Investigating Remote Healthcare Accessibility with AI: Deep Learning and NLP-Based Knowledge Graph for Digitalized Diagnostics

Abstract

Introduction: Recently, vast generational modern AI techniques have facilitated developments for accessing digital healthcare diagnosis with capabilities of detecting illnesses. Problem: There exists a lot of scepticism for e-health couple with high similarities on health symptoms which hinder text data analysis for remote diagnosis limiting remote services and affecting tech development. Objective: This research investigates and substantiates opportunities associated with computational leverage of text data analytics and cognitive extraction of knowledge insights to improve healthcare outcomes. Significance: The study presents an overview of public, an integrated deep learning (DL), and AI knowledge graph (KG) for healthcare accessibility of remote diagnostics with NLP assist. Method: This research applied both qualitative and quantitative analysis. Questionnaires were used to understand the computational analytics and cognitive extraction of AI knowledge graphs on healthcare data. Also, an AI model was built to detect, diagnose based on text data and streamline five (5) related disease symptoms for each given text input. Results: The result of the survey was tested with hypotheses of H1, H2, H3, H4, H5. Results show that deep learning models and knowledge graphs can effectively lead to a well-defined class of data classification. Our model also exhibits a tremendous level of acceptable prediction of health symptoms based on text data. The significant group was accepted as an identified health issue and the non-significant was identified as a non-health issue. Conclusion: The study concludes that a well-defined system based on a rigorous ethical healthcare standard can easily support determining a feasible remote diagnosis.