29 results about "Patient database" patented technology

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

The invention relates to the technical field of medical information, and discloses a dosage regulation and control data storage method and system based on multi-modal diagnosis and treatment data, and the method comprises the steps: obtaining historical diagnosis and treatment data and historical dosage data of a patient; matching the diagnosis result data with health diagnosis result data in the health diagnosis and treatment data, and matching the wearable device data with health wearable device data in the health diagnosis and treatment data; determining whether to adjust the historical dose data to obtain the current dose, and outputting a judgment result needing to be adjusted; and obtaining the adjusted dose data, and storing the diagnosis result data, the diagnosis result similarity, the wearable device data, the wearable data similarity, the historical dose data and the adjusted dose data into a dose adjustment unit in a patient database. According to the invention, multi-modal diagnosis and treatment data can be integrated and applied to dose regulation and control, and a complete medication decision data chain is constructed.

A method for providing a treatment recommendation to a physician for treating a patient is disclosed. The method comprises determining, from a processor in communication with a patient data repository, a first treatment recommendation based on a combination of selected patient demographics from the patient data repository applicable to the patient, and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, the first treatment recommendation having a first survival rate and comprising the use of a first VAD. The method then obtains a first signal from using the first VAD on the patient. The method then determines a second treatment recommendation based on the first signal and the first treatment recommendation, the second treatment recommendation having a second survival rate. The method then provides the second treatment recommendation to the physician if the second survival rate is higher than the first survival rate.

A system for an automated dental patient prioritization and treatment processing based on dental patient-related data, including a processor of a dental patient processing server node configured to host a machine learning (ML) module and connected to an office manager-entity node and to at least one dentist entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire a dental patient report including a list of dental procedures prescribed to a dental patient from the office manager-entity node; parse the dental patient report a to derive a plurality of key ordered features; query a local dental-patient's database to retrieve local historical dental-patients'-related data based on the plurality of the key ordered features; generate at least one feature vector based on the plurality of the key ordered features and the local historical dental patients'-related data; and provide the at least one feature vector to the ML module configured to generate a predictive model for producing at least one dental-patient processing recommendation parameter; and generate at least one procedure performance recommendation and dental patient prioritization based on the at least one dental-patient processing recommendation parameter.

The invention provides a multi-modal fusion Bayesian medical auxiliary diagnosis method and system, belongs to the technical field of medical auxiliary diagnosis, and is used for solving the problems of inquiry redundancy, insufficient adaptation of small sample / emergency treatment / rare disease scenes and large disease probability calculation deviation in related technologies. According to the method, a knowledge graph containing disease-symptom exclusion weight and a patient database are constructed, intelligent inquiry is combined with regional correction, core symptoms are screened through real-time evidence, the disease probability is calculated through multi-modal data processing and Bayesian reasoning, and tool collaborative verification and scene adaptation optimization output are carried out. The system comprises a core calculation unit, a multi-modal acquisition unit, a database unit, a detection interaction unit and a result display unit for supporting method execution. The method can improve the inquiry efficiency and diagnosis precision, is suitable for multiple clinical scenes, and provides reliable assistance for doctors.

A system for predicting clinical trial participation may comprise a participation probability (PP) server connected to a machine learning (ML) module. The PP server may receive clinical trial (CT) parameters from a user device. The PP server may query a patient database using the CT parameters to identify potential participants. The PP server may collect social media data for the potential CT participants. The PP server may generate feature vectors based on the social media data. The PP server may provide the feature vectors to the ML module and receive participation probability index (PPI) scores for the potential CT participants from the ML module. The PP server may generate a ranked list of likely CT participants based on the PPI scores. The PP server may provide targeted content to potential participants with low PPI scores. The PP server may update the PPI scores based on engagement with the targeted content.

According to one embodiment, a method of designing, selecting, dispensing custom finished abutments or performing custom finished abutment minor surgery by a plurality of custom finished abutment sets designed or manufactured based on a patient database is disclosed, the method comprising the steps of: provisioning a plurality of custom finished abutment sets based on a patient database; picking up patient oral data; selecting a suitable abutment among the plurality of custom finished abutment sets using software based on the picked up patient oral data; and dispensing the selected custom finished abutment or performing custom finished abutment minor surgery.

A system and method for automated imaging protocol formulation are provided. The system receives patient data from one or more patient databases. Using a first machine learning model, imaging protocol formulation information is extracted from the patient data. Using a second machine learning model, an imaging protocol template is selected from multiple candidate templates based on the extracted information. Using a third machine learning model, the selected template is fine-tuned based on the patient data. The fine-tuned template is output.

A system for generation of medical recommendation based on patient-related data, including a processor of a medical recommendations server (MRS) node configured to host a machine learning (ML) module and connected to a patient entity node and to at least one medical emergency entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire sensory data from a plurality of biosensors encapsulated into a patient wearable device; acquire patient data from a mobile device of the patient, wherein the patient data may include video data and audio data; parse the sensory data to derive a plurality of key classifying features; extract a plurality of indicators from the patient data based on the plurality of key classifying features; query a local patients'database to retrieve local historical patients'-related data related to previous patients'engagements associated with previous medical recommendations based on the plurality of key classifying features and the plurality of indicators; generate at least one feature vector based on the plurality of key classifying features and the plurality of indicators and the local historical patients'-related data; and provide the feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of medical recommendation parameters from a medical recommendation predictive model generated by the ML module using outputs of the ANN based on the feature vector; and generate medical recommendations based on the plurality of the medical recommendation parameters.

This invention discloses a method for predicting the risk of complications after esophageal surgery. The method includes: acquiring multi-parameter monitoring data collected at fixed time intervals during postoperative monitoring of patients after esophageal surgery, generating a multi-parameter time series dataset; based on a database of historical patients with no complications, grouping and matching historical patients according to age group, surgical type, and surgical duration, calculating the mean and standard deviation of each parameter at each time point after surgery for each group, and generating a standard recovery trajectory reference template containing the expected value and normal fluctuation range of each parameter at each time point after surgery; calculating the difference between the measured values ​​of each parameter of the current patient at each time point and the expected values ​​at the corresponding time points in the standard recovery trajectory reference template, and standardizing the difference by dividing it by the corresponding standard deviation; this invention achieves dynamic postoperative assessment of the risk of complications after esophageal surgery and can capture early abnormal patterns of slight deviations in multiple parameters.

The invention discloses a chronic disease intelligent management system based on multi-modal data fusion, and relates to the technical field of intelligent management, and the system comprises a data collection and preprocessing module which is used for collecting electronic medical record data, historical medication data and living habit data, and constructing a patient database; the cross-domain knowledge fusion module is used for fusing multi-domain related medical data and a knowledge representation technology and constructing a cross-domain medical knowledge graph; the medication plan generation module is used for analyzing the physiological monitoring data, collected in real time, of the patient and generating a personalized medication plan based on the medical knowledge graph; and the dynamic optimization module is used for optimizing the personalized medication scheme by combining the medical knowledge graph according to the medication feedback data and the real-time physiological monitoring data of the personalized medication scheme. According to the method, by adopting ontology mapping and semantic similarity calculation, deep alignment of traditional Chinese medicine knowledge and modern medical knowledge is realized, and the risk of treatment scheme conflict caused by knowledge splitting is reduced.

In an example embodiment, a system, a method, and an apparatus are provided that include an application, configured for: creating a subjective, objective, assessment, and plan (SOAP) report, the creation includes: using a voice-to-text converter to turn a free-form verbal conversation with a patient into a text stream, the text stream is enhanced with metadata from a database to create an enhanced text stream that forms a basis for the SOAP report. Other examples can provide that the metadata includes data from a previous SOAP report, and data associated with a prevision visit of the patient. Still other scenarios can provide that the enhanced text stream is sent to a large language model (LLM) to automatically create a practitioner-specific SOAP report. The SOAP report is structured according to a practitioner's specific requirements, and the SOAP report is automatically entered into a patient database for subsequent access.

A system and method are described for analyzing electronic data records received from a patient database. Analyzing the electronic data records may include identifying one or more common care gaps shared between a group of patients having a common value in a data field of the patient's corresponding electronic data record. The identification of the common care gap can be used to identify treatments that are not being provided to the group of patients, but should be provided to the group of patients.

A system for retrieving patient information using large language models including a computing device configured to receive a natural language query as a function of a user input, input the natural language query into a large language model communicatively connected to the least a processor, receive a computer language query comprising a plurality of nodes from the large language model, map the plurality of nodes to one or more entries in a patient database, receive a database response from the patient database as a function of the mapping, generate a final database query as a function of the database response. query the patient database using the final database query, receive a user response as a function of the final database query, and transmit the user response to a graphical user interface as a function of the final database query.

A method for selecting questions to be answered by a patient includes receiving, from a patient database (3), patient data (5) indicative of a health condition of the patient. The patient data (5) includes sensor data (7) which has been generated by at least one sensor (8) for determining the health condition of the patient. The method further includes inputting the patient data (5) as input data (9) into a question selection algorithm (10) configured for selecting questions, based on the input data (9), from a list (6) of predetermined questions stored in a question database (4). The method further includes outputting at least one selected question or a list (13) of selected questions to be answered by the patient as output data (14) by the question selection algorithm (10). The patient data (5) includes at least one of anamnesis data (16), or medication data (19) of the patient.

A method may include receiving at least one of preoperative activity levels or postoperative activity levels associated with an instant patient from a patient database, receiving a patient preference associated with the instant patient from the patient database, determining, based on at least one of the preoperative activity levels or the postoperative activity, one or more potential connections from a plurality of users, and determining a time interval. The time interval may be based on a date of surgery for the instant patient and a present date. The method may further include determining a search area. The search area may be based on a current location of a user equipment associated with the instant patient. The method may further include determining one or more correlated users, based on (i) at least one of the preoperative activity levels or the postoperative activity levels and (ii) the one or more potential connections.

The invention discloses an excrement drainage management system. The system comprises a medical instrument unit and a patient database, the medical instrument unit is used for counting the types and the number of medical instruments and correspondingly recording the use states of the medical instruments and corresponding patients; the patient database is used for counting data of hospitalized patients and correspondingly distributing the data to the excrement drainage instrument library module in the medical instrument unit according to medical instrument use conditions of the patients; the sphincter of a patient is expanded through the inner side balloon and the outer side balloon, meanwhile, the inner side balloon is inflated through the balloon inflation connector, the inflation change of the inner side balloon can be observed through the external air bag on the balloon inflation connector, and then practice medical staff can operate excrement drainage equipment conveniently; the situation that the body of a patient is injured due to excessive inflation of the inner balloon caused by misoperation is avoided, meanwhile, the drainage catheter can be limited through the arrangement of the outer balloon in cooperation with the inner balloon, and the drainage catheter is prevented from falling off in the using process.

A decision support system for healthcare workflow management including a computer system having a controller, a memory, and a user-interface. The system has a patient database wherein patients are uniquely identifiable and wherein records include at least sought procedures and sought date of appointments for those procedures. The system further has a medical professional database wherein medical professionals are uniquely identifiable and where records include at least skillsets and a calendar. At least one software program is designed to, substantially in real time, assess the calendar and the sought procedures, select changes in the calendar to schedule an appointment set for the sought procedures to the medical professionals and is further designed to weigh performance variables to maximize key performance indicators within resource constraints wherein expected value and variance at a beginning of a period are benchmarked against actual value and variance at period end.

The invention provides an in-hospital wireless call nursing management method and system based on parallel communication. Call requests can be simultaneously sent through a plurality of wireless channels. Therefore, the transmission reliability of the call request is greatly improved, even if a certain channel breaks down or is interfered, other channels can still transmit information normally, and the call request can be ensured to arrive at the communication gateway timely and accurately. And the classification module classifies the call requests according to the demand types, so that nurses can quickly and accurately screen and process different types of requests, and the working efficiency is improved. The resource allocation module is connected with a nurse database and a patient database, and can allocate an optimal nurse for nursing according to factors such as illness state information of patients and working states of nurses. Therefore, nursing resources are reasonably and effectively utilized, and the quality of nursing service is improved. The nursing station can grasp the distribution condition of nurses in real time, and resources can be reasonably allocated.

A medical data and diagnostics management system for processing classified EGM datasets includes a server system that receives transmissions of classified EGM datasets, each corresponding to an arrhythmic episode detected by an IMD, and applies a machine-learning model to each classified EGM dataset stored in a database, thereby determining for each of the classified EGM datasets to which the model is applied a respective indicator of whether the IMD classification is a false positive or a true positive. The system is further configured to remove from the database one or more of the classified EGM data sets for which the respective IMD classification is identified using the machine-learning model as being a false positive, thereby creating a plurality of machine-adjudicated patient database records stored in the database. The system may also provide for display a selected one of the machine-adjudicated EGM datasets and / or facilitate reprograming the IMD.

The invention discloses an intelligent hospital terminal treatment service method and system, and relates to the technical field of medical informationization, and the method comprises the specific steps: enabling a patient to input personal information through an intelligent hospital terminal system, including the name, age, gender and medical history, and storing the information in a patient database, through the deep learning algorithm and the artificial intelligence technology, a large amount of medical image data and other clinical data can be automatically analyzed, key information is rapidly extracted, accurate auxiliary diagnosis information is provided for doctors, the time for the doctors to process and analyze the data is shortened, the diagnosis process is more rapid and efficient, and meanwhile the diagnosis efficiency is improved. By learning a large amount of case data, more accurate disease prediction and diagnosis suggestions can be provided, the risk of diagnosis errors is effectively reduced, and the accuracy and reliability of diagnosis are improved.

The invention relates to the technical field of health management, in particular to an internal medicine chronic disease early-stage individualized intervention model training method, which comprises the following steps of: acquiring electronic medical records of a plurality of patients through a patient database, constructing a chronic disease knowledge map, and establishing a chronic disease knowledge map according to characteristics of common attribute entities among the patients in the chronic disease knowledge map in a distribution relationship; and combining with the degree of disease course similarity between the patients as the possibility of disease evolution from one patient to another patient, so as to obtain the chronic disease evolution path of the patient. According to the chronic disease evolution path of the patient, an intervention method adopted by the patient in each time of disease evolution in the patient database is obtained, and the intervention effect of the corresponding time is determined according to the change condition of the disease course and degree of the chronic disease of the patient. And further determining a preferred intervention method of the patient, and training a machine learning algorithm to obtain an intervention method generation model. According to the method, the individuation degree of early intervention of internal medicine chronic diseases is greatly improved.

A patient portal system may include a patient database module to interpret a patient profile value and a clinical database module to interpret a clinical study description. The patient database module may also interpret patient clinical data in response to the patient profile value and the clinical study description, and include at least a portion of the patient clinical data as longitudinal data of a patient corresponding to the patient profile value. The system may also include a patient interface module to implement a patient engagement interface and to interpret a patient medical event value in response to patient interactions with the patient engagement interface. The patient database may also adjust the longitudinal data in response to the patient medical event value.

A system for an automated dental patient prioritization and treatment processing based on dental patient-related data, including a processor of a dental patient processing server node configured to host a machine learning (ML) module and connected to an office manager-entity node and to at least one dentist entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire a dental patient report including a list of dental procedures prescribed to a dental patient from the office manager-entity node; parse the dental patient report a to derive a plurality of key ordered features; query a local dental-patient's database to retrieve local historical dental-patients'-related data based on the plurality of the key ordered features; generate at least one feature vector based on the plurality of the key ordered features and the local historical dental patients'-related data; and provide the at least one feature vector to the ML module configured to generate a predictive model for producing at least one dental-patient processing recommendation parameter; and generate at least one procedure performance recommendation and dental patient prioritization based on the at least one dental-patient processing recommendation parameter.

A patient monitoring system helps staff personnel, clinicians and physicians who are making their rounds of patients quickly and accurately enter their reports of patient observations. The patient monitoring system has a monitoring device carried by the staff personnel as they make their rounds which wirelessly communicates with a central server. The monitoring device is populated with patient profile information saved within a centralized server when the monitoring device is in operative communication with the central server and allows the rounding personnel to report their observations back to the server. The monitoring device can also scan a code from a proximity device attached to the patient. The patient information and reported observations are stored by the server in a patient database and are not stored by the monitoring device or by the proximity device which reduces the risk that the patient information will be lost, stolen, or otherwise compromised.

The application discloses a kind of based on artificial intelligence's medication strategy generation system design method, it is related to wisdom medical treatment and biological information technology field, including: the drug database comprising drug physicochemical property and therapeutic window parameter and the patient database comprising patient dynamic and static information;Utilize the decision model of the Transformer architecture and output the candidate drug set according to patient symptoms;Optimization model is input to the candidate set and patient characteristics, generate optimal medication strategy by calculating and minimizing preset multidimensional loss function;Run monitoring model to compare real-time blood drug concentration and therapeutic window parameter, predict change trend and generate real-time revised medication strategy.The application solves the subjectivity problem of traditional medical treatment relying on experience through "decision-optimization-monitoring" closed loop, realizes the personalized precision dosing and dynamic adjustment of considering efficacy, safety, economy and targeting.

The present application relates to the technical field of health management, in particular to a kind of early individualized intervention model training method of internal medicine chronic disease, comprising: obtaining the electronic medical record of several patients by patient database and constructing chronic disease knowledge graph, by the feature of the attribute entity shared between patients in chronic disease knowledge graph in distribution relationship, and the degree of the course of disease similar between patients are combined as the possibility of the evolution of disease from one patient to another, to obtain the chronic disease evolution path of patient;According to the chronic disease evolution path of patient, the intervention method used by patient in patient database at each time of disease evolution is obtained, the intervention effect of corresponding time is determined by the change of the course and degree of patient's chronic disease, further determine the preferred intervention method of patient and train machine learning algorithm to obtain intervention method generation model.The present application greatly improves the individualization degree of early intervention of internal medicine chronic disease.

The embodiment of the invention relates to a processing method and device for embedding a multi-modal medical knowledge graph into a coding model. The method comprises the following steps: constructing a patient database by collecting medical data of a patient; constructing a corresponding multi-modal medical knowledge graph based on a preset medical knowledge rule set and a patient database, and recording the multi-modal medical knowledge graph as a knowledge graph M; constructing a corresponding embedded coding model for the knowledge graph M; pre-training the embedded coding model based on the knowledge graph M; and after the pre-training is finished, processing a medical map reasoning task of the knowledge map M based on an embedded coding model. According to the method, not only can the complex entity relationship in the atlas be deeply mined, but also the multi-modal entity relationship in the heterogeneous atlas can be deeply mined.

A remote view playback system for use by medical practitioners to improve patient outcomes, including a patient database and data storage engine including means for collecting and storing data from any number of electronic devices and instructions for transmitting data to any number of electronic devices and for storing data associated with the patient records and / or data to be stored as quality data; a communication hub for collecting, agnostically, data from any number of electronic devices and instructions for transmitting data to any number of electronic devices, and for storing data associated with the patient records to be stored as quality data, and providing access to all data, so that data from multiple disparate sources can be acquired and consolidated within a unified view; optionally, at least one monitoring station; and a user interface rules engine to collect, store and process data in real time, compose views that organize data, notify end-users based on execution steps defined by end-users, display an organized view of data within a timeline of events, change, for end-users, or augment the execution steps, provide notifications at the same time the end-user is reviewing data, and apply one or more rules to the data selected from assessment data from each monitored patient to determine whether a rule for that monitor has been violated or whether best practice rules established by a health care provider are being adhered to as expected.