Work

Reusable Filtering Functions for Application in ICU data: a case study.

Year: 2016

Type: article

Abstract: Complex medical data sometimes requires significant data preprocessing to prepare for analysis. The complexity can lead non-domain experts to apply simple filters of available data or to not use the data at all. The preprocessing choices can also have serious effects on the results of the study if incorrect decision or missteps are made. In this work, we present open-source data filters for an analysis motivated by understanding mortality in the context of sepsis- associated cardiomyopathy in the ICU. We report specific ICU filters and validations through chart review and graphs. These published filters reduce the complexity of using data in analysis by (1) encapsulating the domain expertise and feature engineering applied to the filter, by (2) providing debugged and ready code for use, and by (3) providing sensible validations. We intend these filters to evolve through pull requests and forks and serve as common starting points for specific analyses.

Source: PubMed Central

Authors: Vincent Major, Monique S Tanna, Simon Jones, Yin Aphinyanaphongs

Institution: NYU Langone Health

Language: English

Topic: Sepsis Diagnosis and Treatment

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institutes of Health

Award: Clinical and Translational Science Award