Integration of NLP2FHIR Representation with Deep Learning Models for EHR Phenotyping: A Pilot Study on Obesity Datasets

Sijia Liu, Yuan Luo, Daniel Stone, Nansu Zong, Andrew Wen, Yue Yu, Luke V. Rasmussen, Fei Wang, Jyotishman Pathak, Hongfang Liu, Guoqian Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

HL7 Fast Healthcare Interoperability Resources (FHIR) is one of the current data standards for enabling electronic healthcare information exchange. Previous studies have shown that FHIR is capable of modeling both structured and unstructured data from electronic health records (EHRs). However, the capability of FHIR in enabling clinical data analytics has not been well investigated. The objective of the study is to demonstrate how FHIR-based representation of unstructured EHR data can be ported to deep learning models for text classification in clinical phenotyping. We leverage and extend the NLP2FHIR clinical data normalization pipeline and conduct a case study with two obesity datasets. We tested several deep learning-based text classifiers such as convolutional neural networks, gated recurrent unit, and text graph convolutional networks on both raw text and NLP2FHIR inputs. We found that the combination of NLP2FHIR input and text graph convolutional networks has the highest F1 score. Therefore, FHIR-based deep learning methods has the potential to be leveraged in supporting EHR phenotyping, making the phenotyping algorithms more portable across EHR systems and institutions.

Original languageEnglish (US)
Pages (from-to)410-419
Number of pages10
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2021
StatePublished - 2021

ASJC Scopus subject areas

  • Medicine(all)

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