Conference Papers
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Item Physicians’ Brain Digital Twin: Holistic Clinical & Biomedical Knowledge Graphs for Patient Safety and Value-Based Care to Prevent the Post-pandemic Healthcare Ecosystem Crisis(Springer Science and Business Media Deutschland GmbH, 2022) Talukder, A.K.; Selg, E.; Haas, R.E.The ‘reading to cognition gaps’ and the ‘knowledge to action gaps’ for a physician or a care provider are the root causes of patient harm and the low- value healthcare. Rule-based symptom-checkers often fail when there are multiple co-occurring symptoms. To ensure patient safety and value-based care we have constructed nine AI-driven and evidence based interconnected holistic knowledge graphs covering the entire spectrum of medical knowledge starting from symptoms to therapeutics. These knowledge graphs are in fact the digital twin of all physicians’ brains. These nine knowledge graphs are Symptomatomics, Diseasomics, SNOMED CT, Disease-Gene Network, Multimorbidity, Resistomics, Patholomics, Oncolomics, and Drugomics. These knowledge graphs are constructed from semantic integration of biomedical ontologies like Disease Ontology, Symptom Ontology, Gene Ontology, Drug Ontology, NCI Thesaurus, DisGenomics Network, PharmGKB, ChEBI, WHO AWaRe, and WHOCC. This is further enhanced through thematic integration of the knowledge mined from PubMed, DailyMed, FAERS, Wikipedia and patient data (EHR) from hospitals and cancer registry. These knowledge graphs are interconnected through common vocabularies like SNOMED CT, ICD10, ICDO, UMLS, NCIT, DOID, HGNC, GO, LOINC, ATC, RXCUI, and RxNORM codes that helped us to construct a complete clinical, medical, therapeutic, and conflicting medication knowledge graph with 723,801 nodes and 10,657,694 edges. This knowledge graph is stored in a Neo4j property graph database which is deployed in the cloud accessible 24×7 through REST/JSON-RPC and AIoT API. On top of this integrated knowledge graph we used node2vec to construct digital triplet discovering many unknown and hidden knowledge. This integrated clinical & biomedical knowledge functions as the digital twin of all physicians’ brains. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Drugomics: Knowledge Graph & AI to Construct Physicians’ Brain Digital Twin to Prevent Drug Side-Effects and Patient Harm(Springer Science and Business Media Deutschland GmbH, 2022) Talukder, A.K.; Selg, E.; Fernandez, R.; Raj, T.D.S.; Waghmare, A.V.; Haas, R.E.Unintended toxic effects of a medication occur due to drug-drug interactions (DDI) and drug-disease interactions (DDSI). It is the fourth leading cause of death in the US. To overcome this crisis, we have constructed the Drugomics knowledge graphs comprising DDI and DDSI interactions mined from Drugs@FDA, FAERS (FDA Adverse Events Reporting System), PubMed, literature, DailyMed, drug ontology, and other biomedical data sources. We used Artificial Intelligence and Augmented Intelligence (AI&AI) to translocate this actionable DDI and DDSI knowledge into a network and stored it in a Neo4j property graph database in a cloud for anytime-anywhere access. For the first time, we present here an AI-driven Evidence-Based Clinical Decision Support (AIdEB-CDS) system that accepts human understandable plain text inputs and extracts knowledge from knowledge graphs to offer the right therapeutics for the right disease for the right person at the right time at any Point-of-Care. This functions like a physicians’ brain digital twin to reduce clinical errors, reduce medication errors, and increase general health equity at a reduced cost. This will eliminate the patient harm caused by drug interactions, © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.