Evaluating input data using a deep learning algorithm

Abstract

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 intoa 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 ofhidden 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 eachstatistical 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.

Description

technical field [0001] The present invention relates to the field of deep learning, and more particularly to the field of evaluating object data using deep learning algorithms. Background technique [0002] Deep learning is a branch of machine learning that has recently proven to be very successful in the fields of image analysis, speech recognition, and natural language processing. Deep learning algorithms use a succession of layers of nonlinear transformations to model the nonlinear structure in the input data. While deep learning has only recently become popular due to its success in image and speech analysis tasks, they were first introduced in the machine learning literature decades ago. These algorithms are most successful in tasks where large numbers of training labels are available and feature engineering typically requires significant effort from domain experts. [0003] In machine learning, the quality and quantity of training labels can significantly affect the ...

AI Technical Summary

Problems solved by technology

In the context of deep learning, this problem is very challenging due to the complexity of the optimization process employed by these algorithms

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