Universal Medical Diagnostic System Using Geographic Filtering
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Solution Overview
Problem
Current medical diagnostic systems are limited in their ability to diagnose and treat medical conditions without prior knowledge or referrals, especially in areas with scarce medical resources and during natural disasters, due to the vast number of diseases and the shortage of trained medical professionals.
Innovation Solution
The Universal Medical Diagnostic System (UMDS) uses a combination of geographic location, patient interview, and sensor data analysis with trained classifiers, diagnostic engines, and arbitrators to generate high-confidence diagnoses and treatment plans, incorporating approved medical standards and protocols, and can operate in standalone or remote environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional medical diagnostic systems are used, then they can verify or confirm specific known diseases, but they cannot diagnose diseases without prior knowledge or referrals
Solution Approach 1:
The diagnostic system is segmented into multiple specialized components: a geographic location module that identifies regional diseases, a chief complaint analysis module that processes patient symptoms, and a trained diagnostic engine that integrates multiple classifiers. Each component handles a specific aspect of diagnosis, enabling the system to diagnose diseases without requiring prior knowledge of what disease is suspected.
Solution Approach 2:
The system employs a universal diagnostic engine that can handle multiple disease types and diagnostic scenarios through trained classifiers and state machines. The same core engine processes various chief complaints, geographic locations, and symptom patterns, making the system versatile across different disease categories without requiring separate specialized systems for each disease type.
2Adaptability or versatility
If comprehensive medical diagnostic systems are deployed worldwide, then they can diagnose diseases in any geographic location, but they must account for geographic-specific disease patterns
Solution Approach 1:
The system incorporates local quality by integrating geographic location data into the diagnostic process. The geographic location module identifies the patient's location and retrieves region-specific disease patterns, allowing the system to adapt its diagnostic criteria to local epidemiology. This enables accurate diagnosis in any geographic location while maintaining a manageable database through location-based filtering.
3Measurement precision
If multiple trained diagnostic engines are used to ensure accurate diagnosis, then diagnostic accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by using the geographic location module to pre-filter the disease database based on regional patterns before the diagnostic engine begins analysis. The chief complaint analysis module also pre-processes patient symptoms to identify key diagnostic features. This preliminary filtering reduces the search space for the diagnostic engine, maintaining high diagnostic accuracy while reducing processing time.
Solution Approach 2:
The system applies partial action by activating only the necessary classifiers and diagnostic pathways based on the patient's chief complaint and geographic location. Rather than running all possible diagnostic engines simultaneously, the system selectively engages relevant classifiers, achieving accurate diagnosis with minimal processing time by avoiding unnecessary computational steps.
Data Source
AI summary
A method for controlling operations of a computer system for providing medical diagnoses, comprises diagnosing a patient, wherein the diagnosing comprises receiving a patient identification of the patient; receiving electronic data indicating the geographic location of the computer system; receiving the patient's chief complaint and interview data; receiving electronic data indicating one or more current body characteristics of the patient comprising at least one of pulse rate, body temperature, blood pressure, respiration, and skin condition; creating a current multimedia representation for each of the one or more current body characteristics determined by using the one or more sensors; identifying, in the computer system, previous multimedia representations of each of the one or more body characteristics from other persons using one or more trained classifiers; comparing the current multimedia representation to the previous multimedia representations; and identifying potential matches with corresponding confidence factors in accordance with defined medical standards.