53 results about "Medical profession" patented technology

The invention provides a medical AI question and answer method based on retrieval enhancement, which comprises the following steps: acquiring a medical professional knowledge base, and constructing an entity recognition model and a medical knowledge graph; the method comprises the following steps: splitting a medical professional knowledge base, and creating a vector index and a full-text retrieval reverse index; intention classification is carried out, and initial processing is completed; performing multi-path mixed retrieval on the medical professional knowledge base to obtain a user question related text block; a filtering rule is preset, and the user question related text blocks are filtered; answering the user question according to a preset second prompt word, and answering the user question according to a preset third prompt word when the user question exceeds the range of the medical professional knowledge base or the multi-path mixed retrieval has no result. According to the method, related context information can be accurately searched, reliable reference information is provided for a generative large model to answer professional questions, the interactive experience of a medical question answering system is improved, and the accuracy and specialty of answer generation of the system are remarkably improved.

The invention discloses a medical education-oriented knowledge graph construction, retrieval and application system, and the system comprises a data collection and integration module which is used for collecting and integrating various medical professional knowledge, and forming a medical knowledge material library; the intelligent algorithm module is used for constructing a knowledge graph according to the medical knowledge material library and then providing structured knowledge support for learning and reasoning of the knowledge graph by using a graph neural network model; the knowledge retrieval and content generation module is used for performing retrieval in the knowledge graph according to the input content and providing the retrieved knowledge to the large language model to obtain the corresponding content; and the knowledge application service module is used for providing medical education service according to learning and understanding of the graph neural network model on the knowledge graph and integration of the big language model on semantic information in the knowledge graph. The intelligent development of the medical field is better promoted, and more efficient and accurate support and service are provided for medical education, research and the like.

The invention discloses a teaching corpus construction method and system based on deep learning, and a medium, and belongs to the cross technical field of medical education, artificial intelligence and multi-modal data processing. The method comprises the following steps: a data acquisition and annotation stage: acquiring medical inquiry video data of different scenes; in the model training stage, a multi-modal deep learning model architecture is used for carrying out cross-modal alignment on multi-modal data; then semantic understanding and question classification are carried out based on the pre-training language model after fine tuning; in the corpus construction and optimization stage, multi-modal data are integrated, a corpus management system is built, dynamic updating and self-adaptive optimization are carried out on a corpus, meanwhile, a typical non-language posture library is constructed for a regional culture background, and teaching is carried out in combination with the corpus. According to the invention, in combination with medical professional knowledge, an efficient and intelligent medical inquiry video teaching corpus is constructed through deep fusion of natural language processing, computer vision, speech recognition and multi-modal data fusion technologies.

The invention relates to the technical field of education, in particular to a medical defense fusion skill evaluation method based on a causal-machine model, and the method comprises the steps: 1, generating question feature vectors and knowledge point semantic vectors through employing a natural language processing technology in combination with a deep learning model, and calculating the correlation between the feature vectors through employing cosine similarity; 2, generating a question difficulty coefficient based on a causal-machine model; step 3, generating a student knowledge mastering feature vector according to the answering accuracy and answering speed of the student; 4, recommending the most suitable homework questions to the students based on a deep learning model; and 5, quantitatively analyzing the answering performance of the students through a causal-machine model, and providing feedback suggestions and learning suggestions. According to the invention, weak links of students can be identified, the efficiency of preventive medicine major learning is effectively improved, the teaching decision of teachers is assisted, and personalized cultivation of medical defense fusion and medical defense collaboration talents is promoted.

An AI-based chatbot diagnostic system configured to: receive data from at least one data source related to at least one medical field or disease; store the received data in a medical data repository, wherein the data is categorized according to at least one criterion; access a medical index engine that uses the categorized data and a medical reference database to create and update an index for individual medical fields; utilize interactive data for training a diagnostic model in conjunction with a medical index; select the trained diagnostic model most suitable for a given diagnostic scenario from a model storage database; and manage and interact with the selected diagnostic model using an AI model manager.

The invention discloses a medical big language question and answer method based on data desensitization, and belongs to the technical field of data processing, and the method comprises the steps: obtaining training data in a medical field corpus, carrying out the preprocessing, and obtaining the processed training data through a data desensitization model; establishing a medical big language question and answer model based on the improved enhanced Transform sub-model; inputting the processed training data into a medical big language question and answer model for training to obtain a trained medical big language question and answer model; and performing medical question and answer by using the trained medical big language question and answer model. According to the method, an enhanced Transform module is adopted, the reasoning ability for long-distance context is enhanced, the generalization ability of the model for medical professional knowledge is improved through preprocessing, and more accurate and professional suggestions can be generated; through the data desensitization model, sensitive information in the medical data is identified and converted, and the accuracy and efficiency of privacy protection are improved.

Disclosed herein is a system for remote monitoring of subject by a medical profession.

The invention discloses a characteristic value calculation method based on electrocardio data, belongs to the field of medical data analysis, and provides an electrocardio data analysis and processing method. Comprising the following steps: decomposing different frequency components of an electrocardiosignal by using wavelet transform, and analyzing on different time scales. Wavelet transformation is relatively suitable for detecting QRS wave groups, and due to the fact that the wavelet transformation can highlight transient characteristics of signals on different frequency bands, complex electrocardiosignal processing is more efficient. In addition, wavelet transform is not only suitable for wave crest detection, but also can help to remove signal noise and improve signal definition. The deep learning technology is used for automatically extracting the features from the electrocardiosignals, a feature extractor does not need to be manually designed, related medical professional knowledge and the artificial intelligence technology are combined, wave crests in the electrocardiosignals can be more accurately detected, and meanwhile anomaly detection can be carried out, such as early signs of heart diseases such as sinus arrhythmia and the like.

The invention relates to a medication recommendation method and system. The method comprises the following steps: preprocessing original medical data of a current patient into a standardized time sequence diagnosis and treatment sequence; inputting the time sequence diagnosis and treatment sequence and the medical professional knowledge base into a code-doctor-seeing double-level attention model for processing to obtain code-level aggregation features of each doctor seeing and corresponding doctor-seeing-level attention weight, and performing weighted aggregation on the code-level aggregation features based on the doctor-seeing-level attention weight to obtain a code-level aggregation feature; generating a global health representation of the current patient; based on at least one of the multiple medical feature information of the current patient, similar historical cases are retrieved from a case information base to serve as reference cases; and generating and outputting a medication recommendation list for the current patient at least based on the global health representation of the current patient and the medication scheme of the reference case. By adopting the method and the device, the accuracy and the safety of drug recommendation for the current patient can be improved, so that a doctor can be better assisted to make a drug decision.

Methods, apparatuses, and systems for transcribing and performing analysis on patient data are disclosed. Data is collected from one or more medical professionals as well as sensors and imaging devices positioned on or oriented towards a patient. An analysis is performed on the patient data and the data is presented to a medical professional via a verbal interface in a conversational manner, allowing the medical professional to provide additional data such as observations or instructions which may be used for further analysis or to perform actions related to the patient's care.

Apparatuses, systems, and methods are disclosed to manage and process surgical data, and enable collaboration between two or more surgeons or other clinicians. A repository may be created that includes surgical data (including video data and / or 2024 / 030683 or radiological image data) that may be selectively shared between medical professionals. One or more trained neural networks may process the annotated surgical reports in order to populate the repository. Additional trained neural networks may generate surgical recommendations in response to user requests, based on contents of the repository. Other trained neural networks may generate surgical templates to guide a surgeon during an operation.

The invention discloses an AI-based intelligent accompanying treatment and nursing system, and particularly relates to the technical field of medical assistance, and the system comprises the following modules: a virtual AR interaction module, an AI dialogue module, a relative and friend-medical care communication module, a treatment synchronization engine, a dynamic state analysis module, a virtual image generation module, and a multi-modal intervention module. According to the AI-based intelligent accompanying treatment nursing system, the treatment scene AR interface is generated through the virtual AR interaction module, information such as the focus position and the medicine use condition is displayed in a visual three-dimensional model mode, and compared with traditional complex examination lists and reports, relatives can more easily understand the current physical condition of a patient, and the treatment nursing efficiency is improved. And non-medical relatives can better participate in the treatment process of the patient, the attention and support strength of the relatives to the illness state of the patient are enhanced, a good treatment atmosphere can be created, the positive influence on the psychological state of the patient is generated, and the improvement of the treatment effect is indirectly promoted.

According to the clinical pain management decision support system and the training method based on the severe games, the pain management process is standardized by using the functions of warning, decision making, data storage and the like of the clinical pain management decision support system, a standardized pain nursing document is automatically generated, and timeliness and safety of pain management of a patient are guaranteed. In addition, a relaxed game training mode is adopted, so that clinical medical staff can relieve pain for the patient in a real and safe environment, more learning and practicing opportunities can be provided for the clinical medical staff, the effect better than that of a traditional learning mode is obtained, and the patient cannot be hurt; meanwhile, by means of emerging technical means, skills are trained, professional knowledge is learned or professional guidance is sought anytime and anywhere, the availability of medical professional learning resources is improved, clinical medical staff are not limited by time and space, and the time and economic cost for mastering professional skills is reduced.

In some aspects, a triage artificial intelligence receives a portion of a conversation between a doctor and a patient, determines, based on the conversation, one or more symptoms associated with the patient, determines that the conversation includes a set of trigger words associated with a particular symptom of the one or more symptoms, selects based on the set of trigger words associated with the particular symptom, a first artificial intelligence specialist in a first medical specialty from a plurality of artificial intelligence specialists, and provides the first artificial intelligence specialist with access to the conversation between the doctor and the patient. The first artificial intelligence specialist provides one or more decision support insights that are sent to a computing device associated with the doctor.

Embodiments described herein provide for training an artificial intelligence model to become a preference-aware model. The artificial intelligence model preferences as the artificial intelligence model trains. Reinforcement learning is used to train experts in the artificial intelligence model such that each expert is trained to converge to a unique preference. The architecture of the artificial intelligence model is highly flexible. Upon executing a trained model, users can select automatically images according to various preferences based on medical professional preferences, geographic preferences, patient anatomy, and institutional guidelines.

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 an AI service development system based on an agent technology, which relates to the technical field of medical health and comprises a medical professional module, a digital duplication module, a continuous optimization module, an interactive service module, a safety management module, an ethical and compliance management module, a basic service module and an intelligent diagnosis module. According to the AI service development system based on the agent technology, a multi-dimensional medical knowledge graph which comprises a large number of medical entities and relation edges and a dynamically updated clinical path library is constructed by utilizing a semantic network, accurate and reliable medical knowledge support can be provided, and medical data is deeply analyzed in cooperation with an intelligent diagnosis module; the system provides auxiliary diagnosis of complex diseases, improves the accuracy and efficiency of diagnosis, is high in intelligent level, and can provide accurate and efficient diagnosis service for patients especially in the aspect of diagnosis and treatment decision support of the complex diseases.

The present application relates to a structured information processing method for imaging report text, a lung disease monitoring method and system. The structured information processing method includes: S11, further dividing the part and morphological features in the imaging medical professional entity into two categories, negative and positive, to obtain eight entity labels, namely, vacancy filler, sentence starter, sentence terminator, part-negative, part-positive, morphology-negative, morphology-positive and disease name, performing named entity recognition on the imaging report text based on the eight entity labels, and extracting the named entity output in BIO format; S12, filtering out redundant information marked as O and entity labels as part-negative and morphology-negative based on the extracted named entities, calculating the sentence vector and storing it in the database. The present application realizes the efficient extraction of symptom information and obtains a more expressive sentence vector, so that a more accurate cluster of similar cases can be obtained for spatiotemporal distribution feature analysis.

Proposed concepts aim to provide schemes, solutions, concepts, designs, methods, and systems pertaining to medical image analysis. In particular a difference between annotations relating to an anatomical object in first and second medical images of an area of interest is detected and used to generate a further annotation describing a difference between the anatomical objects. Such an annotation, based on the difference of the anatomical object in the medical images taken at different point in time, may be used for the training of a machine learning model adapted to identify differences in anatomical objects, or simply be provided to medical professionals and / or subjects.

Proposed concepts aim to provide schemes, solutions, concepts, designs, methods, and systems pertaining to medical image analysis. In particular a difference between annotations relating to an anatomical object in first and second medical images of an area of interest is detected and used to generate a further annotation describing a difference between the anatomical objects. Such an annotation, based on the difference of the anatomical object in the medical images taken at different point in time, may be used for the training of a machine learning model adapted to identify differences in anatomical objects, or simply be provided to medical professionals and / or subjects.

To provide an information processing system which evaluates presence / absence or a degree of cognitive function deterioration of a user.SOLUTION: In an information processing system, an information processing device 2, a medical person terminal 3, a sound collecting device 4, and a business operator terminal 5 are communicable through an electric telecommunication line. The information processing device includes a processor. In a presentation step, an instruction is presented for urging a predetermined utterance voice to a user. In a reception step, voice data is received regarding the utterance voice from the user collected by the sound collecting device. In a determination step, it is determined whether or not the sound collecting device is functioning normally based on the voice data. In an evaluation step, in the case where the sound collecting device is functioning normally, based on the voice data and a learned model which has learned beforehand a relation between the voice data, and feature quantity of predetermined utterance voice and presence / absence or a degree of cognitive function deterioration, presence / absence or a degree of cognitive function deterioration of the user is evaluated.SELECTED DRAWING: Figure 1

The invention relates to the technical field of medical information management, and particularly discloses a book recommendation method and device for medicine, electronic equipment and a storage medium. According to the method, a dynamic recommendation model based on medical specialty features is innovatively constructed, and interest measurement models of users in different medical specialty fields for book categories are established by collecting book borrowing data of clinical medical personnel and medical researchers. Aiming at the characteristic that medical users have clear professional directions and research requirements, the system integrates medical feature data such as department attributes, research directions and scientific research projects of the users, and a machine learning algorithm is adopted to establish a correlation model of multi-dimensional medical features and book interests. In the recommendation process, dynamic weighting calculation is carried out by retrieving user group borrowing behavior data with similar medical features and combining a machine learning prediction result, an initial stage emphasizes a machine learning prediction value, and the weight ratio of actual borrowing behavior data is gradually increased along with accumulation of the user borrowing behavior data. The dynamic adjustment mechanism effectively solves the cold start problem of new users in the medical field, ensures that recommendation results are continuously optimized along with evolution of user behaviors, and provides accurate personalized knowledge services for medical professionals. According to the scheme, a medical professional feature analysis module and a weight adaptive adjustment module are specially designed, and complex professional classification systems and dynamically changing book demand features in the medical field can be effectively processed.

To perform an objective evaluation of the QOL of a patient by a medical worker to improve the medical quality.SOLUTION: An electronic medical chart system includes: an input unit that receives an input of an evaluation of a patient by a medical worker or a communication between the medical worker and the patient; a language processing unit that performs language processing for either the evaluation of the patient or communication which is input through the input unit; and a numeralization unit that numeralizes the quality of life (QOL) of the patient by using a result of processing by the language processing unit.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of medical data processing, and discloses a keyword extraction and matching system and method based on a case, and the method comprises the following steps: S1, manually marking keywords in a case text, and obtaining a digital data text; s2, examining the digital data text obtained in the step S1; s3, inputting the digital data text into the hybrid model to extract keywords from the digital data text; s4, standardizing the medical nouns to obtain a standard library; s5, matching the extracted keywords with a standard library, and outputting matching items corresponding to the keywords; the key information in the unstructured text is understood and accurately recognized by using the advanced NLP technology and combining medical professional knowledge and an artificial intelligence algorithm, the processing accuracy and efficiency are improved, and the problem that the key information is difficult to automatically and accurately extract due to complex medical terms and different expression habits of doctors is solved.

A method of providing guidance to medical professionals with a system including electronic devices connected on a network, one or more processors, and a database storing an authoritative knowledge base. The method comprises receiving, at a first electronic device, a prompt from a first medical professional, and converting the prompt into a machine-searchable string. A trained machine learning model is directed to retrieve one or more results from the authoritative knowledge base. A query may be transmitted to a second medical professional if the trained machine learning model determines that the one or more results are not sufficiently relevant. A reply to the query from the second medical professional may be received, and the reply may be outputted on the first electronic device.

The invention relates to a medical information system (10) and method, the system comprising a data storage device (12) configured to receive and store medical data (D) of a plurality of patients acquired by implantable medical devices (14) and / or wearable devices (16), and a user interface (18) configured to provide access to the medical data (D) of a respective individual patient among the plurality of patients stored in the data storage device (12) to multiple users, in particular multiple medical professionals, wherein the data storage device (12) is further configured to receive and store diagnostic and / or treatment information (D1) related to the medical data (D) of each individual patient by at least one of the multiple users, and wherein the user interface (18) is configured to provide access for each of the plurality of users to the stored diagnostic and / or treatment information (D1).

The invention discloses an intelligent diagnosis system and method for multiple myeloma based on multi-modal data fusion, and relates to the technical field of myeloma. Comprising a medical professional module, a digital cloned construction module, a continuous optimization module, an interactive service module, a service requirement module and a basic service module, wherein the medical professional module is responsible for providing professional knowledge support related to multiple myeloma, including clinical manifestations, laboratory examination, diagnostic standards, prognosis layering and therapeutic schedules. According to the intelligent diagnosis system and method for multiple myeloma based on multi-modal data fusion, multi-channel medical knowledge can be acquired and integrated in real time, standardized and structured professional knowledge support is provided through systematic knowledge graph construction and algorithm reasoning, meanwhile, multi-source heterogeneous medical data are comprehensively integrated, and the diagnosis efficiency is improved. And constructing a full-life-cycle data file of the patient so as to provide personalized diagnosis standards and treatment schemes in combination with personal information, medical history and multi-dimensional examination results of the patient.

A computer-implemented and system for remotely monitoring a patient that provides an an online portal to a medical professional enabling, the medical profession to assign an exercise program to the patient, wherein the exercise program consists of a number of exercises to be completed by the patient each time in regular time intervals. Prompt the patient to send feedback data, via a communication network, wherein the patient's compliance with the exercise program is predicted by a machine learning (ML) model based on a gradient boosting algorithm. The machine learning model analyses the feedback data of a patient and predicts the compliance when performing the exercise program the next time after expiry of a number of time intervals to follow.

The invention provides an intelligent question and answer method and device suitable for lung health and electronic equipment, and is applied to a question and answer scene of pulmonary nodule or lung cancer, the method comprises the following steps: analyzing a received user question, and establishing bidirectional mapping between the user question and medical terms; retrieving dynamic knowledge based on bidirectional mapping, and generating a dynamic knowledge graph; generating a personalized decision tree corresponding to the user question according to the dynamic knowledge graph; and generating medical expressions based on the personalized decision tree, converting the medical expressions into text expressions convenient for the user to understand, and outputting the text expressions. According to the method, the latest medical professional knowledge is integrated in real time through the dynamic knowledge graph, and the professional medical expression is converted into the universal text expression to be output, so that the user experience is improved.

This makes it possible for even people without medical expertise to know which medications are appropriate for them. [Solution] The information providing device 1 has an information acquisition unit 131 that acquires health information indicating the user's health condition, a query creation unit 133 that creates a search query for a drug database to search for drugs suitable for the user by inputting a first instruction text together with the health information into a first large-scale language model, a drug identification unit 134 that identifies one or more drugs corresponding to the health information by searching the drug database using the search query, and an output unit 135 that outputs drug information regarding the drugs identified by the drug identification unit 134.