Delirium Risk Prediction Model Using Clinical Data
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Solution Overview
Problem
Delirium is difficult to clinically diagnose and distinguish from other cognitive impairments, often leading to delayed treatment and poor prognosis due to the lack of effective risk prediction systems.
Innovation Solution
A delirium risk prediction system using a prediction model trained on clinical data such as bio signal data, blood data, mental state evaluation, severity evaluation, medication data, and medical treatment data to provide early diagnosis and alert caregivers to high-risk patients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clinical diagnosis methods are used for delirium, then the diagnostic process is simple, but the accuracy and reliability of diagnosis is poor leading to delayed treatment
Solution Approach 1:
The patent introduces a prediction model as an intermediary system that processes multiple clinical parameters (age, comorbidities, medication data, vital signs) to generate delirium risk predictions. This intermediary model bridges the gap between available clinical data and accurate diagnosis, improving measurement precision without requiring direct complex diagnostic procedures.
Solution Approach 2:
The patent replaces traditional mechanical clinical diagnosis methods with an information-processing system that uses algorithms to analyze digital clinical data. This substitution transforms the diagnostic process from manual assessment to automated computational analysis, enhancing accuracy while maintaining operational simplicity through digitalization.
2Reliability
If multiple clinical data types are collected for prediction, then the prediction accuracy improves, but the data collection and processing complexity increases
Solution Approach 1:
The patent segments the clinical data into distinct categories: demographic data (age, sex), comorbidity data, medication data, and vital sign data. This segmentation allows the prediction model to process complex information in manageable chunks, improving reliability through comprehensive data collection while reducing processing complexity through structured organization.
Solution Approach 2:
The prediction model is designed to universally process multiple types of clinical data through a single integrated framework. The model can accommodate various data formats and sources (electronic health records, laboratory results, vital sign monitors), making the system multi-functional and reducing overall system complexity despite handling diverse data types.
3Loss of time
If early prediction of delirium is implemented, then treatment timing improves and survival increases, but the complexity of monitoring and detection increases
Solution Approach 1:
The patent implements preliminary action by continuously monitoring clinical parameters and running prediction models before delirium actually occurs. The system proactively identifies high-risk patients and triggers early intervention, eliminating treatment delay while the automated nature of continuous monitoring reduces the complexity burden on clinical staff.
Solution Approach 2:
The prediction system incorporates feedback mechanisms where prediction results are fed back to clinical teams to adjust monitoring intensity and intervention timing. This feedback loop enables early detection and treatment while the automated feedback processing reduces manual monitoring complexity through algorithmic decision support.
Data Source
AI summary
The present disclosure provides a delirium risk predicting method which includes receiving at least one of blood data, severity evaluation data, mental state evaluation data, and bio signal data, medication data, and medical treatment data, for an subject, predicting a delirium risk for the subject, using a delirium risk prediction model configured to predict a delirium risk, based on at least one data, the medication data, and the medical treatment data, and providing the delirium risk predicted for the subject. The at least one data, the medication data, and the medical treatment data are defined as initial data which is evaluated or measured only once for the subject and a delirium risk predicting device using the same.