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Documentation of Nursing Diagnosis

Documentation of Nursing Diagnosis

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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A Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies.

Jeffrey Page¹, Nadia Moore¹, Gordon Broderick^2,3

¹Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, USA.

Methods in Molecular Biology (Clifton, N.J.)

|November 15, 2024

Summary

Approximate reasoning offers a solution for integrating clinical experience and medical knowledge into patient care. This approach uses explainable AI to systematically manage and reuse diverse data for better healthcare interventions.

Keywords:

Computational models Cross-species data Explainable decision support Integrative models Mechanistic reconciliation Network biology Regulatory dynamics

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Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Clinical Decision Support

Background:

Challenges in extracting, representing, and retaining clinical experience and medical knowledge hinder informed patient care.
Current artificial intelligence (AI) models, like large language models (LLMs), lack human-readable explanations and require extensive, integrated datasets, limiting their clinical utility.
Data scarcity and heterogeneity in medicine contrast with the needs of many AI applications.

Purpose of the Study:

To propose approximate reasoning as a method for systematically incorporating explainable medical knowledge into clinical practice.
To outline a conceptual protocol for utilizing sparse, disparate data from various sources to support clinical decision-making.
To demonstrate an integrative approach for assessing and reconciling data from diverse experimental models and sources.

Main Methods:

Developing a conceptual protocol for approximate reasoning in healthcare.
Focusing on explainable AI that facilitates knowledge retention, sharing, and reuse.
Utilizing sparse, disparate data from multiple sources, including human and animal models.

Main Results:

The proposed approach facilitates the systematic integration of clinical and basic medical knowledge.
Explainable AI through approximate reasoning supports the retention, sharing, and reuse of medical experience.
The conceptual protocol demonstrates utility in assessing and reconciling diverse data for complex health conditions.

Conclusions:

Approximate reasoning offers a viable pathway for integrating explainable medical knowledge into clinical workflows.
This approach addresses the limitations of current AI by leveraging sparse, heterogeneous data and prioritizing clinician understanding.
The proposed framework supports enhanced clinical decision-making by systematically managing and applying collective medical experience.