Medical Care Support Device Anonymization for Prediction Accuracy
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Solution Overview
Problem
Current medical care support devices and systems face challenges in improving prediction accuracy due to limitations in collecting operation histories from diverse users and devices, while also risking personal information leakage, which restricts the ability to accumulate and learn from a wide range of user data.
Innovation Solution
A medical care support device and system that acquires operation histories from terminal devices within medical facilities and uses an external server to predict next operation candidates, ensuring personal information is deleted and securely managed, allowing for improved prediction accuracy without compromising patient data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If operation histories are collected from multiple users and devices to improve prediction accuracy, then the learning effect is enhanced, but the risk of personal information leakage increases
Solution Approach 1:
The patent extracts and removes personal information from operation histories before transmission to the external server. The management device identifies and deletes personal identifiers (names, contact information, etc.) from the collected operation data, retaining only anonymized operational patterns for machine learning analysis. This resolves the contradiction by enabling data collection for improved prediction accuracy while eliminating the personal information leakage risk.
Solution Approach 2:
The management device serves as an intermediary between terminal devices and the external server. It collects operation histories from multiple terminal devices, performs anonymization processing, and then transmits the cleaned data to the external server for model training. This intermediary role enables the system to aggregate diverse operational data for better prediction accuracy while preventing personal information from reaching the external server, thus resolving the security-accuracy contradiction.
2Reliability
If operation histories are accumulated within a single medical facility to ensure data security, then personal information leakage is prevented, but the diversity of user data is limited
Solution Approach 1:
The system enables a single external server to serve multiple medical facilities by receiving anonymized operation histories from various terminal devices across different facilities. The management device facilitates this multi-functional data collection by aggregating diverse operational patterns from different users and devices while maintaining data security through anonymization. This resolves the contradiction by allowing the system to achieve both data security (through centralized controlled collection) and user data diversity (through multi-facility aggregation).
Data Source
AI summary
A medical care support device (11) includes an operation history acquisition unit (63), a prediction execution unit (64), and a display screen generation unit (62). The operation history acquisition unit (63) acquires an operation history in a case where a terminal device is operated. The prediction execution unit (64) predicts a next operation candidate in a case where the terminal device is input and operated by using a trained model generated by an external server learning the acquired operation history, the external server being installed outside the medical facility. The display screen generation unit (62) makes a proposal to the terminal device from the next operation candidate predicted by the prediction execution unit (64).