169 results about "Medicine field" patented technology

Improved novel 3D imaging and reconstruction methods for quantitative and accurate 3D imaging used in multiple industries, segments, and applications within each. Improvement includes methods and hardware configured to improve performance and lower radiation and improve accessibility. Using simplified system matrix and datasets of little scatter interference in tomographic image acquisition and reconstruction, in some cases, using time continuity and spatial continuity to generate parameter data for substances within VOI for a number of key applications in medical and non-medical fields.

A case generation method, used for generating cases in professional fields such as the field of law, the field of medicine or the field of economy. In the method, on the basis of using existing cases as reference cases, similar new cases can be generated by means of using a large language model, and after the new cases are generated, the accuracy of the new cases can be evaluated from different dimensions by means of a large language model; and when the accuracy of the new cases satisfy requirements, the generated new cases are output. The present application uses knowledge repositories and semantic comprehension capabilities of large language models to instruct the large language models to generate new cases on the basis of existing cases, and further evaluate the generated new cases from different dimensions, thus solving the hallucination problem of large language models, and ensuring the accuracy of generated new cases. Moreover, the present solution uses large language models to automatically generate cases without requiring manual construction of cases by people, thereby effectively improving the case construction efficiency.

The invention relates to a professional knowledge graph construction method for the medical field, and the method comprises the following steps: obtaining a multi-modal medical document, carrying out the deep analysis through a Transform-based multi-modal model, and obtaining an analyzed multi-modal medical document; dynamically segmenting the analyzed multi-modal medical document by adopting a context-based adaptive text segmentation method to obtain independent text blocks; based on the independent text blocks, performing structured extraction, and performing verification by adopting a dynamic dual-verification mechanism to form knowledge sub-graphs; and based on the knowledge sub-graph, further constructing a standardized medical knowledge graph. Compared with the prior art, the method has the advantages of high precision, expandability and the like.

The invention discloses high-performance modified calcium carbonate and application thereof in a breathable film. The modified calcium carbonate is prepared by combining a three-element composite modification system composed of stearic acid, titanate and a silane coupling agent with a disc-nest mill three-stage gradient grinding process and antibacterial-hydrophobic dual-functionalization treatment of silver-loaded hydroxyapatite and a fluorinated modifier. The indexes of the prepared modified calcium carbonate are as follows: the median particle size (D50) is 1.8-2.2 mu m, the particle size (D97) of 97% particles is less than 6.5 mu m, the particles with the particle size of 1-5 mu m account for more than 60%, and the particles with the particle size of more than 10 mu m are less than 100 ppm; the content of calcium carbonate exceeds 97%, and the content of harmful impurities such as Fe is less than 200ppm; the whiteness is higher than 95; the oil absorption value is less than 12.5%, and the water content is less than 0.18%. When the composite material is applied to the breathable film in the medical field, the mechanical property, the antibacterial property and the hydrophobic property of the breathable film can be remarkably improved, the special requirements of the medical field on the breathable film are effectively met, and the composite material has good application prospects and industrial value.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

The embodiment of the invention relates to a method and a device for generating a medical report based on evidence-based medicine. The method comprises the following steps: acquiring patient information and an original clinical problem; inputting the patient information, the original clinical question and the first cue word into a target model to generate a structured clinical question; the target model is a medical field large language model; retrieving a medical literature library according to the clinical problem to obtain a plurality of medical literatures; inputting the target medical literature and the second cue word into a target model, so that the target model performs bias risk assessment on the target literature, and a structured initial assessment result of the target literature is generated; inputting the clinical problem, each medical literature, the initial evaluation result of each medical literature and the third cue word into a target model, and enabling the target model to carry out thinking chain gradual reasoning based on a GRADE evaluation process in the third cue word to generate a medical report; the medical report includes medical suggestions for clinical issues, and recommendation intensities and evidence quality levels of the medical suggestions.

The invention relates to a method for processing medical images of human tissue of an area of a patient's body and in particular of the breast by means of a medical imaging device (1) comprising a microwave probe array consisting of K>1 probes spaced apart from one another, the array comprising P>1 different configurations defining transmitting probes and receiving probes for one or more position(s) around the area, in which the transmitting probes are configured to transmit microwave signals so as to illuminate an area of the body and the receiving probes are configured to receive microwave signals after scattering and reflection in the area, the probes being capable, in a complementary manner, of being configured to transmit and receive simultaneously.

The invention discloses an obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

The invention discloses an intelligent pulmonary tuberculosis detection method and system based on images and clinical data, and belongs to the field of medicines.The method comprises the steps that S1, high-resolution chest X-ray images and clinical data such as serum C-reactive protein concentration, erythrocyte sedimentation value, erythrocyte distribution width and body temperature are collected; s2, performing non-linear contrast enhancement on the image, and extracting lung gate texture, lung field transparency and cavity edge features; s3, fusing the image and the clinical features through a graph attention mechanism, and generating a high-dimensional fusion vector; s4, risk grade discrimination is carried out through a multi-scale residual mapping network, and five-grade labels are output; and S5, optimizing the model by utilizing the pathology definite diagnosis sample in combination with the cross entropy and the center loss. The method has the beneficial effects that accurate grading evaluation of the pulmonary tuberculosis risk is realized, and the accuracy of early screening and the clinical interpretability of the model are improved.

The invention belongs to the field of computational medicine, particularly relates to a method and a system for constructing full-parameter digital twinning of a patient and performing treatment simulation, and aims to solve the problems that an existing digital twinning model cannot fuse multi-scale data, lacks dynamic optimization capability and is disjointed with a clinical decision process. The method comprises the following steps: acquiring and integrating multi-source heterogeneous data of a patient; performing cross-scale fusion modeling based on the data, and constructing a personalized digital twinborn initial parameter set containing genetic background correction; driving a multi-physics field coupling engine to simulate an intervention effect, constructing a reverse optimization problem taking real-time monitoring data as a dynamic constraint, and solving an optimal intervention scheme parameter; and carrying out verification simulation on the optimization scheme and generating a treatment report containing quantitative evaluation and risk early warning. According to the invention, by constructing a full-parameter and mechanism model and introducing a dynamic closed-loop optimization verification process, the personalized precision, safety and clinical decision support value of treatment scheme simulation are significantly improved.

The present application relates to the technical field of medical three-dimensional reconstruction, and particularly relates to an endoscopic-image-based three-dimensional reconstruction method and apparatus for a pediatric adenoid situation, which method and apparatus can solve, to a certain extent, the problems whereby existing three-dimensional reconstruction methods in the medical field still lack research on three-dimensional reconstruction of adenoid regions, and reconstruction results in existing technology have low completeness and cannot meet clinical requirements. The method comprises: by means of local plane parameter initialization, establishing relationships between corresponding points in different images; by means of local plane parameter optimization, accurately updating local plane parameters of each pixel point in a reference image; on the basis of a filtering strategy using relative depth difference, implementing depth filtering; by means of a spatial grid surface fitting method, restoring missing depth information of some regions in a depth map after the depth filtering, so as to densify a reconstruction result; and by taking into consideration two metrics, i.e., relative depth difference and reprojection error, converting into a point cloud the depth map which has been subjected to surface fitting, so as to reconstruct a three-dimensional model of an adenoid region.

A spectral sensing device and associated methods for enhanced diagnostic capabilities across medical fields are disclosed. The device incorporates multispectral, hyperspectral, and photoplethysmography technologies to analyze biological features including tongue, oral cavity, saliva, teeth, and other tissues. The system transforms any standard RGB camera into a multispectral imaging device by training machine learning models to associate RGB values with spectral values inside and outside the visible spectrum using measurements from a single pixel detector spectroradiometer. A multimodal neural network with separate processing branches analyzes spectral images, non-spectral images, and tabular data to predict microbiome composition. The system generates health risk assessments by comparing predicted microbial abundances to thresholds associated with various medical conditions. Near-infrared sensing enables blood flow monitoring, subcutaneous imaging, and assessment of physiological parameters. Convolutional neural networks enable spectral reconstruction without requiring the physical device, while supporting continuous monitoring in various form factors.

The invention belongs to the technical field of artificial intelligence and medical information processing, and particularly relates to a medical text big data mining system based on natural language processing, which comprises a medical field pre-training module, a text structured extraction engine module, a knowledge graph guide reasoning module, a clinical path optimization module and a medical question and answer and literature mining platform module, according to the system, a self-adaptive hierarchical attention mechanism is adopted, and medical texts of any length are processed through dynamic fusion of local attention and global attention; constructing an entity dependency relationship graph and coding semantic association between entities by adopting a graph neural network; introducing a medical ontology knowledge graph to carry out conditional random field joint reasoning, and correcting unreasonable annotations; the optimal diagnosis and treatment mode is mined and recognized through the frequent mode, the entity recognition F1 value reaches 96.2%, the long-distance relation recognition accuracy reaches 91%, the structured data accuracy reaches 93.7%, the data preparation time is shortened by 85%, and the document review efficiency is improved by 320%.

According to the PDF document structuring processing and conversion tool, contents (including texts, tables, images, structures and the like) in PDF documents are analyzed and converted into structured data (Markdown) capable of being used for downstream LLM tasks, and the PDF structuring processing and conversion tool is particularly suitable for PDFs of complex typesetting of academic literatures, reports and the like. Through a literature vectorization function, medical professional literatures can be precisely processed, core ideas, key data and logic relations in the literatures can be deeply understood, vectors capable of accurately reflecting the essence of the literatures can be generated, important information is prevented from being lost or misread, and information processing and application effects are improved. Meanwhile, the personalized Agent can perform adaptive optimization aiming at diversified tasks in the medical academic field, and can fit research directions, knowledge backgrounds and working habits of different users, for example, the personalized Agent provides support conforming to a rigorous logic structure and standard academic expression during academic paper writing; and when the academic slide is manufactured, concise and clear contents with highlighted key points can be presented, so that the working efficiency of medical workers and researchers is effectively improved, and the high-standard requirements of the medical field are met.

The invention discloses a multi-modal fusion fact verification method and system for reducing large model medical illusion. The method comprises the following steps: constructing a high-quality multi-modal medical data set; a fusion feature is obtained on the basis of a multi-mode Transform architecture; constructing a fact verification module, executing four verification steps, including semantic layer verification, logic layer verification, evidence layer verification and time sequence layer verification, on the basis of the fact verification module and the fusion features, endowing the preprocessed multi-modal medical data and each verification step with a dynamic confidence score, and determining whether the multi-modal medical data and the verification steps are matched or not on the basis of the confidence score and a preset threshold value. And medical illusion is corrected. According to the invention, cross validation and fact verification of multi-modal data are carried out, and a multi-level and all-around hallucination detection and correction system is constructed, so that the hallucination rate of a large model in the medical field is reduced fundamentally.

The invention discloses a short text matching method based on enhanced contrast learning and multi-task optimization, and belongs to the technical field of natural language processing. According to the method, a multi-source data set is constructed by fusing general field and medical field texts, a generative language model is adopted to generate a high-quality difficult case to form a training triple, and a character-level noise enhancement mechanism based on a medical similar character dictionary is introduced to simulate an OCR error; aLBERT is used as an encoder, supervised comparative learning and mask language modeling tasks are jointly optimized, multi-task cooperative training is achieved through dynamic gradient balance, and finally a short text matching model with high semantic discrimination ability and noise robustness is obtained. On a test set containing 50% OCR noise in the cardiovascular field, the accuracy rate of the method reaches 94.3% and is improved by 12.2% compared with that of a traditional BERT-base model.

The invention discloses a knowledge graph construction method and device in the medical field, a medium and a product, and relates to the technical field of wisdom medical treatment, and the method comprises the steps: obtaining a medical paper data set and open-source medical data; performing fine adjustment on a pre-trained large language model by adopting the open source medical data to obtain a large model in the medical field; adopting a GPT-NER method to perform named entity recognition on the medical paper data set based on the large model in the medical field to obtain a named entity set; performing entity relationship extraction on the medical paper data set based on the large model of the medical field and the named entity set by adopting a cue word project to obtain an entity relationship set; and carrying out alignment and duplicate removal on the basic concepts in the named entity set and the entity relationship set by adopting a clustering model to obtain a knowledge graph in the medical field. According to the method, manual intervention can be reduced, the working efficiency is improved, and the knowledge graph construction accuracy is improved.

The invention relates to the technical field of medicine safety monitoring, in particular to an individual case safety report automatic scoring system based on a large model. The system comprises a report receiving module, a deep semantic understanding module, a dynamic graph structure construction module, a graph neural network scoring module and an interpretability output module. Deep semantic analysis is carried out through a large language model pre-trained in the medical field, a multi-dimensional semantic graph with time sequence characteristics is constructed, a graph neural network based on an attention mechanism is utilized to carry out multi-round iterative reasoning to generate scores, and reasoning process interpretation based on a critical path is provided. According to the method, medical report semantics can be deeply understood, the dynamic relation between entities is accurately captured, the scoring accuracy and interpretability are improved, and reliable technical support is provided for drug safety monitoring.

The invention provides a medical aid decision information processing method and device and a storage medium, and relates to the technical field of computers. The method comprises the steps of generating a question statement for a target medical application scene according to content information of the target medical application scene in response to a calling operation of the target medical application scene for a clinical medical decision application; according to the question statement, performing matching by adopting a preset clinical medical knowledge base to obtain target knowledge content matched with the question statement; filling a cue word template of the target medical application scene with the target knowledge content and the question statement to generate a target cue word of the target medical application scene; according to the target prompt word, calling a preset large language model to obtain reply prompt content of the target medical application scene; and displaying the reply prompt content of the target medical application scene in a display interface of the clinical medical decision application. The reply prompt content is obtained according to the clinical medical knowledge base, and the answer accuracy of the large language model in the medical field is guaranteed.

The invention discloses a CRISPRa tool for activating a target gene and application of the CRISPRa tool. The invention provides a protein. The protein sequentially comprises the following elements from an N terminal to a C terminal: a dCas9 segment and an activation domain segment, n NHRs are arranged in the active domain section; n is a natural number; the NHR is an NHR activating element from a nematode. The NHR has a nonaamino acid trans-activation domain (9aa TAD), consists of only 16 amino acid residues, and shows high transcriptional activity. The invention provides a CRISPRa tool with stronger action effect and more concise action element, can be used for gene transcription regulation (particularly gene transcription activation), can be used in the scientific research field and the clinical medicine field, and has great value.

This invention provides an acoustically transparent skull based on the transfer matrix method. The skull includes a matching layer, a coupling layer, and a biomimetic skull. The biomimetic skull matrix has acoustic windows and a stepped groove design for easy and quick assembly, disassembly, and repositioning. The matching layer module is made of epoxy resin-nano-alumina composite material, and its thickness is designed according to a quarter wavelength of the target frequency band. This effectively optimizes acoustic impedance matching and reduces energy loss, becoming a key breakthrough in improving the transmission efficiency of transcranial ultrasound and expanding its application in non-invasive medical fields. This invention focuses on the field of biomechanics and medicine, addressing the problem of low transmission efficiency in transcranial ultrasound technology caused by differences in acoustic impedance of the skull. It can be used to compare the sound transmission effects of single or multiple matching layers at different frequencies. In the future, ultrasound can be more widely used for diagnosis, treatment, neuromodulation, and health / mental status monitoring of astronauts.

According to the cell membrane bionic conjugated electrolyte molecule taking the porphyrin unit as the core as well as the preparation method and the application of the cell membrane bionic conjugated electrolyte molecule taking the porphyrin unit as the core, the optical performance, the biocompatibility and the stability of a probe are remarkably optimized by introducing the porphyrin unit and metal coordination; and a more accurate and reliable tool is provided for cell membrane related research and disease diagnosis. The innovative design not only improves the spectral characteristics and the imaging effect, but also promotes the application of the fluorescence imaging technology in the medical field, and has wide prospects.

The invention relates to the field of image processing, and provides a medical ultrasonic image segmentation method based on geometric guidance and concept perception fusion, which is characterized in that a geometric guidance text generation module is designed, and high-quality medical text description is automatically generated according to connected component analysis and multi-dimensional geometric analysis; according to the method, text-free segmentation is converted into text-guided segmentation, the problems that high-quality text description is limited in number, difficult to manufacture and high in cost are solved, a concept-aware cross-modal fusion module is designed, multi-scale space alignment and medical knowledge graph modeling are utilized, the problem that cross-modal fusion is insufficient is solved, and the cross-modal fusion efficiency is improved. According to explicit modeling of medical priori knowledge, professional knowledge in the medical field is fully utilized to guide a fusion process and a segmentation decision, a self-adaptive segmentation decoding module is further provided, an optimal strategy is dynamically selected according to feature complexity, the efficiency is improved while the precision is ensured, and the method is suitable for large-scale popularization and application. According to the invention, the precision and efficiency of medical ultrasonic image segmentation are improved.

The invention relates to a medical information extraction method and device, electronic equipment and a storage medium, and can obtain medical information valuable for constructing a medical database, so that the data quality of the medical database can be improved, and the medical information retrieval efficiency of a user can be improved. The method comprises the following steps: acquiring medical literatures; segmenting the medical literature to obtain segmented literature statements; based on knowledge rules in the medical field, the segmented literature statements are classified to obtain classification tags of the literature statements, and the classification tags of the literature statements are medical information or non-medical information; and performing information extraction on the literature statement marked with the medical information to obtain a medical entity and an entity relationship in the literature statement marked with the medical information, and storing the medical entity and the entity relationship in the literature statement marked with the medical information.

1. The name of the design product: ear base prosthesis. 2. The use of the design product: used in medical field, used for ear prosthesis cosmetic surgery. 3. The design points of the design product: in shape. 4. The picture or photo that can best show the design points: perspective view.

In vitro method for predicting mortality risk in patients suffering from shock. The present invention refers to the medical field. Particularly, it refers to an in vitro method for predicting mortality risk among patients suffering from shock, which comprises determining in a biological sample obtained from the patient the concentration level of specific proteins.

The invention provides a text processing method and device, electronic equipment, a computer program product and a computer readable storage medium. The embodiment of the invention can be applied to scenes for carrying out standardization processing on non-standard texts in the fields of medicine, chemistry, geographic information systems and the like; the method comprises the steps of querying a plurality of candidate texts from a term library of a target knowledge domain based on a to-be-converted text of the target knowledge domain; generating a task prompt instruction based on a to-be-converted text and the plurality of candidate texts; obtaining a positive task example and a negative task example based on the to-be-converted text; and based on the positive task example, the negative task example and the task prompt instruction, converting the to-be-converted text to obtain a converted text including the term of the target knowledge domain. Through the method and the device, the accuracy of the converted text can be improved.

The application relates to a medical field knowledge fusion method based on label and content hierarchical fusion, and belongs to the technical field of knowledge fusion and artificial intelligence. Medical knowledge of different knowledge graphs is fused to construct a medical field knowledge base and provide large-scale medical industry knowledge services. The method comprises three steps of defining a meta label of an entity in a medical knowledge graph, data label alignment, and label content fusion. The method has the following characteristics: firstly, in the data label alignment, a data label alignment method based on integration of string measurement and semantic measurement is designed, and data label alignment is performed by using string features and semantic features. Secondly, in the label content fusion, a joint mechanism of content aggregation verification and unstructured knowledge generation is proposed, which aims to extract common knowledge of different granularities in different knowledge graphs, verify contradictory knowledge, and convert common triple knowledge into unstructured sentences to construct a medical knowledge base.

An information processing device stores medical content information viewable or browsable on a Web site and tag information classifying the medical content information to be associated with each other; stores user behavior information that associates user identification information, the tag information corresponding to the medical content information viewed or browsed by a user, and a number of times of viewing or browsing per the tag information with one another; stores user attribute information that associates the user identification information and attribute information including viewing / browsing situation information of the medical content information viewed or browsed by the user with each other; normalizes the number of times of viewing or browsing per the tag information to represent a frequency of viewing or browsing by the user; and applies factor analysis processing to the user behavior information after the normalizing to calculate a predetermined number of common factors.

Embodiments of the present specification relate to a method and device for generating a medical report based on evidence-based medicine, the method comprising: obtaining patient information and an original clinical question; inputting the patient information, the original clinical question and a first prompt word into a target model to generate a structured clinical question; the target model being a large language model in the medical field; retrieving a medical literature database according to the clinical question to obtain a plurality of medical literatures; inputting the target medical literature and a second prompt word into the target model to enable the target model to perform bias risk assessment on the target literature to generate a structured initial assessment result of the target literature; inputting the clinical question, each medical literature and its initial assessment result and a third prompt word into the target model to enable the target model to perform step-by-step reasoning based on a GRADE evaluation process in the third prompt word to generate a medical report; the medical report including a medical recommendation for the clinical question, a recommended strength of the medical recommendation and an evidence quality level.