Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration.

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research.

Generation of Fluorinated Amychelin Siderophores against Pseudomonas aeruginosa Infections by a Combination of Genome Mining and Mutasynthesis.

Pioneering microbial genomic surveys have revealed numerous untapped biosynthetic gene clusters, unveiling the great potential of new natural products. Here, using a combination of genome mining, mutasynthesis, and activity screening in an infection model comprising Caenorhabditis elegans and Pseudomonas aeruginosa, we identified candidate virulence-blocking amychelin siderophore compounds from actinomycetes. Subsequently, we developed unreported analogs of these virulence-blocking siderophores with improved potency by exploiting an Amycolatopsis methanolica strain 239T chorismate to salicylate a biosynthetic subpathway for mutasynthesis. This allowed us to generate the fluorinated amychelin, fluoroamychelin I, which rescued C. elegans from P. aeruginosa-mediated killing with an EC50 value of 1.4 µM, outperforming traditional antibiotics including ceftazidime and meropenem. In general, this paper describes an efficient platform for the identification and production of classes of anti-microbial compounds with potential unique modes of action.

Automated synthesis and visualization of a chemotherapy treatment regimen network.

Cytotoxic treatments for cancer remain highly toxic, expensive, and variably efficacious. Many chemotherapy regimens are never directly compared in randomized clinical trials (RCTs); as a result, the vast majority of guideline recommendations are ultimately derived from human expert opinion. We introduce an automated network meta-analytic approach to this clinical problem, with nodes representing regimens and edges direct comparison via RCT(s). A chemotherapy regimen network is visualized for the primary treatment of chronic myelogenous leukemia (CML). Node and edge color, size, and opacity are all utilized to provide additional information about the quality and strength of the depicted evidence. Historical versions of the network are also created. With this approach, we were able to compactly compare the results of 17 CML regimens involving RCTs of 9700 patients, representing the accumulation of 45 years of evidence. Our results closely parallel the recommendations issued by a professional guidelines organization, the National Comprehensive Cancer Network (NCCN). This approach offers a novel method for interpreting complex clinical data, with potential implications for future objective guideline development.