30 results about "The primary diagnosis" patented technology

The utility model provides an intelligent scheduling command platform with integrated scheduling, and effectively solves the problem that the scheduling work is influenced because an interphone and a camera in the prior art are lack of effective means of effective electric quantity. The dispatching command platform comprises an electric quantity monitoring module, the electric quantity monitoring module comprises a primary diagnosis circuit and a secondary diagnosis circuit, the primary diagnosis circuit detects a current signal of a battery in dispatching equipment during use by using a current sensor, obtains a reminding signal and an early warning signal based on the current signal, and sends the reminding signal and the early warning signal to the secondary diagnosis circuit; the reminding signal and the early warning signal are output to a command center; the primary diagnosis circuit is connected with one end of the secondary diagnosis circuit, and the other end of the secondary diagnosis circuit is connected with the command center.

The invention belongs to the technical field of rail transit, and particularly discloses a train running gear axle box bearing fault monitoring method and system, and the method comprises the following steps: installing vibration temperature sensors on a plurality of axle box bearings of a train running gear to collect temperature and vibration data in real time, and extracting the characteristics of the collected data for cooperative processing; performing feature level fusion on the obtained corresponding collaborative feature values, and preliminarily diagnosing the health condition of the bearing; calculating a corresponding fault probability through the collaborative characteristic value, and secondarily diagnosing the health condition of the bearing; and according to the results of the primary diagnosis and the secondary diagnosis, whether early warning information is sent is judged. By adopting the technical scheme, potential faults can be found earlier and early warning can be performed in time through a coordination and fusion strategy, continuous and stable operation of the rail train is ensured, and a powerful guarantee is provided for the safety of train operation.

The invention relates to the technical field of medical data processing, in particular to a DRG grouping automatic auditing method and system, and the method comprises the steps: S1, extracting all diagnosis and surgical operations from a homepage of an in-hospital medical record of a patient, and generating a Cartesian set of a diagnosis number set and a surgical number set; s2, respectively taking each item combination in the Cartesian set as a main diagnosis or surgical operation, taking other diagnosis or surgical operations of the patient as secondary diagnosis or surgical operations to form a grouping set, and inputting the grouping set into a DRG grouping device to obtain a DRG code of each grouping set; and S3, judging whether # imgabs0 # is true or not, if yes, judging whether the DRG code corresponding to the ACOST is consistent with the original DRG code or not, if yes, passing the auditing, and otherwise, not passing the auditing. The method is simple, efficient and accurate in DRG coding.

The invention relates to the technical field of electronic information, and particularly discloses a medical resource allocation system based on the Internet. By setting the user registration module, the intelligent hospital guide appointment module, the family member guard module, the intelligent question and answer module and the permission granting module, a user can fill basic information through the user registration module, and when the user needs to see a doctor, symptom description is performed through the intelligent hospital guide appointment module; according to the method and the system, a user can select a hospital with rapid reception or good diagnosis and treatment service according to the treatment condition, so that the patient can be helped to rapidly and effectively obtain the medical service, limited medical resources can be efficiently utilized, and in addition, the system and the method can be widely applied to the field of medical treatment. And the users with the family relationship can mutually know the doctor seeing dynamic state, so that the users can care family members in time and know the doctor seeing condition of the family members.

The invention relates to the field of triage in the pneumology department, and particularly provides a primary triage system for the pneumology department, and the system comprises a physical sign obtaining unit which is used for obtaining the body temperature and the disease performance of a registered patient, and the disease performance is the abnormal performance of the patient due to the disease in the recent period; the initial medical record generation unit is used for generating an initial medical record, the initial medical record generation unit obtains basic information of a patient by obtaining registration information of the patient, obtains a patient chief complaint and a current medical history through the physical sign obtaining unit, and pushes the initial medical record of the corresponding patient to a corresponding doctor when the doctor calls a number; according to the primary diagnosis triage system for the pneumology department, the disease performance of the patient is obtained in advance, and the initial medical record is generated, so that the early-stage communication time can be greatly saved during treatment, and the treatment efficiency is improved.

The invention discloses a cross cooperative rail transit vehicle door remote diagnosis system and diagnosis method, and the method comprises the steps: collecting vehicle door operation state information through a slave door controller of each vehicle door, carrying out the classification, feature extraction and dimension reduction processing, generating a low-dimensional feature vector, and carrying out the primary fault diagnosis; each slave door controller uploads the low-dimensional feature vector, the non-time correlation sequence data and the primary diagnosis result to the master door controller of the compartment on the opposite side; the main door controller carries out carriage-level fault diagnosis based on the received opposite-side vehicle door data and carries out edge-side preprocessing on the low-dimensional feature vector; each main door controller uploads the preprocessed data and the carriage-level diagnosis result to a cloud diagnosis platform, and the cloud diagnosis platform carries out deep fault diagnosis and health state prediction; according to the invention, the real-time performance of fault diagnosis is improved.

This invention discloses a method, medium, and device for identifying DRG over-compensation based on invoice reverse engineering and causal imprinting. The method includes: acquiring detailed medical billing data from the medical records to be audited; extracting features of treatment items and mapping them to a medical knowledge graph to obtain a set of feature atoms; inputting the feature atom set into a large language model, outputting real disease phenotypes as pseudo-labels, and performing semantic comparison with the reported primary diagnosis to identify over-compensation of the primary diagnosis; classifying the sensitivity of other reported diagnoses by score, screening high-value complication entities, and verifying their resource consumption matching degree through a clinical pathway constraint library to identify fictitious complications; extracting the main billing items and their low-value billing imprint rule library, and identifying falsely inflated primary intervention items through counterfactual causal comparison; finally, calculating a comprehensive anomaly index based on deviation, matching degree, and divergence, and outputting an audit report. This invention automatically identifies DRG over-compensation anomalies from multiple dimensions, improving the accuracy and comprehensiveness of audits.

The invention discloses a load state diagnosis method and device. The method comprises the following steps: acquiring a load state; when the load state is a fault state, one is added to the first-level diagnosis count value; when the primary diagnosis count value is a first preset threshold value, triggering the secondary diagnosis count value to add one, resetting the primary diagnosis count value and entering a next diagnosis window; taking the next diagnosis window as the current diagnosis window, and returning to execute the acquisition of the load state; and when the secondary diagnosis count value is a second preset threshold value, determining that the load has a fault. Window logic detection is added in the fault identification process, only the period needing to be diagnosed is diagnosed, it can be avoided that an invalid diagnosis period is introduced in the load normal driving process, the diagnosis time is shortened, and therefore the load rate of the bus is reduced. In addition, by regularly collecting the load state and adopting a multi-stage diagnosis counting mechanism, the dependence of fixed time delay is avoided. Even if PWM driving or stepping motor load occurs, the fault state can be continuously monitored and accurately judged in the diagnosis process.

A method and process for providing virtual screening of patients and medical personal implemented as a virtual universe in metaverse, typically with the help of a remote system having Artificial Intelligence with machine learning capability, for collection and presentation of patient's medical data is provided. Currently Doctors conduct virtual visits with patients to help collect patient's medical data and establish initial diagnosis. This is a time taking process and takes up a lot of the physician's and their assistant's valuable time and energy. Using the remote system to do the initial screening and collection of patients medical data for presentation to the physician before the virtual visit, will reduce the time physicians need to spend with the patient to arrive at a diagnosis. The capability will also help a physician to guide an assistant at a remote location to collect additional data of the patient.

The present invention discloses an intelligent auxiliary diagnosis decision system and control method based on an AI chip, which relates to the field of intelligent auxiliary diagnosis technology, including an intelligent handheld auxiliary diagnosis terminal based on an AI chip, a terminal wireless transmission module, and a server-side medical large model analysis module. The present invention can use an intelligent handheld auxiliary diagnosis terminal based on an AI chip to collect health-related data of patients, an edge-side auxiliary diagnosis analysis module based on an AI chip to parse the original data describing the health status information, and perform edge-side health status diagnosis and analysis based on the original data describing the health status information; the data source of the server-side medical large model analysis module can also be combined with historical medical data, and the patient's terminal comprehensive health data can be combined to output the patient's health assessment results, realizing the initial diagnosis from the edge, patient self-screening, to the intelligent auxiliary diagnosis on the server side, and finally providing the attending physician with more accurate intelligent auxiliary diagnosis options.

The invention relates to the technical field of motor diagnosis, in particular to a motor fault diagnosis system based on deep learning, which comprises the steps of acquiring temperature data and vibration data of a motor and transmitting the data to a server, and deploying a diagnosis module, a convolutional neural network and a text large model in the server; and the server diagnoses the temperature data and the vibration data, including the steps of: performing primary diagnosis by the diagnosis module based on a vibration equipment standard; carrying out primary diagnosis by the convolutional neural network; comparing the two primary diagnosis results, wherein the primary diagnosis results comprise normal and abnormal; when the two primary diagnosis results are different and any one of the primary diagnosis results is abnormal, the diagnosis module and the convolutional neural network perform secondary diagnosis based on the initial temperature data and vibration data; and when the secondary diagnosis result is the same as the primary diagnosis result, performing final diagnosis by the text large model based on the initial temperature data and the initial vibration data.

According to the switch cabinet mechanical fault diagnosis method and device, the storage medium and the computer equipment provided by the invention, when the switch cabinet fault is diagnosed, the vibration signal collected by the sensor channel is decomposed into the plurality of intrinsic mode components, and the fitness value of each component is determined, so that the fault can be diagnosed according to the correlation degree between the component in the fitness value and the fault; screening out fault sensitive components from the components; and then component features of the fault sensitive components are extracted, and feature vectors of the component features are generated based on fitness values of the component features, so that the sensitivity degree of the component features can be adaptively adjusted, and a primary diagnosis result obtained by performing primary diagnosis on the feature vectors through a dynamic fault feature vector library has relatively high accuracy. And finally, performing information fusion analysis on the primary diagnosis results of the plurality of sensor channels in the switch cabinet by adopting an improved D-S evidence theory, and generating a fault diagnosis result with relatively high accuracy and reliability for the switch cabinet through multi-dimensional capture of composite fault features.

A system is provided.SOLUTION: A system comprising: means for a user to input a health consultation by text, voice, or image; means for a device to send the user input information to a server; means for the server to analyze the received information by a AI diagnostic module and make an initial diagnosis; means for the server to generate and send additional questions to the user through the device; means for receiving the user's additional answers and providing a final diagnostic result; means for providing a recommended action or action plan based on the diagnostic result; and means for linking with an external service and making necessary reservations and arrangements.SELECTED DRAWING: Figure 1

The application discloses a software diagnosis system, which comprises at least one occurrence domain for recording events triggered by software activities and performing diagnosis on the events; the occurrence domain comprises an event processing source for recording and processing event sources of the software activities; an information block generator connected to the event processing source for generating recording information blocks or event information blocks and sending the recording information blocks or the event information blocks to the event processing source; a primary diagnosis device for performing diagnosis on the recording information blocks or the event information blocks and generating a diagnosis report based on the diagnosis result; and a primary capture device connected to the event processing source and the primary diagnosis device for realizing information docking between the event processing source and the primary diagnosis device and performing information filtering on the recording information blocks or the event information blocks and / or the diagnosis report. Through the above method, the software diagnosis system provided by the application can realize dynamic collection and real-time diagnosis.

The invention relates to an intelligent hospital guide method, a storage medium and a system.The intelligent hospital guide method comprises the steps that a hospital guide program is started in response to a patient, and hospital guide requirements are obtained based on medical record information and comprise primary diagnosis and report interpretation; if the hospital guide demand is report interpretation, the patient is prompted to add a registration through a prompt page; and if the hospital guide demand is the primary diagnosis, entering an inquiry interface. According to the method and the device, hospital guidance for non-appointment patients such as report interpretation and the like is realized.

The invention provides a diagnostic specification management system and method based on intelligent verification, and belongs to the technical field of automobile diagnosis, and the system comprises a template management module which is used for storing and managing at least one first-edition diagnostic questionnaire template and is respectively adaptive to different types of electronic control units; the controller management module is used for creating a new controller project and associating a first-edition diagnosis questionnaire template for the new controller project; the resource distribution module is used for distributing diagnosis fault codes and data identifiers according to the vehicle type information on the basis of the primary diagnosis questionnaire template so as to generate an exclusive diagnosis questionnaire; the data verification module is used for automatically verifying the filled exclusive diagnosis questionnaire; and the knowledge base module is used for constructing a fault tree based on the filled exclusive diagnosis questionnaire. According to the method, the problems of low diagnostic specification design efficiency, error proneness and disordered resource allocation are solved, and standardized design, intelligent verification and knowledge-based application of the diagnostic specification are realized.

An object of a system according to an embodiment is to provide a highly reliable second opinion and select an optimal hospital.SOLUTION: A system according to an embodiment includes a data acquisition unit, a diagnosis unit, and a proposal unit. The data-loading unit uses the generated AI. The diagnosis unit performs a simple diagnosis based on the image data of the disease and the findings captured by the data capturing unit. The proposal unit provides a second opinion based on the diagnosis result obtained by the diagnosis unit. The proposal unit proposes an optimal hospital candidate on the basis of the evaluation information, the facility, and the treatment result of the hospital.SELECTED DRAWING: Figure 1

The present invention relates to the technical field of medical data processing, and in particular to a method and system for automatic review of DRG grouping. The method includes the steps of: S1, extracting all diagnoses and surgical operations from the front page of the patient's inpatient medical record to generate a Cartesian set of the diagnosis set and the surgical operation set; S2, respectively using each combination in the Cartesian set as the primary diagnosis or surgical operation, and the patient's other diagnoses or surgical operations as the secondary diagnoses or surgical operations to form a grouping set, and inputting the grouping set into a DRG grouper to obtain the DRG code of each grouping set; S3, judging whether it holds. If it holds, judge whether the DRG code corresponding to A_COST is consistent with the original DRG code. If it is, the review passes; otherwise, the review fails. The method of the present invention is simple and efficient, and the DRG code is accurate.

The invention relates to the field of artificial intelligence and education science and technology, and particularly discloses a personalized English grammar diagnosis teaching system and method based on a native language background, and the method comprises the steps: receiving input data containing an English text and a user background attribute; the text is processed through an English grammar detection engine, and a primary diagnosis result containing error identification information is generated; on the basis of the primary diagnosis result, an English grammar diagnosis rule base is inquired in combination with context information, and fine-grained error classification information is generated; according to the error classification information, updating a personalized error portrait model corresponding to the user; and based on the updated wrong portrait model and user historical data, retrieving and generating adaptive personalized tutoring content from an English grammar teaching resource library, and outputting the personalized tutoring content. The method has the advantages of being accurate in native language negative migration recognition, high in personalized feedback pertinence, good in teaching closed-loop integrity and the like.

The present disclosure provides a medical device for diagnosing a nervous system disease of a patient, configured to be used in a pre-hospital environment, comprising: a memory; the camera is configured as an image / video data sensor for acquiring a patient and comprises a microphone for acquiring natural language data of the patient; a processor communicatively connected to the memory, the sensor, and the camera, the processor configured to: execute computer instructions to obtain patient data input; performing at least one primary computational algorithm and / or analysis process to evaluate the patient data input and determine a primary diagnosis based on the patient input data; in response to the primary diagnosis indicating that a further diagnosis is necessary, performing at least one auxiliary computational algorithm and / or analysis process to evaluate the patient data input, and determining one or more additional diagnoses based on the patient data input; and determining a final diagnosis and / or treatment regimen for the patient in response to the primary diagnosis and the one or more additional diagnoses.

A method and process for providing virtual screening of patients and medical personal implemented as a virtual universe in metaverse, typically with the help of a remote system having Artificial Intelligence with machine learning capability, for collection and presentation of patient's medical data is provided. Currently Doctors conduct virtual visits with patients to help collect patient's medical data and establish initial diagnosis. This is a time taking process and takes up a lot of the physician's and their assistant's valuable time and energy. Using the remote system to do the initial screening and collection of patients medical data for presentation to the physician before the virtual visit, will reduce the time physicians need to spend with the patient to arrive at a diagnosis. The capability will also help a physician to guide an assistant at a remote location to collect additional data of the patient.

This invention belongs to the field of rail transit technology, specifically disclosing a method and system for monitoring faults in axle box bearings of a train running gear. The method includes the following steps: installing vibration and temperature sensors on multiple axle box bearings of the train running gear to collect temperature and vibration data in real time; extracting features from the collected data and performing collaborative processing to obtain corresponding collaborative feature values; performing feature-level fusion to preliminarily diagnose the bearing's health status; calculating the corresponding fault probability using the collaborative feature values ​​to perform a secondary diagnosis of the bearing's health status; and determining whether to issue an early warning based on the results of the preliminary and secondary diagnoses. By employing this technical solution and using a collaborative and fusion strategy, potential faults can be detected earlier and timely warnings issued, ensuring the continuous and stable operation of rail trains and providing strong protection for train operation safety.

The present invention provides a method and system for screening medical records with incorrect primary diagnoses, including: obtaining all entities in each medical record in a set of medical records to be screened; the entities include: symptoms, diseases, and signs; calculating a weight value of each entity in each medical record based on the primary diagnosis of each medical record; screening out a first set of medical records with incorrect primary diagnoses from the set of medical records to be screened based on the weight values; the first set of medical records is a set of medical records in which the weight value of each entity is less than a preset threshold. The present invention alleviates the technical problem of low screening accuracy of medical records with incorrect primary diagnoses existing in the prior art.

An object of the system according to the embodiment is to reduce a waiting time of a hospital and to improve efficiency of diagnosis and treatment of a patient.SOLUTION: A system according to an embodiment includes an initial diagnosis unit, a hospital cooperation unit, and a pharmacy cooperation unit. The initial diagnostic unit performs initial diagnoses of patients using the generated AI. The hospital cooperation unit transmits the diagnosis result generated by the initial diagnosis unit to the hospital and determines whether the patient needs to receive a medical examination. The pharmacy division sends a prescription to the pharmacy based on the diagnosis result sent by the hospital division, and prepares for the patient to receive the medicine.SELECTED DRAWING: Figure 1

System, methods and computer storage media are disclosed for providing a decision support tool for reducing readmissions of AMI patients through early prediction of readmission. An AMI patient may be identified using a working diagnosis and / or an elevated troponin level. One or more machine learning models may be utilized to predict readmission at a time prior to discharge. Based on the prediction, an intervening action may be automatically initiated. Further embodiments include training machine learning model(s) to predict readmission of an AMI patient. In some embodiments, a first model may be trained using reference patient data as it existed at a predetermined time following the patient's admission (e.g., 12 hours after admission), and a second model may be trained using reference patient data as it existed at a later time (e.g., discharge). Readmission risk scores from each model may be combined to determine an overall risk for an AMI patient.

The application relates to a device fault judgment method and system based on a graph cognitive convolution network, the method comprising the following steps: positioning a fault device according to system data and sensor data to obtain a primary diagnosis graph; introducing expert diagnosis data into an eye movement tracking algorithm to simulate the diagnosis process of expert diagnosis and diagnose the positioned fault device to obtain a high-level diagnosis graph; fusing the primary diagnosis graph and the high-level diagnosis graph according to a set weight value, using a graph cognitive convolution network to extract fault features of the fused graph; and inputting the fault features into a classifier to obtain a fault judgment result. Compared with the prior art, the application has the advantages that fault detection is more accurate.

To provide a diagnostic system, a diagnostic method and a program for obtaining a diagnostic result having reliability of a fixed level or more.SOLUTION: In a diagnosis system including a diagnosis server 200 communicably connected to a plurality of user terminals via a network, the diagnosis server includes an acquisition unit that acquires a captured image obtained by capturing an image of a diagnosis target site, a diagnosis unit that derives primary diagnosis result information indicating a result of diagnosis according to a predetermined diagnosis criterion based on the captured image and derives secondary diagnosis result information obtained by digitizing a predetermined diagnosis item according to the predetermined diagnosis criterion based on the primary diagnosis result information, and an information generation unit that generates diagnosis result-related information including at least a content of the secondary diagnosis result information.SELECTED DRAWING: Figure 6

The invention relates to the technical field of automobile thermal management, in particular to a vehicle thermal management diagnosis method, device and equipment and a medium. According to the method, the primary diagnosis value of the thermal management assembly in the current preset period is determined according to the collected thermal management index information, thermal management diagnosis is performed on the thermal management assembly according to the primary diagnosis value of the current preset period, and the diagnosis type of the thermal management assembly is obtained, so that the thermal management diagnosis abnormal assembly is determined; the thermal management diagnosis value of the vehicle is calculated according to the thermal management influence relation between the thermal management diagnosis abnormal components and the corresponding components, the thermal management components in the whole thermal management system are comprehensively considered, the precision of the thermal management diagnosis value is improved, the thermal management diagnosis level of the vehicle is determined according to the thermal management diagnosis value, and the vehicle thermal management diagnosis efficiency is improved. The thermal management diagnosis level of the vehicle is quantitatively evaluated, and a driver can conveniently know the severity of the thermal management problem of the vehicle.

The invention belongs to the crossing field of a medical information technology and an artificial intelligence technology, and particularly relates to an intelligent medical coding method and device and a storage medium. The intelligent medical coding method comprises the following steps: extracting first feature data for disease preliminary screening from medical data of a patient, and generating a structured description text; inputting the structured description text into a first artificial intelligence model for disease preliminary screening to obtain a main diagnosis candidate set and / or other diagnosis candidate sets; constructing a main diagnosis prompt word and / or other diagnosis prompt words based on the medical data, the external medical knowledge base and the main diagnosis candidate set / or other diagnosis candidate sets; and respectively inputting the main diagnosis prompt words / or other diagnosis prompt words into a second artificial intelligence model for disease discrimination to obtain recommended main diagnosis and / or recommended other diagnosis sets. The method provided by the invention not only can efficiently process the long text, but also has high accuracy and high coverage rate.

The invention discloses an AI-based intelligent auxiliary diagnosis method and system for early Alzheimer's disease. The method comprises the following steps that a patient fills in necessary personal information in the AI diagnosis system to complete registration to form an ID account number corresponding to the patient and automatically log in, and the AI diagnosis system obtains facial expression changes of the patient, obtains voices and intonations of the patient and monitors gaits and reaction time of the patient; the AI diagnosis system sends a voice prompt to guide the patient to fill in a neuropsychological scale on an interactive interface, guides the patient to complete self-evaluation, and inputs the facial expression change of the patient, the voice and intonation of the patient, the monitoring of the gait and reaction time of the patient and the filling in of the neuropsychological scale into a trained machine learning model; according to the Alzheimer's disease diagnosis system and method, the Alzheimer's disease can be rapidly screened and preliminarily diagnosed, and the workload of medical staff is relieved.