate data characteristics and analytic fitness across research questions for multiple uses.

Acupuncture, a key modality in traditional Chinese medicine, is gaining global recognition as a complementary therapy and a subject of increasing scientific interest. However, fragmented and unstructured acupuncture knowledge spread across diverse sources poses challenges for semantic retrieval, reasoning, and in-depth analysis. To address this gap, we developed AcuKG, a comprehensive knowledge graph that systematically organizes acupuncture-related knowledge to support sharing, discovery, and artificial intelligence-driven innovation in the field.