System and method for providing model-based treatment recommendation via individual-specific machine learning models
The present disclosure pertains to a system for providing model-based treatment recommendation via individual-specific machine learning models. In some embodiments, the system (i) obtains an audio recording of an individual, (ii) determines, from the audio recording, one or more utterance-related features of the individual; (iii) performs one or more queries based on the one or more utterance-related features to obtain health information (e.g., utterance-related conditions and treatments provided for the utterance-related conditions) associated with similar individuals having similar utterance-related conditions as the subject; (iv) provides the health information associated with the similar individuals to a machine learning model to train the machine learning model; and (v) provides, subsequent to the training of the machine learning model, the one or more utterance-related features to the machine learning model to determine one or more treatments for the individual.
1. A system for providing model-based treatment recommendation via individual-specific machine learning models, the system comprising:
one or more processors configured by machine-readable instructions to:
obtain an audio recording of an individual;
determine, from the audio recording, one or more utterance-related features of the individual, the one or more utterance-related features corresponding to characteristics of the individual's utterances in the audio recording;
perform one or more queries based on the one or more utterance-related features to obtain health information associated with similar individuals having similar utterance-related conditions as the subject, the health information indicating utterance-related conditions of the similar individuals and treatments provided to the similar individuals respectively for the utterance-related conditions;
provide the health information associated with the similar individuals to a machine learning model to train the machine learning model; and
provide, subsequent to the training of the machine learning model, the one or more utterance-related features to the machine learning model to determine one or more treatments for the individual.
2. The system of claim 1 , wherein the one or more processors are further configured to:
extract, from the audio recording, a set of utterance-related features of the individual, each utterance-related feature of the set of utterance-related features corresponding to one or more characteristics of the individual's utterances in the audio recording;
perform pattern recognition on the set of utterance-related features to determine which of features of the set of utterance-related features have abnormalities;
determine the one or more utterance-related features by identifying the one or more utterance-related features as features having one or more abnormalities based on the pattern recognition; and
perform the one or more queries (i) based on the one or more utterance-related features and (ii) without reliance on one or more other utterance-related features of the set of utterance-related features to obtain the health information associated with the similar individuals.
3. The system of claim 1 , wherein the one or more processors are further configured to:
for each utterance-related feature of the one or more utterance-related features, determine a classification based on demographic information associated with the individual; and
perform the one or more queries based on the classifications of the one or more utterance-related features to obtain the health information associated with the similar individuals.
4. The system of claim 1 , wherein the one or more processors are further configured to:
extract, from the audio recording, a set of utterance-related features of the individual, each utterance-related feature of the set of utterance-related features corresponding to one or more characteristics of the individual's utterances in the audio recording;
perform first pattern recognition on the set of utterance-related features based on a first pattern recognition scheme to determine which of features of the set of utterance-related features have abnormalities;
perform second pattern recognition on the set of utterance-related features based on a second pattern recognition scheme to determine which of features of the set of utterance-related features have abnormalities;
determine the one or more utterance-related features by identifying a first subset of utterance-related features of the individual as features having one or more abnormalities based on the first pattern recognition, the first subset comprising one or more utterance-related features and other utterance-related features, each utterance-related feature of the first subset corresponding to one or more characteristics of the individual's utterances in the audio recording;
perform the one or more queries (i) based on the first subset of utterance-related features to obtain the health information associated with the similar individuals and (ii) based on the second subset of utterance-related features to obtain other health information associated with other similar individuals having similar utterance-related conditions as the subject, the other health information indicating other utterance-related conditions of the other similar individuals and other treatments provided to the other similar individuals respectively for the other utterance-related conditions;
provide the other health information associated with the other similar individuals to a second machine learning model to train the second machine learning model;
provide, subsequent to the training of the second machine learning model, the second subset of utterance-related features to the second machine learning model to determine one or more other treatments for the individual; and
select a set of treatments for the individual from the one or more treatments and the one or more other treatments.
5. The system of claim 4 , wherein the first pattern recognition scheme is related to at least one of speech waveform recognition, acoustic waveform recognition, speech synthesis recognition, or phonetic sound pronunciation waveform recognition, and wherein the second pattern recognition scheme is related to at least a different one of the speech waveform recognition, the acoustic waveform recognition, the speech synthesis recognition, or the phonetic sound pronunciation waveform recognition.
6. A method for providing model-based treatment recommendation via individual-specific machine learning models, the method being implemented by one or more processors configured by machine readable instructions, the method comprising:
obtaining an audio recording of an individual;
determining, from the audio recording, one or more utterance-related features of the individual, the one or more utterance-related features corresponding to characteristics of the individual's utterances in the audio recording;
performing a query based on the one or more utterance-related features to obtain health information associated with similar individuals having similar utterance-related conditions as the subject, the health information indicating utterance-related conditions of the similar individuals and treatments provided to the similar individuals respectively for the utterance-related conditions;
providing the health information associated with the similar individuals to a machine learning model to train the machine learning model; and
providing, subsequent to the training of the machine learning model, the one or more utterance-related features to the machine learning model to determine one or more treatments for the individual.
7. The method of claim 6 , wherein the method further comprises:
extracting, from the audio recording, a set of utterance-related features of the individual, each utterance-related feature of the set of utterance-related features corresponding to one or more characteristics of the individual's utterances in the audio recording;
performing pattern recognition on the set of utterance-related features to determine which of features of the set of utterance-related features have abnormalities;
determining the one or more utterance-related features by identifying the one or more utterance-related features as features having one or more abnormalities based on the pattern recognition; and
performing the one or more queries (i) based on the one or more utterance-related features and (ii) without reliance on one or more other utterance-related features of the set of utterance-related features to obtain the health information associated with the similar individuals.
8. The method of claim 6 , wherein the method further comprises:
for each utterance-related feature of the one or more utterance-related features, determining a classification based on demographic information associated with the individual; and
performing the one or more queries based on the classifications of the one or more utterance-related features to obtain the health information associated with the similar individuals.
9. The method of claim 6 , wherein the method further comprises:
extracting, from the audio recording, a set of utterance-related features of the individual, each utterance-related feature of the set of utterance-related features corresponding to one or more characteristics of the individual's utterances in the audio recording;
performing first pattern recognition on the set of utterance-related features based on a first pattern recognition scheme to determine which of features of the set of utterance-related features have abnormalities;
performing second pattern recognition on the set of utterance-related features based on a second pattern recognition scheme to determine which of features of the set of utterance-related features have abnormalities;
determining the one or more utterance-related features by identifying a first subset of utterance-related features of the individual as features having one or more abnormalities based on the first pattern recognition, the first subset comprising one or more utterance-related features and other utterance-related features, each utterance-related feature of the first subset corresponding to one or more characteristics of the individual's utterances in the audio recording;
performing the one or more queries (i) based on the first subset of utterance-related features to obtain the health information associated with the similar individuals and (ii) based on the second subset of utterance-related features to obtain other health information associated with other similar individuals having similar utterance-related conditions as the subject, the other health information indicating other utterance-related conditions of the other similar individuals and other treatments provided to the other similar individuals respectively for the other utterance-related conditions;
providing the other health information associated with the other similar individuals to a second machine learning model to train the second machine learning model;
providing, subsequent to the training of the second machine learning model, the second subset of utterance-related features to the second machine learning model to determine one or more other treatments for the individual; and
selecting a set of treatments for the individual from the one or more treatments and the one or more other treatments.
10. The method of claim 9 , wherein the first pattern recognition scheme is related to at least one of speech waveform recognition, acoustic waveform recognition, speech synthesis recognition, or phonetic sound pronunciation waveform recognition, and wherein the second pattern recognition scheme is related to at least a different one of the speech waveform recognition, the acoustic waveform recognition, the speech synthesis recognition, or the phonetic sound pronunciation waveform recognition.
11. A system for providing model-based treatment recommendation via individual-specific machine learning models, the system comprising:
means for obtaining an audio recording of an individual;
means for determining, from the audio recording, one or more utterance-related features of the individual, the one or more utterance-related features corresponding to characteristics of the individual's utterances in the audio recording;
means for performing one or more queries based on the one or more utterance-related features to obtain health information associated with similar individuals having similar utterance-related conditions as the subject, the health information indicating utterance-related conditions of the similar individuals and treatments provided to the similar individuals respectively for the utterance-related conditions;
means for providing the health information associated with the similar individuals to a machine learning model to train the machine learning model; and
means for providing, subsequent to the training of the machine learning model, the one or more utterance-related features to the machine learning model to determine one or more treatments for the individual.
12. The system of claim 11 , wherein the one or more processors are further configured to:
means for extracting, from the audio recording, a set of utterance-related features of the individual, each utterance-related feature of the set of utterance-related features corresponding to one or more characteristics of the individual's utterances in the audio recording;
means for performing pattern recognition on the set of utterance-related features to determine which of features of the set of utterance-related features have abnormalities;
means for determining the one or more utterance-related features by identifying the one or more utterance-related features as features having one or more abnormalities based on the pattern recognition; and
means for performing the one or more queries (i) based on the one or more utterance-related features and (ii) without reliance on one or more other utterance-related features of the set of utterance-related features to obtain the health information associated with the similar individuals.
13. The system of claim 11 , further comprising:
means for, for each utterance-related feature of the one or more utterance-related features, determining a classification based on demographic information associated with the individual; and
means for performing the one or more queries based on the classifications of the one or more utterance-related features to obtain the health information associated with the similar individuals.
14. The system of claim 11 , further comprising:
means for extracting, from the audio recording, a set of utterance-related features of the individual, each utterance-related feature of the set of utterance-related features corresponding to one or more characteristics of the individual's utterances in the audio recording;
means for performing first pattern recognition on the set of utterance-related features based on a first pattern recognition scheme to determine which of features of the set of utterance-related features have abnormalities;
means for performing second pattern recognition on the set of utterance-related features based on a second pattern recognition scheme to determine which of features of the set of utterance-related features have abnormalities;
means for determining the one or more utterance-related features by identifying a first subset of utterance-related features of the individual as features having one or more abnormalities based on the first pattern recognition, the first subset comprising one or more utterance-related features and other utterance-related features, each utterance-related feature of the first subset corresponding to one or more characteristics of the individual's utterances in the audio recording;
means for performing the one or more queries (i) based on the first subset of utterance-related features to obtain the health information associated with the similar individuals and (ii) based on the second subset of utterance-related features to obtain other health information associated with other similar individuals having similar utterance-related conditions as the subject, the other health information indicating other utterance-related conditions of the other similar individuals and other treatments provided to the other similar individuals respectively for the other utterance-related conditions;
means for providing the other health information associated with the other similar individuals to a second machine learning model to train the second machine learning model;
means for providing, subsequent to the training of the second machine learning model, the second subset of utterance-related features to the second machine learning model to determine one or more other treatments for the individual; and
means for selecting a set of treatments for the individual from the one or more treatments and the one or more other treatments.
15. The system of claim 14 , wherein the first pattern recognition scheme is related to at least one of speech waveform recognition, acoustic waveform recognition, speech synthesis recognition, or phonetic sound pronunciation waveform recognition, and wherein the second pattern recognition scheme is related to at least a different one of the speech waveform recognition, the acoustic waveform recognition, the speech synthesis recognition, or the phonetic sound pronunciation waveform recognition.