322 results about "Mental dysfunction" patented technology

The invention discloses a multi-modal large model identification and intelligent intervention method and system for mental disorder, and belongs to the technical field of intelligent identification and intervention of mental disorder, and the method comprises a multi-modal data acquisition stage, a data preprocessing stage, a feature extraction stage, a cross-modal feature fusion stage, a mental disorder identification stage, and a personalized non-drug intervention stage. In the multi-modal data acquisition stage, skin electricity, electrocardio, electroencephalogram and facial expression multi-modal data are acquired in real time, and a dialogue-feedback-adjustment closed-loop mechanism is constructed in combination with a cognitive behavior therapy theory. According to the multi-modal large model recognition and intelligent intervention method and system for mental disorder, accurate recognition and personalized intervention of mental disorder are achieved by integrating electroencephalogram, electrocardio, skin electricity, expression analysis and text data multi-modal physiological and behavior data and combining a large language model and a cross-modal attention mechanism.

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

Disclosed herein is a system and method for implementing a personalized ultrasound neuromodulation system and methods with neural sensing of personalized electrophysiological data, guiding ultrasound targeting and dosage using such personalized data. Specific ultrasound transducer solutions are provided for various neural targets that enable the low-intensity focused ultrasound to modulate the central nervous system and peripheral nervous systems to treat a variety of neurological and mental disorders. A portable ultrasound console device, a wearable guiding apparatus and a compact solution for effectively and safely enabling point-of-care neuromodulation, e.g., home healthcare, are provided. Further methods for ultrasound waveform customization are also described to improve effectiveness of ultrasound neuromodulation.

Pharmaceutically active benzofuran compositions for the treatment of mental disorders or for mental enhancement, including for entactogenic therapy. The present invention also includes benzofuran compounds, compositions, and methods for generally modulating central nervous system activity and treating central nervous system disorders.

The invention discloses a multi-dimensional personality evaluation method for dynamic emotion changes, and relates to the technical field of personality evaluation, the system is composed of a plurality of functional modules, and the method comprises the following steps: step 1, recording physiological data in real time through a wearable device, capturing micro-reactions by using a computer vision technology, and determining whether the micro-reactions exist or not; the micro-reactions comprise facial micro-expressions, limb actions and voice and intonation changes; based on a preset dynamic simulation social scene, observing an emotion regulation strategy of the subject in pressure; step 2, using an LSTM model to carry out time sequence modeling on the multi-modal acquisition data, capturing dynamic evolution of emotions, and combining an attention mechanism to dynamically adjust the weight of each modal; based on six basic emotions and nine mental disorder tendencies, establishing a classification model, and capturing association between emotions and personalities; reinforcement learning is introduced, and model parameters are dynamically optimized according to feedback of a subject; and predicting the emotion trend of the subject in a period of time in the future through an autoregressive integral moving average model.

The invention discloses a semi-structured mental disorder interview method based on large model enhancement, and relates to the cross field of clinical psychology and artificial intelligence, and the method comprises the steps: extracting key information according to a medical text to construct an interview question set, generating a standardized question through a large model, and obtaining a standardized question set; the expression style is adjusted, and the problem quality is optimized in combination with a self-evaluation feedback mechanism; identifying similar points and identification points between symptoms by using a BERT classifier, and generating a decision tree structure in combination with an ICL technology and an XOT framework; a multi-Agent collaborative dialogue framework fused with an RAG mechanism is adopted, a large model and a simulated patient are used for multiple rounds of dialogues, context memory, fine-grained questioning and decision rule retrieval are processed respectively, and the inquiry process is guided; a mental disorder interview evaluation system is constructed, expert knowledge and large model ability are fused, interview content is subjected to multi-dimensional evaluation, and a diversified data set is generated through interview simulation. The method can effectively improve the efficiency and accuracy of mental disorder auxiliary diagnosis.

The invention discloses a structured information extraction system for a mental disorder electronic medical record based on a knowledge graph and a natural language, and belongs to the technical field of medical information processing. According to the system, firstly, core concepts such as diseases, symptoms and drugs are extracted from an authoritative guide to construct a mental disorder knowledge graph, and a standardized clinical knowledge base is established; then pre-training and fine-tuning the deep learning model on a large number of biomedical texts and desensitized medical records to enable the deep learning model to have a medical language understanding ability; performing preprocessing, named entity recognition and entity linking on the electronic medical record text, and mapping spoken expressions to standard medical terms; inference is carried out by utilizing a relation extraction model and combining with a knowledge graph to complement implicit clinical information; and finally, structured data output meeting the standards of FHIR and the like is generated. According to the method, through deep fusion of knowledge driving and data driving, the problems of insufficient semantic understanding and weak generalization ability of a traditional method are effectively solved, and the accuracy and clinical value of electronic medical record structured information extraction are remarkably improved.

A system for and a method of diagnosing a mental illness in a patient are disclosed. The method measures signals, such as EEG signals, on a patient and applies a trained machine learning model on these signals. The model classifies the patient as having a mental illness or of being a normal control. The results are communicated to a user. In addition, a sub-type of a mental disorder may be identified by using other machine learning techniques on the features of the signals, such as a neural network, clustering, dimension reduction, and visualization algorithms. One such technique is t-distributed stochastic neighbor embedding (t-SNE).

The present invention discloses compounds, compositions, methods for modulating central nervous system activity, methods for treating inflammatory or metabolic disorders, and methods for treating central nervous system disorders, such as depression, anxiety, and trauma- and stressor-related disorders, comprising the indolizines having the structures disclosed herein.

The present disclosure provides compounds, pharmaceutical compositions, and methods for treating a mental disorder in a subject, which may include administering to the subject a pharmaceutical composition including a therapeutically effective amount of a non-hallucinogenic 5-HT2A receptor agonist or a pharmaceutically acceptable salt thereof, thereby treating the mental health disorder in the subject.

The invention discloses a mental disorder intelligent discrimination system based on a brain structure image similarity map, and belongs to the technical field of artificial intelligence medical image analysis. The system firstly collects brain structure magnetic resonance image data of a multi-center mental disorder patient and a healthy control and carries out standardization preprocessing; then constructing a brain structure standardized distribution model of the cross-age gender, and extracting individualized deviation degree features; establishing a typical brain structure characteristic spectrum database of various mental disorders through a neural network; after to-be-diagnosed individual features are vectorized, multi-dimensional parallel comparison calculation is carried out on the to-be-diagnosed individual features and the atlas database by using a special similarity quantization algorithm; and the system outputs a quantitative similarity score vector containing the matching degree with various mental disorders and health modes, and generates a structured differential diagnosis report. According to the invention, diagnosis normal form transformation from absolute classification to flexible matching is realized, the problem of identification of mental disorder heterogeneity and common diseases is effectively solved, and the interpretability and clinical credibility of an intelligent diagnosis system are remarkably improved.

The disclosure provides a compound of Formula Ib or Formula IIb, or a hydrate thereof, a pharmaceutical composition such as a spray-dried pharmaceutical composition comprising the compound of Formula Ib or Formula IIb, or a hydrate thereof, as well as methods of preparation thereof, and use thereof in the treatment and / or prevention of one or more of the following dysfunctions or disorders: a sexual dysfunction, erectile dysfunction, ejaculatory dysfunction, hypoactive sexual desire disorder, psychiatric disorder, neurological disorder.

The invention relates to the technical field of knowledge graph construction, in particular to a method for constructing a knowledge graph of autism multi-source heterogeneous big data. Original data is obtained from an electronic medical record system, a gene database, a research literature library and a social media multi-source heterogeneous data source in a real-time or timing mode; 2, cleaning, denoising, missing value completion and de-identification are carried out on the original data, and standardized mapping is carried out on terms based on a unified medical language system, the fifth edition of 'Mental Diagnostic and Statistical Handbook' and a human phenotype ontology medical ontology; 3, on the basis of the medical ontology, autism related concept subsets are expanded, version control is carried out, and a special autism ontology is formed. According to the method, high-precision fusion of multi-source entities is achieved, and the technical bottlenecks that standardization mapping is difficult, extraction precision is low, fusion alignment is difficult and the like are fundamentally solved.

The disclosure provides methods for the treatment of psychiatric disorders caused by viral, bacterial, or autoimmune encephalitis, and for treatment of psychiatric symptoms of viral, bacterial, and autoimmune encephalitis, and for protecting or reinforcing the blood-brain barrier, comprising administering to a patient in need thereof, a therapeutically effective amount of a 5-HT2A or 5-HT2A / D2 receptor ligand.

This invention pertains to a cognitive-coping therapy (CCT) method, system, and application that is a computer-implemented method for implementing AI-based CCT for a patient using natural language processing (NLP) and / or human-machine dialogue with / without an avatar. Based on the cognitive psychology and stress-coping theory, the CCT alleviates the excessive fear of negative event(s) and can be used to address mental disorders / conditions like obsessive-compulsive disorder (OCD), anxiety disorders (e.g., panic disorder, social phobia), and more. The AI system uses the NLP to comprehend the patient's symptoms and provide relevant interventions. Additionally, the system can monitor the patient's progress over time and adapt the interventions accordingly.

The present application relates to heterobicyclic compounds having glucagon-like peptide receptor (GLP-1R) agonist activity, processes for their preparation, pharmaceutical compositions comprising them and their use as GLP-1R agonists or for the treatment or prophylaxis of GLP-1R associated diseases or disorders. In particular, the compounds of the present application are useful for body weight management, for lowering blood sugar and / or for the treatment or prevention of diabetes, diabetic complications, obesity, overweight, dyslipidemia, fatty liver diseases (e.g., non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH)), metabolic diseases, cardiovascular diseases, nervous system disorders, mental disorders, kidney diseases, etc. , gastrointestinal diseases, autoimmune diseases, inflammatory diseases, lung diseases, hypothalamic-pituitary-gonad axis related diseases or disorders, cancers and the like.

The present disclosure relates to compositions comprising racemic ketamine, or a pharmaceutically acceptable salt thereof, for use in treating psychiatric disorders such as suicidality, suicidal ideation, major depressive disorder, treatment-resistant depression, and post-traumatic stress disorder.

The invention discloses a mental disease classification method fusing prior knowledge and a multi-mode self-attention mechanism, and belongs to the technical field of medical image processing. In order to solve the problems that in the current mental disease classification process, biomarker information is dispersed, and reproducibility is insufficient, multi-mode nerve image data, field priori knowledge and the intelligent analysis ability of a large language model are fully fused, and existing mental disease biomarkers are integrated, optimized and utilized. And related medical knowledge is efficiently extracted and integrated by using a large language model, so that efficient and accurate classification of mental diseases is realized. The method provides an innovative technical path and theoretical support for precise diagnosis and mechanism research of mental disorders.

To provide an AI support system that allows people to consult without feeding hurdles to prevent mental problems.SOLUTION: An AI support system for preventing mental problems is provided, the system comprising means of accumulating past consultation cases in a database, means of providing appropriate support based on consultation details of a user, and means of protecting privacy of the user.SELECTED DRAWING: Figure 1

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

To provide an expression construct for a protein for enhancing spines that can be introduced into neurons to increase a spine volume.SOLUTION: Provided are: [1] an expression construct for a protein for enhancing spines including a promoter for expression that functions in nerve cells, a domain of a Kalirin-7 protein required for at least spine enhancement, operably linked to the promoter, and a nucleotide sequence encoding a protein J1 that forms a heterodimer in the presence of a dimer-inducing compound; and [2] an expression construct for a protein for enhancing spines including a promoter for expression that functions in nerve cells, a domain of a PSD-95 protein required for the localization to at least a postsynaptic membrane, operably linked to the promoter, and a nucleotide sequence encoding a protein J2 that forms a heterodimer in the presence of a dimer-inducing compound.SELECTED DRAWING: None

The invention discloses a mental disease prediction method based on a reciprocal multi-network noise label learning model, and belongs to the technical field of medical image processing. In order to solve the problems that inherent subjectivity exists in the mental disease classification process at present, and a wrong diagnosis label is generated due to the fact that pathology and neural mechanisms behind mental disorders generally do not completely meet clinical diagnosis standards, the method comprises the steps that neuroimaging data are utilized; and the diagnosis accuracy is improved under the condition that potential clinical diagnosis deviation or error exists. By maximizing the consistency of deep neural networks in identifying and utilizing subjects with accurate diagnostic tags and suspected diagnostic tags, the present method effectively utilizes multiple deep neural networks to learn from data of suspected diagnostic tags. According to the method, on the basis of the original diagnostic tag, the re-determined biotype shows more obvious inter-group differences, and more significant functional connections exist.

The invention discloses a juvenile depressive disorder diagnosis marker, a depressive disorder diagnosis model and application thereof, and relates to the technical field of mental disorder diagnosis. Adiponectin is applied to auxiliary diagnosis or diagnosis of adolescent depressive disorder, the AUC value of a diagnosis model is larger than 0.7, and the adiponectin is high in diagnosis sensitivity and specificity and can serve as a diagnosis or auxiliary diagnosis marker of adolescent depressive disorder. Adiponectin and other indexes are combined for detection, the diagnosis performance of auxiliary diagnosis of juvenile depressive disorder can be improved, and the method has a good clinical application prospect.

The invention discloses a precise follow-up visit real-time evidence storage system and method based on robot process automation, and belongs to the technical field of information processing, and the method comprises the steps: carrying out the calculation based on first original data to obtain a scheduling score, sorting patients based on the scheduling score to obtain a priority sequence, and outputting a task structure based on the priority sequence; performing multi-modal data acquisition on the patient in the follow-up visit process, performing hash encryption on second original data to obtain a first encrypted fingerprint, verifying the second original data, and constructing a semantic consistency score and a behavior rationality score; reporting the priority based on the semantic consistency score and the behavior rationality score, and performing task simulation execution and data entry; performing behavior compliance verification and data consistency judgment, and if both the behavior compliance verification and the data consistency verification pass and are successfully returned, performing task closed loop; otherwise, re-checking is triggered. According to the scheme, the credibility and the execution degree of follow-up visit of mental disorders are improved through fusion of process chain optimization and data credibility modeling.

The present disclosure relates to methods of treating at least one symptom of a mental disorder or disease of the central nervous system in a subject by modulating the amount of GABA produced in the subject's gut. The present disclosure also relates to methods of culturing the bacterial strain new bacterial strains. Also disclosed are methods of identifying bacterial strains capable of producing GABA, and engineering strains to produce GABA.

The present invention relates to novel compounds of Formula (I), wherein P, Q, A, B, m, n, R1, Z, J and L are defined as in Formula (I); which are gamma-(y)-aminobutyric acid type B receptor ("GABAB-R") positive allosteric modulators which are useful for the treatment or prevention of neurological and psychiatric disorders associated with GABA dysfunction and diseases in which the GABAB receptor is involved. The invention is also directed to pharmaceutical compositions comprising such compounds, to processes of preparing such compounds and such compositions, and to the use of such compounds for the prevention or treatment of neurological and psychiatric disorders and diseases in which GABAB-R is involved.

The present disclosure provides compounds of Formula (I), (II), and (III). The provided compounds are able to bind protein kinases (e.g., SIK) and may be useful in modulating (e.g., inhibiting) the activity of a protein kinase (e.g., SIK, (e.g., SIK1, SIK2, or SIK3)) in a subject or cell. The provided compounds may be useful in treating or preventing a disease (e.g., proliferative disease, musculoskeletal disease, genetic disease, hematological disease, neurological disease, painful condition, psychiatric disorder, or metabolic disorder) in a subject in need thereof. Also provided are pharmaceutical compositions, kits, methods, and uses that include or involve a compound described herein.

The present disclosure provides pharmaceutical compositions comprising a cannabinoid, and dosage forms comprising same, for oral delivery, such as sublingual or buccal delivery. The compositions of the present disclosure are substantially homogeneous and remain stable and monophasic upon storage. Also provided are methods of using the compositions for pain management and to treat various diseases and conditions, including dementia, sleep disorders, movement disorders, mental disorders, and multiple sclerosis.