5 research outputs found
Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping
Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph
Biomedical Literature Mining and Knowledge Discovery of Phenotyping Definitions
Indiana University-Purdue University Indianapolis (IUPUI)Phenotyping definitions are essential in cohort identification when conducting
clinical research, but they become an obstacle when they are not readily available.
Developing new definitions manually requires expert involvement that is labor-intensive,
time-consuming, and unscalable. Moreover, automated approaches rely mostly on
electronic health records’ data that suffer from bias, confounding, and incompleteness.
Limited efforts established in utilizing text-mining and data-driven approaches to automate
extraction and literature-based knowledge discovery of phenotyping definitions and to
support their scalability. In this dissertation, we proposed a text-mining pipeline combining
rule-based and machine-learning methods to automate retrieval, classification, and
extraction of phenotyping definitions’ information from literature. To achieve this, we first
developed an annotation guideline with ten dimensions to annotate sentences with evidence
of phenotyping definitions' modalities, such as phenotypes and laboratories. Two
annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text
observational studies’ methods sections (n=86). Percent and Kappa statistics showed high
inter-annotator agreement on sentence-level annotations. Second, we constructed two
validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level.
We applied the abstract-level classifier on a large-scale biomedical literature of over
20 million abstracts published between 1975 and 2018 to classify positive abstracts
(n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from
their methods sections and used the full-text sentence-level classifier to extract positive
sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the
positively classified sentences. Lexica-based methods were used to recognize medical
concepts in these sentences (n=19,423). Co-occurrence and association methods were used
to identify and rank phenotype candidates that are associated with a phenotype of interest.
We derived 12,616,465 associations from our large-scale corpus. Our literature-based
associations and large-scale corpus contribute in building new data-driven phenotyping
definitions and expanding existing definitions with minimal expert involvement