47 results about "Clinical knowledge" patented technology

A system and associated methods provide healthcare entities with analyzing capabilities for records of a patient population. The analyzing process seeks to find matches between a piece of clinical knowledge introduced to a healthcare entity and data contained in the patient population records. According to one method, a service generates executable code based on the piece of clinical knowledge. Through a communication with the service, the healthcare entity makes a determination as to what extent the clinical knowledge has relevance to a patient population of the healthcare entity. Based on this determination, the healthcare entity receives at least a portion of the executable code. Then, the records of the patient population may be analyzed by the executable code to register matches of the clinical knowledge with data contained within the records. System activity may then be initiated based on the matches registered. Examples of system activity include compiling a contact list of patients and/or healthcare providers associated with specific records where matches occurred, generating a report about the matches registered, and flagging specific records where matches occurred so that care providers may discuss issues surrounding the clinical knowledge with associated patients in a future encounter with the healthcare entity, among others.

The invention relates to a clinical-knowledge-graph construction method based on electronic medical records. The method is characterized by including the following steps: step 1, concept layer designing, step 2, instance layer learning, and step 3, knowledge graph updating. Compared with the prior art, the method is better in a construction effect. Construction of a graph is automatic, partial manpower is saved, for unique data features of medical data, professionalism, data semantics, connection, knowledge time sequences and the like of the medical data are maintained, updating of the clinical knowledge graph is supported, and subsequent use is facilitated.

The present invention, defined as MEGICS (Medical+Logics), has been developed in order to improve quality of care and enhance the efficiency of operation of healthcare facilities and providers. When front-line healthcare doctors and nurses make various clinical decisions, MEGICS management system can provide them with relevant clinical knowledge in a timely manner. MEGICS management system can, therefore, increase the level of user satisfaction and provide patients with better quality of healthcare services. The Inventor believes that the present invention, MEGICS, can penetrate a new market and be easily adopted by healthcare facilities, due to its flexible system structure, experience-based approach, and user-friendly features.

The present invention proposes a method for automatically creating a dose prediction model based on existing clinical knowledge that is accumulated from multiple sources without collaborators establishing communication links between each other. According to embodiments of the claimed subject matter, clinics can collaborate in creating a dose prediction model by submitting their treatment plans into a remote computer system (such as a cloud-based system) which aggregates information from various collaborators and produces a model that captures clinical information from all submitted treatment plans. According to further embodiments, the method may contain a step where all patient data submitted by a clinic is made anonymous or the relevant parameters are extracted and condensed prior to submitting them over the communications link in order to comply with local regulations.

The invention discloses a robot active inquiry method based on a traditional Chinese medicine clinical knowledge graph. In the inquiry process, the robot has the initiative right of the knowledge graph, the information of a patient is collected through multiple times of inquiry, diagnosis and iteration are conducted to complete an inquiry task, and then final diagnosis is conducted. The method relates to the knowledge graph, knowledge expression learning, knowledge reasoning technology and a syndrome type diagnosis iteration model. According to the invention, the problems of high complexity and big difficulty of artificial intelligence traditional Chinese medicine inquiry can be solved.

The invention provides a method for carrying out DRGs grouping on medical record data. The method comprises the following steps: judging whether any code of an operation and treatment mode is matchedwith MDCA group information or not: if so, entering an MDCA group, or else, entering the next step of classification; judging age or out-of-hospital diagnosis: if age is less than or equal to 1 and ifyes, entering the next step of classification, or else, performing checking and correcting diagnosis; screening out data conforming to the MDCP disease encoding information, and classifying the datainto an MDCP group; dividing the data of the MDCA group and the MDCP group into corresponding first-stage ADRGs groups according to the operation and operation codes; and combining the obtained first-stage ADRGs groups according to clinical knowledge to obtain a second-stage ADRGs group class. According to the method, the accuracy of the processed data is greatly improved, and subsequent secondaryclassification through an algorithm is facilitated; the grouping scheme is more flexible, the problem of current Chinese medical record home page data quality is fully considered, and the formed grouping scheme better meets related requirements of prefecture and municipal medical insurance bureau and is more suitable for prefecture and municipal medical current situations.

Embodiments of the present invention relate to clinical knowledge driven healthcare scheduling. There is disclosed a method of healthcare scheduling, comprising receiving a request to generate an appointment for a target user, the request at least containing identification of the target user; obtaining clinical knowledge associated with the target user by accessing a knowledge base, the clinical knowledge at least indicating time constraints and at least one required resource for the appointment; generating a group of prioritized candidate appointments by processing the request at least partially based on the obtained clinical knowledge, each of the candidate appointments being represented at least by appointment time and a priority; and selecting at least one recommended appointment from the group of prioritized candidate appointments based on availability of the at least one required resource. Corresponding apparatus, system and computer program product are also disclosed.

The invention provides a minimally invasive surgery training and evaluation method, and a teacher computer and student computers are applied to perform open type minimally invasive surgery skill training and evaluation. The teacher computer and the student computers are provided with displays and operating boards; each operating board is partitioned into a box inside part and a box outside part; a test body is placed into box inside part; a minimally invasive surgery apparatus is arranged at the box outside part; and the minimally invasive surgery apparatus is inserted into the box for acting on the test body to simulate the process of minimally invasive endoscope surgery. The minimally invasive surgery training and evaluation method has the beneficial effects that the requirements from resident doctors to specialists on teaching, training and evaluation on different structures in different levels at the same stage can be fully met; a simulated training center is related to every subject, so that students can learn and grasp clinical knowledge more systematically; atraumatic training can be performed repeatedly by using a simulation center; and the advantage of situational teaching can be realized by using the simulated clinical environment of the simulation center.

The invention relates to an analysis method and a system of a dynamic disease model base. The method comprises the following steps of S1, establishing a disease model according to a symptom; S2, inputting an effective medical record set into the disease model so as to carry out analysis and processing; S3, according to effective medical records, updating the disease model and then acquiring a newdisease model; and S4, inputting patient correlation symptom information into the model, selecting a disease list according with a strong relation symptom in the disease model according to the symptominformation, then using a disease derivation formula of the corresponding disease list to calculate various diseases and probabilities and pushing a result to a clinician. The system comprises a dynamic reasoning machine. The dynamic reasoning machine is connected to an information inputting end and a user end in a communication connection mode. In the invention, a static clinical knowledge baseand a large data method are effectively fused so as to generate a dynamic disease model. Simultaneously, medical data is converted into a factor used for promoting the disease model to be optimized atany time and the disease model base is dynamically adjusted. A diagnosis speed and accuracy are increased and clinical data is instantly converted into experience.

The invention provides a clinical guideline knowledge representing model and method. The model comprises a knowledge graph, a guideline metadata dictionary and a decision variable set; the knowledge graph represents main knowledge of a guideline; the guideline metadata dictionary represents meta information related to the guideline; the decision variable set which is global represents all decisionvariables, and may include variables of multiple guidelines, different decision variables represent decision items which are most basic and cannot be divided further, and a value of the decision variable is decided according to the practical condition of a patient in a clinical practical process; and an electronic guideline that can be explained by a computer can be obtained by integrating and coding the knowledge graph, the guideline metadata dictionary and the decision variable set. Thus, guideline knowledge is represented effectively, the model after representation can be explained and executed by the computer, a doctor is helped master the clinical knowledge rapidly, suggestion about a diagnosis and treatment scheme is provided for the doctor, the doctor is helped overcoming memory limitation and confusion, the possibility of misdiagnosis and mistreatment is reduced, and the diagnosis effect of the doctor is improved.

Systems and methods for measuring physician expertise are disclosed. Furthermore, systems and methods for ranking physicians based on said measure of expertise are disclosed. The systems and methods may comprise (a) capturing and mining large-scale, up-to-date medical and clinical knowledge sources (e.g., biomedical corpora, clinical guidelines, clinical trials, professional physician data); (b) building models of medical conditions that link each condition to relevant concepts (e.g., specialties, medical procedures, drug regimens) and relevant clinical research (e.g., published articles, clinical trials); (c) transforming biomedical concepts (e.g., conditions, procedures, drugs) extracted from text in natural language into terms and codes of biomedical ontologies; (d) enriching mission-critical biomedical ontologies and creating mappings between them; (e) matching physician data against a medical condition model in order to rank physicians according to their relevance to a given condition.

Presented herein are methods, systems, devices, and computer-readable media for image annotation for medical procedures. The system operates in a parallel manner. In one flow, the system starts from clinical terms and image and applies image detection module in order to get visual candidates for related radiological finding and provide them with semantic descriptors. In the second (parallel) flow, the system produces a list of prioritized semantic descriptors (with probabilities). The second flow is done by application of a reverse inference algorithm that uses clinical terms and expert clinical knowledge. The results of both flows combined by matching module for detection the best candidate and with limited user input images can be annotated. The clinical terms are extracted from clinical documents by textual analysis.

The invention discloses a disease cause finding method and system based on big data, and relates to the field of medical assistance. The system comprises an intelligent terminal, a PC (personal computer) terminal and a disease cause finding platform, the disease cause finding platform comprises a clinical knowledge base, a health file base, an analysis module, a scheme generation module and a communication module, the clinical knowledge base is used for acquiring and storing a lot of clinical knowledge information through big data, the health file base is used for storing vital sign data of users, the analysis module is used for analyzing symptom information inputted by the users according to the clinical knowledge base, the scheme generation module is used for generating corresponding treatment schemes according to analysis results, and the communication module is used for achieving online communication of the users and experts. According to the method and system, big data clinical knowledge is counted, symptoms inputted by the users and the clinical knowledge base are compared, and treatment schemes suitable for the symptoms are analyzed. When the users need further confirmation,the system is connected with the experts, the experts directly communicate with the users, disease cause confirming time and queuing and registering time of the users are shortened, storage of the clinical knowledge base is widened, and symptom judging accuracy is improved.

Custom Order Sets for use by doctors and clinicians allow the user to utilize Order Sets and Orderables in clinical encounters. Closed-loop processes and systems for creating a Custom Order Set and using the Order Set to get feedback from Physicians and make modifications to Orderables and Order Sets used in a hospital are described. A Physician can create her own Order Sets and submit them to the hospital for approval. If approved, they can be included in the hospital's Order Sets Library and used by other Physicians. A Physician can create a Framework of Order Sets. A Framework is intended to reflect how the Physician organizes clinical knowledge in her specialty and is intended to match the way the Physician thinks about a Clinical Encounter with a patient. The Physician selects Order Sets from categories within the Framework. These Order Sets are organized or blended together using a detailed process to form a Custom Order Set. A Custom Order Set provides a visual display of Order Sets based on Views and categories and provides various types of information by way of visual markups and text. The Physician can provide Feedback at several levels, such as at the Order Set or Orderable levels or make requests for new content.

The invention provides an area clinical path management system based on big data technology, wherein the system relates to the field of intelligent medical science. The system comprises a clinical data resource database, a clinical data processing module, a clinical diagnosis and treatment behavior rule database, a clinical path template management module and a clinical path issuing module. The clinical data resource database is used for acquiring and storing the clinical data. The clinical data processing module is used for processing the clinical data and constructing a clinical knowledge atlas. The clinical diagnosis and treatment behavior rule database faces an individual patient and recommends an appropriate clinical diagnosis and treatment rule through similarity measure based on deep learning. The clinical path issuing module is disposed in an area medical cooperation platform and is loaded through configuring a dynamic module. When the area medical cooperation platform is started or reloaded, the clinical path issuing module is dynamically loaded. According to the area clinical path management system based on big data technology, a clinical path template is disposed to thearea medical cooperation platform, thereby improving popularization of the clinical path template.

The invention provides a clinical knowledge base construction method and device. The method comprises the steps: obtaining corresponding first text data from a medical teaching material, and obtaining second text data from a network resource; obtaining a first triple set based on the first text data, and obtaining a second triple set based on the second text data; combining the first triple set and the second triple set, and performing duplicate removal on triples in the combined triple set to obtain a target triple set; and constructing a corresponding medical knowledge graph based on the target triple set. According to the scheme, in the process of constructing the triad, the entity pair is determined by using the first corresponding relation between the chapter theme and the chapter text carried in the data and the second corresponding relation between the medical theme and the web text, so that the efficiency of constructing the medical knowledge graph is improved, a data source, namely the medical textbook, with high accuracy is introduced, and the efficiency of constructing the medical knowledge graph is improved. And the accuracy of the constructed medical knowledge graph is improved.

A system and method for ordering healthcare services wherein a list of selectable orders for medical services for a given patient is dynamically generated based on medical information of the patient (e.g., the patient's condition or set of conditions) and presented to a user (e.g., a physician). In one aspect, a system for ordering patient specific healthcare services comprises a user interface that enables a user to input a medical condition of a patient and to select desired medical services from a list of orderable medical services presented to the user; a services database comprising a plurality of predetermined medical services that are each associated with a medical condition; and an engine for compiling the list of orderable medical services from one or more predetermined medical services in the services database that are associated with the input medical conditions of the patient. A clinical knowledge model is used by the engine to identify a medical condition that is potentially associated with a known or identified medical condition of the patient, based upon potential etiology, potential complication, clinical associations, or any combination thereof.

The invention provides a clinical knowledge graph link prediction method and system based on a relational graph attention network, and the scheme achieves the link prediction of a medical record knowledge graph of a clinical medical patient based on the architecture of an encoder and a decoder. According to the scheme, a relational graph attention network is adopted, patient medical record information in a knowledge graph is coded, and vector representation is carried out on a triple in a more effective mode; meanwhile, a novel entity and relation embedded representation is provided; and finally, by using a ConvKB model, a scoring function is defined, the possibility of effective triples is judged, and according to the ranking of the triples, whether a link relationship exists between patient medical record entities is inferred, so that a link prediction task is completed.

The invention relates to the field of medical information, and especially relates to a clinical knowledge base system based on a pathophysiological path. The system comprises a public knowledge base unit which is used for managing a public knowledge base; a private knowledge base unit which is connected with the public knowledge base unit and is used for managing a private knowledge base; and a knowledge base engine unit which is connected with the private knowledge base unit, triggers the operation according to a triggering mode, and outputs a knowledge conclusion. The system takes the pathophysiological changes as the basis, and reflects the reasonability and individuation of the disease diagnosis and treatment. A semi-open and semi-close mode that the public knowledge base and the private knowledge base coexist meets the common demands of a clinical knowledge base and the personalized demands of a medical unit. Various types of triggering modes provide convenience for medical workers in flexibly using the system. To sum up, the system facilitates the standardization of the diagnosis behaviors of a hospital and medical workers and improves the medical quality.

The embodiment of the invention discloses a medical knowledge graph construction method and device, electronic equipment and a medium. A specific embodiment of the method comprises the steps of collecting a medical related data set; classifying medical related data in the medical related data set to obtain at least one category of medical related data subsets; constructing at least one medical knowledge sub-graph based on the at least one category of medical related data subset; fusing the medicine knowledge sub-atlas, the clinical knowledge sub-atlas, the common life medical knowledge sub-atlas and the emergency treatment knowledge sub-atlas to generate a medical knowledge atlas; and transmitting the medical knowledge graph to the medical related intelligent equipment, and controlling the medical related intelligent equipment to store and display the medical knowledge graph. According to the embodiment, the user can quickly find the required information from the map, and convenience is provided for the user to know medical knowledge.