27 results about "Treatment characteristics" patented technology

The present invention is a personal care device comprising an elongated handle for manually moving the personal care device along a body surface, a working head attached to the handle for effecting a personal care treatment to the body surface, wherein an adjustment device including at least one adjustment actuator is provided for adjusting at least one treatment characteristic, wherein the adjustment actuator is configured and controlled to adjust the at least one treatment characteristic from a first setting to a second setting within a transition time period of not less than 0.1 seconds and not more than 1.5 seconds.

The invention relates to the technical field of data synchronization, in particular to a digestive system department clinical information acquisition and synchronization system, which comprises a multi-source heterogeneous acquisition module for generating a standardized diagnosis and treatment sequence, a characteristic spectrum construction module for mapping a text and an image into vector nodes and constructing a dynamic diagnosis and treatment characteristic spectrum, the state fingerprint verification module calculates a map fingerprint Hamming distance to position a difference feature node, and the incremental collaborative synchronization module constructs an incremental data packet and sends the incremental data packet to the central server. According to the method, the multi-dimensional characteristic spectrum based on the diagnosis and treatment time sequence is constructed, discrete images and texts are converted into topological association units, heterogeneous data semantic level alignment and integrity verification are achieved, version conflicts and information faults are eliminated, meanwhile, a dynamic hash fingerprint difference comparison strategy is adopted, accurate recognition is achieved, and only substantial change nodes are transmitted; and the network load is reduced, and high real-time consistency and zero-loss circulation of whole-flow information are ensured.

The invention provides a large-model dynamic diagnosis and treatment system based on cross-modal data mining and medical experience, which mainly comprises a multi-modal data input module, a first inquiry prompt model, a second inquiry prompt model and a third iteration inquiry module, and a third iteration inquiry module at least utilizes an emotion pacifying information driving factor and a medical diagnosis information driving factor generated according to the first inquiry indication information and the second inquiry indication information to guide a third large language model so as to enable the third large language model to output inquiry feedback and treatment feedback, and the third iterative inquiry module further influences the multi-modal data input module in an iterative manner based on the output of the third large language model, so that the system finally outputs a treatment scheme according with the expectation through the third large language model. The invention embodies a dynamic diagnosis and treatment large model based on cross-modal medical data mining and medical experience guidance, and the dynamic diagnosis and treatment large model has an iteration characteristic and a dynamic diagnosis and treatment characteristic.

The invention provides a clinical data management system, a clinical data privacy protection method, equipment and a medium. Obtaining an identity identifier corresponding to the medical institution identity information of the requester and a patient anonymous identifier associated with the medical record text data; performing medical semantic analysis on clinical contents in the medical record text data, identifying a plurality of sensitive entities containing diagnosis and treatment features, physiological indexes and personal identifiers of patients, associating medical contexts of the sensitive entities, and generating a specified replacement seed value for each sensitive entity based on anonymous identifiers of the patients; and for each sensitive entity, determining corresponding replacement content from a preset desensitization rule base according to the medical context of the sensitive entity and the replacement seed value. And finally, replacing each sensitive entity with the corresponding replacement content, and generating a desensitized clinical text associated with the anonymous identifier of the patient. According to the scheme, context-aware privacy protection can be realized in a clinical data sharing environment.

The invention belongs to the technical field of biomedical engineering, and discloses a multi-frequency intelligent bioelectric current cooperative modulation method. The method comprises the following steps: collecting human physiological state data of each sub-treatment area in real time, performing bioelectrical impedance analysis and connectivity analysis respectively, and constructing a differentiated bioelectrical impedance area; comprehensively evaluating the tissue penetration degree of each bioelectrical impedance region; collecting treatment characteristic data, combining the treatment characteristic data with the tissue penetration degree, extracting the energy conduction efficiency of each bioelectrical impedance region, and generating an energy conduction efficiency curve; performing quantitative evaluation on the tissue response factors of each bioelectrical impedance region, and generating a response type value matrix; integrating the energy conduction efficiency curve, the response type value matrix and the treatment characteristic data, and dynamically formulating a physiotherapy parameter optimal configuration scheme; the adaptive capacity and the application effect of the bioelectric current in refined regulation and personalized treatment can be improved, so that the treatment effect and the comfort are effectively improved.

Methods for treating ocular diseases such as dry age-related macular degeneration include administering non-invasive photobiomodulation light therapy in the context of a prior PBM treatment, on the basis of a subject response to prior PBM treatment, on the basis of a patient pre-treatment characteristic, and combinations thereof. In some embodiments, methods comprise administering PBM therapy to subjects who have received PBM about four to six months prior to the present administering. In some embodiments, methods comprise administering PBM therapy to subjects in early stages of dry age-related macular degeneration.

The invention provides a DRGs payment-oriented diagnosis basis retrieval method and system, and relates to the technical field of DRGs payment, and the method comprises the steps: obtaining the identity information, employment information and historical diagnosis information of diagnosis doctors, carrying out the calculation of a comprehensive evaluation value for each doctor, carrying out the evaluation of the physical change characteristics and mental health degree of a patient, and carrying out the calculation of the comprehensive evaluation value. Forming a diagnosis feature set with disease information of a patient, a preliminary diagnosis result, a treatment scheme and identity information of a doctor, judging a treatment resource use index according to actual treatment data, and forming a treatment feature data set with rehabilitation of a main diagnosis disease and existence of complications; an assessment feature set is formed according to a treatment feature data set, a diagnosis feature set and a comprehensive assessment value of a doctor, assessment values of main diagnosis and auxiliary diagnosis are analyzed by establishing a diagnosis assessment model, and a retrieval data set is formed according to adjusted diagnosis results, actual treatment data and complications which are converted into clinical codes. And performing grouping through the established DRGs grouping model.

The invention relates to a traditional Chinese medicine medical insurance reimbursement process management method and device, equipment and a medium. According to the method, traditional Chinese medicine diagnosis and treatment feature data (including prescription composition, dose use and diagnosis and treatment item data) of a patient is acquired, compatibility analysis is performed by using a traditional Chinese medicine knowledge graph, and a dose deviation degree and therapy suitability are evaluated in combination with a clinical pathway specification database. And generating a comprehensive risk scoring level through a dynamic risk assessment model trained based on historical data. And when the score reaches a corresponding preset threshold value, generating a reimbursement decision instruction containing the risk level to guide the reimbursement process. By adopting the method, the efficiency and accuracy of traditional Chinese medicine medical insurance reimbursement are improved, the risk monitoring capability is enhanced, and safe use of medical insurance funds is effectively guaranteed.

The present invention discloses a system for determining a venous thromboembolism prevention plan based on diagnosis and treatment information, and relates to the field of data processing technology. The system for determining a venous thromboembolism prevention plan includes: a feature matching module, which is used to establish a symptom matching model based on the symptom characteristics of venous thromboembolism, and collect the real-time diagnosis and treatment characteristics of the target object and input them into the symptom matching model for feature matching; a risk assessment and grading module, which is used to predict the risk probability of venous thromboembolism in the target object according to the feature matching results; and a personalized plan formulation module, which is used to formulate personalized venous thromboembolism prevention plans for target objects of different risk levels in combination with the preference characteristics of the target object. The present invention can accurately capture multiple factors such as the target object's treatment preference, drug selection preference, and risk tolerance preference through the preference analysis module, and then further optimize the prevention plan based on these personalized data.

Methods for treating ocular diseases such as dry age-related macular degeneration include administering non-invasive photobiomodulation light therapy in the context of a prior PBM treatment, on the basis of a subject response to prior PBM treatment, on the basis of a patient pre-treatment characteristic, and combinations thereof. In some embodiments, methods comprise administering PBM therapy to subjects who have received PBM about four to six months prior to the present administering. In some embodiments, methods comprise administering PBM therapy to subjects in early stages of dry age-related macular degeneration.

The application discloses a multi-source clinical data aided decision method and system for childhood leukemia, and belongs to the field of medical informatization, and comprises the following steps: acquiring a clinical data set for representing disease characteristics, treatment characteristics and physiological characteristics of a target object; performing similarity retrieval in a case database based on the clinical data set to obtain a comparable case set of the target object; and generating medication aided information for the target object based on the clinical data set and the comparable case set. The application realizes fine support for the medication aided decision process of childhood leukemia, improves the accuracy of comparable case screening, enhances the reliability of medication aided information, and reduces decision deviation caused by insufficient data utilization.

The present invention discloses a method and system for intelligently generating doctor portraits for whole course management, which relates to the technical field of medical management. The method obtains clinical data, academic achievement data and patient treatment records corresponding to a number of doctors and encapsulates and generates corresponding data sets respectively. The data sets corresponding to the several doctors are cleaned and integrated to generate corresponding normalized data sets. Feature analysis and extraction are performed on the normalized data sets to obtain personalized diagnosis and treatment feature information corresponding to each doctor. A corresponding doctor portrait model is constructed according to the personalized diagnosis and treatment feature information corresponding to the several doctors, and personalized course description information corresponding to the doctor portrait model is generated. The treatment course of the current patient is divided into a number of sub-process courses, and the patient treatment-related data of each sub-process course and the several personalized course description information are matched in sequence, so as to match the corresponding doctor for each sub-process course.

APPLICATOR WITH CAMERA AND SENSOR FOR SKIN HEALTH MAPPING A personal care system includes an applicator (100) including a body (105) and an applicator surface (110) configured to apply a formula to the skin, a position sensor module; a skin condition sensor module, and a processor (140).The personal care system is configured to obtain, via the position sensor module, position sensor data; calculate, based on the position sensor data, a position of the applicator on the skin; apply a treatment to the skin at the calculated position; determine treatment characteristics at the calculated position; obtain, via the skin condition sensor module, skin condition sensor data; determine, based on the skin condition sensor data, skin condition information at the calculated position; and store the calculated position, the treatment characteristics, and the skin condition information. The stored information may be used to generate a visualization of a change in skin condition over time, obtain a recommendation from a recommendation engine, or adjust one or more treatments. Figure for abstract: [Fig 1c].

The present application belongs to the tumor precision medicine and artificial intelligence medical technology field, and particularly relates to a non-small cell lung cancer neoadjuvant immunotherapy efficacy prediction method based on transfer learning. The method first pretreats the non-small cell lung cancer immunotherapy cohort and the neoadjuvant immunotherapy cohort, selects immunotherapy and neoadjuvant immunotherapy characteristic genes, and based thereon, carries out deep learning training on the immunotherapy cohort to obtain a basic model, finally, the basic model is fine-tuned to the neoadjuvant immunotherapy training data set through transfer learning, and finally used to the neoadjuvant immunotherapy cohort to predict the neoadjuvant immunotherapy effect. The present application improves the stability and generalization ability of the model in the small sample neoadjuvant treatment scene through gene selection and transfer fine-tuning, and can be used for clinical auxiliary decision and population stratification.

The application relates to the technical field of electric digital data processing, and discloses a big data fast indexing and querying system based on general surgery diagnosis and treatment characteristics, which comprises a data stream acquisition module, a topological sequence reorganization module, a physical space mapping module, an index key synthesis module and a preloading execution engine; the topological sequence reorganization module analyzes heterogeneous diagnosis and treatment data streams and reorganizes the data streams into logically continuous patient whole-course data aggregates; the physical space mapping module sequentially maps the aggregates to a continuous physical address space of a bottom-layer storage; the index key synthesis module generates composite index items according to physical address offsets among data blocks; and the preloading execution engine drives the bottom-layer storage to burst read, the application converts cross-modal diagnosis and treatment correlation queries into one-way sequential reading of the storage, reduces the track delay and access overhead in the searching process, and guarantees the searching response delay certainty under the condition that the data volume continuously grows.

The invention discloses a traditional Chinese medicine dynamic prescription generation method and device based on a graph attention network, and a medium. The method comprises the following steps: collecting and processing multi-source heterogeneous medical data; defining a node and edge relationship, constructing a heterogeneous medical diagram, and generating a diagram node embedding vector based on a Node2Vec algorithm; a graph attention network model is modeled, and training and optimization are carried out; and deploying the trained and optimized graph attention network model for predicting a recommended prescription. Through structural data acquisition and graph attention network fusion, the defects of a traditional model in data noise processing, complex relation modeling and dynamic adaptability are overcome, and collaborative optimization of prescription generation accuracy, interpretability and real-time performance is achieved while the traditional Chinese medicine syndrome differentiation and treatment characteristics are kept, so that the method is suitable for large-scale popularization and application. The accuracy and clinical applicability of prescription generation are improved, and an innovative technical path is provided for intelligent development of traditional Chinese medicine.

The invention provides an ICU-AW risk prediction method fusing early activity quantity quantitative evaluation, and through analysis, it is found that age, kidney replacement therapy and propofol usage amount are independent risk factors of ICU-AW after departure, and the total amount of early activity in the first week after departure is an independent protection factor of the ICU-AW after departure and has good prediction value for the ICU-AW. Therefore, the early activity amount is taken as a predictive factor to be incorporated into the ICU-AW prediction model, so that the prediction efficiency of the model is improved, and the prediction precision and clinical practical application significance of the ICU-AW risk are improved. According to the method, an ICU-AW risk prediction model is constructed based on the verified ICU-AW risk influence factors, the ICU-AW diagnosis and treatment characteristics of the ICU patient are identified, and the corresponding ICU-AW occurrence rate after departure is predicted, so that accurate ICU-AW risk prediction is realized, and a hospital can conveniently quantify and report intervention measures on the ICU-AW movement of the ICU patient.

This invention discloses an intelligent evaluation and optimization method and system for osteoarthritis treatment plans, relating to the field of medical technology. The intelligent evaluation and optimization method for osteoarthritis treatment plans includes the following steps: S1, collecting parameters and dividing them into time-series stages; S2, normalizing the data and constructing a task node index table and a resource dependency mapping table; S3, extracting treatment features and consumption features, and calculating weights to form a treatment efficacy feature parameter set; S4, associating the task node index table with the treatment efficacy feature parameter set to obtain the patient node treatment parameter set; S5, matching the patient parameter set with data of similar diseases based on a cosine similarity algorithm to evaluate treatment effectiveness and cost; S6, optimizing and adjusting the treatment parameters, verifying and dynamically updating medical resource consumption parameters. This invention achieves efficient monitoring and accurate evaluation by introducing a combination of time-series stage division and a task node index table.

The invention provides an asthma acute attack risk prediction method and system based on asthma characteristics, and is applied to the field of medical data processing. Therapeutic feature data are processed, an abnormal value is detected through a Kuk distance, a range standardization method is utilized to standardize a continuous variable to an interval of [0, 1], a preprocessed balanced data set is generated, feature selection and extraction are performed, and a long-short-term memory neural network is adopted to extract features from the multi-modal therapeutic feature data. Calculating a time step importance weight through an attention mechanism, and carrying out weighted fusion on the final hidden state of each modal to generate a target feature set; performing model training on the target feature set, constructing a prediction model based on the feature set and an input data set generated by the category label, and generating an initial prediction model; performing performance evaluation and parameter optimization on the initial prediction model to generate an acute asthma attack risk prediction model; and processing the target patient information to generate an acute asthma attack risk prediction result.

The present invention relates to the field of medical information technology, and in particular to a method and device for automatically generating clinical medical records based on a spatiotemporal sequence model, the method comprising: obtaining the diagnosis and treatment features in the diagnosis and treatment records of several diagnosis objects stored historically; constructing a corresponding standard spatiotemporal sequence model; determining the degree of matching between the single diagnosis and treatment features and the standard spatiotemporal sequence model; determining whether the individual spatiotemporal sequence model of the target object needs to be adjusted according to the matching degree; inputting the target time interval corresponding to the target time point into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment features, and generating the clinical medical record corresponding to the target object. The device comprises: a data acquisition module, a model construction module, a model matching module, a model adjustment module, and a record generation module. The present invention can automatically generate clinical diagnosis and treatment records when the target object has no historical diagnosis and treatment records, and can improve the accuracy of automatic generation of clinical medical records.

The application discloses a cancer patient psychological health evaluation and intervention method and system, and the method comprises the following steps: acquiring community interaction behavior data and clinical diagnosis and treatment characteristic data of a target user on a platform; performing natural language processing and voice emotion recognition on the community interaction behavior data, and extracting original emotion characteristics; generating a current physiological state baseline and a treatment side effect expected window of the target user based on the clinical diagnosis and treatment characteristic data; inputting the original emotion characteristics into a joint analysis model, weighting and correcting original emotion characteristic values by using the physiological state baseline and the treatment side effect expected window, and calculating a psychological health index; comparing the dynamic psychological health index with a preset risk threshold, determining a psychological health risk level of the user, and triggering a corresponding grading intervention mechanism. The application can accurately distinguish between physiological distress and pathological depression, and realizes dynamic monitoring and accurate management of the psychological state of a cancer patient in an off-hospital rehabilitation period.

The invention provides a prediction method, device, equipment, storage medium and product for adverse skin reaction in immunotherapy, and relates to the technical field of artificial intelligence, and the method comprises the steps: determining the baseline features of a patient based on the medical record data of the patient; the baseline features comprise at least one of a demographic feature, a clinical feature, a treatment feature and a biomarker feature; inputting the baseline features into a pre-trained first prediction model to obtain a skin adverse reaction occurrence probability output by the first prediction model; and inputting the baseline features into a pre-trained second prediction model to obtain a skin adverse reaction degree grade output by the second prediction model. By means of the mode, accurate prediction and evaluation of the occurrence probability and the severity of the untoward effects of the skin of the patient are achieved.

A system and method for using pre-treatment and intra-treatment serial imaging in at least one of predictive modeling or multi-modal predictive modeling of therapeutic agent response. The method includes obtaining pre-treatment characteristics of one or more target lesions associated with a pre-treatment scan of a target subject prior to treating the target subject according to a treatment plan. The method includes using the pre-treatment features to determine a set of features indicative of changes in the one or more target lesions. The method includes providing the feature set to one or more predictive models trained to predict therapeutic agent response based on the features of the target lesion. The method includes generating a predicted treatment response score for the treatment plan based on the feature set and one or more predictive models prior to treating the target subject according to the treatment plan.

The invention discloses a traditional Chinese medicine syndrome mode intelligent identification method based on big data analysis, and the method comprises the following steps: 1, collecting traditional Chinese medicine diagnosis and treatment data of a patient at a plurality of time points, and generating a diagnosis and treatment original data sequence; 2, constructing a diagnosis and treatment feature sequence; 3, constructing a traditional Chinese medicine knowledge graph, and converting the triple relationship into a regular path with time constraint; 4, performing rule learning and screening by adopting an improved AnyBURL algorithm, and outputting a rule path embedding sequence; 5, aligning the diagnosis and treatment feature sequence with the regular path embedding sequence according to time, inputting the sequence into a RetNet model, and generating a syndrome identification representation sequence by introducing a syndrome causal ripple mechanism; and 6, outputting a syndrome identification result and a syndrome trend prediction result at the current time point. According to the method, the improved AnyBURL algorithm and the causal ripple enhanced RetNet model are fused, and intelligent identification of the traditional Chinese medicine syndrome mode is realized.

The invention relates to the technical field of traditional Chinese medicine treatment, and discloses an intelligent mode classification system based on auxiliary traditional Chinese medicine treatment of Jiaji acupoint, which comprises a multi-dimensional data acquisition module, a diagnosis evaluation module and a classification matching module, and is characterized in that firstly, an intelligent sensor and medical imaging equipment are integrated to obtain a patient image feature YXtz and a patient pathological feature HBtz; the method comprises the following steps of: calculating a patient treatment index data set ZLZBz by combining a Xiaji acupoint treatment characteristic ZLtz in a medical information database, calculating a Xiaji acupoint treatment index ZLzs of the current patient according to the patient treatment index data set ZLZBz, and finally matching different treatment schemes according to the Xiaji acupoint treatment index ZLzs to comprehensively evaluate the overall condition and disease risk of the patient. The condition of purely relying on subjective judgment is reduced, the pertinence of treatment is improved, and the treatment effect is optimized.

This application is applicable to the field of tumor care technology, and particularly relates to an intelligent classification prediction method, system and device for tumor care. The method includes: obtaining the detection values of tumor-related molecular markers and clinical diagnosis and treatment characteristics of each target patient to comprehensively integrate the multi-dimensional medical information of the patients; constructing a scientifically quantified multi-dimensional prognosis stratification index set based on the correlation between molecular markers and clinical characteristics to form an accurate stratification evaluation framework; inputting the medical data into a pre-trained multi-layer neural network model, and generating a high-resolution tumor characterization map through cross-modal feature fusion technology; based on the dynamic matching of the map and the stratification index set, combining with a random forest model to calculate the stratification contribution degree parameter and the prognosis risk score, completing the accurate quantitative evaluation of the patient's condition, providing a highly reliable quantitative basis for clinical decision-making, effectively reducing the risk of over-treatment and improving the effect of patient prognosis management.

Provided is a system. The present invention relates to an applicator comprising an applicator having a body, one or more reservoirs within the body configured to contain a formulation, an applicator surface configured to apply the formulation, a sensor configured to detect a condition of the skin before the formulation is applied to the skin, a position sensor configured to map the skin as the applicator surface moves over the skin, and a base device including a processor configured to receive the status from the sensor and direct the applicator to apply a skin treatment based on the status. The system may be configured to determine a processing feature; obtaining skin state sensor data; determining skin state information at the calculated position; and generate a visual presentation of skin state changes, recommendations obtained from a recommendation engine, or conditioning processes.