5 results about "Patient volume" patented technology

To provide a technique capable of accurately estimating the number of patients, including patients with attributes not covered, when using health insurance information based primarily on employee insurance.SOLUTION: An information processing device for estimating the number of prescriptions of medical products, the information processing device comprises: acquisition means for acquiring a first number of patients, the number of patients with a first attribute to whom a medical product has been prescribed, from health insurance information based on employee insurance; estimation means for estimating a second number of patients, the number of patients with a second attribute different from the first attribute to whom the medical product has been prescribed, using the health insurance information; and output means for outputting the sum of the first number of patients and the second number of patients as the number of patients to whom the medical product has been prescribed. Estimating the second number of patients in the estimation means includes estimating the second number of patients with the second attribute to whom the medical product has been prescribed, which should be obtained on the basis of information of medical fees from an insurance medical institution, using the number of patients with the second attribute to whom the medical product has been prescribed, which is obtained on the basis of information of medical fees from a specific insurance medical institution, and the number of patients obtained from insurer information of the medical care system for the latter-stage elderly.SELECTED DRAWING: Figure 3

The application relates to the technical field of medical transportation, in particular to a post-hospital non-emergency transportation platform recommendation method based on artificial intelligence, which comprises the following steps: determining combination recommendation or direct recommendation according to the number of patients and a recommendation time length comparison value; in the combination recommendation, determining the illness change degree according to the examination parameter completeness, performing combination analysis based on the illness change degree and the medical record text correlation degree to determine the associated combination, and determining the recommendation priority coefficient corresponding to each associated combination based on the evaluation mean value; in the direct recommendation, determining the recommendation priority coefficient corresponding to each patient according to the comprehensive evaluation value; determining to perform generation analysis according to the number of patients in a single recommendation, or generating feature keywords according to the feature keyword emergence proportion; and determining the recommended hospital corresponding to each patient or each associated combination according to the keyword matching degree and the diagnosis waiting time length. The application can improve the accuracy of the recommended hospital.

The invention relates to the technical field of medical treatment prediction, in particular to a medical institution treatment number prediction method based on interpretable deep learning, and the method comprises the following steps: S1, collecting the number of daily treatment patients of a full-caliber outpatient department in past several years through a medical institution business system; s2, deleting records of which the names of the doctor-seeing departments contain'nucleic acid 'characters, then performing grouping statistics on the data according to dates, and performing smooth filling on date records without statistical data; s3, carrying out in-block structure design; the method can effectively solve the problems that an existing algorithm is too simple in structure, many in preconditions, too low in characterization complexity, low in generalization ability, large in error, too high in requirement for the feature engineering level, needs to additionally input other features to improve the precision, long in optimization process, difficult to decompose the trend from the feature engineering level, low in cost and the like. And components concerned in the actual operation process, such as high precision and explainability, cannot meet the actual requirements of high precision and explainability.

A method of patient management including receiving therapy data associated with a plurality of durable medical devices and a first set of patients, and displaying an overview of patient compliance and a plurality of customizable tiles. The overview of patient compliance includes at least the number of patients in the first set of patients, current compliance data, compliance history data, and follow-up data. The plurality of aligned tiles includes at least a title and a plurality of selectable subtitles, each of the plurality of selected subtitles associated with one or more rules. Upon selection of a first selectable subtitle, displaying at least an indication of patients in a second set of patients, wherein the second set of patients is a subset of the first set of patients; upon selection of a first patient, displaying at least all of the rules triggered by the first patient.

The present application belongs to the field of medical information technology and clinical decision support technology, and in particular to a patient capacity management method, system, device and medium based on bioelectrical impedance data, comprising S1, multi-source data acquisition and convergence: acquiring bioelectrical impedance data, clinical data and behavior data of a target patient; S2, intelligent grading of capacity state: based on real-time or phased data obtained in step S1; S4, personalized scheme comparison and optimization recommendation: S5, data closed loop and model continuous optimization; the present application adopts a counterfactual reasoning method, estimates the expected improvement range of the patient's capacity state after the implementation of each candidate scheme, and sorts the improvement ranges from large to small, automatically selects the optimal scheme, then, through a natural language generation module, converts the optimal scheme into a personalized management plan that can be directly read by the patient or the doctor, so that the model can be continuously optimized with the accumulation of data, and better adapt to the capacity change law of individual patients.