Audio Waveform Processing for Medical Condition Prediction
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Solution Overview
Problem
Current machine learning models for detecting health conditions from audio data face challenges in accurately predicting conditions like post-partum depression, anxiety, drug addiction, Parkinson's disease, and respiratory disorders using raw waveform audio, as they require sophisticated feature extraction and temporal dependence analysis.
Innovation Solution
A computer system is developed that includes a machine learning model trained with historical feature vector inputs combining audio data and claims data, utilizing techniques such as keyword processing, natural language processing, and Long Short-Term Memory (LSTM) layers to generate condition likelihood outputs, and transforms user interfaces to display results based on these outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning models use raw waveform audio for detecting health conditions, then the model can process unprocessed audio data directly, but the model requires sophisticated feature extraction and temporal dependence analysis which increases system complexity
Solution Approach 1:
The patent pre-processes audio data into feature vectors before model training, extracting relevant acoustic features in advance. This preliminary action simplifies the model's operation while maintaining detection accuracy, as the complex feature extraction is performed once during data preparation rather than during each inference operation.
Solution Approach 2:
The patent introduces feature vectors as an intermediary between raw audio data and the machine learning model. These feature vectors serve as a simplified representation that captures essential acoustic characteristics without requiring the model to perform complex feature extraction during operation, thus reducing system complexity while preserving ease of use.
2Measurement precision
If machine learning models process audio data with sophisticated feature extraction, then the detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs sophisticated feature extraction in advance during data preparation, creating pre-computed feature vectors. This allows the model to achieve high detection accuracy using these pre-extracted features without incurring the computational cost of real-time feature extraction, thus reducing processing time while maintaining precision.
Solution Approach 2:
The patent segments the audio processing task into distinct stages: feature extraction during data preparation and classification during inference. By separating these tasks temporally, the system can use computationally intensive feature extraction methods during offline processing while maintaining fast, simple classification during online operation, thereby reducing overall processing time without sacrificing accuracy.
3Measurement precision
If the system integrates multiple audio features like intensity, fundamental frequency, and MFCCs, then the condition prediction accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent combines multiple audio features (intensity, fundamental frequency, MFCCs) into unified feature vectors that are processed together by the machine learning model. This merging approach allows the system to leverage the complementary information from different feature types to improve prediction accuracy while managing processing complexity through integrated handling of all features in a single processing pipeline.
Solution Approach 2:
The patent creates a universal feature vector structure that can accommodate multiple types of audio features simultaneously. This multi-functional feature representation allows the same processing pipeline to handle diverse acoustic characteristics, improving prediction accuracy across different health conditions without requiring separate processing paths for each feature type, thus managing complexity through standardized handling.
Data Source
AI summary
A computer system includes memory hardware and processor hardware configured to execute stored instructions. The instructions include training a machine learning model with the historical feature vector inputs including multiple audio data entries and multiple claims data entries, to generate a condition likelihood output indicative of a specified condition associated with one of multiple historical database entities. The instructions include for each of a set of multiple database entities, generating a feature vector input according to audio data and the claims data associated with the entity, processing the feature vector input with the machine learning model to generate the condition likelihood output, and assigning the database entity to an identified condition subset in response to determining that the condition likelihood output is greater than a specified likelihood threshold. The instructions include transforming a user interface to display the condition likelihood output associated with the database entity.