17 results about "Patient database" patented technology

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.

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 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 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.

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.

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 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.