Creation and curation of knowledge graphs at scale can be used to exponentially accelerate the discovery, matching, and analysis of diseases in real-world data. While disease ontologies are useful for annotation, integration, and analysis of biological data, codified disease and procedure categories e.g. SNOMED-CT, ICD10, CPT, etc. rarely capture all of the nuances in a patient condition or, in the case of rare disease, may not even exist. Furthermore, there are multiple disease definitions used in data sources and publications, each having its own structure and hierarchy. Mapping between ontologies, finding disease clusters, and building a representation of the chosen disease area are resource-intensive, often requiring significant human capital. We propose the creation and curation of a patient knowledge graph utilizing large language model extraction techniques. In order to expand in volume and scale, knowledge graphs with generalized language capability allow for data to be extracted using natural language rather than being constrained by the exact terminology or hierarchy of existing ontologies. We develop a method of mapping back to existing ontologies such as MeSH, SNOMED-CT, RxNORM, HPO, etc. to ground the extracted entities to known entities in the medical community.We have access to one of the largest ambulatory care EHR databases in the country. To demonstrate the effectiveness of our method, we benchmark our extraction in a test set with over 33.6M unique patients, in the area of patient search. In this case study, we perform a patient search for a rare disease: Dravet syndrome. Dravet syndrome was codified as an ICD10 recognizable disease in October 2020. In the following research, we describe our method of the construction of patient-specific knowledge graphs and subsequent searches for patients who exhibit symptoms of a particular disease. Using patients with confirmed ICD10 codes for Dravet syndrome as our ground truth, we utilize our LLM-based entity extraction techniques and formalize an algorithmic way of characterizing patients in a grounded ontology to assist in mapping patients to specific diseases. Finally, we present the results of a real-world discovery method on Beta-propeller protein-associated neurodegeneration (BPAN), identifying patients with a rare disease, where no ground truth currently exists.

In this cohort study, we analyzed data collected from Drexel University students, faculty, and staff (age 18 – 79) using machine learning models to gain insight into the significance and predictive capabilities of the the features collected. Data from 126,983 SARS-CoV-2 tests was collected from 16,914 unique individuals from March 4, 2020 to April 24, 2022. Associated symptom data (551,257 reports) was collected through the Drexel University Health Checker App powered by the industry partner, Respond Health. 4,457 people had a positive SARS-CoV-2 test result within the study timeframe. 2,074 of the 4,457 (46.53%) positive cases were in individuals that were fully vaccinated. Given the comprehensive data collected over the entire period of the pandemic, we are able to explore the trends and importance of features to their predictive capabilities. In our experiments and results, we analyze the relative importance of the collected features during different time periods of the COVID-19 evolution and present the trends over time.

MOTIVATION: As the use of microarrays in human studies continues to increase, stringent quality assurance is necessary to ensure accurate experimental interpretation. We present a formal approach for microarray quality assessment that is based on dimension reduction of established measures of signal and noise components of expression followed by parametric multivariate outlier testing. RESULTS: We applied our approach to several data resources. First, as a negative control, we found that the Affymetrix and Illumina contributions to MAQC data were free from outliers at a nominal outlier flagging rate of alpha=0.01. Second, we created a tunable framework for artificially corrupting intensity data from the Affymetrix Latin Square spike-in experiment to allow investigation of sensitivity and specificity of quality assurance (QA) criteria. Third, we applied the procedure to 507 Affymetrix microarray GeneChips processed with RNA from human peripheral blood samples. We show that exclusion of arrays by this approach substantially increases inferential power, or the ability to detect differential expression, in large clinical studies. AVAILABILITY: http://bioconductor.org/packages/2.3/bioc/html/arrayMvout.html and http://bioconductor.org/packages/2.3/bioc/html/affyContam.html affyContam (credentials: readonly/readonly)