40 results about "Medical profession" patented technology

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.

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.

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.

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.

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 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.

The present invention discloses a medical big language question-answering method based on data desensitization, which belongs to the field of data processing technology and includes: obtaining training data from a medical field corpus and preprocessing it, and obtaining processed training data through a data desensitization model; establishing a medical big language question-answering model based on an improved enhanced Transformer sub-model; inputting the processed training data into the medical big language question-answering model for training to obtain a trained medical big language question-answering model; and performing medical question-answering using the trained medical big language question-answering model. The present invention adopts an enhanced Transformer module to enhance the reasoning ability of long-distance contexts. Through preprocessing, the model's generalization ability of medical expertise is improved, and it can generate more accurate and professional suggestions; through the data desensitization model, sensitive information in medical data is identified and converted, thereby improving the accuracy and efficiency of privacy protection.

The invention provides a medical question and answer risk control method and device, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the legality detection of the medical question content inputted by a user; when the legality detection result is passed, generating current answer content corresponding to the medical question content by using a question and answer large model, and performing streaming output of the current answer content; according to the continuously iteratively updated medical risk control knowledge base, performing medical risk control detection on the current answer content in real time by using a preset auditing model to obtain a current risk control detection result; and filtering and outputting the current answer content according to the current risk control detection result. According to the method, wrong medical information output caused by AI model defects can be effectively reduced, and the accuracy, safety and reliability of medical consultation and suggestions are ensured, so that the content safety risk problem of AI medical products is solved, and it is ensured that medical contents generated by AI conform to laws and regulations and medical professional standards.

The present invention discloses a medical machine translation method based on reinforcement learning, comprising the steps of constructing a parallel corpus, establishing a machine translation model, pre-training the basic parameters of the machine translation model, constructing a test set to calculate the translation quality score, constructing a reinforcement learning training set, and fine-tuning the optimized parameters of the machine translation model. This invention makes the translation of professional content in the medical vertical field more accurate and convenient, helping medical professionals, research institutes, institutions, and pharmaceutical and medical device companies to quickly understand and learn about the world's most advanced medical information knowledge and medical industry development trends, effectively improving the clinical diagnosis and treatment level and consultation efficiency of medical practitioners.

Abstract SYSTEM AND METHOD FOR PATHOLOGY SPECIMEN COLLECTION ADDRESSING NETWORK DISRUPTIONS AND DATA PRIVACY The present invention provides a system and method for pathology collection. A computer-implemented method for improved functioning of electronic pathology systems to overcome network disruptions is disclosed. This method encompasses receiving patient information and a list of requested medical tests, including the medical professional's identification, then determining suitable container types for the tests and generating a unique string encapsulating the test details, container indications, and patient information. The generated string comprising the decision tree indication and the scores assigned to each set of container indication. The generated string is further secured through encryption. Subsequently, a collector's device captures and processes this pattern, extracting and decrypting the necessary information using the corresponding public key. The container indications are presented to the collector, complete with images and quantities of required containers. A server and a computing device are also disclosed. Figure 1 Abstract SYSTEM AND METHOD FOR PATHOLOGY SPECIMEN COLLECTION ADDRESSING NETWORK DISRUPTIONS AND DATA PRIVACY The present invention provides a system and method for pathology collection. A computer-implemented method for improved functioning of electronic pathology systems to overcome network disruptions is disclosed. This method encompasses receiving patient information and a list of requested medical tests, including the medical professional's identification, then determining suitable container types for the tests and generating a unique string encapsulating the test details, container indications, and patient information. The generated string comprising the decision tree indication and the scores assigned to each set of container indication. The generated string is further secured through encryption. Subsequently, a collector's device captures and processes this pattern, extracting and decrypting the necessary information using the corresponding public key. The container indications are presented to the collector, complete with images and quantities of required containers. A server and a computing device are also disclosed. Figure 1 20 25 20 01 96 10 J an 2 02 5 A b s t r a c t S Y S T E M A N D M E T H O D F O R P A T H O L O G Y S P E C I M E N C O L L E C T I O N A D D R E S S I N G N E T W O R K D I S R U P T I O N S A N D D A T A P R I V A C Y 2 0 2 5 2 0 0 1 9 6 1 0 J a n 2 0 2 5 Fi gu re 1 C om pu tin g de vi ce R eg is te r f or a cc ou nt U pl oa d lis t o f o rd er ab le te st s St or ag e M ed ia Pa tie nt M ed ic al Pr of es si on al La bo ra to ry R ec ei ve te st li st R eg is te r f or ac co un t Se le ct a p at ie nt re co rd in C IS St ar t n ew pa th ol og y re qu es t R en de r in te rfa ce Q ue ry C IS fo r cu rre nt p at ie nt A Vi si t i n pe rs on o r vi rtu al ly B 10 2 10 0 10 4 20 0 20 2 20 4 20 6 30 0 30 2 30 4 40 0 Se rv er 10 1 / 9 20 25 20 01 96 1 0 Ja n 20 25 2025200196 10 Jan 2025 10 102 Computing Patient device 400 Visit in person or virtually 302 100 B Render interface A

This invention enables targeting, personalized measurement, and management of cognitive skills development by users, clinicians, teachers, and parents. The invention features a game based virtual learning curriculum for targeting and developing the underlying cognitive skills of executive functions. The methods and systems of the invention provide an effective and rapid video game-based training curriculum to improve the cognitive skills such as focused attention, sustained attention, cognitive inhibition, behavioral inhibition, selective attention, alternating attention, divided attention, interference control, novelty inhibition, delay of gratification, inner voice, motivational inhibition, and self-regulation. This curriculum utilizes: (i) each of the cognitive processes that underlie attention control and impulse inhibition; (ii) the identification of measurable and trainable cognitive skills; and (iii) game design and game mechanics that effectively train and enable retention of those skills. The game-based system provides a medical professional, clinician, parent, teacher and user with the ability to measure and manage training of targeted cognitive skills to reach a desired performance goal.

The present invention relates to the field of educational technology, and in particular to a method for evaluating medical-preventive integration skills based on a causal-machine model, comprising the following steps: generating a question feature vector and a knowledge point semantic vector using natural language processing technology combined with a deep learning model, and calculating the correlation between the feature vectors using cosine similarity; generating a question difficulty coefficient based on the causal-machine model; generating a student knowledge mastery feature vector based on the student's answer accuracy and answering speed; recommending the most suitable homework questions to the student based on the deep learning model; and providing feedback and learning suggestions by quantitatively analyzing the student's answering performance through the causal-machine model. The present invention can identify students' weak links, effectively improve the efficiency of preventive medicine professional learning, assist teachers in making teaching decisions, and promote the personalized training of medical-preventive integration and medical-preventive collaborative talents.

Systems and methods for machine learning of medical records are provided. The system can execute multiple machine learning models on the medical records in parallel using multi-threaded approach wherein each machine learning model executes using its own, dedicated computational thread in order to significantly speed up the time with which relevant information can be identified from documents by the system. The multi-threaded machine learning models can include, but are not limited to, sentence classification models, comorbidity models, ICD models, body parts models, prescription models, and provider name models. The system can also utilize combined convolutional neural networks and long short-term models (CNN+LSTMs) as well as ensemble machine learning models to categorize sentences in medical records. The system can also extract senice provider, medical specializations, and dates of service information from medical records.

Provided herein is an apparatus and method for ultrasound compatible ECMO training of medical professionals, specifically to train them in performing cannulation in real surgical scenarios. The apparatus can include a manikin for Extracorporeal Membrane Oxygenation (ECMO) training including: a body of the manikin formed to resemble at least a portion of a human torso; a closed-circuit circulation loop within the body; at least one cavity within the body proximate a location corresponding to a thigh; at least one removable gel insert received within the at least one cavity; a medial chest plate including one or more springs attached to a top side of the medial chest plate; and a top chest plate attached to the one or more springs opposite the medial chest plate, where a compartment is defined below the medial chest plate, and wherein the closed-circuit circulation loop is disposed at least partially within the cavity.

To prevent a medical worker who is a user from misunderstanding that there is some relation between medical articles displayed continuously.SOLUTION: The doctor terminal 30 is provided with an article moving part R201 for moving the medical article to be displayed in the article display area 31J corresponding to the operation of a user to the doctor terminal 30, a normal display part 31K for displaying the medical article in a normal state that the user can read the medical article and a visibility lowering processing part 31F for executing visibility lowering processing for lowering the visibility of the medical article more than the normal state to the medical article. The article display control part 31C displays the first medical article AT1 in a part of the article display area R201 in a normal state and displays the result of the visibility reduction processing executed to the second medical article R201 in another part of the article display area AT2 in the article transition stage.SELECTED DRAWING: Figure 4B

Systems, methods, and software for accessing, processing and displaying data related to an implanted medical device. A system may include at least one server. The at least one server may be configured to receive raw data from the implanted medical device. The at least one server may be further configured to translate the raw data to readable data by machine learning algorithms trained under supervision of one or more medical professionals. The at least one server may be further configured to transmit the readable data to a computing device configured to display the readable data to a user.

In a wearable device and a method of using limb perimeter and acceleration to monitor changes in volume and property of human interstitial fluid associated with both normal and disease conditions, raw data is transmitted from the wearable device to an external device for storage and evaluation. This data is compared and compared to reference information for individuals and larger populations, and assessed to achieve health and clinical significance. Relevant messages relating to the state of the individual may be transmitted to relevant parties, such as patients, caregivers, and medical professionals.

The invention relates to the technical field of medical education and training, in particular to an augmented reality (XR) medical learning system based on a Miller pyramid medical evaluation model. The system comprises a medical professional knowledge and operation theory layer, a medical knowledge application capability layer, a clinical operation presentation layer and a system evaluation and feedback mechanism, and provides a progressive learning path through technical means such as high-precision three-dimensional model generation, multi-person cross-space cooperation, real-time feedback and personalized learning. The system can significantly improve the learning intuition, interactivity and authenticity, solves the problems of limited resources, lack of systematicness and insufficient collaborative learning opportunities in traditional medical education, provides a comprehensive and systematic surgical operation training platform for medical students, and assists the comprehensive improvement of surgical skills and comprehensive quality.