7 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.

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 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 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.