Symptom-Centric Decision Tree for Rapid Medical Diagnosis
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Solution Overview
Problem
Current conversation-based diagnosis systems are inefficient in quickly identifying diseases and providing effective treatments, as they do not adequately account for symptom and disease probabilities and emergency values, leading to unnecessary detail and frustration in the diagnostic process.
Innovation Solution
A symptom-centric decision tree is developed that considers probabilities and emergency values for symptoms and diseases, allowing for rapid disease identification and treatment by navigating a disease-centric knowledge graph, which groups diseases with shared treatments together, enabling quick and effective treatment administration without specific disease identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional conversation-based diagnosis systems are used without considering symptom probabilities and emergency values, then the diagnostic process becomes lengthy and detailed, but this leads to increased diagnostic time and user frustration
Solution Approach 1:
The patent transforms the traditional decision tree into a prioritized decision tree by introducing new parameters: probability values for symptoms and emergency values for diseases. These parameters change the structure and navigation of the diagnostic process, allowing the system to weigh symptoms by their likelihood and prioritize diseases by their urgency, thereby reducing diagnostic time while maintaining accuracy
Solution Approach 2:
The system performs preliminary calculations of symptom probabilities and disease emergency values before the actual diagnosis process. By pre-computing these parameters and organizing the decision tree accordingly, the system prepares the optimal diagnostic path in advance, enabling faster navigation through high-priority diseases first without compromising diagnostic thoroughness
2Reliability
If the system identifies every specific disease before providing treatment, then treatment precision is maximized, but this increases diagnostic complexity and time consumption
Solution Approach 1:
The patent merges multiple diseases that share the same treatment into unified treatment groups within the decision tree. By combining diseases with identical or similar treatment protocols, the system reduces the number of distinct diagnostic paths needed while ensuring that appropriate treatments are still provided for each underlying disease, thus simplifying the diagnostic process without sacrificing treatment reliability
Data Source
AI summary
Methods and systems for treating a disease include navigating a symptom-centric decision tree which accounts for probabilities for symptoms and diseases and emergency values for the symptoms and diseases, based on information provided by a user, to determine a disease. A treatment is provided to the user based on the determined disease.