SymptomGraph: Identifying Symptom Clusters from Narrative Clinical Notes using Graph Clustering

dc.contributor.authorTahabi, Fattah Muhammad
dc.contributor.authorStorey, Susan
dc.contributor.authorLuo, Xiao
dc.contributor.departmentElectrical and Computer Engineering, School of Engineering and Technology
dc.date.accessioned2024-03-11T13:48:42Z
dc.date.available2024-03-11T13:48:42Z
dc.date.issued2023
dc.description.abstractPatients with cancer or other chronic diseases often experience different symptoms before or after treatments. The symptoms could be physical, gastrointestinal, psychological, or cognitive (memory loss), or other types. Previous research focuses on understanding the individual symptoms or symptom correlations by collecting data through symptom surveys and using traditional statistical methods to analyze the symptoms, such as principal component analysis or factor analysis. This research proposes a computational system, SymptomGraph, to identify the symptom clusters in the narrative text of written clinical notes in electronic health records (EHR). SymptomGraph is developed to use a set of natural language processing (NLP) and artificial intelligence (AI) methods to first extract the clinician-documented symptoms from clinical notes. Then, a semantic symptom expression clustering method is used to discover a set of typical symptoms. A symptom graph is built based on the co-occurrences of the symptoms. Finally, a graph clustering algorithm is developed to discover the symptom clusters. Although SymptomGraph is applied to the narrative clinical notes, it can be adapted to analyze symptom survey data. We applied Symptom-Graph on a colorectal cancer patient with and without diabetes (Type 2) data set to detect the patient symptom clusters one year after the chemotherapy. Our results show that SymptomGraph can identify the typical symptom clusters of colorectal cancer patients’ post-chemotherapy. The results also show that colorectal cancer patients with diabetes often show more symptoms of peripheral neuropathy, younger patients have mental dysfunctions of alcohol or tobacco abuse, and patients at later cancer stages show more memory loss symptoms. Our system can be generalized to extract and analyze symptom clusters of other chronic diseases or acute diseases like COVID-19.
dc.eprint.versionAuthor's manuscript
dc.identifier.citationTahabi FM, Storey S, Luo X. SymptomGraph: Identifying Symptom Clusters from Narrative Clinical Notes using Graph Clustering. Proc Symp Appl Comput. 2023;2023:518-527. doi:10.1145/3555776.3577685
dc.identifier.urihttps://hdl.handle.net/1805/39161
dc.language.isoen_US
dc.publisherACM
dc.relation.isversionof10.1145/3555776.3577685
dc.relation.journalProceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
dc.rightsPublisher Policy
dc.sourcePMC
dc.subjectSymptom clusters
dc.subjectGraph neural networks
dc.subjectClinical notes
dc.subjectGraph clustering
dc.subjectElectronic health records
dc.titleSymptomGraph: Identifying Symptom Clusters from Narrative Clinical Notes using Graph Clustering
dc.typeArticle
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