WO2022019514A1 - Appareil, procédé et support d'enregistrement lisible par ordinateur pour prise de décision à l'hôpital - Google Patents

Appareil, procédé et support d'enregistrement lisible par ordinateur pour prise de décision à l'hôpital Download PDF

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WO2022019514A1
WO2022019514A1 PCT/KR2021/008298 KR2021008298W WO2022019514A1 WO 2022019514 A1 WO2022019514 A1 WO 2022019514A1 KR 2021008298 W KR2021008298 W KR 2021008298W WO 2022019514 A1 WO2022019514 A1 WO 2022019514A1
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patient
hospital
information
medical
decision
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PCT/KR2021/008298
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English (en)
Korean (ko)
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김영학
허신영
전태준
안임진
조용현
채정우
강희준
허재희
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주식회사 라인웍스
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0633—Workflow analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting
    • G06Q10/1093—Calendar-based scheduling for persons or groups
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the present invention relates to a hospital decision-making apparatus, method, computer-readable recording medium and computer program.
  • An object of the present invention is to provide a hospital decision-making apparatus, a method, a computer-readable recording medium, and a computer program.
  • this hospital decision-making device predicts a patient's medical event through a deep learning learning model, and comprehensively considers the predicted medical event, the patient's medical information, and the hospital's state information to make the hospital's decision. and the like may be included in the problems to be solved by the present invention.
  • a hospital decision-making apparatus includes an input/output unit for receiving medical treatment information about a patient; a memory in which medical information for a plurality of patients is converted into data of a predetermined structure format and stored; and a processor electrically connected to the memory, wherein the processor receives the data in the predetermined structural format as an input, and learns by answering at least one medical event that may occur for the plurality of patients as an answer.
  • a doctor of the hospital predicts at least one medical event for the patient through a deep learning learning model, and considers the predicted at least one medical event for the patient, medical treatment information for the patient, and state information of the hospital decision can be made.
  • the medical treatment information for the patient includes medical treatment information according to the time when the patient first visits the hospital, medical information according to the time when the patient visits the hospital after the initial visit to the hospital, and other information other than the hospital It may include at least one of the medical information obtained from the hospital.
  • the medical event may include a mortality rate within a predetermined period after the patient's discharge, a readmission rate within a predetermined period after the patient's discharge, a period from the time the patient reads into the hospital to a discharge time, and within a predetermined period after the patient's discharge at least one of the incidence of heart disease.
  • the decision-making of the hospital may include determining the patient's treatment schedule, determining the bed assignment of the patient, and determining the operating room schedule of the patient.
  • the state information of the hospital includes the number of treatment rooms in the hospital, the number of treatment rooms used by each medical staff of the hospital, outpatient support personnel information for each medical staff, session opening status information for each day of the other medical staff in the same department of the hospital, and the hospital's It may include at least one of bed information in a hospital room, patient status information of the hospital, status information for each department, the type and number of operating rooms in the hospital, schedule information for each operating room, and estimated time required for each operation of the patient. .
  • the decision-making of the hospital includes determining the patient's medical schedule, and determining the patient's medical care ranking based on the medical treatment information for the patient and the predicted patient's medical event,
  • the patient's treatment schedule may be determined by further considering the determined patient's treatment order and medical staff information of the hospital.
  • the medical staff information of the hospital includes the medical treatment schedule information for each medical staff, the information by the patient type, the time information generated by the patient's treatment, the schedule information of at least one of the surgery, the procedure, and the research by the medical staff, and the medical staff It may include at least one of performance information and information on whether or not a medical staff has been placed in the hospital.
  • the decision-making of the hospital includes determining the bed assignment of the patient, and the processor determines the bed assignment rank of the patient based on the medical event information of the patient and the predicted patient, The bed assignment of the patient may be determined by further considering the determined bed assignment order of the patient.
  • a hospital decision-making method includes converting medical treatment information for a plurality of patients into data of a predetermined structural format, and generating data in the predetermined structural format as an input for the plurality of patients Learning a deep learning learning model with at least one possible medical event as the correct answer, receiving medical treatment information about a patient, and predicting at least one medical event for the patient through the deep learning learning model and performing the decision-making of the hospital in consideration of the predicted at least one medical event for the patient, medical treatment information for the patient, and state information of the hospital.
  • the medical treatment information for the patient includes medical treatment information according to the time when the patient first visits the hospital, medical information according to the time when the patient visits the hospital after the initial visit to the hospital, and other information other than the hospital It may include at least one of the medical information obtained from the hospital.
  • the medical event may include a mortality rate within a predetermined period after the patient's discharge, a readmission rate within a predetermined period after the patient's discharge, a period from the time the patient reads into the hospital to a discharge time, and within a predetermined period after the patient's discharge at least one of the incidence of heart disease.
  • the decision-making of the hospital may include determining the patient's treatment schedule, determining the bed assignment of the patient, and determining the operating room schedule of the patient.
  • the state information of the hospital includes the number of treatment rooms in the hospital, the number of treatment rooms used by each medical staff of the hospital, outpatient support personnel information for each medical staff, session opening status information for each day of the other medical staff in the same department of the hospital, and the hospital's It may include at least one of bed information in a hospital room, patient status information of the hospital, status information for each department, the type and number of operating rooms in the hospital, schedule information for each operating room, and estimated time required for each operation of the patient. .
  • the decision-making of the hospital includes determining the treatment schedule of the patient, and the performing of the decision-making of the hospital includes the treatment of the patient based on the treatment information about the patient and the predicted medical event of the patient.
  • the treatment order may be determined, and the treatment schedule of the patient may be determined by further considering the determined treatment order of the patient and the medical staff information of the hospital.
  • the medical staff information of the hospital includes the medical treatment schedule information for each medical staff, the information by the patient type, the time information generated by the patient's treatment, the schedule information of at least one of the surgery, the procedure, and the research by the medical staff, and the medical staff It may include at least one of performance information and information on whether or not a medical staff has been placed in the hospital.
  • the decision-making of the hospital includes determining the bed assignment of the patient, and the step of performing the decision-making of the hospital may include, based on the medical information about the patient and the predicted patient's medical event, the patient's The bed assignment order may be determined, and the bed assignment of the patient may be determined by further considering the determined bed assignment order of the patient.
  • a computer-readable recording medium is a computer-readable recording medium storing a computer program.
  • the computer program When the computer program is executed by a processor, it stores medical information about a plurality of patients as data in a predetermined structure format. transforming into , and learning a deep learning learning model with at least one medical event that may be generated for the plurality of patients as an input as an input of the data in the predetermined structural format as a correct answer; receiving information and predicting at least one medical event for the patient through the deep learning learning model; It may include instructions for causing the processor to perform a method comprising the step of performing decision-making of the hospital in consideration of the information.
  • the decision-making device of the hospital uses a deep learning learning model to perform medical events (eg, mortality within a predetermined period after patient discharge, readmission rate within a predetermined period after patient discharge, and time of patient readmission) It is possible to predict the incidence rate of heart disease (from the period from to the time of discharge to Therefore, it is possible to provide a patient-specific hospital process.
  • medical events eg, mortality within a predetermined period after patient discharge, readmission rate within a predetermined period after patient discharge, and time of patient readmission
  • FIG. 1 is a block diagram of a decision-making apparatus for a hospital according to an exemplary embodiment.
  • FIG. 2 is a data table for explaining an example of medical treatment information for a patient according to an embodiment.
  • FIG. 3 is a diagram illustrating an example in which medical treatment information for a plurality of patients is converted into data in a predetermined structural format according to an exemplary embodiment.
  • 4A to 4B are diagrams illustrating medical treatment information for a plurality of patients stored in a memory of a decision-making apparatus of a hospital according to an exemplary embodiment.
  • 5A and 5B are diagrams for explaining that data stored in a memory of a decision-making apparatus of a hospital is converted into a standardized structure format and stored according to an exemplary embodiment.
  • FIG. 6 is a flowchart for explaining training a deep learning learning model according to an embodiment.
  • FIG. 7 is a diagram for explaining an example of data input to a deep learning learning model according to an embodiment.
  • FIG. 8 is a flowchart illustrating an example of performing decision-making in a hospital according to an exemplary embodiment.
  • FIG. 9 is a flowchart illustrating an example of performing decision-making in a hospital according to another embodiment.
  • FIG. 10 is an exemplary flowchart of a decision-making method in a hospital according to an embodiment.
  • FIG. 1 is a block diagram of a hospital decision-making apparatus 100 according to an embodiment.
  • a hospital decision-making apparatus 100 may include an input/output unit 101 , a communication unit 102 , a memory 110 , and/or a processor 120 .
  • the input/output unit 101 transmits a command or data input from a user or other external device to other component(s) of the hospital decision-making apparatus 100 according to an embodiment, or in one embodiment A command or data received from other component(s) of the decision-making apparatus 100 of a hospital according to an example may be output to a user or other external device.
  • the input/output unit 101 may receive medical treatment information for a patient.
  • the medical treatment information for the patient may include medical treatment information according to the time when the patient first visits the hospital and medical information according to the time when the patient visits the hospital after the first visit to the hospital. That is, the medical treatment information for the patient may include the patient's past medical treatment information and the patient's current medical information.
  • the medical treatment information for the patient may include treatment information in other hospitals other than the hospital where the patient has currently obtained the medical treatment information.
  • FIG. 2 is a data table for explaining an example of medical treatment information for a patient according to an embodiment.
  • medical treatment information for a patient input to the input/output unit 101 may include hospital visit information (visit table) and medication information (medication table).
  • the hospital visit information includes information about the patient's visit number, visit classification (for example, it can be classified as outpatient, hospitalized, or emergency), hospitalization time and discharge time, or among them (patient's visit number, information on the classification of hospitalization, hospitalization time, and discharge time)) may be included.
  • Medication information includes information on the patient's visit number, drug prescription date, drug type, and drug dose, or information about the patient's visit number, drug prescription date, drug type, and drug dose. ) may include some.
  • the communication unit 102 may support establishment of a wired or wireless communication channel between the hospital decision-making apparatus 100 and an external device, and communication through the established communication channel.
  • Memory 110 is a variety of data used by at least one component (processor 120, input/output unit 101 and/or communication unit 102) of the hospital decision-making apparatus 100, for example, software It can store input data or output data for (eg, program) and related commands.
  • the memory 110 may include a volatile memory or a non-volatile memory.
  • the memory 110 may store medical treatment information for a plurality of patients converted into data of a predetermined structure format.
  • the memory 110 may store a pre-learned deep learning learning model.
  • the medical treatment information for the plurality of patients may be accumulated data of the medical treatment information for the patient input from the input/output unit 101 .
  • treatment information for a plurality of patients stored in the memory 110 will be described with reference to FIGS. 2 to 5 , which is converted into data of a predetermined structural format and stored.
  • FIG. 3 is a diagram illustrating an example in which medical treatment information for a plurality of patients is converted into data in a predetermined structural format according to an exemplary embodiment.
  • the hospital decision-making apparatus 100 may convert medical treatment information for a plurality of patients into data of a predetermined (or standardized) structure format and store the converted data in the memory 110 .
  • text data included in medical treatment information for a plurality of patients may be standardized into numeric data.
  • the division information divided into “outpatient”, “hospitalization” and “emergency” can be converted into “373864002", “416800000” and “4525004", respectively, and the drug information is "stain", Divide by "lnsulin” and “aspirin” can be converted to "1158753”, "106892” and "315431", respectively.
  • the treatment information for a plurality of patients may be converted into data in a standardized structure format using a linkage database, but is not limited thereto.
  • 4A to 4B are diagrams illustrating medical treatment information for a plurality of patients stored in the memory 110 of the hospital decision-making apparatus 100 according to an exemplary embodiment.
  • information about a plurality of patients stored in the memory 110 includes visit time points (Visit 1 to Visit 5) and treatment categories (eg, "hospitalization”, “test”, “medication” and " The data may be sorted according to "operation”).
  • the hospital decision-making apparatus 100 converts the stored data (medical information about a plurality of patients) to other data (visit 1 to visit 5) adjacent to the visit time point (visit 1 to visit 5). It is possible to embed a correlation with medical information for a plurality of patients). In this case, embedding the correlation between the time of each data and other data adjacent in this way is called history-aware embedding.
  • 5A and 5B are diagrams for explaining that data stored in the memory 110 of the hospital decision-making apparatus 100 is converted into a standardized structural format and stored according to an exemplary embodiment.
  • information about a plurality of patients stored in the memory 110 includes visit time points (Visit 1 to Visit 5) and treatment category (eg, “hospitalization”, “examination”, “medication”). and “surgery”) may be sorted according to data, and information on a plurality of patients may be stored as " ⁇ ".
  • information on a plurality of patients when information on a plurality of patients is converted into a standardized structure format and stored, as shown in FIG. 5B , information on a plurality of patients may be changed from “ ⁇ ” to “ ⁇ ”.
  • the processor 120 (also referred to as a control unit, a control device, or a control circuit) includes at least one other component (eg, a hardware component (eg, an input/output unit 101 , a communication unit 102 ) of the connected hospital decision-making device 100 . ) and/or memory 110) or software components), and may perform various data processing and operations.
  • a hardware component eg, an input/output unit 101 , a communication unit 102
  • the processor 120 also referred to as a control unit, a control device, or a control circuit
  • the processor 120 includes at least one other component (eg, a hardware component (eg, an input/output unit 101 , a communication unit 102 ) of the connected hospital decision-making device 100 . ) and/or memory 110) or software components), and may perform various data processing and operations.
  • the processor 120 receives information stored in the memory 110 from the memory 110 (data in which medical information about a plurality of patients is converted into a standardized structural format) as an input, and provides the information to a plurality of patients. After loading a deep learning learning model that has been learned by using at least one medical event that may occur for a patient as a correct answer, it is possible to predict at least one medical event for a patient through the deep learning learning model.
  • the processor 120 may predict at least one medical event for the patient through the deep learning learning model.
  • the processor 120 may be generated for a plurality of patients by inputting information stored in the memory 110 (data in which medical information about a plurality of patients is converted into a standardized structure format) as an input. At least one medical event for the patient may be predicted through the deep learning learning model, which is learned by taking at least one medical event as the correct answer.
  • FIG. 6 is a flowchart for explaining training a deep learning learning model according to an embodiment.
  • the processor 120 receives medical information about a plurality of patients as an input, and at least one medical event that may be generated for the plurality of patients as a correct answer.
  • a deep learning learning model may be trained (step S1).
  • the processor 120 may input some of the medical information for a plurality of patients to the deep learning learning model as input, and check whether a desired medical event is output (step S2). ).
  • the processor 120 may check whether the error value of the medical event output from the deep learning learning model is equal to or less than a preset value (step S3), and the error value of the medical event output from the deep learning learning model is a preset value. is exceeded, the deep learning learning model may be trained again in step S2.
  • the processor 120 when the error value of the medical event output from the deep learning learning model is less than or equal to a preset value, the deep learning learning model may be stored in the memory 110 or other external device (step S4) .
  • the processor 120 may check whether new data (eg, patient's medical information) is introduced (step S5), and in this case, when new data (eg, patient's medical information) is introduced, step Go to S1, you can input new data as input data for training the deep learning learning model. However, it may be terminated if no new data is introduced.
  • new data eg, patient's medical information
  • treatment information for a plurality of patients may be input, and in this case, the treatment information for the plurality of patients is the hospital in which the patient has acquired the current treatment information. It may include medical information from other hospitals other than (Institution A) (Institution B).
  • the processor 120 may predict at least one medical event for the patient by inputting the patient's medical treatment information into the deep learning learning model.
  • the medical event includes the mortality rate within a predetermined period after the patient's discharge, the readmission rate within a predetermined period (for example, may be 30 days) after the patient's discharge, the period from the time the patient is re-admitted to the time of discharge, and the discharge of the patient. It may include at least one of the incidence rates of heart disease within a predetermined period (eg, may be 30 days) after.
  • the mortality rate within a predetermined period after discharge of a patient may include in-hospital death and death due to cancer.
  • the mortality rate within a predetermined period after the patient's discharge was the total number of patients in the hospital (total number of hospitalizations) and the number of patients who died within 30 days from the time of discharge (30 at the time of discharge) among all patients in the hospital. number of deaths within one day).
  • AUROC is a value indicating the accuracy of mortality within a predetermined period after discharge of a patient predicted through a deep learning learning model.
  • the accuracy of mortality within a predetermined period after discharge of a patient predicted through a deep learning learning model is 92.39% by multiplying the value of AUROC by 100.
  • the readmission rate within a predetermined period (for example, may be 30 days) after a patient's discharge is information about readmission within 30 days from the patient's discharge in consideration of the patient's medical information before and during hospitalization.
  • readmission refers to a case where the time of re-admission from the time of discharge is within 30 days only for patients who have been admitted to the hospital at least once.
  • the rate of long-term hospitalized patients among patients re-hospitalized within a predetermined period (for example, may be 30 days) after the patient's discharge was within 30 days among all patients in the hospital.
  • a predetermined period for example, may be 30 days
  • the number of patients and the number of patients whose period from readmission to discharge exceeds 7 days (the number of long-term hospitalizations during readmission (more than 7 days)) among all patients in the hospital can be calculated by
  • AUROC in Table 2 is a value indicating the accuracy of the long-term hospitalization rate among patients re-hospitalized within a predetermined period (for example, it may be 30 days) after discharge of the patient predicted through the deep learning learning model.
  • the accuracy of the long-term hospitalization rate among patients re-hospitalized within a predetermined period (for example, 30 days) after patient discharge through a deep learning learning model is 81.26% by multiplying the value of AUROC by 100 It can be said that
  • Table 3 shows data on patients who were readmitted within 30 days after the patient was discharged, and among them, it is a table indicating whether the period from the time the patient was readmitted to the time of discharge was less than or equal to a predetermined period.
  • the predetermined period is exemplarily expressed as 10 days.
  • the period from the time the patient was re-admitted to the time of discharge is the number of patients who were readmitted within 30 days (the number of readmissions within 30 days) and the total number of patients in the hospital, as shown in Table 3 below. It can be calculated using the number of patients who were readmitted within 30 days and who were hospitalized for less than 10 days (the number of hospitalizations for less than 10 days among them).
  • R ⁇ 2 is an index that can confirm how accurately the number of hospitalization days or hospitalization period was predicted.
  • the mean absolute error may mean the error rate of the period from the time when the predicted patient is re-hospitalized to the time of discharge.
  • the incidence rate of heart disease within a predetermined period (eg, may be 30 days) after a patient's discharge is an event that predicts whether a heart disease will occur within 30 days after discharge among all patients in the hospital.
  • the heart disease includes at least one of death, revascularization, myocardial infarction, and stroke, and the earliest event within 30 days is considered.
  • the incidence rate of heart disease within a predetermined period is the number of patients who visited the hospital again after being discharged from the hospital (the total number of visits) ) and the total number of patients in the hospital with heart disease (eg, death, revascularization, myocardial infarction, stroke) (the number of major cardiac events).
  • the deep learning learning model used to predict medical events is a feed-forward model (FFNN: Feed-forward Neural Network) with boosted time-aware embeddings (or features) and a gradient boosting tree. It may be at least one model among models (GBM: Gradient Boosting Machine).
  • FFNN Feed-forward Neural Network
  • GBM Gradient Boosting Machine
  • the processor 120 performs hospital decision-making in consideration of the predicted patient's medical event, medical information about the patient input from the input/output unit 101, and state information of the hospital (which may be the hospital where the patient has been treated).
  • the hospital status information includes the number of clinics in the hospital, the number of treatment rooms used by each medical staff in the hospital, outpatient support personnel information by medical staff, session opening status information by day of the week for other medical staff in the same department of the hospital, bed information in the hospital, and hospital resources It may include at least one of patient status information, status information for each department, type and number of operating rooms, schedule information for each operating room, and estimated time required for each operation of the patient, but is not limited thereto.
  • the decision-making of the hospital may include at least one of a patient's treatment schedule determination, a patient's operating room schedule determination, and a patient's bed assignment determination.
  • the processor 120 may determine the patient's treatment priority based on the patient's medical event predicted using the patient's medical treatment information and a deep learning learning model.
  • the processor 120 makes a hospital decision in consideration of the patient's medical event predicted using the deep learning learning model, the patient's medical information, the hospital's status information, the patient's medical priority, and the hospital's medical staff information. can be performed.
  • the medical staff information of the hospital includes treatment schedule information by medical staff, information by patient type, time information generated by patient treatment, schedule information of at least one of surgery, procedure and research by medical staff, performance information by medical staff, and medical staff position in the hospital It may include at least one of whether or not information, but is not limited thereto.
  • the processor 120 may determine the patient's bed allocation ranking based on the patient's medical event predicted using the patient's medical treatment information and a deep learning learning model.
  • the processor 120 makes a hospital decision in consideration of the patient's medical event predicted using the deep learning learning model, the medical information about the patient, the state information of the hospital, and the patient's bed (or ward) assignment order can be performed.
  • FIG. 8 is a flowchart illustrating an example of performing decision-making in a hospital according to an exemplary embodiment.
  • the hospital decision-making apparatus 100 may first receive the patient's medical treatment information through the input/output unit 101 (step S11).
  • the hospital decision-making apparatus 100 includes the patient's medical information in the past, including hospitals other than the hospital where the patient obtained the medical information (for example, the patient's hospitalization history record) ) can be checked whether or not there is (step S12).
  • the patient's medical information does not contain the patient's past medical information (for example, the patient's hospitalization history record) including hospitals other than the hospital from which the patient obtained the medical information, the patient's medical priority cannot be maintained. have.
  • the patient's past medical information for example, the patient's hospitalization history record
  • the initial treatment order of the patient may be set as the last priority among the treatment orders of all patients in the hospital, but is not limited thereto.
  • the hospital decision-making device 100 includes past patient medical information (eg, patient's hospitalization history record) including hospitals other than the hospital where the patient obtained medical information in the patient's medical information. It is possible to predict the mortality rate within a predetermined period after a patient's discharge during a medical event through a deep learning learning model on the patient's medical information, and check whether the predicted mortality rate within a predetermined period after discharge exceeds a preset probability (step S13) ).
  • patient medical information eg, patient's hospitalization history record
  • the patient's treatment order may be maintained as it is.
  • the patient's medical treatment order may be increased (step S14).
  • the hospital decision-making apparatus 100 provides the predicted patient's medical event, the patient's medical information, the hospital's state information, the patient's medical priority, and the hospital's medical staff information. Taking this into account, it is possible to determine the patient's treatment schedule during the decision-making of the hospital (step S15).
  • the treatment information about the patient includes the mortality rate within a predetermined period after the patient's discharge, the number of hospital visits before the reference point of the hospital visit, the waiting time for treatment, and the time required for treatment.
  • diagnostic information, medication history information, surgery/procedure/treatment information may include at least one information of hospital visit information including at least one of body measurement information.
  • the medical staff information may be the treatment schedule (for example, the number of openings per week, days of the week, morning and afternoon, total treatment hours, number of patients per time, outpatient return rate (discontinuance rate, additional treatment rate) information), by patient type Information (for example, patient type ratio, capacity, re-treatment rate, reservation rate, actual treatment rate compared to reservation, non-visiting rate, ratio of critically ill patients may be information), average waiting time for each patient, patient's treatment time, It may include information about at least one of patient termination delay time information, medical staff surgery/procedure/research schedule, number of surgical/procedure/research results, post-outpatient surgery linkage rate, and position status information.
  • the treatment schedule for example, the number of openings per week, days of the week, morning and afternoon, total treatment hours, number of patients per time, outpatient return rate (discontinuance rate, additional treatment rate) information
  • patient type Information for example, patient type ratio, capacity, re-treatment rate, reservation rate, actual treatment rate compared to reservation
  • the hospital status information may include at least one of the number of clinics in each department, the number of offices used by medical staff/department, outpatient support personnel for each medical team, and session opening status information by day of the week for other medical staff in the same department.
  • the patient may receive treatment according to the determined patient's treatment schedule.
  • FIG. 9 is a flowchart illustrating an example of performing decision-making in a hospital according to another embodiment.
  • the hospital decision-making apparatus 100 may receive information about a list of patients reserved for hospitalization among medical information about the patient (step S21).
  • the hospital decision-making apparatus 100 predicts the incidence rate of heart disease within a predetermined period after the patient's discharge during a medical event through a deep learning learning model with respect to the patient's medical information (reserved patient list information), , it is possible to check whether the predicted incidence of heart disease within a predetermined period after discharge exceeds a preset probability (step S22), and when the predicted incidence of heart disease within a predetermined period after discharge of the patient is less than or equal to a preset probability, the patient's The hospital bed (or ward) allocation order may be maintained as it is.
  • the initial bed (or ward) assignment order of the patient may be set to the last rank among the bed (or room) assignment orders of all patients in the hospital, but is not limited thereto.
  • the patient's bed (or ward) assignment order may rise (step S23).
  • the hospital decision-making apparatus 100 may provide a patient's medical event predicted according to the patient's bed (or room) assignment order, medical treatment information for the patient, hospital status information, and the patient's bed (or The patient's bed assignment may be determined during decision-making in the hospital by considering the ward) assignment order (step S24).
  • the medical information about the patient includes the patient's registration number (or visit number), name, gender, age, patient type (new/secondary/revisit), etc.
  • Basic information including, diagnosis information, including name of diagnosis, department, doctor in charge, severity level, precautions, special notes, patient status information including type of isolation/receipt date/required period, operation/surgery name, scheduled surgery date, At least one of information about the patient's surgery including the surgeon and the type of anesthesia, the medicine within the hospital stay, the patient's treatment information including the type/prescription/treatment of materials, and the result information of various diagnostic tests and imaging tests. can
  • hospital status information includes total bed information including ward, ward, name/number/number of beds, etc., the number of beds used by all hospitalized patients per ward/ward/bed, At least among inpatient patient status (hospitalization period, surgery/procedure/treatment status, etc.), ward/ward/bed status by department, type and number of operating rooms, schedule status by operating room (surgery name, surgery timetable), and scheduled operation time for each surgery It may contain one piece of information.
  • the total number of patients with a predicted hospitalization period of 31 days or more at the time of admission (the total number of patients with a long hospitalization period), the predicted value of the hospitalization period at the time of admission Predetermined value less than 30 days
  • Total number of patients expected to be hospitalized within N days (eg, N is 3 days) (total number of short-stay patients) and total patients likely to be readmitted within 30 days
  • At least one piece of information among the number of pieces of information may be considered, but is not limited thereto.
  • the patient's bed assignment may succeed or fail, and when the patient's bed assignment succeeds, the patient may perform a hospitalization procedure according to the determined bed assignment.
  • FIG. 10 is an exemplary flowchart of a decision-making method in a hospital according to an embodiment.
  • the hospital decision-making method shown in FIG. 10 can be performed by the hospital decision-making device 100 shown in FIG. 1 .
  • the hospital decision-making method shown in FIG. 10 is merely exemplary.
  • the hospital decision-making apparatus 100 may convert medical treatment information for a plurality of patients into data having a predetermined structure (step S100).
  • the hospital decision-making apparatus 100 may train the deep learning learning model by using data of a predetermined structural format as an input, and at least one medical event that may be generated for a plurality of patients as a correct answer (step S200).
  • the hospital decision-making apparatus 100 may receive medical treatment information for the patient and predict at least one medical event for the patient through the deep learning learning model (step S300 ).
  • the hospital decision-making apparatus 100 may perform the hospital decision-making in consideration of the predicted at least one medical event for the patient, medical treatment information for the patient, and state information of the hospital (step S400 ).
  • the hospital decision-making device uses a deep learning learning model to perform medical events (eg, mortality within a predetermined period after a patient's discharge, a readmission rate within a predetermined period after a patient's discharge, (the period from the patient's re-hospitalization to the time of discharge and the incidence rate of heart disease within a predetermined period after the patient's discharge) Because the system can be managed, it is possible to provide patient-specific hospital processes.
  • medical events eg, mortality within a predetermined period after a patient's discharge, a readmission rate within a predetermined period after a patient's discharge, (the period from the patient's re-hospitalization to the time of discharge and the incidence rate of heart disease within a predetermined period after the patient's discharge).
  • Combinations of each block in the block diagram attached to the present invention and each step in the flowchart may be performed by computer program instructions.
  • These computer program instructions may be embodied in the encoding processor of a general purpose computer, special purpose computer, or other programmable data processing equipment, such that the instructions executed by the encoding processor of the computer or other programmable data processing equipment may correspond to each block of the block diagram or
  • Each step of the flowchart creates a means for performing the functions described.
  • These computer program instructions may also be stored in a computer-usable or computer-readable memory which may direct a computer or other programmable data processing equipment to implement a function in a particular way, and thus the computer-usable or computer-readable memory.
  • the instructions stored in the block diagram may also produce an item of manufacture containing instruction means for performing a function described in each block of the block diagram or each step of the flowchart.
  • the computer program instructions may also be mounted on a computer or other programmable data processing equipment, such that a series of operational steps are performed on the computer or other programmable data processing equipment to create a computer-executed process to create a computer or other programmable data processing equipment. It is also possible that instructions for performing the processing equipment provide steps for carrying out the functions described in each block of the block diagram and in each step of the flowchart.
  • each block or each step may represent a module, segment, or portion of code comprising one or more executable instructions for executing specified logical function(s). It should also be noted that in some alternative embodiments it is also possible for the functions recited in blocks or steps to occur out of order. For example, it is possible that two blocks or steps shown one after another may in fact be performed substantially simultaneously, or that the blocks or steps may sometimes be performed in the reverse order according to the corresponding function.

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Abstract

La présente invention concerne un appareil pour la prise de décision à l'hôpital. Plus spécifiquement, la présente invention peut : convertir des informations médicales pour une pluralité de patients en données dans un format structurel prédéterminé; entraîner un modèle d'apprentissage profond tout en marquant, en tant que réponse correcte, au moins un événement médical qui peut se produire pour la pluralité de patients, en entrant les données dans le format structurel prédéterminé; recevoir des informations médicales concernant un patient et prédire au moins un événement médical pour le patient par l'intermédiaire du modèle d'apprentissage profond; et prendre une décision à l'hôpital en tenant compte du ou des événements médicaux prédits pour le patient, des informations médicales concernant le patient, et des informations d'état de l'hôpital.
PCT/KR2021/008298 2020-07-21 2021-06-30 Appareil, procédé et support d'enregistrement lisible par ordinateur pour prise de décision à l'hôpital WO2022019514A1 (fr)

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CN116052887B (zh) * 2023-03-01 2023-06-27 联仁健康医疗大数据科技股份有限公司 一种过度检查的检测方法、装置、电子设备及存储介质

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