559 results about "Medical knowledge" patented technology

System for diagnosis, medical decision support, and healthcare information management that performs analysis of serial health data, adapts to the individual data, and represents dynamics of the most significant parameters (indicators), using at least two scales. The system uses the first-scale (low-resolution) analysis of a snapshot measurement of at least one indicator (primary element) such as heart rate or blood pressure and uses a second-scale (higher-resolution) analysis to determine serial changes in each of the said primary elements. The system optimizes information flow, usage of medical knowledge, and improves accuracy of analysis of serial changes, and adaptability to each individual's data. The information can be distributed in parallel to separate databases at different locations.

he invention discloses a medical information extraction system and method based on depth learning and distributed semantic features. The system is composed of a pretreatment module, a linguistic-model-based word vector training module, a massive medical knowledge base reinforced learning module, and a depth-artificial-neural-network-based medical term entity identification module. With a depth learning method, generation of the probability of a linguistic model is used as an optimization objective; and a primary word vector is trained by using medical text big data; on the basis of the massive medical knowledge base, a second depth artificial neural network is trained, and the massive knowledge base is combined to the feature leaning process of depth learning based on depth reinforced learning, so that distributed semantic features for the medical field are obtained; and then Chinese medical term entity identification is carried out by using the depth learning method based on the optimized statement-level maximum likelihood probability. Therefore, the word vector is generated by using lots of unmarked linguistic data, so that the tedious feature selection and optimization adjustment process during medical natural language process can be avoided.

A palm APP based medical service system includes a query module, a user module, a medicine data module; through connection and information exchange among all the modules, a user can know information about hospitals, diseases and medicine data, and can record personal effective medical history data information; the palm APP based medical service system can effectively integrate all kinds of medical information resources, and integrate guidance, examination, payment, medical history information and medicine knowledge into a whole. The palm APP based medical service system can achieve collection of mobile terminal data through connection of all the databases, can save the time of seeing a doctor, embodies a quick service, and creates favorable conditions for hospital market development.

The invention discloses an intelligent medical aid decision making system on basis of a cloud computing technique and a medical knowledge base technique, and belongs to the technical field of medical information. The intelligent medical aid decision making system includes an inquiry knowledge base module and a doctor-patient communication inquiry module; the doctor-patient communication inquiry module includes a consultation launching unit, an intelligent inquiry unit, a manual inquiry unit, a diagnosis and treatment scheme generation unit and a medical history generation unit; and the inquiry knowledge base module is a purchased third party knowledge base service or a self-established knowledge base, and includes basic data of an expert knowledge base which are collected based on departments or disease entities, a personalization inquiry module corresponding to doctors, and an intelligent inquiry path associated with inquiry questions and patient answers. Through a cloud server, a neural network technique, and an intelligent mobile device, the intelligent medical aid decision making system can improve the efficiency and the accuracy of a whole diagnosis process, and effectively relieve the difficulty of getting medical service.

The invention discloses an electronic medical record text structuring method. The method comprises the following steps that 1, a medical knowledge database is loaded; 2, an electronic medical record text is read; 3, word segmentation is carried out on short sentences through a forwards maximum match algorithm, and words in sentences, word properties and relative position relations are obtained; 4, whether the semantics in the sentences for disease information description is positive or negative is judged; 5, disease information elements are extracted; 6, the steps 2-5 are repeated till all interesting contents in the electronic medical record are obtained; 7, different expressions of the disease information elements are combined, the same disease information is combined according to a medical synonym bank, and redundant information is removed; 8, disease description information elements are stored in a structure/class mode, the structuring process is finished, disease relevant information can be effectively extracted from medical record description text, structural expression for disease information is formed, and therefore disease occurrence regularity, definite diagnosis ways, treatment effects and the like are deeply explored.

The invention discloses a patient portrait drawing method and device based on medical big data. A professional medical clinic knowledge base is established, a new calculation model and a new method are adopted on the basis to construct a patient label, and the problems that universal user portrait drawing is low in accuracy and is not suitable to apply in the medical field and the individual privacy of the patient is difficult in protection are solved. In the method and the device, the extraction and the calculation of a label system are established on the basis of a medical knowledge base so that the method and device exhibit strong speciality, and therefore, the accuracy of the patient portrait drawing system is improved to a great extent; secondly, the risk factor, the increasing-decreasing factor and the correlation factor of label characteristics are considered so that the method and device are more in line with the disease development law; thirdly, the patient participates in the confirmation and the management of portrait drawing so as to eliminate the calculation deviation of the label; and finally, a block chain technology is introduced to the aspect of the individual privacy of patient portrait drawing data, and a risk that sensitive medical data is leaked is technically lowered.

An intelligent care provider medical practice system which learns the care provider's preferences and historical diagnosis for predicting and treating patients based on provided information. The system makes use of one or more medical knowledge bases and utilizes artificial intelligence and reasoning to learn the provider's preferences and tendencies. The system also automatically generates the provider's notes, treatment plans, and other medical practice actions for treating, billing, and processing the patient after an exam or visit.

A system and method for providing computerized, knowledge-based medical diagnostic advice. The medical advice is provided to the general public over a network, such as a telephone network with the use of a telephone or the Internet with the use of an Internet access device. Alternatively, the medical advice can be provided to a patient in a stand-alone mode by use of a computer. The invention utilizes a list-based processing method of generating and executing diagnostic scripts. For the purpose of diagnosing a health problem of a patient, medical knowledge is organized into a list of the diseases to be considered. Each disease on the disease list includes a list of symptoms that is checked in a patient. Each symptom on the symptom list is then further described as a response to a list of one or more questions asked of the patient about the symptom. This triply-nested list structure is converted by suitable data structure transformations into a script that is stored. When a patient requires diagnosis, the script is played back as a sequence of questions. The responses of the patient are analyzed and converted into symptoms. The symptoms are accumulated into diseases. Finally the diseases are selected and reported as a diagnosis.

The invention discloses a knowledge map construction method for medical images, belonging to the field of knowledge maps. The construction process includes a step of knowledge representation with a framework theory representation method, a step of knowledge acquisition, wherein the knowledge sources of entity, attribute and attribute value extraction is unstructured data, a step of knowledge fusion with the integration of acquired new knowledge and the elimination of ambiguity, a step of knowledge processing with the knowledge reasoning and quality assessment of the data which is subjected tothe knowledge fusion and the adding of qualified data to a knowledge map, and a step of knowledge update with the update of the knowledge map according to the update and development of medical image knowledge. According to the characteristics of the medical image knowledge, and the knowledge acquisition rate is greatly improved with the unstructured data such as textbooks and academic journals asa source of knowledge.

The invention discloses a method for establishing a medical image atlas based on image segmentation and the CNN. A CNN method is used to segment medical images finely, image segmentation identification is carried out on the collected medical images, medical knowledge unit segmentation is carried out on medical diagnosis knowledge, and knowledge units of different medical images are formed; an image relation of image segmentation is analyzed to connect the different image knowledge units and communicate with an additional medical illness knowledge unit, a knowledge base association relation isestablished on the basis of connection between medical image identification and medical diagnosis knowledge; characteristic matching is carried out on the knowledge units after image segmentation, entities with the same type of attribute serves as entity nodes in the knowledge atlas, a knowledge base entity is established by fusion, and entities are aligned; and a knowledge atlas is established byusing the entities and the associations therebetween. Suspected diagnosis prompt and treatment suggestion are provided, and AI diagnosis is made possible.

The invention discloses a medical knowledge base construction method based on a question-and-answer system, and relates to a medical knowledge base. The medical knowledge base construction technology based on the question-answering system mainly includes data processing, data analysis and an expert question-and-answer platform. Firstly, entity and correlation relations are extracted from clinical data by a hierarchical partitioning method at a data processing stage. Secondly, extracted results are analyzed by an association rule algorithm, entity correlation is mined, the mined entity correlation is matched with a medical dictionary, and correctly matched results are directly stored into the knowledge base. Finally, questions are automatically generated from entity correlation which cannot be confirmed by crowd-sourcing technology, the questions are submitted to an expert question-and-answer platform for answering the questions, and final results are selected from answers of experts by a majority voting algorithm and stored into the medical knowledge base.

The invention discloses a construction method and device for a medical knowledge map. The method comprises the following steps of obtaining all medical treatment key words in sample data and associations among the medical treatment key words; according to the sample data and the associations, calculating out association strength among the medical treatment key words; based on all the medical treatment key words and the association strength, constructing the medical knowledge map. By the adoption of the technical scheme, through the sample data, all the medical treatment key words obtained from the sample data and the associations among all the medical treatment key words, firstly, the association strength among all the medical treatment key words is calculated out, and then according to all the medical treatment key words and the association strength, the medical knowledge map is constructed. Not only can the associations among all the medical treatment key words be visually reflected, but also the strength of the associations among all the medical treatment key words can be determined, the rearrangement of medical knowledge in the sample data is facilitated, the experience is summarized, and the development of medicine is advanced.

A clinical decision support system enabling sharing of medical knowledge in a machine executable format, implementable as a network-based system in which individual executable knowledge modules (EKMs) define the data requirements for assessing a patient, the conclusions that can be drawn using that data, and instructions on how to generate those conclusions. Using standards-based messages transmitted over a network, client decision support applications provide patient data to the system and receive patient-specific assessments and recommendations. The system permits re-use of executable medical knowledge across diverse applications and care settings, easy authoring of knowledge modules, and use of the system framework to implement decision support applications having significant clinical utility.

The present invention discloses a method and a device for constructing a medical knowledge graph and an assistant diagnosis method. The assistant diagnosis method based on a medical knowledge graph comprises the steps of: acquiring complaint data and examination data of a patient and processing the data to obtain symptom entities and sign entities of the patient; searching, from the medical knowledge graph, disease entities associated with the symptom entities and the sign entities, calculating a posterior probability of each disease entity separately under a set of its corresponding symptom entities and sign entities; and outputting a disease entity with a maximum posterior probability as well as data corresponding to its associated nodes. The present invention provides an intelligent assistant diagnosis for clinical medicine to reduce the work burden of medical staff, relieve the medical pressure and reduce medical accidents.

A system generates medical knowledge base information by searching at least one repository of medical information to identify sentences including a received medical term. A data processor searches the identified sentences to identify sentences including a medical term different to the received term in response to a predetermined repository of medical terms and excludes sentences without a term different to the received term, to provide remaining multiple term sentences. The data processor groups different terms of individual sentences of the multiple term sentences to provide grouped terms, determines whether a medically valid relationship occurs between different terms of an individual group of terms of the grouped terms by using predetermined sentence structure and syntax rules and outputs data representing grouped terms having a medically valid relationship.

The invention relates to a disease risk predication method and system. The method comprises the following steps of: S1, receiving input gene sequencing result information; analyzing the input gene sequencing result information to obtain all mutant gene information; S2, looking up disease gene mutant information of corresponding gene sites from a precision medical knowledge base according to the gene sites corresponding to the mutant gene information, so as to obtain at least one piece of disease gene mutant information; S3, matching each piece of the disease gene mutant information with all the disease gene mutant information to obtain the similarity of each piece of the mutant gene information, and all the disease gene mutant information; and S4, obtaining a risk predication table aiming at the gene sequencing result information according to the similarity of each piece of the mutant gene information, and all the disease gene mutant information. By analyzing an association relation of susceptibility genes, mutation sites, mutation types and diseases, disease risk predication is carried out on healthy people and general disease risk grade predication is provided; disease risks in the future are reduced.

A computer-implemented method of detecting an object in a three-dimensional medical image comprises determining the values of a plurality of features at each voxel in at least a portion of the medical image. Each feature characterises a respective property of the medical image at a particular voxel. The likelihood probability distribution of each feature is calculated based on the values of the features and prior medical knowledge. A probability map is generated by using Bayes' law to combine the likelihood probability distributions, and the probability map is analysed to detect an object.