Self-Tuned Deep Learning for Clinical and Genomic Data Evaluation
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Solution Overview
Problem
Deep learning algorithms face challenges in producing accurate results without a large set of labeled training data and require substantial user effort for optimization, particularly in tasks involving complex optimization processes.
Innovation Solution
A method using a deep learning algorithm that tunes itself based on data clusters, performs statistical clustering, and obtains markers from these clusters to derive medically relevant data, such as survival rates, by leveraging Gaussian mean width and convergence rates to select optimal hidden layers and employing unsupervised learning with autoencoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning algorithms are used to evaluate clinical and genomic data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The deep learning algorithm is divided into multiple hidden layers, each performing specific transformations on the input data. This segmentation allows the complex evaluation task to be broken down into manageable sequential steps, improving measurement precision while organizing the device complexity into structured components
Solution Approach 2:
The patent introduces intermediate data clusters as mediators between the raw input data and the final medical evaluation results. These clusters serve as intermediate representations that simplify the processing pipeline, allowing the algorithm to achieve high precision through staged transformations rather than direct complex processing
2Measurement precision
If a large number of training labels are used to train deep learning algorithms, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies partial action by using only the necessary subset of data clusters for training rather than requiring all possible labeled data. The algorithm identifies and processes only the critical clusters needed to achieve accurate medical evaluations, reducing the time-consuming labeling effort while maintaining measurement precision
Solution Approach 2:
The patent performs preliminary clustering of the input data before the main evaluation process. By pre-organizing data into meaningful clusters, the algorithm reduces the amount of labeled training data needed, as the clustering structure provides prior organization that accelerates the learning process and reduces labeling time
3Measurement precision
If extensive user input is required for optimizing deep learning algorithms, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The deep learning algorithm performs self-service by automatically tuning its own parameters based on the input data clusters. The system self-optimizes without requiring extensive user input, yet still achieves high measurement precision in medical data derivation, thereby improving ease of operation while maintaining accuracy
Data Source
AI summary
The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.