Veterans Health Administration

About: Veterans Health Administration is a government organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Veterans Affairs. The organization has 63820 authors who have published 98417 publications receiving 4835425 citations. The organization is also known as: VHA.

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data.

Abstract: A promise of machine learning in health care is the avoidance of biases in diagnosis and treatment; a computer algorithm could objectively synthesize and interpret the data in the medical record. Integration of machine learning with clinical decision support tools, such as computerized alerts or diagnostic support, may offer physicians and others who provide health care targeted and timely information that can improve clinical decisions. Machine learning algorithms, however, may also be subject to biases. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and measurement error. There is concern that biases and deficiencies in the data used by machine learning algorithms may contribute to socioeconomic disparities in health care. This Special Communication outlines the potential biases that may be introduced into machine learning–based clinical decision support tools that use electronic health record data and proposes potential solutions to the problems of overreliance on automation, algorithms based on biased data, and algorithms that do not provide information that is clinically meaningful. Existing health care disparities should not be amplified by thoughtless or excessive reliance on machines.

Innate Lymphoid Cells Promote Anatomical Containment of Lymphoid-Resident Commensal Bacteria

Abstract: The mammalian intestinal tract is colonized by trillions of beneficial commensal bacteria that are anatomically restricted to specific niches. However, the mechanisms that regulate anatomical containment remain unclear. Here, we show that interleukin-22 (IL-22)-producing innate lymphoid cells (ILCs) are present in intestinal tissues of healthy mammals. Depletion of ILCs resulted in peripheral dissemination of commensal bacteria and systemic inflammation, which was prevented by administration of IL-22. Disseminating bacteria were identified as Alcaligenes species originating from host lymphoid tissues. Alcaligenes was sufficient to promote systemic inflammation after ILC depletion in mice, and Alcaligenes-specific systemic immune responses were associated with Crohn's disease and progressive hepatitis C virus infection in patients. Collectively, these data indicate that ILCs regulate selective containment of lymphoid-resident bacteria to prevent systemic inflammation associated with chronic diseases.

Methods for constructing and assessing propensity scores.

Abstract: Objectives To model the steps involved in preparing for and carrying out propensity score analyses by providing step-by-step guidance and Stata code applied to an empirical dataset. Study Design Guidance, Stata code, and empirical examples are given to illustrate (1) the process of choosing variables to include in the propensity score; (2) balance of propensity score across treatment and comparison groups; (3) balance of covariates across treatment and comparison groups within blocks of the propensity score; (4) choice of matching and weighting strategies; (5) balance of covariates after matching or weighting the sample; and (6) interpretation of treatment effect estimates. Empirical Application We use data from the Palliative Care for Cancer Patients (PC4C) study, a multisite observational study of the effect of inpatient palliative care on patient health outcomes and health services use, to illustrate the development and use of a propensity score. Conclusions Propensity scores are one useful tool for accounting for observed differences between treated and comparison groups. Careful testing of propensity scores is required before using them to estimate treatment effects.