8/7/2019 Albert Lai - Information Fusion Poster

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Abstract

An Information Fusion Pipeline for Longitudinal Health RecordsAlbert M. Lai,1,2 Preethi Raghavan,2 and Chunhua Weng3

1Departments of Biomedical Informatics and2Computer Science and Engineering, The Ohio State University, Columbus, OH3Department of Biomedical Informatics, Columbia University, New York, NY

Conclusion

http://bmi.osu.eduDEPARTMENT OF BIOMEDICAL INFORMATICS

BMI

Existing EHRs often store unstructured clinical narrativesand structured data in an uncoordinated manner, which

poses complex challenges for extracting a longitudinal viewof a patient across clinical encounters. The various clinical

data sources may contain redundant or inconsistent infor-

mation, and hence involve a large number of temporally orsemantically overlapping or contradictory events, compli-

cating automatic generation of a coordinated Longitudinal

Health Record (LHR). We propose an information fusionpipeline, which leverages both semantic and temporal infor-

mation in conjunction with a machine learning approach to

integrate structured and unstructured clinical information, togenerate the foundations of an LHR. This LHR offers the

potential to improve the efficiency and accuracy of decisionsupport tools.

In the proposed research, we will characterize the overlapof clinical information across various data sources at two

large integrated medical centers. We will develop algo-

rithms for extracting temporal expressions and relationsfrom clinical narratives obtained from the institutional infor-

mation warehouses. Utilizing the temporal information ex-tracted from these narratives, we will develop information

fusion algorithms and combine the temporal information

with structured data from EHR systems, leveraging both se-mantic and temporal information, developing a longitudinal

health record.

We will perform temporal relation extraction using a ma-

chine learning approach, mapping medical events and tem-

poral expressions.

Temporal interval overlap and semantic alignment of onto-logically coded medical events that can be used in conjunc-

tion with heuristics or as features in a machine learning ap-

proach to resolving intra-narrative event coreferences.

We have presented an information fusion approach toLHRs. Successfully creating an LHR using an information

fusion approach would benefit multiple clinical scenarios.

For instance, it could be used to improve the automaticidentification of cohorts of patients who satisfy specific clini-

cal trial eligibility criteria and are hence eligible for a particu-lar clinical trial. The ability to utilize an automated, data-

driven approach to cohort identification would enable the

pace at which clinical research progresses. However, with-out a coherent LHR created through information fusion, au-

tomated approaches are likely to be fraught with errors due

to poor information consistency when extracting clinical in-formation from various clinical data sources.

Acknowledgements

This study is partially funded by NLM R01 LM009886 andNCRR UL1-RR025755.

We will also perform inter-narrative co-reference resolutionby integrating the list of events generated from clinical nar-

ratives with various other sources of information. This in-

cludes similar lists from other narratives, such as relevantdata from structured narratives (lab values), tagged con-

cepts (like UMLS), entities and ontology mappings obtainedfrom MetaMap. We propose to combine all events gener-

ated from clinical narratives together with the aforemen-

tioned structured data and perform information fusion.

This includes performing both inter-narrative and multi-

source co-reference resolution, leveraging semantic andtemporal information. In addition, we intend to also leverage

the metadata (e.g. medical record number, admission

number, encounter number, admission date, encounterdate, date note was written) associated with clinical narra-

tives to improve the quality of the information fusion.Once the inter-narrative coreferences are resolved resulting

in a distinct set of chronologically ordered events, we have

a longitudinal model of the clinical record for a patient.

Name: John Doe Admit Date: 4/3/2010

MRN: #AB123 Discharge Date: 4/13/2010Attending Physician: Dr. Mike Smith

HISTORY OF PRESENT ILLNESS

Mr. Doe came in with a problem of a leaking G-tube.

The G-tube had been in place since 2008...HOSPITAL COURSE

The G-tube was surgically removed at laparotomywith closure of the stomach. After approximately 2

days of nothing by mouth status, we started bringing

him around. It took some time for his diet to be appre-ciated and he was returned to an ECF (Extended care

facility) with a regular diet and wound care.

Sample Discharge Summary

Information

Fusion

UMLS &Knowledge

Base

Longitudinal

Health

Record

StructuredData

(Labs, ADT)

Clinical

Narratives

Clinical

Narrative1

Clinical

Narrativen

NLPextracted

concepts

Temporal

Relations

Clinical

Events

NLP

extracted

concepts

Temporal

Relations

Clinical

Events

Events with TemporalConstraints & Ontologically

Coded Entities

Methods