Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
4 results
Search Results
Item 2AI&7D Model of Resistomics to Counter the Accelerating Antibiotic Resistance and the Medical Climate Crisis(Springer Science and Business Media Deutschland GmbH, 2021) Talukder, A.K.; Chakrabarti, P.; Chaudhuri, B.N.; Sethi, T.; Lodha, R.; Haas, R.E.The antimicrobial resistance (AMR) crisis is referred to as ‘Medical Climate Crisis’. Inappropriate use of antimicrobial drugs is driving the resistance evolution in pathogenic microorganisms. In 2014 it was estimated that by 2050 more people will die due to antimicrobial resistance compared to cancer. It will cause a reduction of 2% to 3.5% in Gross Domestic Product (GDP) and cost the world up to 100 trillion USD. The indiscriminate use of antibiotics for COVID-19 patients has accelerated the resistance rate. COVID-19 reduced the window of opportunity for the fight against AMR. This man-made crisis can only be averted through accurate actionable antibiotic knowledge, usage, and a knowledge driven Resistomics. In this paper, we present the 2AI (Artificial Intelligence and Augmented Intelligence) and 7D (right Diagnosis, right Disease-causing-agent, right Drug, right Dose, right Duration, right Documentation, and De-escalation) model of antibiotic stewardship. The resistance related integrated knowledge of resistomics is stored as a knowledge graph in a Neo4j properties graph database for 24 × 7 access. This actionable knowledge is made available through smartphones and the Web as a Progressive Web Applications (PWA). The 2AI&7D Model delivers the right knowledge at the right time to the specialists and non-specialist alike at the point-of-action (Stewardship committee, Smart Clinic, and Smart Hospital) and then delivers the actionable accurate knowledge to the healthcare provider at the point-of-care in realtime. © 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.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.Item GREAT AI in Medical Appropriateness and Value-Based-Care(Springer Science and Business Media Deutschland GmbH, 2023) Datta, V.D.; Ganesh, S.; Haas, R.E.; Talukder, A.K.Fee For Service, also known as Volume Based Care (VBC) model of healthcare encourages service volume – more service more reward. This model of care results in unnecessary, inappropriate, and wasted medical services. In the US, Fraud, Waste, and Abuse (FWA) ranges between $760 billion to $935 billion, accounting for approximately 25% of total healthcare spending. In India, the waste caused by FWA is estimated to be as high as 35%. This is due to a lack of smart digital health, absence of AI models, and lack of preventive vigilance against inappropriate medical interventions. Inappropriate medical intervention costs valuable resources and causes patient harm. This paper proposes GREAT AI (Generative, Responsible, Explainable, Adaptive, and Trustworthy Artificial Intelligence) in Medical Appropriateness. We show how GREAT AI is used to offer appropriate medical services. Moreover, we show how GREAT AI can function in vigilance role to curb FWA. We present two GREAT AI models namely MAKG (Medical Appropriateness Knowledge Graph) and RAG-GPT (Retrieval Augmented Generation – Generative Pretrained Transformer). MAKG is used as an autonomous coarse-grained medical-inappropriateness vigilance model for payers and regulators. Whereas RAG-GPT is used as a fine-grained LLM, with human-in-the-loop for medical appropriateness and medical inappropriateness model where the actor human-in-the loop can be anybody like providers, patients, payers, regulators, funders, or researchers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.