AI-Powered Mental Health Diagnostics

A system to dynamically determine the risk level of patients with mental disorders using sensors, mobile devices, and machine learning. The system collects physiological data, activity, voice, and sleep from patients using sensors and devices. It extracts features and patterns using signal processing and machine learning algorithms. A risk level judgment model is trained based on labeled data to predict patient risk. The system monitors patients in real-time, identifies risk levels, and triggers alarms and interventions.

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Diagnosing attention deficit hyperactivity disorder (ADHD) using machine learning and natural language processing techniques. The method involves preprocessing text data using NLTK, then applying machine learning or deep learning algorithms to the preprocessed data to create classified output. When a patient's input text is compared to the classified output, if it meets the criteria, an ADHD diagnosis is made.

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A depression diagnosis system that uses multi-paradigm electroencephalography (EEG) and machine learning to distinguish between depressed and normal states. The system measures multiple types of EEG signals from a subject, extracts and preprocesses the necessary signals to minimize noise, generates a diagnostic model using machine learning, and diagnoses depression using the model. The extracted signals include steady-state EEG, P3 waves induced by sound stimulation, and LDAEP waves. This multi-paradigm EEG approach aims to provide more comprehensive and accurate depression diagnosis compared to using a single EEG signal type.

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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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A multi-level feature fusion classification method for mental illness text that uses deep learning techniques to accurately diagnose mental illnesses based on doctor-patient dialogue texts. The method involves extracting keywords from the texts using algorithms like TFTDF and CHI, then fusing the keyword features with lower-level features extracted using convolutional and recurrent neural networks. This multi-level feature fusion provides a comprehensive representation of the text for more accurate mental illness classification.

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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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Diagnosing mental disorders using encephalography (EEG) signals and deep learning. The method involves feeding EEG signals from a patient into a trained neural network to classify them as having a specific mental disorder like depression, bipolar disorder, or schizophrenia. The network learns discriminative features from the EEG signals to accurately diagnose the disorders. It can also identify subtypes of disorders like treatable depression. This allows objective, neuroimaging-based diagnosis of mental disorders using EEG signals and deep learning models.

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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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Diagnosing mental disorders with improved reliability using fuzzy techniques. The method involves obtaining a diagnostic sheet result, deriving symptoms from it, treating them with an artificial neural network, diagnosing if the user has a disorder based on the symptoms and a first disease code, and re-diagnosing using the network again if initial diagnosis is positive. Learning is done using fuzzy hierarchical and decision tree algorithms. This iterative, multi-step diagnosis process improves reliability for mental disorders where initial and late symptoms differ or not all symptoms appear.

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