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Despite the recent adoption of large language models (LLMs) for biomedical information extraction (IE), challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete IE pipelines.

To develop and disseminate a technical framework for administering the Research Participant Perception Survey (RPPS) and aggregating data across institutions using REDCap.

To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship.

Sepsis recognition among infants in the Neonatal Intensive Care Unit (NICU) is challenging and delays in recognition can result in devastating consequences. Although predictive models may improve sepsis outcomes, clinical adoption has been limited. Our focus was to align model behavior with clinician information needs by developing a machine learning (ML) pipeline with two components: (1) a model to predict baseline sepsis risk and (2) a model to detect evolving [...]

Demonstrate a methodology for improving discoverability of rare disease datasets by enriching source data with biological associations.

Artificial intelligence (AI) technology promises to be a powerful tool in addressing the global health challenges posed by tuberculosis (TB). However, evidence for its real-world impact is lacking, which may hinder safe, responsible adoption. This case study addresses this gap by assessing the technical performance, usability and workflow aspects, and health impact of implementing a commercial AI system (qXR by Qure.ai) to support Australian radiologists in diagnosing pulmonary TB.

Digital health (patient portals, remote monitoring devices, video visits) is a routine part of health care, though the digital divide may affect access.

Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1).

Examine the accuracy of privacy preserving record linkage (PPRL) matches in real world data (RWD).

To evaluate large language models (LLMs) for pre-test diagnostic probability estimation and compare their uncertainty estimation performance with a traditional machine learning classifier.