Model-Assisted Performance Status Prediction from Unstructured Medical Records
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Solution Overview
Problem
Current methods for assessing a patient's survivability and treatment suitability are subjective and rely on individual doctor assessments, leading to incomplete and inconsistent data, which hinders accurate prognosis and treatment planning, especially when Eastern Cooperative Oncology Group (ECOG) scores are absent or incomplete.
Innovation Solution
A model-assisted system and method using a processor to analyze structured and unstructured patient data, including medical records, to generate a performance status prediction without relying on ECOG scores, employing trained machine learning models or natural language processing algorithms to provide a quantifiable assessment of survivability and treatment suitability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ECOG scores are used to assess patient performance status, then a standardized prognostic evaluation can be obtained, but the data becomes incomplete and inconsistent when ECOG scores are absent or subject to individual doctor judgment variations
Solution Approach 1:
The patent replaces the manual, subjective ECOG scoring system with an automated machine learning-based performance status assessment system. The system uses natural language processing to extract information from unstructured medical records and structured data to generate objective performance status predictions, eliminating reliance on individual doctor judgments and ECOG score subjectivity.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw medical data and performance status assessment. This intermediary processes both structured and unstructured data through trained algorithms to produce consistent, reproducible performance status predictions, serving as a mediator that transforms diverse data sources into reliable prognostic evaluations.
2Adaptability or versatility
If individual doctor assessments are used for ECOG scoring, then clinical judgment can be applied, but the assessments become subjective and vary between doctors
Solution Approach 1:
The patent replaces subjective human assessment with an automated machine learning system that consistently processes medical data. The system maintains clinical relevance by training on data that reflects clinical judgment while eliminating inter-doctor variability, achieving both adaptability to clinical contexts and measurement precision through standardized algorithmic processing.
Solution Approach 2:
The patent transforms the assessment from a subjective categorical score (ECOG 0-5) to a continuous prediction derived from multiple data parameters. By changing the assessment parameters from single-point clinical judgment to multi-factor algorithmic analysis, the system achieves greater consistency while preserving clinical adaptability through the flexibility of the machine learning model.
3Ease of manufacture
If only structured medical record data is analyzed, then data processing is straightforward, but comprehensive prognosis assessment is limited without unstructured information
Solution Approach 1:
The patent merges structured and unstructured medical data into a unified analysis framework. The system combines tabular structured data (lab results, vitals) with unstructured text data (physician notes, discharge summaries) through natural language processing and feature extraction, creating a comprehensive prognostic assessment that leverages the strengths of both data types without sacrificing processing efficiency.
Solution Approach 2:
The patent creates a multi-functional data processing system that can handle both structured and unstructured data formats through a single machine learning pipeline. The system performs multiple functions: extracting entities from text, processing tabular data, integrating features, and generating predictions, all within one unified framework that efficiently processes diverse data types.
Data Source
AI summary
A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.