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Diseasomics: Actionable machine interpretable disease knowledge at the point-of-care.

Asoke K Talukder1,2, Lynn Schriml3, Arnab Ghosh4

  • 1SRIT India, Bangalore, India.

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An AI-powered knowledge graph integrates disease information to aid physicians in accurate diagnosis at the point-of-care. This tool supports healthcare workers, especially in resource-limited settings, to improve diagnostic accuracy and democratize medical knowledge.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Knowledge Representation

Background:

  • Physicians face time constraints and increasing workloads, making it challenging to stay updated with rapidly evolving medical guidelines.
  • Access to updated medical knowledge is often limited at the point-of-care, particularly in resource-constrained environments.
  • Accurate diagnosis relies on integrating diverse patient data and comprehensive disease information.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-based approach for integrating comprehensive disease knowledge.
  • To support physicians and healthcare workers in achieving accurate diagnoses at the point-of-care.
  • To construct a machine-interpretable diseasomics knowledge graph for improved diagnostic decision-making.

Main Methods:

  • Integrated multiple disease knowledge sources including Disease Ontology, SNOMED CT, DisGeNET, PharmGKB, and Symptom Ontology.
  • Constructed a comprehensive diseasomics knowledge graph representing disease-symptom associations.
  • Utilized node2vec for link prediction in disease-symptom networks to identify potential missing associations.
  • Incorporated spatial and temporal comorbidity data from electronic health records (EHR) for two population datasets.

Main Results:

  • Developed a disease-symptom network with 84.56% accuracy, integrating data from various sources like EHR, Wikipedia, and PubMed.
  • Created a knowledge graph stored as a digital twin in a graph database.
  • Demonstrated the potential for identifying missing disease-symptom associations through link prediction.
  • Integrated comorbidity knowledge from EHR data from Spain and Sweden.

Conclusions:

  • The AI-driven diseasomics knowledge graph can democratize medical knowledge and empower healthcare workers.
  • The tool aims to assist in making evidence-based decisions, contributing to universal health coverage.
  • The knowledge graphs provide associations and can serve as a guide for differential diagnosis, focusing on signs and symptoms.