Suicide Risk Prediction System Using Logistic Regression
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Solution Overview
Problem
Current methods for monitoring and managing mental health conditions, particularly in-patient suicide risk, are inefficient due to uncertain effectiveness of intensified monitoring, high costs, and lack of objective diagnostic tools, leading to inadequate prevention of suicide attempts and inefficient resource allocation.
Innovation Solution
A system and method that utilize a logistic regression model to automatically monitor mental health conditions, predict suicide attempts, and dynamically tailor observation levels based on real-time patient data, enabling timely interventions and resource allocation, while facilitating remote patient management and behavior modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intensified monitoring is implemented on all patients, then patient safety is improved, but cost increases dramatically
Solution Approach 1:
The patent applies local quality by implementing differential monitoring strategies based on individual patient risk profiles. High-risk patients receive intensified monitoring while low-risk patients receive standard monitoring, allowing the system to maintain patient safety for those who need it most while avoiding unnecessary costs for low-risk patients.
Solution Approach 2:
The system dynamically adjusts monitoring intensity as a parameter based on changing patient conditions and risk assessments. Monitoring levels are not fixed but can be escalated or de-escalated based on clinical indicators, allowing flexible resource allocation that responds to actual patient needs rather than applying uniform intensive monitoring to all patients.
2Reliability
If 100% of patients receive intensified monitoring, then patient safety is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent implements local quality by tailoring monitoring intensity to individual patient needs rather than applying uniform monitoring across all patients. This allows the healthcare system to concentrate resources on high-risk patients where they provide maximum safety benefit while reducing or eliminating intensive monitoring for low-risk patients, thereby improving overall resource efficiency.
Solution Approach 2:
The system applies partial action by providing intensified monitoring only to the portion of the patient population that actually requires it (high-risk patients) rather than applying excessive monitoring to all patients. This selective approach ensures adequate safety coverage for vulnerable patients while avoiding waste of resources on patients who do not require such intensive oversight.
3Reliability
If close observation procedures are increased, then suicide prevention capability is improved, but expense increases dramatically
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting observation intensity based on calculated suicide risk scores and clinical indicators. Patients with higher risk parameters receive increased observation while those with lower risk parameters receive standard observation, allowing the system to optimize suicide prevention capability while controlling expenses through evidence-based, risk-stratified monitoring.
Solution Approach 2:
The system incorporates feedback mechanisms where monitoring data and clinical outcomes are continuously analyzed to refine risk assessments and adjust observation levels. This feedback loop allows the system to learn from actual patient trajectories and optimize resource allocation, ensuring that expensive intensive monitoring is applied only when and where it demonstrably prevents suicide attempts.
Data Source
AI summary
Methods, systems, and computer-readable media are provided for managing health status of persons with a chronic condition including providing dynamic, adaptive monitoring, detection, and prediction of suicide risk to a person at risk for suicide related to mental health. In an embodiment, a patient-assessment application is used to obtain information periodically on a patient's mental health status. Based on this information, a logistic regression model is employed to determine a patient's probability of attempting suicide. The probability is evaluated against a default threshold to determine if the patient's status has changed significantly, and if the threshold is exceeded, an action is evoked. In one embodiment, the action includes providing notice to the patient's care provider, caregiver, or case manager.