Sentiment analysis of mental health disorder symptoms
First Claim
1. A computer-implemented method, comprising:
- monitoring continuously and in real-time, by an audio input device associated with a first party, speech from the first party;
generating, by the audio input device, audio data based on monitored speech;
transcribing, using a hardware processor of a computing device, the audio data to text;
analyzing, by the computing device, the text of the audio data to determine a sentiment;
training 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;
storing the audio data, the text, the determined sentiment and an output of the trained machine learning model in a database as historical data, wherein the trained machine learning model is trained based at least in part on the historical data that is stored in the database; and
developing, over time, a behavioral baseline condition for the first party based on a history of audio data and corresponding text sentiment analysis results;
comparing a result of analyzing the text of the current audio data against a baseline condition of said first party, anddetermining, based on said comparison, whether the first party is exhibiting a new or different symptom; and
scheduling, via an interface device, a checkup or appointment with a health care practitioner regarding said new or different symptom,wherein a symptom includes a mood swing event, said method further comprising;
analyzing, by the hardware processor, 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;
determining, by the hardware processor, over time, a pattern and frequency of each identified mood swing event;
comparing a determined pattern and frequency of the mood swing events against a database of known mood swing patterns;
predicting, based on said comparing determined frequency and pattern of mood swing events, a mood swing occurrence exhibited by said first party in the future, andgenerating 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.
15 Citations
9 Claims
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1. A computer-implemented method, comprising:
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monitoring continuously and in real-time, by an audio input device associated with a first party, speech from the first party; generating, by the audio input device, audio data based on monitored speech; transcribing, using a hardware processor of a computing device, the audio data to text; analyzing, by the computing device, the text of the audio data to determine a sentiment; training 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; storing the audio data, the text, the determined sentiment and an output of the trained machine learning model in a database as historical data, wherein the trained machine learning model is trained based at least in part on the historical data that is stored in the database; and developing, over time, a behavioral baseline condition for the first party based on a history of audio data and corresponding text sentiment analysis results; comparing a result of analyzing the text of the current audio data against a baseline condition of said first party, and determining, based on said comparison, whether the first party is exhibiting a new or different symptom; and scheduling, via an interface device, a checkup or appointment with a health care practitioner regarding said new or different symptom, wherein a symptom includes a mood swing event, said method further comprising; analyzing, by the hardware processor, 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; determining, by the hardware processor, over time, a pattern and frequency of each identified mood swing event; comparing a determined pattern and frequency of the mood swing events against a database of known mood swing patterns; predicting, 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 generating 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)
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Specification