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US 2023/0402180 A1Published application
Techniques for generating predictive outcomes relating to spinal muscular atrophy using artificial intelligence
OwnerSilvia Elena MOLERO LEON, Hélène Jeanne SAHRI +2inventors
InventorsSilvia Elena MOLERO LEON, Hélène Jeanne SAHRI, Turap TASOGLU, Cigdem TOURKMEN
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Abstract
Disclosed are techniques for using artificial intelligence (AI) to facilitate the treatment of subjects diagnosed with spinal muscular atrophy (SMA). Methods and systems disclosed herein relate to techniques for using AI to predict the disease progression in subjects diagnosed with SMA, detect latent commonalities across subjects with SMA to identify candidate subjects for new or existing clinical studies, and intelligently select subject-specific therapeutic treatments for treating SMA.
Claims20
A computer-implemented method comprising:
- retrieving a subject record associated with a subject, the subject record including a set of features characterizing the subject, and the subject having been diagnosed with spinal muscular atrophy (SMA);
- extracting a subset of the set of features included in the subject record, each feature of the subset of the set of features being associated with an SMA characteristic;
- generating a partial word sequence by combining the subset of the set of features into a sequence of one or more words, each word of the one or more words representing a feature of the subset of features;
- transforming the partial word sequence into a numerical representation using a trained word-to-vector model;
- inputting the numerical representation of the partial word sequence into a natural language processing (NLP) model having been trained to predict a completion word or phrase for completing the partial word sequence;
- generating, based on the completion word or phrase outputted by the NLP model, a disease progression representing a predicted progression of one or more SMA phenotypes specific to the subject over a period of time;
- and
- outputting an indication that the subject is predicted to exhibit the one or more SMA phenotypes included in the disease progression.
5 more elements
A system, comprising:
- one or more processors;
- and
- a non-transitory, computer-readable storage medium containing instructions which, when executed on the one or more processors, cause the one or more processors to perform a set of actions including:
- retrieving a subject record associated with a subject, the subject record including a set of features characterizing the subject, and the subject having been diagnosed with spinal muscular atrophy (SMA);
- extracting a subset of the set of features included in the subject record, each feature of the subset of the set of features being associated with an SMA characteristic;
- generating a partial word sequence by combining the subset of the set of features into a sequence of one or more words, each word of the one or more words representing a feature of the subset of features;
- transforming the partial word sequence into a numerical representation using a trained word-to-vector model;
- inputting the numerical representation of the partial word sequence into a natural language processing (NLP) model having been trained to predict a completion word or phrase for completing the partial word sequence;
- generating, based on the completion word or phrase outputted by the NLP model, a disease progression representing a predicted progression of one or more SMA phenotypes specific to the subject over a period of time;
- and
- outputting an indication that the subject is predicted to exhibit the one or more SMA phenotypes included in the disease progression.
8 more elements
A computer-program product tangibly embodied in a non-transitory, machine-readable storage medium, including instructions configured to cause one or more data processors to perform a set of actions including:
- retrieving a subject record associated with a subject, the subject record including a set of features characterizing the subject, and the subject having been diagnosed with spinal muscular atrophy (SMA);
- extracting a subset of the set of features included in the subject record, each feature of the subset of the set of features being associated with an SMA characteristic;
- generating a partial word sequence by combining the subset of the set of features into a sequence of one or more words, each word of the one or more words representing a feature of the subset of features;
- transforming the partial word sequence into a numerical representation using a trained word-to-vector model;
- inputting the numerical representation of the partial word sequence into a natural language processing (NLP) model having been trained to predict a completion word or phrase for completing the partial word sequence;
- generating, based on the completion word or phrase outputted by the NLP model, a disease progression representing a predicted progression of one or more SMA phenotypes specific to the subject over a period of time;
- and
- outputting an indication that the subject is predicted to exhibit the one or more SMA phenotypes included in the disease progression.
5 more elements
Dependencies are inferred from the claim text.