A rule-based semantic approach for data integration, standardization and dimensionality reduction utilizing the UMLS: Application to predicting bariatric surgery outcomes

Minoo Modaresnezhad,Ali Vahdati,Hamid Nemati,Ali Ardestani,Fereidoon Sadri

doi:10.1016/j.compbiomed.2019.01.019

Abstract

Utilization of existing clinical data for improving patient outcomes poses a number of challenging and complex problems involving lack of data integration, the absence of standardization across inhomogeneous data sources and computationally-demanding and time-consuming exploration of very large datasets. In this paper, we will present a robust semantic data integration, standardization and dimensionality reduction method to tackle and solve these problems. Our approach enables the integration of clinical data from diverse sources by resolving canonical inconsistencies and semantic heterogeneity as required by the National Library of Medicine's Unified Medical Language System (UMLS) to produce standardized medical data. Through a combined application of rule-based semantic networks and machine learning, our approach enables a large reduction in dimensionality of the data and thus allows for fast and efficient application of data mining techniques to large clinical datasets. An example application of the techniques developed in our study is presented for the prediction of bariatric surgery outcomes.

Highlights

With the rising interest in utilizing patients' medical history for efficient and effective prediction of clinical outcomes, Clinical Decision Support Systems (CDSS) have become an area of research that shows tremendous potential for enhancing medical care while reducing the associated costs
CDSS can help a clinician in several ways including, 1) using patients' medical history in helping to decide the most appropriate treatment for the patients, 2) monitoring and recording the patients’ medical information prior and after the start of treatment and alerting the clinician in case of any changes, and 3) using the interrelationships or findings learned from the medical data of other patients relating to a particular condition, to help in early diagnosis and treatment for future patients [4]
The pressing need for data standardization across diverse Electronic Health Record (EHR) systems was highlighted by the President's Council of Advisors on Science and Technology (PCAST) in a 2010 report titled: “Realizing the full potential of health information technology to improve healthcare for americans: the path forward” [12]

Summary

Introduction

With the rising interest in utilizing patients' medical history for efficient and effective prediction of clinical outcomes, Clinical Decision Support Systems (CDSS) have become an area of research that shows tremendous potential for enhancing medical care while reducing the associated costs. The report argued for improved medical data standardization through a “universal exchange language whose semantics is intrinsically extensible” and for “managing and storing data for advanced data-mining techniques through breaking it down into the smallest individual pieces” [12]. One example of such an interoperable and universal exchange language for medical data is the QUEL [[13], [14], [15]]. Efficient dimensionality reduction of big medical data is of utmost importance when computationally-demanding machine learning techniques are employed to analyze the data with the goal of improving clinical decision making

Methods

Results

Conclusion