Re-admission risk and hospitalization time risk assessment model based on data driving

Abstract

The invention discloses a re-admission risk and hospitalization time risk assessment model based on data driving. In the first stage, the re-admission prediction model is used for carrying out classification prediction on whether a case is avoidable admission or inevitable re-admission; and in the second stage, for patients capable of avoiding admission, the model takes intervention measures to prevent the patients from entering the hospital again, so that the hospital cost can be effectively reduced. For inevitable hospitalized patients, a hospitalization time prediction model is provided topredict the hospitalization time of the inevitable hospitalization patients so as to improve the scheduling and management of medical resources; the risk factors of re-admission and LOS of the patientare determined, the diagnosis efficiency of doctors is improved, and the satisfaction degree of the patient is improved; a clinical database of a patient is perfected, so that the medical service level and efficiency of a hospital are improved, more accurate re-admission prediction is provided, and meanwhile, the method has important significance for guiding practice.

Description

technical field [0001] The invention belongs to the technical field of risk assessment, and in particular relates to a data-driven risk assessment model for readmission risk and hospitalization time. Background technique [0002] In recent years, the living environment (smog, global warming, etc.) and living conditions (smoking, obesity, etc.) have affected people's health. With the continuous improvement of people's living standards, people's demand for health care has greatly increased. With the rapid development of Internet, computer technology and cloud computing technology, intelligent medical treatment and medical digitalization will become the development trend of public health and hospital management industry. With the development of medical digitalization, the efficiency of medical data collection has been greatly improved, including not only the basic clinical information of the patient population, available medical information resources, and patient treatment inf...

AI Technical Summary

Problems solved by technology

However, simply identifying avoidable readmissions is not enough

At present, the number of hospitalized patients has increased significantly, and the hospital capacity is insufficient, the number of beds is insufficient, and the ratio of nurses to patients is insufficient, which often leads to inefficien...

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