23 results about "Patient report" patented technology

The described technology is generally directed towards automated and dynamic creation of a consolidated summary of medical information pertaining to a patient. Rather than a caregiver having to trawl through numerous reports and medical information, utilizing consolidated reports enables efficient review of medical information to enable expedited determination and treatment of the patient. In an example sequence of operation, a collection of patient / medical reports can be individually summarized to form a collection of summarized versions of the original reports. The summarized reports can be further collectively summarized to generate a consolidated summary. Hence, the consolidated summary is a summary of the initial collection of patient / medical records. A consolidated summary can comprise patient information, medical history, medical images, pertinent medical research, etc. A consolidated summary can be presented on an interactive display, with the respective initial patient / medical reports accessible via selectable hyperlinks presented in the consolidated summary.

The present disclosure provides systems and methods for providing neurostimulation therapy according to patient features. The patient features may be analyzed to develop a patient model between physiological and / or patient reported features and optimal settings for a neurostimulation therapy using machine learning operations. The model is trained using data corresponding to time intervals in which electrical pulses are applied to the patient, including video kinematic data, patient reported pain levels, implanted or worn sensor patient data, and stimulation parameters. The model is used to control ongoing neurostimulation therapy for the patient.

Disclosed herein are systems and methods for evaluating implant condition and associated patient condition. A method for determining an implant condition can include the steps of receiving implant vibration data from an implant coupled to a bone of a patient, receiving patient reported data from the patient, and generating an implant loosening score from the implant vibration data and the patient reported data. The implant vibration data can be generated by a sensor associated with the implant. The implant loosening score can be related to movement of implant with respect to the bone.

The invention discloses a patient report outcome management method and system, and relates to the technical field of medical information. The method comprises the following steps: firstly, dividing role permissions, and defining an information viewing and operating range; key operation behaviors are collected and coded according to a unified format to generate a role behavior chain; extracting a content degradation factor from the behavior chain, determining a weight through a random forest algorithm, and carrying out weighted calculation on a content degradation index; a term vector is generated by using a biomedical model, and a medical compliance index is calculated through a cosine distance; content optimization indexes are calculated by fusing the two indexes, and fields needing to be optimized are marked by comparing threshold values; when the field content is the content optimization item, optimizing the original field content; and comparing data of the experimental group and the control group through A / B test, calculating to obtain an optimization effect index, and automatically replacing the optimization effect index with optimization content if the optimization effect index reaches the standard to form a final patient report. According to the optimization effect index, whether the optimized field content is replaced with the original field content or not is determined, and a final patient report is obtained.

A method and system of audibly broadcasting responses to a user based on user queries about a specific patient report, the method comprising receiving an audible query from the user to a microphone coupled to a collaboration device, identifying at least one intent associated with the audible query, identifying at least one data operation associated with the at least one intent, associating each of the at least one data operations with a first set of data presented on the report, executing each of the at least one data operations on a second set of data to generate response data, generating an audible response file associated with the response data and providing the audible response file for broadcasting via a speaker coupled to the collaboration device.

The invention relates to the technical field of medical data management, and particularly discloses an acute postoperative pain assessment and management decision-making system based on a patient report result, which is used for solving multiple challenges facing postoperative acute pain management, including subjectivity of a traditional assessment method, lack of personalized treatment, insufficient intervention coordination and lack of real-time monitoring ability. The problems that an existing decision support system is insufficient in intelligence and complex in user interaction experience, so that the efficiency of medical staff is low, and finally rehabilitation and life quality of a patient are affected are solved, and the system comprises a patient pain data collection module, a data analysis and processing module, a decision support module and a user interface. The data analysis processing module analyzes the collected data by adopting a machine learning algorithm so as to evaluate the severity of the pain; according to the invention, individualized pain assessment is realized through a machine learning algorithm, patient pain data is analyzed in real time, a management scheme is rapidly adjusted, patient satisfaction is improved, a pain management scheme is optimized, and manual intervention is reduced.

The present disclosure provides systems and methods for providing neurostimulation therapy according to patient features. The patient features may be analyzed to develop a patient model between physiological and / or patient reported features and optimal settings for a neurostimulation therapy using machine learning operations. The model is used to control ongoing neurostimulation therapy for the patient.

We provide a flexible, AI-powered wearable patch system for non-invasive medication compliance monitoring. [Solution] This system 100 integrates physiological sensors 101 to detect heart rate variability, skin temperature, electrocutaneous activity, and swallowing-related muscle signals. A drug detection sensor 102 identifies interactions with drug containers, and a medication event sensor 103 detects gestures or physiological signals indicating medication. An embedded AI processor 104 receives multimodal sensor data and performs digital filtering, normalization, artifact removal, and machine learning-based classification to determine medication confirmation events, missed doses, or irregular medication patterns. A power management unit 105 controls the power supply to ensure continuous operation. A wireless communication module 106 transmits processed medication data to an external device. This enables reliable, objective, and real-time medication confirmation without relying on invasive ingestion sensors or patient reports.

A pacemaker control system includes a pacemaker; a plurality of sensors which are internal to the pacemaker, a plurality of sensors which are external to the pacemaker, a circuit for entering patient reports; and a circuit for using artificial intelligence to process outputs from the plurality sensors internal and external to the pacemaker and from the circuit for entering patient reports, which are collectively identified as a labeled dataset, to reiteratsvely learn a function which determines the labeled dataset most likely to provide optimal pacemaker function for the patient. The means for using artificial intelligence comprises a database of archive outputs from the plurality sensors internal and external to the pacemaker and from the means for entering patient reports for the patient used for optimization of rate modulation to intelligently, continuously and physiologically control the pacemaker.

The invention relates to the technical field of data management and analysis, in particular to a liver cancer TACE curative effect evaluation method and system based on patient report outcome, and the method comprises the steps: obtaining a request set monitoring request transmitted by an evaluation server, the request set monitoring request being configured with a first request set label; acquiring an associated request report and an evaluation record report corresponding to the request report according to the first request set label; performing credibility evaluation on the request report according to the evaluation record report to obtain a first credibility evaluation record; if it is determined that the request report is credible through the first credibility evaluation record, the request time point is executed according to the monitoring priority of the request time point and the perioperative key point so as to complete the target patient evaluation item. According to the invention, the problem of liver cancer TACE curative effect evaluation can be solved, the development of self-curative effect monitoring and curative effect management work of patients in clinic is promoted, and the prognosis and life quality of the patients can be improved.

The invention discloses a lung cancer immunotherapy adverse reaction patient report management system, which relates to the technical field of medical information and comprises a data acquisition module, a phase deviation calculation module, a characteristic parameter calculation module, a real-time data processing module, a characteristic decomposition module and an evaluation generation module. The method comprises the following steps: firstly, acquiring a drug administration time sequence and multi-dimensional symptom data of a patient, calculating a phase offset at a symptom acquisition moment by using a drug administration period, constructing a periodic phase coordinate system, and mapping a symptom feature vector to the coordinate system, calculating a first characteristic parameter representing drug resonance intensity and a second characteristic parameter representing a time sequence topological relation between symptoms, constructing a multi-dimensional signal decoupling model, decomposing a symptom data vector into a first component vector and a second component vector, and generating a grading evaluation result according to module values of the two components; according to the invention, precise decoupling and identification of immune adverse reactions and tumor progression are realized, and the problem of misjudgment caused by confusion of signal sources is effectively solved.

1. Name of the product in this design: Graphical User Interface (Armline) for Electronic Devices with Export of Patient Training Report Data. 2. The purpose of this design product is for program operation. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design points: Change state diagram 1. 5. The purpose of the graphical user interface is for program operation and for therapists to export patient training report data. 6. Human-Computer Interaction Methods of the Graphical User Interface: Main View: The displayed graphical user interface is the login interface; Change State Diagram 1: The displayed graphical user interface is the main interface after successfully logging in by entering the device account and password in the main view; Change State Diagram 2: The displayed graphical user interface is the interface for displaying the patient list and statistical data after clicking the patient management button in the left navigation bar in Change State Diagram 1; Change State Diagram 3: The displayed graphical user interface is the interface for displaying the selected patient report list by clicking the report button in the upper right corner of Change State Diagram 2; Change State Diagram 4: The displayed graphical user interface is the interface for selecting reports to be exported and activating the batch export button in Change State Diagram 3; Change State Diagram 5: The displayed graphical user interface is the interface for displaying the export confirmation pop-up after clicking the batch export button in Change State Diagram 4; Change State Diagram 6: The displayed graphical user interface is the interface for displaying the export status after clicking the confirmation button in Change State Diagram 5; Change State Diagram 7: The displayed graphical user interface is the result feedback interface for successfully exporting reports in Change State Diagram 6.

The present disclosure describes methods of using an alimemazine or a pharmaceutically acceptable salt thereof for treating pain associated with trigeminal neuralgia. The methods of the present disclosure can include administering to a patient in need thereof a therapeutically effective amount of an alimemazine or a pharmaceutically acceptable salt thereof, thereby treating the patient. The methods can include reducing pain in the patient, as determined by patient report or clinician-administered outcome assessments.

A system for managing and distributing patient reports regarding medical devices to end users includes at least one processor configured to perform instructions to receive, from at least one therapeutic medical device, first physiological information relating to at least one therapeutic medical device patient and receive, from at least one monitoring medical device, second physiological information relating to at least one monitoring medical device patient. The at least one processor is also configured to perform instructions to process the first physiological information and the second physiological information, generate a plurality of patient reports from the processed first physiological information and from the processed second physiological information, receive a selection from an end user via an end user terminal, and provide at least one patient report of the plurality of patient reports to the end user terminal based on the received selection.

The invention discloses a tumor patient individualized medication dose optimization method, which comprises the following steps: establishing a patient file, calculating an initial dose based on evidence-based knowledge, making a structured follow-up visit plan, integrating data from communities, families and patient reports, analyzing by using a fusion rule and an intelligent algorithm, and generating a dose optimization suggestion. After doctor-patient auditing, a situational medication reminding and recording tool is automatically updated and pushed, and finally continuous loop feedback and iteration of a treatment scheme are achieved; according to the invention, long-term follow-up visit and dosage decision are bound, and medication reminding, patient education and clinical support are combined, so that real-time, accurate and individualized adjustment of the medication dosage is effectively realized in a basic scene, and the treatment safety and compliance are improved.

The invention provides a patient life quality assessment analysis method and system based on a patient report, and relates to the technical field of data assessment management, the patient life quality assessment analysis method based on the patient report comprises the following steps: screening out a patient report outcome symptom by using pre-collected symptom information of a peripheral artery disease patient, constructing a multi-dimensional life quality index scale; based on the multi-dimensional life quality index scale, comprehensive risks of the life quality of the patient under different symptoms are calculated through a dynamic symptom tree model in combination with a Markov method; and evaluating the importance of each symptom on the life quality, and selecting an adjustment measure according to an importance evaluation result in combination with a pre-configured adjustment measure. The comprehensive risk of the life quality of the patient under different symptoms can be dynamically calculated, a doctor can track the disease change of the patient in real time, a treatment scheme can be adjusted in time, and the risk of decline of the life quality of the patient is reduced.

Methods and systems for generating a report that identifies wound and fall risks for patients are disclosed. A method includes obtaining historical patient data via one or more databases communicatively coupled with the computer system. The method includes generating a training set including a subset of the historical patient data, training the injury detection system using the training set, and determining, based on new patient data provided to the injury detection system, a patient's fall risk and / or wound risk. The method further includes generating, based on the patient's fall risk and / or wound risk, a patient report and providing caretakers remote access to the patient report. The patient report presents the patient's fall risk and / or wound risk and includes one or more user interface elements for receiving additional information about the patient.

A large language model (LLM) may extract information from unstructured data from a clinical setting, including for example freeform physicians' notes and / or patient reports. Extracted information may be used to populate a structured database (e.g., a clinical data registry), which may then be queried via database queries from various applications, for example to identify clinical trial cohorts or generate data visualizations or reports. Additionally, the LLM integrated with the structured database may support natural language queries from users (e.g., medical personnel) to support on-demand retrieval of relevant patient parameters.

Disclosed herein are systems and methods for evaluating implant condition and associated patient condition. A method for determining an implant condition can include the steps of receiving implant vibration data from an implant coupled to a bone of a patient, receiving patient reported data from the patient, and generating an implant loosening score from the implant vibration data and the patient reported data. The implant vibration data can be generated by a sensor associated with the implant. The implant loosening score can be related to movement of implant with respect to the bone.

Systems and methods for controlling access to a medication include an interactive vial communicatively connected to an offsite server storing an artificial intelligence model. The interactive vial includes a housing configured to receive and store the medication prescribed for a patient and a controllable lock configured to inhibit access into the housing for a period of time. The offsite server includes a processor configured to execute the at least one artificial intelligence model to generate a patient report, wherein executing the at least one artificial intelligence model comprises: parsing through the patient database to locate data associated with the patient; and inputting the data associated with the patient into a patient report workflow to generate the patient report.

A pacemaker control system includes a pacemaker; a plurality of sensors which are internal to the pacemaker, a plurality of sensors which are external to the pacemaker, a circuit for entering patient reports; and a circuit for using artificial intelligence to process outputs from the plurality sensors internal and external to the pacemaker and from the circuit for entering patient reports, which are collectively identified as a labeled dataset, to reiteratively learn a function which determines the labeled dataset most likely to provide optimal cardiac function of the patient. The means for using artificial intelligence comprises a database of archive outputs from the plurality sensors internal and external to the pacemaker and from the means for entering patient reports for the patient used for optimization of rate modulation to intelligently, continuously and physiologically control the pacemaker.

The application discloses a kind of based on double self-adapting weighted multimodal fusion rheumatoid arthritis activity intelligent evaluation method, belong to medical information technology field.This method inputs clinical test, imageology, patient report and joint sign multimodal data, after abnormal value processing, missing value filling and standardization preprocessing, carries out modal internal feature purification enhancement;With modal reliability factor and bidirectional cross-modal attention weight, realize double self-adapting weighted depth fusion;Output disease activity grade and DAS28 score, and adopt global clinical correction factor to complete result calibration.The application integrates multi-dimensional data complementary value, realizes rheumatoid arthritis activity precision, objectification, forward-looking evaluation, provides reliable basis for clinical intervention.

The present invention discloses an inflammatory bowel disease patient report evaluation system based on artificial intelligence, which relates to the technical field of medical treatment. The age group analysis module calculates the historical intestinal damage factor based on the historical data of patients of different ages, and analyzes the impact of different ages on intestinal damage, constructs the corresponding influence coefficient, and combines the blood extraction data to obtain the activity coefficient of the current patient, so as to more accurately judge the patient's inflammation intensity and make up for the shortcomings of traditional evaluation methods under age differences. The intestinal state analysis module uses deep learning technology to construct a morbidity prediction model, and constructs a morbidity complexity coefficient based on imaging data, and obtains a risk assessment coefficient in combination with model fitting. This application of deep learning can more accurately evaluate the complexity of the morbidity and provide a more scientific risk assessment. The report evaluation module compares and analyzes the risk assessment coefficient through preset evaluation thresholds to generate complete and exclusive symptom reports of different levels.