Heterogeneous electronic medical record representation for similarity computing

04/29/2021
by   Hoda Memarzadeh, et al.
0

Due to the widespread use of tools and the development of text processing techniques, the size and range of clinical data are not limited to structured data. The rapid growth of recorded information has led to big data platforms in healthcare that could be used to improve patients' primary care and serve various secondary purposes. Patient similarity assessment is one of the secondary tasks in identifying patients who are similar to a given patient, and it helps derive insights from similar patients' records to provide better treatment. This type of assessment is based on calculating the distance between patients. Since representing and calculating the similarity of patients plays an essential role in many secondary uses of electronic records, this article examines a new data representation method for Electronic Medical Records (EMRs) while taking into account the information in clinical narratives for similarity computing. Some previous works are based on structured data types, while other works only use unstructured data. However, a comprehensive representation of the information contained in the EMR requires the effective aggregation of both structured and unstructured data. To address the limitations of previous methods, we propose a method that captures the co-occurrence of different medical events, including signs, symptoms, and diseases extracted via unstructured data and structured data. It integrates data as discriminative features to construct a temporal tree, considering the difference between events that have short-term and long-term impacts. Our results show that considering signs, symptoms, and diseases in every time interval leads to less MSE and more precision compared to baseline representations that do not consider this information or consider them separately from structured data.

READ FULL TEXT

page 9

page 27

02/13/2015

How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?

Electronic health records capture patient information using structured c...
01/26/2020

Secondary Use of Electronic Health Record: Opportunities and Challenges

In present technological era, healthcare providers generate huge amount ...
09/23/2021

MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records

Clinical documentation can be transformed by Electronic Health Records, ...
07/07/2021

MedGPT: Medical Concept Prediction from Clinical Narratives

The data available in Electronic Health Records (EHRs) provides the oppo...
01/22/2019

CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model

Background: Widespread adoption of electronic health records (EHRs) has ...
09/20/2017

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning

Objective: Electronic medical records (EMRs) contain an amount of medica...
12/10/2016

Data Curation APIs

Understanding and analyzing big data is firmly recognized as a powerful ...