206 results about "Mental dysfunction" patented technology

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.

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 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 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 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 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.

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.

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 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.

There is provided a computer implemented method of diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising: receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject, clustering the plurality of EEG datasets into a plurality of clusters, computing a p-adic representation of the plurality of clusters, extracting a quantum potential value from p-adic representation of the plurality of clusters, and diagnosing the medical state associated with the neuro-psychiatric disorder according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.

Disclosed are a wearable mental disorder automatic diagnosis system and a method based on contrastive learning. The system is realized by a wearable device and includes a data acquisition unit for obtaining multi-modal physiological data of a user; a user registration unit for executing the following steps under the condition that the user is determined to be a new user: fine-tuning a first feature encoder which is pre-trained offline by using the multi-mode physiological data in a self-supervised contrastive learning mode to obtain a second feature encoder; extracting data features from labeled multi-modal physiological data through a second feature encoder; training a personalized classifier using the data features as input to obtain a mental disorder recognition model; and a recognition unit for obtaining recognition results using the mental disorder recognition model when it is determined that the user is not a new user.

The invention discloses a task-driven fNIRS decoding neural feedback training method and a task-driven fNIRS decoding neural feedback training system, which are characterized in that an individualized decoder is constructed by utilizing task state fNIRS data in a pretest stage, and a preselected feature set with the strongest discriminating power is screened out, so that a stable brain activity mode highly related to a specific cognitive function is targeted to neural feedback training; therefore, a foundation is laid for realizing accurate nerve regulation and control; in a real-time training stage, the system continuously decodes the brain activity into a positive state instead of a negative state as a feedback index, so that a subject can induce a target neural mode through self-regulation without being exposed to external unfavorable stimulation completely, and the target neural mode can be used as a target neural mode. The training pertinence and nerve specificity of advanced cognitive functions such as interference control are enhanced, a closed-loop training mode based on data driving and an individualized model can be applied to treatment of psychological and mental disorders, and discomfort and risks possibly brought by a traditional exposure therapy are effectively avoided.

The invention discloses a dynamic trajectory construction and visual mapping method for association of mental disorder brain network damage and systemic system diseases, and belongs to the field of artificial intelligence medical application. According to the method, high-resolution MRI images, biomarkers and clinical information of major mental disorder patients are collected, and the influence of factors such as age, gender, medication and diagnosis on the braingut axis and the cardio-cerebral axis is evaluated through multi-modal data fusion. By constructing a disease dynamic trajectory model, brain structures and function change modes corresponding to different mental disorders are identified. Large-scale samples are analyzed through machine learning, potential risks and protection factors are extracted, and a visual tool is developed to visually display changes of the brain under different disease systems. A closed-loop feedback mechanism is established through follow-up visit, the disease progress and the intervention effect are dynamically tracked, and key evaluation indexes are identified. According to the invention, theoretical basis and practical guidance are provided for early screening, precise intervention and personalized treatment of mental disorders, and the diagnosis and treatment accuracy and efficiency are remarkably improved.

The present invention relates to the technical field of pharmaceutical synthesis and development of drugs, and to a process for preparing cyclic 2-amino-1-one derivatives and to the reaction products and intermediates obtainable by this process. The present invention further relates to pharmaceutical compositions, in particular drugs or medicaments, comprising the cyclic 2-amino-1-one derivatives and to their use as medicaments, in particular in the prophylactic or therapeutic treatment of diseases of the human or animal body, preferably of neurodegenerative diseases or psychiatric disorders.

This invention relates to the finding that testis development related protein (TDRP; also termed Immuno-moodulin or Imood herein) is a circulatory anxiogenic factor that modulates anxiety-like behaviour in mammalian models through the regulation of the immune system. Methods of treatment of mental disorders, such as anxiety, and TDRP antagonists for use in such methods are provided. Methods of diagnosing or monitoring a mental disorder in an individual by determining levels of TDRP in a sample and methods of screening for compounds that reduce levels of TDRP are provided.

To provide a self-care system for stress-related diseases or mental health issues caused by stress.SOLUTION: In a system for self-care of mental health, the system causes an information processing terminal included in the system to provide a user with information of a plurality of therapeutic interventions based on psychotherapy registered in the system on a screen of the information processing terminal in a selectable manner, either sequentially or at any time, continuously for a certain period of time, and further to provide the user with information of a plurality of therapeutic interventions based on the psychotherapy newly registered in the system, either sequentially or at any time, continuously for a certain period.SELECTED DRAWING: Figure 1

To provide modification of the gut microbiome to treat psychiatric disorders or diseases of the central nervous system.SOLUTION: The present disclosure relates to a method of treating at least one symptom of a psychiatric disorder or disease of the central nervous system in a subject by modulating the amount of GABA produced in the gut of the subject. The present disclosure also relates to a method of culturing novel bacterial strains. A method of identifying bacterial strains capable of producing GABA and engineering strains to produce GABA are also disclosed. The present disclosure also describes a method of identifying bacterial strains that can produce, for example, GABA under physiologically appropriate conditions, such as, physiologically appropriate pH.SELECTED DRAWING: None

5, 6, 7, 8-tetrahydro-1, 6-naphthyridines and derivatives thereof, which are substituted in the 6 position with 6, 7-dihydro-5H-pyrrolo [3, 4-b] pyridin-5-one, furo [3, 4-b] pyridin-5 (7H)-one, 6, 7-dihydro-5H-cyclopenta [b] pyridin-5-one, or [1, 2, 4] triazolo [4, 3-a] pyrimidine-3 (2H)-one, are positive allosteric modulators of muscarinic acetylcholine receptor M4 (mAChR M4), and can be used for treating neurological and psychiatric disorders associated with muscarinic acetylcholine receptor dysfunction.

The invention discloses a mental disease typing method and system integrating DNA methylation and neuroimaging, and the method is a neuroimaging biological annotation method integrating DNA methylation and brain connection group data, and comprises the steps: recognizing brain network features related to a specific molecular phenotype in a whole brain range through a machine learning model; and mechanism-sensitive layering of the mental disorder heterogeneity group is realized. The method does not need to depend on a prior classification or hypothesis mechanism, can be suitable for different types of mental disorder people, provides technical support for exploring potential biological mechanisms and identifying targeted therapy groups, and has high generalizability and clinical application prospects.

A brain information display device and a brain information display method are provided that can help a user identify a mental disorder by utilizing the association between a mental disorder and a brain region associated with the mental disorder. [Solution] The brain information display device 1 comprises a brain region display unit 22 that displays brain regions within the brain separately, a case information acquisition unit 23 that acquires case information on the brain region for the mental illness, an evaluation unit 24 that assigns an evaluation value to the brain region in the brain region display unit based on the case information acquired by the case information acquisition unit, and an association display unit 25 that displays the association between the mental illness and the brain region that produces symptoms associated with the mental illness based on the evaluation value assigned by the evaluation unit.

The present invention relates to novel compounds of Formula (I'), wherein P, Q, A, B, m, n, R1, R2, R9, R10, R11 and R12 are defined as in Formula (I'). These compounds may be useful as muscarinic M4 receptor subtype ("M4-mAChR") positive allosteric modulators which are useful for the treatment or prevention of neurological and psychiatric disorders associated with muscarinic M4 receptor dysfunction and diseases in which the M4 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 such as for example, cognitive decline, both positive and negative symptoms in schizophrenia as well as other disorders modulated by M4 muscarinic receptors.

The present disclosure includes azetidine tryptamines and methods of treating psychiatric disorders with such compounds. Also provided are pharmaceutical compositions comprising azetidine tryptamine.

Novel compositions of N-desmethyl ruboxistaurin L-lactate salt and L-lactate salt polymorphs. The use of compositions of N-desmethyl ruboxistaurin L-lactate salt and polymorphs to modulate GSK-3 signaling is disclosed, as is the use of compositions of N-desmethyl ruboxistaurin L-lactate salt and polymorphs to inhibit protein kinase C. Methods are also disclosed of using compositions of N-desmethyl ruboxistaurin L-lactate salt and polymorphs in the treatment of subjects having a neurological disease and / or psychiatric disorder, including Alzheimer's disease, bipolar disorder, depression, schizophrenia, Parkinson's disease, or neuroinflammation, as well as methods of using compositions of N-desmethyl ruboxistaurin L-lactate salt and polymorphs in treating conditions associated with diabetes mellitus or its complications, or ischemia, inflammation, pulmonary hypertension, congestive heart failure, cardiovascular disease, dermatological disease, or cancer. In addition, compositions of N-desmethyl ruboxistaurin L-lactate salt and polymorphs administered in combination with lithium or other treatments for bipolar disorder are also disclosed.

The present invention relates to a single cell cylindrical battery, such as a button cell or a button battery, that can be regarded to have the shape of a slice of a cylinder, and to a method preventing upper digestive and upper respiratory tract injury after accidental ingestion of the single cell cylindrical battery. It is noted that in some case ingestion has even led to the death in particular of children or small size adults, or people with a mental limitation, or people with a prior narrowing of structures in which the battery can be lodged. The structures in which the battery can be lodged are both the pharyngeal, upper digestive and upper respiratory tracts. As ingestion itself can not always be prevented, a relatively safe battery has been developed, which mitigates problems associated with ingestion.