Abstract
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.
Keywords
- Physicians’ brain digital twin
- Digital triplet
- Symptomatomics
- Diseasomics
- Resistomics
- Patholomics
- DisGenomics
- Oncolomics
- Drugomics
- Knowledge graph
- Healthcare ecosystem crisis
- Patient safety
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Low Value Care. https://vbidcenter.org/initiatives/low-value-care/#:~:text=Low%2Dvalue%20care%20can%20be,annually%20in%20wasteful%20health%20spending
The Learning Healthcare System. IOM. https://www.ncbi.nlm.nih.gov/books/NBK53494/ (2006)
Srirama, S.N., Lin, J.-W., Bhatnagar, R., Agarwal, S., Reddy, P.K. (eds.): BDA 2021. LNCS, vol. 13147. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-93620-4
To Err is Human: Building a Safer Health System. IOM. https://pubmed.ncbi.nlm.nih.gov/25077248/ (2000)
Makary, M.A., Daniel, M.: Medical error—the third leading cause of death in the US BMJ 2016;353:i2139. https://www.bmj.com/content/353/bmj.i2139 (2016)
Preventable Adverse Drug Reactions: A Focus on Drug Interactions. https://www.fda.gov/drugs/drug-interactions-labeling/preventable-adverse-drug-reactions-focus-drug-interactions
Patient Safety in Healthcare, Forecast to 2022. https://store.frost.com/patient-safety-in-healthcare-forecast-to-2022.html
Americas report surge in drug-resistant infections due to misuse of antimicrobials during pandemic. https://www.paho.org/en/news/17-11-2021-americas-report-surge-drug-resistant-infections-due-misuse-antimicrobials-during (2021)
COVID-19 aggravates antibiotic misuse in India. https://medicine.wustl.edu/news/covid-19-aggravates-antibiotic-misuse-in-india/#:~:text=After%20statistically%20adjusting%20for%20seasonality,period%20of%20peak%20COVID%2D19 (2021)
O’Neill, J.: (Chair). Antimicrobial resistance: tackling a crisis for the health and wealth of nations. https://amr-review.org/sites/default/files/AMR%20Review%20Paper%20-%20Tackling%20a%20crisis%20for%20the%20health%20and%20wealth%20of%20nations_1.pdf (2014)
World Health Organization, Noncommunicable Diseases (2021). https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases
Diabetics make up 40% of COVID deaths in US, experts say: https://nypost.com/2021/07/16/diabetics-make-up-40-of-covid-deaths-in-us-experts-say/amp/
World’s older population grows dramatically. https://www.nih.gov/news-events/news-releases/worlds-older-population-grows-dramatically
Medical knowledge doubles every few months; how can clinicians keep up?. https://www.elsevier.com/connect/medical-knowledge-doubles-every-few-months-how-can-clinicians-keep-up (2018)
The answer is 17 years, what is the question: understanding time lags in translational research. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3241518/ (2011)
Covid-19: Antimicrobial misuse in Americas sees drug resistant infections surge, says WHO. https://www.bmj.com/content/375/bmj.n2845 (2021)
Hou, H., et al.: Factors Associated with Turnover Intention Among Healthcare Workers During the Coronavirus Disease 2019 (COVID-19) Pandemic in China. https://www.dovepress.com/factors-associated-with-turnover-intention-among-healthcare-workers-du-peer-reviewed-fulltext-article-RMHP (2021)
Talukder, A.K., Haas, R.E.: Oncolomics: digital twins & digital triplets in cancer care. In: 7th Computational Approaches for Cancer Workshop (CAFCW21), Supercomputing Conference (SC21). https://sc21.supercomputing.org/presentation/?id=ws_cafcw103&sess=sess434 (2021)
Jensen, A., Moseley, P., Oprea, T., et al.: Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat Commun 5 (2014). https://doi.org/10.1038/ncomms5022
Surveillance, Epidemiology, and End Results https://seer.cancer.gov/data/
Talukder, A.K., Schriml, L., Ghosh, A., Biswas, R., Chakrabarti, P., Haas, R.E.: Diseasomics: actionable machine interpretable disease knowledge at the point-of-care. PLoS Digit. Health 1(10), e0000128 (2022). https://doi.org/10.1371/journal.pdig.0000128
Campbell, W.S., Pedersen, J., McClay, J.C., Rao, P., Bastola, D., Campbell, J.R.: An alternative database approach for management of SNOMED CT and improved patient data queries. J. Biomed. Inform. https://doi.org/10.1016/j.jbi.2015.08.016. https://www.sciencedirect.com/science/article/pii/S1532046415001847 (2015)
DisGeNET: https://www.disgenet.org/
Talukder, A.K., Sanz, J.B., Samajpati, J.: ‘Precision health’: balancing reactive care and proactive care through the evidence based knowledge graph constructed from real-world electronic health records, disease trajectories, diseasome, and patholome. Springer, Cham, LNCS 12581. https://www.springerprofessional.de/en/precision-health-balancing-reactive-care-and-proactive-care-thro/18718294 (2020)
Talukder, A.K., Chakrabarti, P., Chaudhuri, B.N., Sethi, T., Lodha, R., Haas, R.E.: 2AI&7D model of resistomics to counter the accelerating antibiotic resistance and the medical climate crisis. In: Big Data Analytics 2021. Springer, Cham, LNCS, volume 13147. https://www.springerprofessional.de/2ai-7d-model-of-resistomics-to-counter-the-accelerating-antibiot/19984472 (2021)
Neo4j Graph database: https://neo4j.com/
Irving, G., Neves, A.L., Dambha-Miller, H., et al.: International variations in primary care physician consultation time: a systematic review of 67 countries. BMJ Open 2017, 7: e017902. https://doi.org/10.1136/bmjopen-2017-017902. https://bmjopen.bmj.com/content/7/10/e017902 (2017)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD ‘16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2016, pp. 855–864. https://doi.org/10.1145/2939672.2939754 (2016)
Drugs@FDA: FDA-Approved Drugs. https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm
FDA Adverse Event Reporting System (FAERS) Public Dashboard. https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-public-dashboard
Talukder, A.K., Haas, R E.: AIoT: AI meets IoT and Web in Smart Healthcare. (2021). https://dl.acm.org/doi/fullHtml/10.1145/3462741.3466650
Unified Medical Language System (UMLS): https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/index.html
Kim, S., et al.: Patient safety over power hierarchy: a scoping review of healthcare professionals’ speaking-up skills training. https://pubmed.ncbi.nlm.nih.gov/32149868/ (2020)
50 years of the inverse care law. The Lancet Editorial 2021. https://doi.org/10.1016/S0140-6736(21)00505-5. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)00505-5/fulltext
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Talukder, A.K., Selg, E., Haas, R.E. (2022). Physicians’ Brain Digital Twin: Holistic Clinical & Biomedical Knowledge Graphs for Patient Safety and Value-Based Care to Prevent the Post-pandemic Healthcare Ecosystem Crisis. In: Villazón-Terrazas, B., Ortiz-Rodriguez, F., Tiwari, S., Sicilia, MA., Martín-Moncunill, D. (eds) Knowledge Graphs and Semantic Web . KGSWC 2022. Communications in Computer and Information Science, vol 1686. Springer, Cham. https://doi.org/10.1007/978-3-031-21422-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-21422-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21421-9
Online ISBN: 978-3-031-21422-6
eBook Packages: Computer ScienceComputer Science (R0)