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Bag-of-Attributes Representation: A Vector Space Model for Electronic Health Records Analysis in OMOP | IEEE Conference Publication | IEEE Xplore

Bag-of-Attributes Representation: A Vector Space Model for Electronic Health Records Analysis in OMOP


Abstract:

Several studies have been performed worldwide to improve health services using data generated by digital medical systems. The increasing volume of data generated by these...Show More

Abstract:

Several studies have been performed worldwide to improve health services using data generated by digital medical systems. The increasing volume of data generated by these systems is making the use of knowledge discovery and data analysis techniques essential to improve the quality of the health services, which are offered by the medical facilities. However, it is possible to observe a gap, in the literature, about generic and flexible vector space models (VSM) that are well adapted to handle electronic health records (EHR), requiring that each knowledge discovery effort develop their own VSM or other representation model. This restriction can turn a knowledge discovery task over clinical pathways nonviable for comparative evaluations among different methods. Targeting such scenario, we propose the Bag-of-Attributes Representation (BOAR). BOAR represents an EHR as an n-dimensional vector space. Since BOAR takes advantage of the OMOP (Observational Medical Outcomes Partnership) standard, BOAR is able to represent records retrieved from different data models. The experimental results show that BOAR is flexible and robust to representing EHR from several sources, and allows the execution and evaluation of several clustering algorithms.
Date of Conference: 28-30 July 2020
Date Added to IEEE Xplore: 01 September 2020
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ISSN Information:

Conference Location: Rochester, MN, USA

I. Introduction

Analyzing the healthcare interventions received by a patient admitted in a hospital, the so-called “clinical pathways”, has gained more attention from researchers around the world [1]–[3]. The main reason is that extracting useful information from Electronic Health Records (EHR) aids to improve the quality of the health services. Following well-defined clinical pathways during the analysis of a patient's symptoms can bring important benefits, such as early diagnosis, which increases the likelihood of recovery and reduction of operating costs [2]. Clinical pathways are composed of several pieces of information: medical procedures, drug exposures, patient complaints, and procedure occurrences.

References

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