Conference Papers
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Item Bridging the Inferential Gaps in Healthcare(Springer Science and Business Media Deutschland GmbH, 2021) Talukder, A.K.Inferential gaps are the combined effect of reading-to-cognition gaps as well as the knowledge-to-action gaps. Misdiagnoses, medical errors, prescription errors, surgical errors, under-treatments, over-treatments, unnecessary lab tests, etc. – are all caused by inferential gaps. Late diagnosis of cancer is also due to the inferential gaps at the primary care. Even the medical climate crisis caused by misuse, underuse, or overuse of antibiotics are the result of serious inferential gaps. Electronic health records (EHR) had some success in mitigating the wrong site, wrong side, wrong procedure, wrong person (WSWP) errors, and the general medical errors; however, these errors continue to be quite significant. In the last few decades the disease demography has changed from quick onset infectious diseases to slow onset non-communicable diseases (NCD). This changed the healthcare sector in terms of both training and practice. In 2020 the COVID-19 pandemic disrupted the entire healthcare system further with change in focus from NCD back to quick onset infectious disease. During COVID-19 pandemic misinformation in social media increased. In addition, COVID-19 made virtual healthcare a preferred mode of patient-physician encounter. Virtual healthcare requires higher level of audit, accuracy, and technology reliance. All these events in medical practice widened the inferential gaps further. In this position paper, we propose an architecture of digital health combined with artificial intelligence that can mitigate these challenges and increase patient safety in the post-COVID healthcare delivery. We propose this architecture in conjunction with diseasomics, patholomics, resistomics, oncolomics, allergomics, and drugomics machine interpretable knowledge graphs that will minimize the inferential gaps. Unless we pay our attention to this critical issue immediately, medical ecosystem crisis that includes medical errors, caregiver shortage, misinformation, and the inferential gaps will become the second, if not the first leading cause of death by 2050. © 2021, Springer Nature Switzerland AG.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.