Machine Learning Decision Support for Neonatal Shock Resuscitation
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Solution Overview
Problem
The management of neonatal shock syndromes lacks standardized, data-driven guidelines for resuscitation modalities, leading to variability in clinical protocols and increased risk of adverse outcomes due to inadequate fluid and pressor administration.
Innovation Solution
A method and tool utilizing machine learning techniques such as decision trees, SVMs, RNNs, LSTM, and Bayesian methods to analyze neonatal medical data, predicting optimal fluid boluses or pressor support based on perfusion status, etiology, and severity of shock.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinicians use traditional non-standardized approaches to manage neonatal shock, then they can make treatment decisions based on experience and judgment, but there is high variability in clinical protocols and lack of data-driven guidance leading to suboptimal outcomes
Solution Approach 1:
The patent replaces the mechanical system of manual clinical judgment and experience-based decision-making with an automated machine learning system. The ML model processes neonatal medical data and automatically predicts optimal resuscitation modalities, substituting human expertise with an algorithmic system that provides consistent, data-driven recommendations without variability.
Solution Approach 2:
The system enables self-service by automatically analyzing medical data, extracting features, selecting appropriate ML models, and generating treatment recommendations without requiring clinician intervention in the decision-making process. The system serves itself by autonomously processing information and producing actionable insights based on trained patterns from medical data.
2Reliability
If clinicians administer fluid boluses to treat neonatal shock, then perfusion may improve, but excessive fluid administration can cause rapid deterioration due to respiratory compromise and fluid overload
Solution Approach 1:
The patent implements feedback by continuously monitoring neonatal responses to treatment and using this information to refine ML model predictions. The system analyzes outcomes of fluid administration and pressor support, learning from actual patient responses to optimize future recommendations and avoid harmful effects through adaptive, response-based decision-making.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting treatment recommendations based on changing clinical parameters such as perfusion status, shock etiology, and severity. The ML model modifies fluid bolus volumes, pressor dosages, and treatment sequencing in real-time based on monitored parameters, optimizing the balance between improving perfusion and avoiding fluid overload.
3Reliability
If clinicians initiate pressor support for neonatal shock, then cardiovascular function may improve, but the decision lacks standardized guidance and evidence-based approaches
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive datasets of neonatal shock cases and outcomes before clinical use. The system performs preliminary learning and pattern recognition during model training, storing optimized decision rules and treatment protocols that can be rapidly applied to new cases without requiring real-time evidence review or clinician expertise in complex scenarios.
Data Source
AI summary
This disclosure relates to method and tool assisting clinicians in making real time decisions for resuscitation modalities in neonatal shock syndromes. The method includes receiving medical data corresponding to a neonate diagnosed with shock. The method further includes extracting one or more features from the medical data. The one or more features are indicative of perfusion status, possible etiology, type, and severity of shock, in the neonate. Further, the method includes selecting at least one machine learning model (ML) from a plurality of ML models based on the one or more features. Further, the method may include predicting via at least one ML model, an optimal treatment modality for the neonate. The method further includes assisting a clinician to provide the optimal treatment modality to the neonate based on predicting.