Dialysis Machine Control Settings via Predictive Patient Response Modeling
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Solution Overview
Problem
Current renal replacement therapy techniques lack flexibility in scheduling treatment sessions and often require significant caregiver effort, leading to potential deviations from desired patient status, which can result in adverse conditions.
Innovation Solution
A dialysis system that computes individualized control settings for each treatment session based on predictive models estimating patient response to machine-related parameters, allowing for flexible scheduling and reduced caregiver effort by directly targeting desired physiological parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If treatment sessions are scheduled according to a fixed treatment plan with prescribed control settings, then the treatment plan can be strictly adhered to, but flexibility in scheduling is lost and patient may have difficulty accommodating to fixed schedule
Solution Approach 1:
The dialysis machine dynamically adjusts control settings for each treatment session based on actual patient status data, rather than following fixed predetermined settings. The system computes individualized control settings that adapt to the patient's current physiological state, allowing flexible scheduling while maintaining treatment effectiveness through real-time data-driven adjustments.
2Ease of manufacture
If control settings are determined manually by caregiver based on clinical experience, then treatment plan can be created, but significant caregiver effort is required and treatment may deviate from desired patient status
Solution Approach 1:
The system uses feedback from actual patient status data (obtained through measurements during treatment) to automatically compute and adjust control settings. This closed-loop feedback mechanism replaces manual caregiver estimation with data-driven automated computation, improving both the ease of creating treatment plans and the precision of achieving desired patient status through continuous measurement and adjustment.
Solution Approach 2:
The dialysis machine performs self-adjustment of control settings by automatically computing individualized settings based on measured patient status data, reducing dependence on manual caregiver intervention. The system serves itself by autonomously determining optimal control parameters without requiring significant caregiver effort for manual estimation and adjustment.
3Reliability
If treatment plan is adjusted frequently to achieve desired patient status, then patient status can be optimized, but caregiver effort increases significantly
Solution Approach 1:
The dialysis machine autonomously adjusts control settings by computing individualized settings based on actual patient status measurements, eliminating the need for frequent manual adjustments by caregivers. The system performs self-optimization automatically, maintaining reliable achievement of desired patient status while preserving caregiver productivity by reducing manual intervention requirements.
Data Source
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Figure 3~4A
AI summary
A dialysis machine is configured to perform treatment sessions of renal replacement therapy. Prior to each treatment session for an individual patient, a machine controller in the dialysis machine obtains from a logic device a set of current control settings of the machine-related parameters to be applied in the treatment session. In generating the control settings, the logic device obtains (21B) a set of therapeutic targets comprising a target value that represents part of a desired status of the patient after the treatment session, in terms of one or more physiological parameters for the patient. The logic device also obtains (21A) status data that represents the current status of the patient prior to the treatment session, and computes (22) the set of current control settings of the machine-related parameters, as a function of the set of therapeutic targets and the status data and at least partly based on a predictive model, which estimates the physiological response of the patient to the machine-related parameters during the treatment session. Thereby, the dialysis machine may be inherently controlled to achieve the desired status at the end of the treatment session, based on automatically computed control settings, which reduces the need for the patient to strictly adhere to a predetermined treatment plan, and for the caregiver to manually estimate control settings.