Sentiment analysis of mental health disorder symptoms
First Claim
1. A computer system comprising:
- an audio input device associated with a first party and receiving a speech from the first party; and
at least one hardware processor of said computer system in communication with the audio input device and configured to;
cause the audio input device to continuously and in real-time monitor the speech of said first party;
cause the audio input device to generate audio data based on the monitored speech;
transcribe the audio data to text;
analyze the text of the audio data to determine a sentiment;
train a model, using machine learning, to correlate the text and the determined sentiment to clinical information associated with one or more symptoms of a health disorder;
store the audio data, the text, the determined sentiment and an output of the trained machine learning model in a database as historical data, said machine learning model being trained to correlate based at least in part on the historical data that is stored in the database;
the at least one hardware processor is further configured to;
develop, over time, a behavioral baseline condition for the first party based on a history of audio data and corresponding text sentiment analysis results;
compare a result of analyzing the text of the current audio data against a baseline condition of said first party, anddetermine, based on said comparison, whether the first party is exhibiting a new or different symptom; and
schedule, via an interface device, a checkup or appointment with a health care practitioner regarding said new or different symptom, wherein the at least one hardware processor is further configured to;
analyze the sentiment of the speech and a duration of said sentiment to identify a mood swing event including a time of occurrence, how quickly the mood swing event occurs, and for how long a mood swing event occurs;
determine, over time, a pattern and frequency of each identified mood swing event;
compare a determined pattern and frequency of the mood swing events against a database of known mood swing patterns;
predict, based on said comparing determined frequency and pattern of mood swing events, a mood swing occurrence exhibited by said first party in the future, andgenerate an output message via an interface, said message indicating said predicted potential mood swing of said first party.
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Accused Products
Abstract
Monitoring and analysis of a user'"'"'s speech to detect symptoms of a mental health disorder by continuously monitoring a user'"'"'s speech in real-time to generate audio data based, transcribing the audio data to text and analyzing the text of the audio data to determine a sentiment of the audio data is disclosed. A trained machine learning model may be applied to correlate the text and the determined sentiment to clinical information associated with symptoms of a mental health disorder to determine whether the symptoms are a symptom event. The initial determination may be transmitted to a second device to determine (and/or verify) whether or not the symptom event was falsely recognized. The trained machine learning model may be updated based on a response from the second device.
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Citations
15 Claims
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1. A computer system comprising:
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an audio input device associated with a first party and receiving a speech from the first party; and at least one hardware processor of said computer system in communication with the audio input device and configured to; cause the audio input device to continuously and in real-time monitor the speech of said first party; cause the audio input device to generate audio data based on the monitored speech; transcribe the audio data to text; analyze the text of the audio data to determine a sentiment; train a model, using machine learning, to correlate the text and the determined sentiment to clinical information associated with one or more symptoms of a health disorder; store the audio data, the text, the determined sentiment and an output of the trained machine learning model in a database as historical data, said machine learning model being trained to correlate based at least in part on the historical data that is stored in the database;
the at least one hardware processor is further configured to;develop, over time, a behavioral baseline condition for the first party based on a history of audio data and corresponding text sentiment analysis results; compare a result of analyzing the text of the current audio data against a baseline condition of said first party, and determine, based on said comparison, whether the first party is exhibiting a new or different symptom; and schedule, via an interface device, a checkup or appointment with a health care practitioner regarding said new or different symptom, wherein the at least one hardware processor is further configured to; analyze the sentiment of the speech and a duration of said sentiment to identify a mood swing event including a time of occurrence, how quickly the mood swing event occurs, and for how long a mood swing event occurs; determine, over time, a pattern and frequency of each identified mood swing event; compare a determined pattern and frequency of the mood swing events against a database of known mood swing patterns; predict, based on said comparing determined frequency and pattern of mood swing events, a mood swing occurrence exhibited by said first party in the future, and generate an output message via an interface, said message indicating said predicted potential mood swing of said first party. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 12, 14)
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10. A computer program product, said computer program product comprising a non-transitory computer readable storage medium having instructions stored thereon that, when executed by at least one processor:
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cause an audio input device associated with a first party and receiving a speech from the first party, to continuously and in real-time monitor the speech of said first party; cause the audio input device to generate audio data based on the monitored speech; transcribe the audio data to text; analyze the text of the audio data to determine a sentiment; train a model, using machine learning, to correlate the text and the determined sentiment to clinical information associated with one or more symptoms of a health disorder; store the audio data, the text, the determined sentiment and an output of the trained machine learning model in a database as historical data, said machine learning model being trained to correlate based at least in part on the historical data that is stored in the database; develop, over time, a behavioral baseline condition for the first party based on a history of audio data and corresponding text sentiment analysis results; compare a result of analyzing the text of the current audio data against a baseline condition of said first party, and determine, based on said comparison, whether the first party is exhibiting a new or different symptom; and schedule, via an interface device, a checkup or appointment with a health care practitioner regarding said new or different symptom, wherein the instructions when executed by at least one processor further cause the at least one processor to; analyze the sentiment of the speech and a duration of said sentiment to identify a mood swing event including a time of occurrence, how quickly the mood swing event occurs, and for how long a mood swing event occurs; determine, over time, a pattern and frequency of each identified mood swing event; compare a determined pattern and frequency of the mood swing events against a database of known mood swing patterns; predict, based on said comparing determined frequency and pattern of mood swing events, a mood swing occurrence exhibited by said first party in the future, and generate an output message via an interface, said message indicating said predicted potential mood swing of said first party. - View Dependent Claims (11, 13, 15)
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Specification