196 results about "Cognitive diseases" patented technology

The invention discloses a multi-modal interview automatic quality analysis and evaluation method and system based on a large model, and the method comprises the steps: collecting and storing multi-modal data, such as texts, audios, videos and behavior interaction, and carrying out the preprocessing of the multi-modal data to form a standardized data set; utilizing a preset interview structure and a large model to dynamically guide the process, adjusting the topic rhythm according to real-time feedback, and recording stage conversion information to form logic trajectory data for process coherence management; automatically coding text data through a large language model, extracting features such as keywords and performing topic clustering, performing cross validation and semantic fusion in combination with data analysis results of each modal, and generating deep analysis results such as psychological states; and generating a comprehensive assessment report containing qualitative description, quantitative score and psychological abnormality or cognitive disorder risk prompts based on a deep analysis result, thereby providing a basis for psychological health assessment and cognitive competence evaluation. According to the method, automatic analysis of multi-modal data is realized, and evaluation scientificity and efficiency are improved.

The invention relates to the technical field of cognitive disease risk assessment, and discloses a cognitive disease intelligent risk management method based on multi-modal voiceprint data analysis, the method comprises multi-modal feature fusion, dynamic risk assessment and real-time intervention linkage, by setting a multi-modal feature fusion module, when cognitive disease risk assessment is carried out, a multi-modal voiceprint data analysis module is set, the risk of the cognitive disease is evaluated, and the risk of the cognitive disease is evaluated. The recognition specificity of different cognitive impairment types is guaranteed by fusing acoustic characteristics, rhythm rules and multi-dimensional voiceprint characteristics of semantic logic, meanwhile, a comprehensive feature vector is constructed based on depth feature extraction, neural function degeneration signals implied in voiceprint data can be captured in real time, the misjudgment risk caused by traditional single-mode analysis is reduced, and the recognition accuracy of the cognitive impairment types is improved. According to the method, the reliability of early cognitive impairment screening is improved, a dynamic risk assessment mechanism is constructed, a cognitive state score and an adaptive threshold are compared in real time during individualized cognitive tracking, and abnormal fluctuation of a cognitive function is recognized in time.

The invention discloses a rehabilitation assisting method and device suitable for cognitive disorder treatment and a medium, and relates to medical rehabilitation. A tablet personal computer, a millimeter wave radar and the like are included, artificial intelligence technologies such as signal processing and deep learning are utilized, and rehabilitation training of patients with mild cognitive impairment caused by brain injury and cerebral apoplexy is assisted. Basic information, medical history and current cognitive conditions of the patient are recorded and collected through the tablet computer, and a rehabilitation file is generated; based on the cognitive disorder type and severity of the patient, a rehabilitation training plan is intelligently made; a patient performs rehabilitation training through a gamified training interface, and the physiological state in the training process is monitored in real time in a non-contact mode based on millimeter wave radar; a deep learning model is used for analyzing training data of the patient, and rehabilitation progress is evaluated; training and evaluation data are summarized, and a subsequent rehabilitation scheme is optimized. Full-process automatic management from patient information collection, training plan making, training implementation, rehabilitation evaluation to data analysis report generation can be rapidly and accurately achieved, and the cognitive rehabilitation efficiency and effect are improved.

The invention belongs to the technical field of cognitive disorder rehabilitation, and particularly relates to a dynamic self-adaptive middle-aged and elderly cognitive and mental health intervention system which comprises a VR interaction module, a physiological data acquisition and processing module, a dynamic self-adaptive module and a cloud management platform. Interaction scenes of the VR interaction module comprise an occupational scene and a nostalgic scene; the physiological data acquisition and processing module acquires and processes multi-modal physiological data of a user in real time; the dynamic self-adaptive module is used for calculating a cognitive load index and an emotion pressure index based on physiological data, dynamically adjusting occupational task complexity through a fuzzy logic controller, triggering restalgic scene switching, and embedding a mild cognitive task in a restalgic scene to maintain training continuity; and the cloud management platform stores user historical data, and optimizes a scene recommendation strategy through a machine learning model. A personalized intervention strategy can be customized according to the actual situation of the user, and the user experience is improved.

The invention discloses an early judgment system for cognitive impairment of Parkinson's disease based on transcranial magnetic-electroencephalogram multi-dimensional features. The early judgment system comprises a transcranial magnetic-electroencephalogram combined acquisition module, a synchronous signal preprocessing module, a multi-dimensional feature extraction module, a feature intelligent fusion module and a storage and display module, the transcranial magnetic-electroencephalogram combined acquisition module is used for disturbing the neural activity of a patient through monopulse transcranial magnetic stimulation coding and acquiring an evoked state electroencephalogram signal; the synchronizing signal preprocessing module is used for denoising and filtering the evoked state electroencephalogram signals; a multi-dimensional feature extraction module performs feature extraction on the processed evoked state electroencephalogram signals by utilizing multi-dimensional electroencephalogram feature analysis to obtain multi-parameter electroencephalogram neural activity features; the feature intelligent fusion module is used for carrying out weight distribution and feature fusion on the multi-parameter electroencephalogram neural activity features to obtain cognitive disorder multi-modal neural indexes; and the storage and display module is used for storing the cognitive disorder multi-modal nerve indexes and visualizing the cognitive disorder multi-modal nerve indexes.

Systems and methods for providing recommended doses and instructions for pharmaceutical treatments, and more particularly systems and methods for measuring physiological and / or electrophysiological signals from a subject and providing recommended doses and instructions of pharmaceuticals based at least in part on the measured signals are described. Further, the recommended doses and instructions are based at least in part on a subject's disease(s), disorder(s), or injuries and / or symptom(s) of such, and / or side effects of medications or treatments the subject is receiving. The recommended doses and instructions are personally tailored to the subject to precisely and personally address the subject's needs, and may be based in part on correlation with database(s) comprising historical data of the subject and / or other subjects to treat the subject's movement disorders, injuries to the body or brain, cognitive diseases and disorders, sleep diseases and disorders, chronic pain, or other conditions.

The invention discloses an auditory cognitive impairment evaluation and screening system, which belongs to the technical field of data analysis, and specifically comprises the following steps: setting basic test parameters and initial stimulation parameters, and synchronously acquiring voice, eye movement trajectory data and brain wave signals by combining space-time anchoring marks to form a multi-modal data set with a timestamp; a multi-stage cognitive test is carried out in an adaptive test engine, and stimulation parameters are dynamically adjusted by using a forgetting curve prediction algorithm according to real-time accuracy and response time; based on the adjusted stimulation parameters, performing time domain alignment on the multi-modal data set by adopting a dynamic time warping algorithm and taking a space-time anchoring mark as a benchmark, extracting features and combining the features into a cross-modal feature vector; and the cross-modal feature vectors are input into a pre-trained LSTM-decision tree fusion diagnosis model, and a quantitative evaluation report of obstacle type and degree grading is output, so that the accuracy of cognitive disorder diagnosis is improved.

Provided are compositions comprising compounds or precursors to compounds which may be used for a variety of therapeutic applications including, for example, treating and / or preventing a disease or disorder related to reduced or inadequate mitochondrial activity, including aging or stress, diabetes, obesity, and neurodegenerative diseases. The compounds relate generally to urolithins and precursors thereof, including but not limited to ellagitannins and urolithin A. In certain embodiments the compositions are presented in or as food products or nutritional supplements. These same compounds and compositions can also be used advantageously in generally healthy individuals to increase or maintain metabolic rate, decrease percent body fat, increase or maintain muscle mass, manage body weight, improve or maintain mental performance (including memory), improve or maintain muscle performance, improve or maintain mood, and manage stress.

The invention discloses a cognitive disorder early screening and diagnosis system based on multi-modal data, and the system comprises the following modules: (1) a multi-modal data collection module which is used for obtaining image data, gene data, behavior data and clinical narrative information; the clinical narrative information comprises at least two of a patient chief complaint text, an interference factor hooking option and a symptom time axis; the causal purification module is used for filtering pseudo-correlation among the multi-modal data through clinical narrative anchoring and reverse intervention deduction, and comprises a narrative anchoring unit which is used for identifying data conflicts through a structured clinical narrative field based on a preset causal conflict rule base, and triggering freezing or weight descending operation; the intervention deduction unit is used for generating a reverse intervention verification packet containing a low-cost intervention measure and a review plan, and updating a causal rule base based on review data; according to the method, the statistical association limitation of the traditional technology is broken through, and the diagnosis and treatment normal form upgrading from correlation to causality is realized.

The invention relates to 2 '-fucosyllactose, in particular to application of the 2'-fucosyllactose to preparation of immune balance drugs or food. The immune balance comprises regulation and control of the ratio of Th17 cells to Treg cells of Th17 / Treg immune imbalance offspring activated by maternal immune or induced by virus infection; according to the application disclosed by the invention, the fact that filial generations generated after the maternal body of a mammal generates immune reaction activation can regulate and control the proportion of Th17 and Treg cells through the influence of 2 '-fucosyllactose on intestinal flora is found, so that Th17 and Treg immune balance is realized; in addition, social and cognitive disorders and repetitive behaviors related to filial generation autism induced by Th17 / Treg immune imbalance can be improved.

The invention discloses a cognitive disorder nostalgic audio-video intervention therapy method, which comprises the following steps of: building a scene containing nostalgic music art, classical movies and retro original sound, playing matched audio and visual materials to a patient, and guiding the patient to perform structured intervention activities such as puzzle, theme recall or emotion sharing. Therefore, memory is stimulated, cognition is exercised, and emotion expression is promoted; according to the invention, by integrating three layouts of audios of nostalgic music art, classical movies and retro original sounds with matched visual elements, a multi-scene nostalgic sensory environment is constructed, and auditory and visual channels of a patient can be synchronously stimulated; compared with single auditory or visual stimulation, the compound sensory stimulation can more effectively activate a plurality of brain areas related to memory and emotion in the brain, so that long-term memory of deep sleep is more intensively aroused, and the strength and awakening efficiency of reminiscence stimulation are greatly improved.

The invention discloses portable intelligent assessment and rehabilitation training integrated equipment for cognitive disorder patients, and belongs to the technical field of cognitive disorder assessment. Comprising a device body composed of a base and a display screen, and the device body is electrically connected with an intelligent evaluation training system. The problem that an existing assessment method has limitation is solved, and the portability of existing medical equipment is improved by performing integrated design on the equipment in a notebook computer form; multi-dimensional evaluation data of the patient are collected through a MoCA Monte-Evaluation scale, scores and change trends of all cognitive functions are extracted by using a correlation analysis method, and the scores and change trends are predicted through a neural network model to obtain a prediction result of the patient; corresponding rehabilitation training suggestions are retrieved from the decision tree classification model, optimization is carried out in combination with actual conditions, and then rehabilitation training is carried out on the patient; and by introducing an intelligent technology, personalized design and comprehensive intervention, the evaluation and rehabilitation training effects are further improved.

The invention discloses a user analysis method, device and equipment based on a large model and a storage medium, and relates to the technical field of user analysis, and the method comprises the steps: collecting cross-modal interaction data to construct a representation data set, calculating a cognitive load, extracting an implicit aversion anchor point deviating from dominant feedback, optimizing the large model through incremental adversarial training to generate dynamic user representation, and analyzing the dynamic user representation. And formulating a compensation strategy based on a cognitive disorder theory. The device comprises a representation acquisition module, an anchor point extraction module, a model optimization module and a strategy making module. The device executes the steps of the program implementation method through the processor, and the storage medium stores the corresponding computer program. According to the method, through multi-modal data fusion and dynamic modeling, hidden aversion identification and active compensation are realized, a user experience blind area can be positioned, interface interaction can be optimized, recommendation accuracy can be improved, and the hidden aversion perception capability of a large model can be optimized.

The invention discloses a cognitive impairment screening method and system based on face analysis. The cognitive impairment screening method comprises the following steps: selecting a video clip with a preset duration for a user to watch, wherein the video clip comprises a plurality of smile points; setting a corresponding time window according to the appearing time of each laughing point; based on spontaneous facial expressions after the user watches the video, facial motion features of the user in each time window are collected, and the facial motion features comprise values of a plurality of facial motion units of the user; inputting the facial motion features of the user in each time window into a preset model to output a cognitive impairment screening result; wherein the preset model carries out model training based on historical big data, so as to form a mapping relation from the facial movement features to the cognitive impairment screening result. Therefore, by utilizing the method, the cognitive impairment can be screened in a diversified scene, the dependence on professionals is reduced, a user does not need to be wounded, and the method is particularly suitable for early screening of the cognitive impairment.

The invention discloses a hybrid federal learning modeling method and device for a neurocognitive impairment evolution trajectory, and the method comprises the steps that a child node receives an expert global model distributed by a central node, and the expert global model is determined by the central node from an expert global model group according to metadata pre-registered by the child node; taking the expert global model as an initial model, and performing localization training by using the local multi-modal non-invasive physiological signal data to obtain a trained local model; extracting global sharing parameters in the trained local model, and uploading the global sharing parameters to a central node; and for the corresponding expert global model, the central node aggregates global shared parameters uploaded by all the child nodes training the expert global model to obtain an updated corresponding expert global model, issues the updated expert global model to the child nodes, and circulates until a termination condition is met. According to the method, the heterogeneous data of the child nodes are adapted, privacy is protected, and the adaptability and accuracy of trajectory prediction are improved.

The invention discloses a cognitive disorder progress trend prediction method based on multi-modal data fusion, which comprises the following steps: firstly, acquiring MR I images, PET images, EEG and scale information which comprises clinical cognitive score data, patient age, intelligence quotient score, gender and education degree, and carrying out standardization processing; a feature extraction network composed of a generator and a discriminator is constructed, the MRI image serves as input, high-quality PET image features are generated through the generator, the generated PET image features and real PET image features are discriminated through the discriminator, and then the generator is optimized; constructing an enhanced multi-modal classification model, and performing deep fusion on the MR I image, the generated PET image features, the EEG signals and the clinical cognitive scores to obtain multi-modal features; and 4, inputting multi-modal features, carrying out time sequence modeling by adopting a Mamba model, and predicting the cognitive decline trend of the patient in a future time period.

A medical system useful in the determination of future disease progression in a subject. More specifically the present invention applies machine learning techniques to aid prediction of disease pathology and clinical outcomes in subjects presenting with symptoms of cognitive decline and to expedite clinical development of novel therapeutics.

The invention discloses a big language model credible reasoning system and method based on double-flow cognitive loss and solution synthesis, and aims to solve the problem of'compliance illusion 'caused by the fact that a big language model cannot process internal and external knowledge conflicts. The invention provides a double-flow cognitive engine, which is used for executing a'this-my 'flow of internal knowledge of a simulation model and a'super-my' flow strictly following an external document in parallel so as to generate two opposite cognitive state vectors. The system calculates a semantic conflict score between the two vectors in real time through a cognitive disorder detector. When the conflict is significant, the system activates and demodulates the synthesis module, and actively generates a text for solving the conflict; when there is no conflict, the output of the "super-my" stream is continued to ensure factuality. According to the method, cognitive conflicts are explicitly and actively solved, and a large language model is transformed into an inference engine with inherent critical thinking and high credibility.

The invention discloses an application method of a serum Raman spectrum as a biomarker in cognitive impairment diagnosis, a cognitive impairment diagnosis system is used for diagnosis, the cognitive impairment diagnosis system comprises a Raman spectrum detection and preprocessing module, a database module and a cognitive diagnosis module, and the application method comprises the following steps: step 1, collecting a sample; step 2, pre-treating a sample; 3, collecting and preprocessing a serum sample by using a Raman spectrum detection and preprocessing module; 4, performing model construction and optimization by utilizing a database module; step 5, performing efficiency evaluation; and 6, performing cognitive grading diagnosis on the new sample by using a cognitive diagnosis module. The method has remarkable technical progress, no cognitive impairment diagnosis blood test method suitable for the whole crowd exists clinically at present, cognitive impairment can be diagnosed through the Raman spectrum of serum, detailed cognitive function grading can be carried out, and the cost of a single sample is less than 100 yuan.

The invention discloses a risk assessment method for rheumatoid arthritis accompanied with cognitive impairment based on big data, and belongs to the field of data feature extraction, and the method specifically comprises the steps: carrying out the unified calling of collected clinical, laboratory and iconography features as an original feature set, and completing the preprocessing through multiple interpolation and Z-score standardization; inputting the original feature set into a self-attention network, and generating interaction feature representation for mining high-order interaction; low-dimensional potential feature vectors are compressed, and combined training is carried out through reconstruction and discriminant loss; inputting the potential feature vectors into an XGBoost model, and screening out a preliminary candidate feature subset; performing global and local importance evaluation on the candidate feature subsets by adopting SHAP, and iteratively rejecting features of which the contribution degrees are lower than a threshold value epsilon until the final feature subset is stable; and respectively inputting the final feature subsets into the simplified ResNet and LightGBM to obtain two groups of risk scores, and then generating a fusion risk score through weighted fusion.

Provided is a Bifidobacterium breve for improving an aging-related cognitive disorder and use thereof. The Bifidobacterium breve for improving the aging-related cognitive disorder is named Bifidobacterium breve BBr16 strain with a deposit number CGMCC No. 24471. The strain can significantly improve the aging-related cognitive disorder and can be used for preparing a product with an efficacy of preventing, improving or treating the aging-related cognitive disorder.

The invention belongs to the technical field of traditional Chinese medicines, and particularly relates to a traditional Chinese medicine composition for improving cognition, emotion, memory and depression states and a preparation method and application thereof. The traditional Chinese medicine composition is prepared from the following components in parts by mass: radix scutellariae, rhizoma coptidis, radix puerariae, rhizoma anemarrhenae, bitter gourd, rhizoma zingiberis, stiff silkworm, American ginseng and radix notoginseng. Wherein the radix scutellariae and the rhizoma coptidis are monarch drugs and mainly attack the core pathogenesis; the radix puerariae, the rhizoma anemarrhenae and the pseudo-ginseng serve as ministerial drugs, assist monarch drugs and treat secondary pathogenesis; bitter gourd, rhizoma zingiberis, stiff silkworm and American ginseng are used as adjuvant drugs to regulate drug properties and treat and treat both symptoms and root causes; and radix puerariae and pseudo-ginseng are used as conductant drugs for guiding channels or harmonizing the drugs. The traditional Chinese medicine composition disclosed by the invention can be used for preventing and treating cognitive disorder, mood disorder and depressive state caused by metabolic disorder. The traditional Chinese medicine composition is small in side effect, high in individuation and suitable for being taken for a long time, and has an obvious treatment effect.

The invention discloses a perioperative cognitive impairment predicting and monitoring device, relates to the technical field of medical instruments, and aims to solve the problem of low accuracy of perioperative cognitive impairment predicting and monitoring for children in the prior art. An electroencephalogram signal acquisition module, an eye movement tracking module, a behavior interaction module, a physiological parameter monitoring module and a data processing and analyzing module are integrated in the head-mounted VR glasses; the electroencephalogram signal acquisition module is used for acquiring an electroencephalogram signal of a child, processing the electroencephalogram signal and then transmitting the electroencephalogram signal to the data processing and analyzing module; the eye movement tracking module is used for capturing motion images of eyeballs of children, obtaining fixation points, eye jump frequencies and pupil diameter change parameters of the eyeballs and transmitting the parameters to the data processing and analyzing module. The method has the advantage that the accuracy of predicting and monitoring the cognitive impairment in the perioperative period of children is improved.

The invention discloses a cognitive disorder recognition system and method based on audio and video modal decoupling representation learning, and the system comprises an integrated processing and display terminal module which is used for operating data, presenting visual stimulation, controlling a collection process, and preliminarily storing data; the visual stimulation presentation and task instruction module is used for clearly and standardly displaying a preset cognitive evaluation task to the participant and informing the participant of specific operation needing to be completed; the audio and video synchronous acquisition module is used for capturing dynamic visual information when a participant executes a task and acoustic information when the participant executes the task; the cognitive state and auxiliary information input module is used for allowing an operator to input information; the control and management module is used for realizing standardized operation and ensuring consistency and high efficiency of data acquisition; and the cognitive impairment recognition module is used for performing multi-modal analysis processing on the data. The early cognitive impairment screening method can provide efficient, accurate and generalizable early cognitive impairment screening for the elderly groups in the community.

The Parkinson's cognitive disorder classification prediction model is constructed based on a neural network structure, the neural network structure comprises an input layer, a hidden layer and an output layer, the input layer is used for receiving feature data related to a Parkinson's patient, and the output layer is used for outputting a classification result of the Parkinson's cognitive disorder. The classification method based on the Parkinson cognitive disorder classification and prediction model comprises the following steps: acquiring clinical data of Parkinson and non-Parkinson patients; preprocessing the data; performing feature selection on the processed data, and screening out features related to cognitive impairment; training the data by adopting a neural network, and establishing a cognitive impairment classification prediction model; evaluating the model by using various indexes and optimizing the model; and performing cognitive disorder prediction on a patient to be predicted according to the trained model, and outputting a prediction result. The cognitive impairment of a new patient is effectively predicted, the decision-making process of a clinician is supported, and then the treatment effect and life quality of the patient are improved.

The invention discloses a brain small vascular disease cognitive disorder assessment method based on gait features and ensemble learning, and relates to the technical field of medical and industrial combination. According to the technical scheme, a wearable device is used for collecting gait data; the collected original data are preprocessed; the cognitive impairment prediction method comprises the following steps: predicting the cognitive degree of a CSVD patient by adopting a plurality of types of models, and establishing a cognitive impairment prediction model by using an integrated learning method based on a voting method and adopting a model selection method based on an elimination method; and evaluating the performance of the model by using the accuracy, the precision, the recall rate, the F1 score and the area under the curve as evaluation indexes. The method has the beneficial effects of accurate, objective and reliable evaluation, early prediction and intervention, high adaptability, simplicity and convenience in operation, capability of providing scientific basis and the like; the popularization and application of the method can provide new thoughts and tools for cognitive impairment evaluation of the CSVD patient, and the cognitive health and life quality of the patient can be improved.

The present invention relates to a solid nutraceutical composition for the prevention and treatment of cognitive disorders, consisting of an emulsion and a mixture of seed and oil, all of which are naturally derived. The emulsion contains plukenetia volubilis oil; an extract of plant origin, such as turmeric, pepper or cocoa; an emulsifier selected from lecithin, saponaria or maguey extract, or egg white; the sweetening agent is selected from fructo-oligosaccharide, stevia rebaudiana or maguey extract, Italian bee honey and cane sugar; and air is contained. Seeds and oil include pumpkin seeds and plukenetia volubilis oil. Other ingredients selected from cocoa powder, coconut oil, fruit or seed mass, natural juice and protein are also added. Thus, the nutraceutical comprises 3 to 5 sources of fatty acids, phytosterols and triacylglycerol, 2 to 8 sources of antioxidants, up to 3 sources of hypoglycemic agents, and up to 4 sources of anti-cholesterol and anti-inflammatory compounds. In addition, the nutritional health-care product also contains proteins, vitamins, macroelements, microelements and substances for promoting chewing and swallowing of subjects and patients suffering from cognitive impairment. The nutritional health-care product is suitable for preventing and treating Alzheimer's disease; mental, behavioral and developmental disorders, including depression; neurological, digestive, immune, endocrine and circulatory disorders; cardiovascular, cerebrovascular, infectious, parasitic, neoplastic and blood diseases; fatigue, fatigue and malnutrition.

The utility model discloses a cognitive disorder patient rehabilitation training system based on games. The system at least comprises sensor gloves, an electronic computer and VR glasses. The sensor glove comprises a curvature sensor, a WiFi chip and a power supply battery; the electronic computer at least comprises a display, a central processing unit, a graphic adapter, a memory, an input / output interface, a power adapter and a rehabilitation training game module; the VR glasses comprise a head-mounted skeleton and two lenses. The rehabilitation training device is simple and easy to operate, can improve the body function and reaction speed of a patient, promote nerve recovery, improve the BDNF protein level of the patient, repair neurons and improve the cognitive function through cooperative training of the sensor glove and the VR glasses, solves the problem that traditional rehabilitation training wastes time and labor, and improves the rehabilitation training efficiency. The enthusiasm of the patient participating in rehabilitation training is greatly improved, and the physical and psychological conditions of the cognitive disorder patient are effectively improved.