Machine Learning Patient Labeling Automation
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Solution Overview
Problem
Current electronic patient systems require manual labeling by administrators, which is time-consuming and inefficient, and struggle to incorporate non-standard and non-medical patient records into treatment planning, limiting the ability to identify similar patients for effective care management.
Innovation Solution
A machine learning model is developed to automate patient labeling and data processing, using a large language model to integrate and format diverse patient information from multiple sources, enabling dynamic retraining and improved categorization of patients for better care planning and treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual labeling by administrators is used, then patient data can be labeled and categorized, but the process is time-consuming and inefficient
Solution Approach 1:
The machine learning model enables the system to automatically label and categorize patient data without requiring continuous human intervention. The model learns from labeled examples and independently applies this knowledge to new patient records, making the system self-sufficient for the labeling task while administrators only need to provide initial training data and validate results.
Solution Approach 2:
The patent replaces the mechanical manual process of administrators reading and labeling patient records with an automated machine learning system. The ML model processes patient data through computational algorithms, substituting human cognitive effort with automated information processing that can handle large volumes of data much faster than manual methods.
2Adaptability or versatility
If traditional search queries are used to find similar patients, then clinicians can compare treatment histories, but the system cannot effectively incorporate non-standard and non-medical patient records
Solution Approach 1:
The machine learning model is designed to process multiple types of patient records through a single unified system. It can handle both structured medical data and unstructured non-medical records (such as social determinants of health, lifestyle information, and narrative patient reports) using the same labeling framework, making the system versatile across different data formats and sources.
Solution Approach 2:
The patent transforms diverse patient record formats into a standardized representation that the machine learning model can process. By converting various data types (structured and unstructured) into consistent features and labels, the system changes the parameters of different data formats to a common framework, enabling uniform analysis across all patient records regardless of their original format.
3Adaptability or versatility
If static patient categories are used in the database, then patients can be organized, but the system cannot dynamically add new classes of patients
Solution Approach 1:
The machine learning model provides dynamic patient categorization by continuously learning from new labeled data and adapting its classification criteria. As administrators label new patient records with emerging patient types or conditions, the model updates its understanding and can automatically recognize and categorize similar new cases, allowing the system to evolve with changing patient populations without requiring manual reconfiguration of categories.
Data Source
AI summary
Server-implementing methods include receiving a selection of records for patients from an external source coupled to the server, and vectorizing at least set of healthcare record items associated with the selected patients and other patients from the data source. At least one vector element may be determined from healthcare record items for each patient. Vectors may be formed from at least one of the healthcare record items and the separate vectors concatenated together to form final vectors for the patients. A similarity search may be performed using the final vectors to determine a group of similar patients from the vectorized patients of the system. Selected patients that are within a same dimensional space as a focal patient may be labelled in the batch and presented on computer display. Further, intake of patient data from non-medical record sources may be automated and facilitated through the use of a large language model.