AI for Mental Health Diagnosis

Cloud-based, machine learned models for pairing mental health help seekers with care providers based on collaborative relationship patterns. The models use demographic, personality, and other variables to predict a strong therapeutic alliance between a client and provider. This aims to improve matchmaking and reduce dropouts by leveraging machine learning to optimize pairing based on factors beyond just client characteristics.

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Using generative adversarial networks (GANs) to predict changes in behavior, physiology, and well-being due to starting a new job or other stressful life event. The GANs are trained using wearable sensor data and self-reported assessments to generate predictions of feature changes. These predictions are then analyzed to identify indicators of resilience, i.e., maintaining mental health during stress. The GANs use multi-task learning with separate discriminators for features and clusters to improve performance on complex, multivariate datasets.

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Digital mental health assessment platform that provides comprehensive screening, testing, and reporting to help individuals accurately identify potential mental health conditions. The platform uses a tiered process with screening questions, confirmatory tests, and targeted rating scales to assess symptoms against diagnostic criteria. It generates reports with diagnostic indicators and suggestions for further evaluation by healthcare providers. The platform aims to educate and empower individuals to make informed decisions about their mental health and encourage seeking professional care.

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Automated automatic monitoring of mental health state of users based on data acquired through a mobile computing device. The automated monitoring includes obtaining data from a sensor or user-interface of a mobile computing device gathered during use of the mobile computing device by a user, inferring, from the data, with a trained machine learning and/or artificial intelligence (AI) model, a mental-health state of the user; and storing the mental health state in memory.

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An AI-based screening tool for mental health that uses natural language processing and machine learning to assess emotional well-being and stress levels. The tool presents a questionnaire, processes the responses, assigns weights based on importance, generates additional questions, captures facial expressions, and calculates an emotional wellness index for each domain. It then generates a report describing distress levels and specific domains of concern. The tool aims to triage mental health issues and refer users to professional help as needed.

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Using AI to recommend personalized mental health treatment plans by ingesting medical assessment data and identifying factors of patients, applying a machine learning model, and generating treatment recommendations that consider factors like age, gender, income, etc. The AI learns from assessment results and can suggest providers, prescriptions, and care regimens. It also updates based on new assessments to improve recommendations.

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Interacting with large language models (LLMs) like GPT-3 to improve their output and performance. The method involves using structured, machine-readable representations of data in a universal language to provide new context to LLMs, analyze their output, and train them. This allows leveraging the benefits of LLMs like generation and understanding while mitigating issues like hallucination, originality, and factual accuracy. The structured representation allows representing complex concepts and relationships in a consistent, scalable way that LLMs can better understand. It involves using a processing system with a language like Cyc to generate continuation text for LLM prompts, validate factual accuracy, add citations, avoid hallucination, and ensure originality.

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Understanding and managing a stress level of an individual user using a machine learning model during daily life. The model predicts a stress of the user with respect to a previous specific time interval.

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Automated analysis and use of natural language data using large language models (LLMs) in a way that improves their output and accuracy compared to traditional LLMs. The method involves using structured, machine-readable representations of data in a universal language to provide new context to LLMs, analyze LLM output, train LLMs, and validate natural language for factual accuracy. This allows leveraging the benefits of LLMs while mitigating issues like hallucination, plagiarism, and lack of understanding. It also involves techniques like generating citations and avoiding copyright infringement in LLM output.

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Using a structured, machine-readable language to improve the output of large language models (LLMs) and enable fact checking of LLM output. The method involves providing structured data to an LLM as context to improve its continuation text generation. The LLM output is analyzed by a processing system using the structured language to identify variations and potentially hallucinated parts. These are replaced or shown less prominently to the user. The LLM can also be trained using structured data to improve its performance.

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Artificial intelligence device to detect and manage psychological stress using sensors and machine learning. The device collects environmental data like light spectrum, along with physiological and behavioral data, to predict and manage stress levels. It uses recurrent neural networks (RNN) and support vector machines (SVM) to analyze the sensor data and extract personalized stress indicators. This allows quantifying and tracking stress levels over time. The device can provide personalized insights and recommendations to manage stress based on the analyzed data.

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Mental state classification system that provides accurate and reliable mental state classification to individuals and organizations in a non-face-to-face manner. The system uses a server with two platforms - a service platform for user interaction and a mental state classification platform for analysis. The service platform provides questionnaires and face image capture to users. The mental state classification platform extracts heart rate variability (HRV) from faces and uses algorithms to classify mental states based on answers and HRV. It generates reports indicating probabilities of multiple mental disorders. The system aims to provide objective and comprehensive mental state classification via remote interaction.

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Improving the accuracy of estimating a person's psychological state by analyzing their mood over time. The method involves calculating probabilities of mood transitions and average persistence times based on historical mood data. These metrics are then learned by a neural network to estimate psychological states. This allows identifying which specific mood transitions and durations have the strongest influence on psychological well-being, beyond just overall mood statistics.

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A gamified, AI-enabled mental health screening tool for children and adults that uses games, facial recognition, voice analysis, and machine learning to collect more comprehensive and accurate mental health data compared to traditional paper-pencil assessments. The tool engages users in age-appropriate gamified interviews that collect natural language responses, facial expressions, and voice shifts. AI analyzes the data to flag risk factors, generate personalized recommendations, and calculate risk-protective scores. The tool aims to improve early identification and treatment of mental health disorders by leveraging technology to overcome barriers like stigma, lack of training, and burden on providers.

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Using intracranial electrodes implanted during brain surgery to develop AI models that can accurately identify, classify, and predict psychological brain states based on the high-quality, continuous intracranial EEG data. The implanted electrodes capture high signal-to-noise EEG signals that are not feasible from scalp EEG. These long-duration, labeled iEEG datasets are used to train AI models like deep neural networks to recognize patterns associated with psychological states. The trained models can then be deployed to analyze real-time iEEG or scalp EEG for decision-making in applications like mental health monitoring.

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Automated system to provide personalized stepped mental health care without requiring frequent clinician consultations. The system continuously monitors physiological parameters using wearables and smartphones. It extracts features from the data, detects changes in mental state, and adapts the content presented to the user based on the detected changes. This allows tailored mental health resources to be delivered to individuals as their needs evolve. The system integrates physiological and conscious mental state data to improve accuracy.

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A method for building a health predictive model that uses purchase data and symptom data to predict health risks and provide personalized health advice. The model correlates merchandise categories from purchase history with symptom severity to predict health risks for individuals. It can predict conditions like menopause symptoms, sleep disorders, immunocompromised states, and nutritional deficiencies. The model can also predict health risks related to factors like body water balance, exercise habits, oral health, skin health, intestinal health, mental health, exercise efficacy, body temperature regulation, and eye health. By linking purchase data with symptom data, the model can identify merchandise categories that indicate higher risk of specific health conditions. This allows targeted health interventions based on the predicted risks.

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Estimating a user's psychological state from their behavior data using a machine learning model that considers past reference points. The method involves extracting features from the behavior data for each time point, calculating the difference between the current feature and features from past times, and training a model using the features and their differences. This allows the model to estimate the user's psychological state based on how it compares to past states.

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Using facial expressions captured over time to predict mental indices like dementia or depression in individuals. The method involves analyzing emotion tags from images of the subject's facial expressions, calculating integrated emotion tags over time periods, extracting preferred features highly correlated with predicted results, and training a mental index prediction model based on those features. This allows predicting the subject's mental index by analyzing their long-term emotional expression patterns.

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Using psychiatric markers to refine nourishment programs for preventing, treating, and reversing psychiatric disorders. The method involves retrieving a user's psychiatric marker, identifying a nutrient variation based on the marker, establishing nourishment possibilities, and generating a customized nourishment program using machine learning. The program considers the psychiatric marker, nutrient variation, and trained model to optimize diet for the user's specific needs related to their psychiatric condition.

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