Medical Data Classification for Preterm Birth Risk Alerts
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Solution Overview
Problem
Current diagnostic approaches for preterm birth risk fail to effectively communicate complex and uncertain risk factors to expectant mothers, leading to inadequate understanding and behavior change, particularly due to limited time and training among healthcare providers, resulting in missed indicators of adverse pregnancy outcomes and unnecessary healthcare visits.
Innovation Solution
A structured medical data classification system that uses a processor and memory to parse and classify medical data, generating a risk profile and transmitting alerts to healthcare providers, incorporating graph-learning and machine learning techniques to provide personalized risk assessments and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If healthcare providers use current diagnostic approaches for preterm birth risk, then they can identify risk factors, but they fail to effectively communicate complex and uncertain risk factors to expectant mothers, leading to inadequate understanding and behavior change
Solution Approach 1:
The patent introduces an automated classification system as an intermediary between healthcare providers and patients. This system processes complex medical data and generates simplified risk profiles that patients can understand, bridging the communication gap without requiring providers to directly explain complex statistical risks.
Solution Approach 2:
The patent replaces the manual communication process with an automated machine learning-based classification system. The system automatically analyzes medical records, identifies risk factors, and generates personalized risk profiles, eliminating the need for providers to manually communicate complex risk information.
2Loss of information
If healthcare providers provide comprehensive risk information to patients, then patients can make fully informed decisions, but the limited time available with each patient prevents in-depth discussions
Solution Approach 1:
The system performs preliminary analysis of medical data and generates comprehensive risk profiles before the provider-patient interaction. This preliminary action includes automatically identifying risk factors, calculating risk scores, and preparing personalized recommendations, so that when the provider meets the patient, they only need to discuss the pre-processed information rather than analyzing data in real-time.
Solution Approach 2:
The automated classification system performs the information processing and analysis work that would otherwise require provider time. The system self-processes medical records, identifies risks, and generates patient-friendly explanations, allowing patients to receive comprehensive information without consuming provider time.
3Adaptability or versatility
If healthcare providers use standard risk communication approaches, then they can convey general recommendations, but they fail to provide personalized information tailored to individual patient risk profiles
Solution Approach 1:
The patent applies local quality by generating risk profiles that are specifically tailored to each patient's unique characteristics. The system analyzes individual medical records, identifies patient-specific risk factors, and creates customized recommendations rather than using generic risk communication templates, ensuring that each patient receives information relevant to their personal situation.
Solution Approach 2:
The system dynamically adjusts risk assessment parameters based on individual patient data. The machine learning model processes varying medical parameters, historical data, and clinical characteristics to generate personalized risk scores and recommendations, adapting the complexity and content of risk information to each patient's specific profile.
4Reliability
If healthcare providers monitor all risk factors, then they can detect indicators of adverse pregnancy outcomes, but they miss indicators due to inadequate training and understanding of complex risk interactions
Solution Approach 1:
The patent replaces provider-based risk monitoring with an automated machine learning system that continuously analyzes medical data. The system detects patterns and interactions between multiple risk factors that would be difficult for humans to identify, using computational algorithms to monitor for adverse outcomes and trigger alerts when thresholds are exceeded.
Solution Approach 2:
The system implements continuous feedback by monitoring patient data, comparing it against established risk thresholds and patterns, and providing real-time alerts when adverse outcomes are detected or predicted. This feedback mechanism enables reliable detection of risk indicators while automatically handling the complexity of multi-factor interactions through programmed response protocols.
Data Source
AI summary
A system for classifying structured medical data, with each item of structured medical data, the system comprising a processing module that parses items of structured medical data to retrieve values of respective fields of the one or more items of structured medical data, the one or more retrieved values representing a set of medical attributes; a classification module that selects a classifier based at least one of the attributes in the set and applies the classifier to the set of attributes to classify one or more items of structured medical data into a particular risk profile; a user interface that renders one or more controls for input data that confirms one or more of the risk factors of the risk profile; and a transmitter to transmit to a remote medical device, an alert that specifies confirmation of the one or more of the risk factors.