Machine Learning Query Construction for Hereditary Angioedema Diagnosis
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Solution Overview
Problem
Current methods are inefficient in identifying rare genetic conditions like Hereditary Angioedema due to misdiagnosis, prolonged diagnostic times, and the inability to process large volumes of data effectively, leading to delayed treatment and ineffective therapies.
Innovation Solution
A method using machine-readable data sets and distributed computing to identify common features in patient data, generate machine learning algorithms, and determine the probability of a medical condition's presence by weighting features based on frequency and mutual information, enabling earlier diagnosis and reducing misdiagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If passive surveillance relying on existing medical records is used, then cost is reduced, but diagnostic accuracy deteriorates due to incomplete and unreliable data
Solution Approach 1:
The patent introduces an intermediary layer of machine learning algorithms and pattern recognition systems that process existing medical records. This intermediary transforms incomplete and unreliable data into actionable diagnostic insights, allowing the system to maintain cost efficiency while improving diagnostic accuracy through computational analysis of patterns across multiple data points
Solution Approach 2:
The system changes the parameters of data analysis by applying machine learning models that can extract meaningful patterns from existing medical records. Instead of relying on complete and reliable data, the system transforms the approach to work effectively with incomplete data by using probabilistic models and pattern recognition that can infer diagnostic information from partial evidence
2Measurement precision
If active surveillance with questionnaires and tests is used, then diagnostic accuracy is improved, but cost and practicality deteriorate
Solution Approach 1:
The patent creates a virtual copy of the active surveillance process through machine learning algorithms that simulate the diagnostic reasoning of active surveillance. Instead of physically implementing questionnaires and tests on large populations, the system copies the diagnostic logic into computational models that can screen populations efficiently, maintaining diagnostic accuracy while eliminating the practical burdens of active surveillance
Solution Approach 2:
The system replaces the mechanical system of active surveillance (physical questionnaires, manual testing, human review) with an automated computational system. Machine learning algorithms process medical records and identify potential cases without requiring physical interaction with patients, thereby maintaining diagnostic accuracy while dramatically improving practicality and reducing costs
3Device complexity
If traditional diagnostic methods are used, then simplicity is maintained, but diagnostic time deteriorates leading to delayed treatment
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on extensive datasets of medical records and diagnostic outcomes. These pre-trained algorithms can then rapidly screen new patients without requiring complex real-time analysis, maintaining simplicity at the point of use while achieving fast diagnostic results through prior computational preparation
Solution Approach 2:
The system enables self-service diagnosis by allowing the machine learning algorithms to autonomously analyze medical records and identify potential cases without requiring extensive human intervention. The algorithms automatically process data, apply diagnostic criteria, and generate results, reducing both diagnostic time and the complexity of human expertise required while maintaining diagnostic quality
Data Source
AI summary
A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features and weighting the common features based on frequency of occurrence in the data set(s) to generate mutual information. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.