AI Donor Matching System for Transplant Timeliness
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Solution Overview
Problem
Current healthcare systems lack a comprehensive strategy to pair potential organ donors with appropriate recipients in a timely manner, leading to challenges in finding suitable donors and predicting the success or failure of organ transplantation.
Innovation Solution
An artificial intelligence-based method that uses a gated recurrent unit with trainable decays (GRU-D) neural network to predict the in-hospital death or survival of potential organ donors, estimate the time of death, and pair donors with recipients based on predicted transplant success probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional recipient-centered matchmaking methods are used, then the focus is on finding appropriate organ donors based on quality and time of transplantation, but the unbalanced number of organ donors and recipients leads to challenges in finding appropriate donors in due time
Solution Approach 1:
The patent inverts the traditional recipient-centered approach by implementing a donor-centered matchmaking system. Instead of starting with recipients and searching for donors, the system identifies potential donors first, predicts their survival probability and time of death, then proactively matches them with suitable recipients. This inversion resolves the contradiction by shifting the initiative to the donor side, enabling timely identification and matching before organs become unavailable.
Solution Approach 2:
The system performs preliminary actions by predicting donor survival probability and estimated time of death before actual organ donation occurs. By using machine learning models to forecast donor outcomes in advance, the system can proactively initiate matching processes, secure recipient allocations, and prepare transplantation logistics beforehand, thereby reducing the time to find suitable donors while maintaining high matching accuracy.
2Measurement precision
If comprehensive donor assessment and prediction models are implemented, then the ability to predict transplant success and identify suitable donors improves, but the complexity of the healthcare system increases
Solution Approach 1:
The patent replaces complex manual assessment processes with automated machine learning models. Instead of relying on manual evaluation of donor suitability and transplant success probability, the system uses trained algorithms that automatically analyze donor data, predict outcomes, and generate matching recommendations. This substitution maintains high measurement precision while reducing operational complexity by automating previously manual tasks.
Solution Approach 2:
The system introduces an intermediary layer of prediction models and matching algorithms between raw clinical data and decision-making processes. These intermediary components process and interpret complex donor and recipient data, translating them into actionable insights about survival probability and compatibility. This intermediary layer simplifies the overall system by providing structured, data-driven recommendations that guide clinicians without requiring them to manually analyze complex datasets.
3Productivity
If the system pairs donors and recipients based on predicted transplant success, then the success rate of organ transplantation improves, but the requirement for accurate death time estimation and data processing increases
Solution Approach 1:
The system implements feedback mechanisms where predicted death times and survival probabilities are continuously refined based on actual transplantation outcomes. By comparing predicted versus actual results, the machine learning models learn and improve their accuracy over time. This feedback loop enables the system to maintain high transplant success rates while progressively improving the accuracy of death time estimations through iterative learning from real-world data.
Data Source
AI summary
A method for identifying a plurality of intended organ donors among a plurality of organ donor candidates. The method includes obtaining a donor clinical dataset by acquiring each donor clinical data from a respective organ donor candidate, obtaining a recipient clinical dataset by acquiring each recipient clinical data from a respective recipient candidate, predicting one of an in-hospital death or survival of an intended organ donor candidate, estimating a time of death of the intended organ donor candidate, obtaining a paired donor-recipient by pairing the intended organ donor candidate with an intended recipient for organ transplantation, estimating a probability of organ transplant success for the paired donor-recipient, and pairing the intended recipient with the plurality of intended organ donors for organ transplantation based on the probability of organ transplant success.