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Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs.

Increased scrutiny of artificial intelligence (AI) applications in healthcare highlights the need for real-world evaluations for effectiveness and unintended consequences. The complexity of healthcare, compounded by the user- and context-dependent nature of AI applications, calls for a multifaceted approach beyond traditional in silico evaluation of AI. We propose an interdisciplinary, phased research framework for evaluation of AI implementations in healthcare. We draw analogies to and highlight differences from the clinical [...]

Informatics tools that support next-generation sequencing workflows are essential to deliver timely interpretation of somatic variants in cancer. Here, we describe significant updates to our laboratory developed bioinformatics pipelines and data management application termed Houston Methodist Variant Viewer (HMVV).

The study sought to explore information needs arising from a gap in clinicians' knowledge that is not met by current evidence and identify possible areas of use and target groups for a future clinical decision support system (CDSS), which will guide clinicians in cases where no evidence exists.

The objective of this study was to assess the clinical and financial impact of a quality improvement project that utilized a modified Early Warning Score (mEWS)-based clinical decision support intervention targeting early recognition of sepsis decompensation.

One primary consideration when developing predictive models is downstream effects on future model performance. We conduct experiments to quantify the effects of experimental design choices, namely cohort selection and internal validation methods, on (estimated) real-world model performance.

This article describes the system architecture, training, initial use, and performance of Watson Assistant (WA), an artificial intelligence-based conversational agent, accessible within Micromedex®.

To describe a configurable mobile health (mHealth) framework for integration of physiologic and environmental sensors to be used in studies focusing on the domain of pediatric asthma.

Phenotypes are the result of the complex interplay between environmental and genetic factors. To better understand the interactions between chemical compounds and human phenotypes, and further exposome research we have developed "phexpo," a tool to perform and explore bidirectional chemical and phenotype interactions using enrichment analyses. Phexpo utilizes gene annotations from 2 curated public repositories, the Comparative Toxicogenomics Database and the Human Phenotype Ontology. We have applied phexpo in 3 [...]