Clinical decision support system and computer program specialized for infectious diseases

Abstract

The present invention is characterized in that it comprises: an initial evaluation unit that calculates the severity and risk of an infectious disease using patient information and determines a treatment type among home treatment, inpatient treatment, and ICU (Intensive Care Unit) by inputting the calculated severity and risk of the infectious disease into an artificial intelligence model or comparing it with preset criteria; a treatment guide unit that determines monitoring guidelines and treatment guidelines for a patient determined to receive home treatment based on patient information, the calculated severity and risk of the infectious disease, and the results of a medical history interview; and a monitoring unit that performs monitoring of the patient receiving home treatment according to the monitoring guidelines determined by the treatment guide unit.

Description

Technology Field

[0001] The present invention relates to a clinical decision support system specialized for infectious diseases, and more specifically, to a clinical decision support system specialized for infectious diseases that performs evaluation, medical treatment, prescription, and monitoring, etc., for patients who require or are undergoing home treatment. Background Technology

[0002] During a pandemic, the explosive increase in patients leads to shortages of hospital beds and medical personnel. This results in the collapse of the medical system, leaving not only infectious disease patients but also general emergency patients unable to receive treatment, potentially causing a significant number of deaths. Due to the nature of infectious diseases, patients are typically isolated in negative pressure rooms to prevent transmission; however, during a pandemic, the reality is that the number of beds available in hospitals is insufficient to handle the volume of patients.

[0003] To effectively treat and manage both infectious disease patients and general emergency patients, Korea adopts a policy of admitting severely ill patients to hospitals while having mild cases self-isolate. Furthermore, to prevent the spread of infection and minimize face-to-face contact between patients and medical staff, Korea implements telemedicine for those in self-isolation. Telemedicine allows for the efficient utilization of limited medical resources and enables the provision of medical services, particularly to patients residing in medically underserved areas.

[0004] Problems arising from home treatment and telemedicine implemented during the COVID-19 pandemic include issues regarding liability, uncertainty regarding target institutions, inadequate medical delivery systems, and the lack of regulatory measures for telemedicine platforms. Additionally, infrastructure limitations, workload overload and negligence in home care management, the absence of rapid response measures for emergencies, and delays in patient transport have emerged as issues. In particular, as the hospital-centered home care system becomes overloaded, there is a demand for a system shift centered on local clinics. However, it has been pointed out that local clinics lack the expertise regarding novel infectious diseases and thus do not have the time to acquire the latest information. Furthermore, time constraints are inevitable under a home care system where a small number of doctors treat a large number of patients, which could lead to a decline in the quality of care.

[0005] In addition, it is being raised as a problem that conventional home treatment and telemedicine do not properly manage intensive care groups, such as patients with underlying conditions, who are at high risk of their condition worsening.

[0006] Therefore, there is a need for a clinical decision support system that resolves the problems of conventional home treatment and telemedicine and assists physicians in their medical practice. Prior art literature

[0007] Korean Patent Publication No. 10-2023-0078527 The problem to be solved

[0008] The present invention aims to provide a clinical decision support system specialized for infectious diseases that performs evaluation, medical treatment, prescription, and monitoring of infectious disease patients undergoing home treatment, while specially managing patients with underlying diseases and rapidly determining whether to transfer patients undergoing home treatment, thereby providing medical resources efficiently and effectively. means of solving the problem

[0009] To achieve the above objective, the present invention is characterized by comprising: an initial evaluation unit that calculates the severity and risk of an infectious disease using patient information and determines one of the treatment types among home treatment, inpatient treatment, and ICU (Intensive Care Unit) by inputting the calculated severity and risk of the infectious disease into an artificial intelligence model or comparing it with preset criteria; a treatment guide unit that determines monitoring guidelines and treatment guidelines for a patient determined to receive home treatment based on patient information, the calculated severity and risk of the infectious disease, and the results of a medical history interview; and a monitoring unit that performs monitoring of the patient receiving home treatment according to the monitoring guidelines determined by the treatment guide unit.

[0010] Preferably, the initial evaluation unit may use underlying disease information to calculate the severity of the infectious disease or the risk of the infectious disease.

[0011] Preferably, the underlying disease information may be the presence or absence of an underlying disease and the type of underlying disease.

[0012] Preferably, the monitoring unit can detect whether there is an abnormality in the patient's vital signs by analyzing a biosignal acquired in real time by one or more biosignal measuring devices.

[0013] Preferably, the monitoring unit distinguishes whether there is an underlying disease using the patient's underlying disease information, and if the patient has an underlying disease, can determine whether the detected abnormal vital signs are due to an infectious disease or the underlying disease.

[0014] Preferably, the monitoring unit can receive diagnostic information capable of diagnosing an underlying disease when the patient has an underlying disease.

[0015] Preferably, the monitoring unit inputs the patient information, the underlying disease information, the biosignal, the diagnostic information, and the real-time medical history results into a trained power decision model and can output one of discontinuation of non-face-to-face treatment (visit to a medical institution), discontinuation of non-face-to-face treatment (recommendation for hospitalization), and continuation of non-face-to-face treatment.

[0016] Preferably, the monitoring unit performs patient monitoring at regular intervals, and the medical guide unit determines monitoring guidelines and medical guidelines by analyzing the underlying disease information of a patient for whom home treatment has been decided, and if it is confirmed through the underlying disease information that the patient has an underlying disease, it may transmit a monitoring guideline to the monitoring unit to perform monitoring at least twice a day.

[0017] A clinical decision support system specialized for infectious diseases, further comprising a non-face-to-face medical department that preferably holds non-face-to-face medical consultations for patients undergoing home treatment at regular intervals, and holds multidisciplinary consultations if it is confirmed that the patient has an underlying disease through the underlying disease information.

[0018] Preferably, when the telemedicine department holds a multidisciplinary consultation, it requests a telemedicine consultation from medical staff related to infectious diseases and medical staff related to underlying diseases, shares and displays the patient information, underlying disease information, vital signs, diagnostic information, and real-time medical history results to the requested medical staff, and can receive a consultation opinion from each medical staff.

[0019] Furthermore, the present invention is characterized by another feature comprising a computer program that includes instructions stored on a computer-readable storage medium to cause a computer to perform the following operations, wherein the operations include: an initial evaluation operation that calculates the severity and risk of an infectious disease using patient information, inputs the calculated severity and risk of the infectious disease into an artificial intelligence model or compares it with preset criteria to determine a treatment type among home treatment, inpatient treatment, and ICU (Intensive Care Unit); a treatment guide operation that determines monitoring guidelines and treatment guidelines for a patient determined to receive home treatment based on patient information, the calculated severity and risk of the infectious disease, and the results of a medical history interview; and a monitoring operation that performs monitoring of the patient receiving home treatment according to the monitoring guidelines determined by the treatment guide operation. Effects of the invention

[0020] The present invention has the advantage of being able to provide medical resources efficiently and effectively by varying the treatment type, medical care type, and monitoring method in consideration of the severity and risk level of patients with infectious diseases.

[0021] In addition, the present invention has the advantage of being able to provide special care to patients with underlying diseases by determining the type of treatment, type of medical care, and monitoring method based on the presence and type of underlying diseases in patients with infectious diseases. Brief explanation of the drawing

[0022] Figure 1 shows a configuration diagram of a clinical decision support system specialized for infectious diseases according to an embodiment of the present invention. FIG. 2 shows a schematic diagram of a clinical decision support system specialized for infectious diseases according to an embodiment of the present invention. FIG. 3 shows a schematic diagram of an initial evaluation unit according to an embodiment of the present invention. Figure 4 shows a configuration diagram of an initial evaluation unit according to an embodiment of the present invention. FIG. 5 shows a schematic diagram of a treatment type determination unit that determines a treatment type by considering the severity and risk of an underlying disease according to an embodiment of the present invention. Figure 6 shows the structure of an overall artificial intelligence model for determining a treatment type according to an embodiment of the present invention. FIG. 7 shows a schematic diagram of a monitoring unit according to an embodiment of the present invention. FIG. 8 shows a configuration diagram of a monitoring unit according to an embodiment of the present invention. FIG. 9 shows a schematic diagram of a computing environment according to an embodiment of the present invention. Specific details for implementing the invention

[0023] The present invention will be described in detail below with reference to the contents described in the attached drawings. However, the present invention is not limited or restricted by exemplary embodiments. Identical reference numerals in each drawing indicate components that perform substantially the same function.

[0024] The purpose and effects of the present invention may be naturally understood or become clearer through the following description, and the purpose and effects of the present invention are not limited solely to the description below. Furthermore, in describing the present invention, if it is determined that a detailed description of known technology related to the present invention may unnecessarily obscure the essence of the present invention, such detailed description will be omitted.

[0025] The terms used in this invention are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the description of the invention, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0026] Terms such as "first," "second," etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component.

[0027] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which this invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this invention.

[0028] In interpreting the components, they are interpreted to include a margin of error even without a separate explicit indication. In the case of descriptions regarding temporal relationships, for example, where the temporal sequence is described using 'after,' 'following,' 'next,' 'before,' etc., cases that are not continuous are included unless 'immediately' or 'directly' is used.

[0029] Hereinafter, the technical configuration of the present invention will be described in detail with reference to the attached drawings.

[0030] FIG. 1 shows a configuration diagram of a clinical decision support system (10) specialized for infectious diseases according to an embodiment of the present invention. Referring to FIG. 1, the clinical decision support system (10) specialized for infectious diseases may include an initial evaluation unit (100), a medical guide unit (300), a monitoring unit (500), and a non-face-to-face medical treatment unit (700).

[0031] FIG. 2 shows a schematic diagram of a clinical decision support system (10) specialized for infectious diseases according to an embodiment of the present invention. Referring to FIG. 2, the clinical decision support system (10) specialized for infectious diseases may include an initial evaluation CDSS for determining whether to provide home treatment to a patient, a treatment guide CDSS for providing medical care to a patient, a customized drug prescription guide CDSS for providing prescriptions based on the results of treatment, a notification CDSS for detecting worsening symptoms of a home treatment to a patient for monitoring the home treatment to a patient, and a mobile multidisciplinary consultation CDSS for providing mobile multidisciplinary consultation. Here, the initial evaluation CDSS for determining whether to provide home treatment to a patient corresponds to an initial evaluation unit (100), the treatment guide CDSS for providing medical care to a patient and the customized drug prescription guide CDSS for providing medical care to a patient correspond to a treatment guide unit (300), the notification CDSS for detecting worsening symptoms of a home treatment to a monitoring unit (500), and the mobile multidisciplinary consultation CDSS corresponds to a non-face-to-face medical treatment unit (700).

[0032] A clinical decision support system (10) specialized for infectious diseases may include an adaptive CDSS. The adaptive CDSS is equipped with artificial intelligence and knowledge base update automation technology, which can maximize the reflection and utilization of new knowledge, such as variant viruses and new treatments, as well as the latest clinical research. The adaptive CDSS can update new knowledge and treatment methods for previously applied infectious diseases, and can update the reference disease to the new infectious disease (infectious disease response guidelines, new clinical data, etc.) in the event of a new infectious disease outbreak in accordance with international organizations and government guidelines. The adaptive CDSS can notify the occurrence of new knowledge through keyword analysis of internet portals, recommend similar knowledge based on an infectious disease embedding model, support CDSS update automation by linking with a model editor for data loading standardization, a knowledge inputter, and an AI CDSS development and operation platform, and provide supporting materials as a summary when searching for new knowledge information.

[0033] FIG. 3 shows a schematic diagram of an initial evaluation unit (100) according to an embodiment of the present invention. Referring to FIG. 3, the initial evaluation unit (100) calculates the severity and risk of an infectious disease using patient information, and inputs the calculated severity and risk of the infectious disease into an artificial intelligence model or compares it with a preset standard to determine one of the treatment types: home treatment, inpatient treatment, and ICU (Intensive Care Unit).

[0034] The initial evaluation unit (100) may use underlying disease information to calculate the severity of the infectious disease or the risk of the infectious disease. Here, underlying disease information may include the presence or absence of an underlying disease, the type of underlying disease, the time of occurrence of the underlying disease, the duration of treatment for the underlying disease, whether related treatment was received, the type of related treatment, the severity of the underlying disease, and the risk of the underlying disease. Preferably, the underlying disease information may be the presence or absence of an underlying disease and the type of underlying disease.

[0035] FIG. 4 shows a configuration diagram of an initial evaluation unit (100) according to an embodiment of the present invention. Referring to FIG. 4, the initial evaluation unit (100) may include a receiving unit (110), a first analysis unit (130), a second analysis unit (150), and a treatment type determination unit (170).

[0036] The receiver (110) can receive patient information including at least one of biometric information, medical information, medical history information, and underlying disease information. The information collected by the receiver (110) can be used as data to determine the severity and risk of an infectious disease. Additionally, the information collected by the receiver (110) can be used as data to determine the severity and risk of an underlying disease.

[0037] Here, bio-information may include biosignals (heart rate, blood pressure, body temperature, oxygen saturation, respiratory rate, SpO2, ECG, EEG, etc.), body composition-related information (weight, muscle mass, body fat percentage, body water content, BMI), blood-related values ​​(blood glucose levels, cholesterol levels, hemoglobin levels, hormone levels), brain and nervous system activity information (EEG, ECG), lung function (vital capacity, peak expiratory flow), physical activity and sleep (daily step count, calorie expenditure, sleep duration and quality), stress indicators (cortisol levels, galvanic skin response), and the immune system (white blood cell count, antibody levels).

[0038] Medical information may include basic personal information (age, gender, race, occupation, living environment), medical history (history of past diseases, surgical history, current treatment), family history (genetic diseases, history of major diseases in family members), lifestyle information (smoking, drinking status, exercise habits, dietary habits), allergy information (drug allergies, food allergies), information on medications being taken (prescription drugs, over-the-counter drugs, health supplements), physical examination results, lab data (blood tests, urine tests, stool tests), imaging test results (chest X-ray, CT, MRI, ultrasound), special test results (pulmonary function tests), mental health assessments (depression screening, anxiety assessment), and vaccination records (type and date of vaccination).

[0039] The medical history information may include patient-reported symptoms (cough, phlegm, headache, pain, etc.) and tests on daily living functions.

[0040] The first analysis unit (130) inputs patient information into an infectious disease severity classification algorithm to calculate one of the following infectious disease severity levels: asymptomatic, mild, moderate, severe, and severe.

[0041] The infectious disease severity classification algorithm can calculate one of the following infectious disease severity levels: asymptomatic, mild, moderate, severe, or critical, based on whether the symptoms match pre-set infectious disease symptoms, the presence of respiratory distress, chest X-ray results, oxygen saturation, respiratory failure, septic shock, and multiple organ failure.

[0042] Specifically, the infectious disease severity classification algorithm may classify as asymptomatic if the patient shows a positive reaction to an infectious disease test but has no symptoms consistent with the infectious disease, as mild if there are symptoms consistent with the infectious disease but no abnormalities on a chest X-ray or shortness of breath, as moderate if the patient is judged to have a respiratory disease and has an oxygen saturation of 94% or higher, as severe if the oxygen saturation is less than 94%, the PaO2 / FiO2 is less than 300 mmHg, the respiratory rate is greater than 30 breaths per minute, or the lung parenchymal infiltration is greater than 50%, and as severe if at least one of respiratory failure, septic shock, and multiple organ failure is present.

[0043] In another embodiment, the infectious disease severity classification algorithm may be an artificial intelligence model that is an infectious disease severity model. In this case, the infectious disease severity classification algorithm may be trained by taking as inputs the patient's positive status for the infectious disease, symptoms of the infectious disease, presence of dyspnea, results of chest X-ray examination, presence of respiratory disease, oxygen saturation, PaO2 / FiO2 levels, respiratory rate, lung parenchymal infiltration levels, presence of respiratory failure, presence of septic shock, presence of multiple organ failure, etc., and by outputting one of the infectious disease severity levels among asymptomatic, mild, moderate, severe, and critical. The infectious disease severity classification algorithm may be composed of XGBoost, which utilizes the CART (Classification And Regression Tree) method among boosting algorithms. All artificial intelligence models described below may likewise be composed of XGBoost, which utilizes the CART (Classification And Regression Tree) method among boosting algorithms.

[0044] The second analysis unit (150) inputs patient information into a learned infectious disease risk model to calculate the infectious disease risk of either the low-risk group or the high-risk group.

[0045] An infectious disease risk model can be trained by taking at least one of the patient's age, height, weight, presence and type of underlying disease, and medications being taken as input, and by outputting a low-risk group or a high-risk group.

[0046] The high-risk group may be patients who are 65 years of age or older, 50 years of age or older with underlying diseases, or immunocompromised.

[0047] Patients with underlying conditions including diabetes, hypertension, cardiovascular disease, chronic kidney disease, chronic lung disease, and a Body Mass Index (BMI) of 30 kg / m² 2 It can be defined as a person who falls under at least one of the following: a person with a neurodevelopmental disorder, or a person with a mental illness.

[0048] An immunocompromised person may be defined as a person currently receiving treatment for a tumor or blood cancer, a patient within 2 years of hematopoietic stem cell transplantation, a person within 1 year of receiving B-cell immunotherapy, a person receiving treatment for sickle cell anemia, hemoglobinemia or thalassemia, a person being treated for primary immunodeficiency, a lung transplant patient, a patient within 1 year of solid organ transplantation, a patient with HIV infection, a patient with combined immunodeficiency, a patient with autoimmune or autoinflammatory rheumatoid arthritis, a patient who has had a splenectomy, and a patient with functional anatomical asplenia or splenic dysfunction, or a person taking at least one of high-dose corticosteroids, alkylating agents, antagonists, transplant-associated immunosuppressants, cancer chemotherapy agents, tumor necrosis blockers, other biological agents that are immunosuppressants or immunomodulators, or Burton tyrosine kinase inhibitors.

[0049] The treatment type determination unit (170) can determine one of the treatment types, such as the home treatment group, the inpatient treatment group, and the ICU group (Intensive Care Unit), by inputting the infectious disease severity and infectious disease risk calculated by the first analysis unit (130) and the second analysis unit (150) into a learned initial evaluation model.

[0050] The initial assessment model can be trained using infectious disease severity and infectious disease risk as inputs, and one of the home treatment group, inpatient treatment group, and ICU group as output.

[0051] The home treatment group may be defined as patients with an infectious disease who have completed home treatment by the end of their isolation period. The inpatient treatment group may be defined as patients with an infectious disease who have discontinued home treatment before the end of their isolation period and visited a medical institution or were admitted to a general ward. The ICU group may be defined as patients with an infectious disease who have discontinued home treatment before the end of their isolation period and received oxygen therapy or died.

[0052] FIG. 5 shows a schematic diagram of a treatment type determination unit (170) that determines a treatment type by considering the severity and risk of an underlying disease according to an embodiment of the present invention. The treatment type determination unit (170) may additionally consider the severity and risk of an underlying disease when determining a treatment type. Here, the severity of the underlying disease may be calculated in the third analysis unit (140), and the risk of the underlying disease may be calculated in the fourth analysis unit (160). That is, the treatment type determination unit (170) may determine one of a treatment type among a home treatment group, an inpatient treatment group, and an ICU group (Intensive Care Unit) by inputting the severity of the infectious disease, the risk of the infectious disease, the severity of the underlying disease, and the risk of the underlying disease calculated in the first analysis unit (130) to the fourth analysis unit (160) into a learned initial evaluation model.

[0053] FIG. 6 illustrates the structure of an overall artificial intelligence model for determining a treatment type according to an embodiment of the present invention. Referring to FIG. 6, the third analysis unit (140) inputs biosignals, medical information, underlying disease information, and medical history data into a learned underlying disease severity model to calculate the severity of an underlying disease among asymptomatic, mild, moderate, severe, and severe. The underlying disease severity model can be learned by taking the patient's biosignals, medical information, underlying disease information, and medical history data as inputs, and the asymptomatic / mild / moderate / severe / severe status previously classified by medical staff as outputs.

[0054] The fourth analysis unit (160) inputs gender, age, and underlying disease information into a learned underlying disease risk model to calculate the underlying disease risk of either a low-risk group or a high-risk group. The underlying disease severity model can be learned by taking the patient's gender, age, and underlying disease information as input and the low-risk / high-risk group status previously classified by medical staff as output.

[0055] The medical guide section (300) can determine monitoring guidelines and medical guidelines for patients for whom home treatment has been decided based on patient information, calculated severity and risk of infectious disease, and medical history results. The medical guide section (300) can additionally use information on underlying diseases when determining the patient's monitoring guidelines and medical guidelines.

[0056] As shown in Table 1 below, the medical guide section (300) can determine medical guidelines based on a combination of the severity of the infectious disease, the risk of infection, or the presence or absence of an underlying disease.

[0057] Severity Risk level Presence or absence of underlying diseases Clinical guidelines Asymptomatic low risk you Case 1 radish high risk you radish Mild low risk you radish high risk you Case 2 radish Moderate low risk you radish high risk you Case3 radish Case 2 severe low risk you Case3 radish high risk you Case 4 radish Case3 perjury low risk you Case 5 radish high risk you Case 6 radish Case 5

[0058] The medical guide department (300) analyzes the underlying disease information of a patient who has been decided for home treatment and determines monitoring guidelines and medical guidelines, but if it is confirmed through the underlying disease information that the patient has an underlying disease, it can transmit a monitoring guideline to the monitoring department (500) to perform monitoring at least twice a day.

[0059] FIG. 7 shows a schematic diagram of a monitoring unit (500) according to an embodiment of the present invention. Referring to FIG. 7, the monitoring unit (500) can perform monitoring of a patient receiving home treatment according to monitoring guidelines determined by the medical guide unit (300). The monitoring unit (500) can detect whether there is an abnormality in the patient's vital signs by analyzing a biosignal acquired in real time by one or more biosignal measuring devices.

[0060] The monitoring unit (500) can distinguish whether there is an underlying disease using the patient's underlying disease information, and if the patient has an underlying disease, it can determine whether the detected abnormal vital signs are due to an infectious disease or the underlying disease.

[0061] The monitoring unit (500) can receive real-time diagnostic information that can diagnose the underlying disease if the patient has an underlying disease. The monitoring unit (500) can detect symptoms of the underlying disease through the real-time diagnostic information that can diagnose the patient's underlying disease.

[0062] The monitoring unit (500) inputs patient information, the underlying disease information, the biosignal, the diagnosis information, and the real-time medical history results into a trained power determination model and can output one of discontinuation of non-face-to-face treatment (visit to a medical institution), discontinuation of non-face-to-face treatment (recommendation for hospitalization), and continuation of non-face-to-face treatment.

[0063] The monitoring unit (500) can perform patient monitoring at regular intervals. The monitoring unit (500) can set the monitoring cycle differently depending on the severity or risk of the patient's infectious disease or the severity or risk of the underlying disease, and can set the monitoring cycle shorter as the risk and severity increase.

[0064] FIG. 8 shows a configuration diagram of a monitoring unit (500) according to an embodiment of the present invention. Referring to FIG. 8, the monitoring unit (500) may include a receiving unit (510), a vital sign detection unit (530), and a power determination unit (550). The monitoring unit (500) may be configured independently by being combined with the aforementioned initial evaluation unit (100).

[0065] The receiving unit (510) can receive patient information including at least one of biometric information, medical information, medical history information, and underlying disease information, and diagnostic information capable of diagnosing an underlying disease. The receiving unit (510) of the monitoring unit (500) may be configured independently of or integrally with the receiving unit (110) of the initial evaluation unit (100).

[0066] The receiver (510) receives information on underlying diseases, but may receive different information depending on the patient's underlying disease. For example, if the patient's underlying disease is hypertension, it may receive blood pressure; if the patient's underlying disease is diabetes, it may receive blood sugar levels; and if the patient's underlying disease is a respiratory disease, it may receive oxygen saturation levels.

[0067] The vital sign detection unit (530) can detect whether there is an abnormality in the patient's vital signs by analyzing a vital signal acquired in real time by one or more vital signal measuring devices. Preferably, the vital sign detection unit (530) can detect whether there is an abnormality in the patient's vital signs by analyzing one or more vital signals and underlying disease information.

[0068] In one embodiment, the vital sign detection unit (530) may determine that there is a vital sign abnormality if one or more vital signs and underlying disease information match a preset threshold.

[0069] In another embodiment, the vital sign detection unit (530) can detect whether there is an abnormality in the patient's vital signs by using a vital sign detection model, which is an artificial intelligence model. The vital sign detection model can be learned by taking one or more vital signs and information regarding the patient's underlying disease as inputs, and normal or abnormal as outputs.

[0070] In another embodiment, the vital sign detection model can distinguish between symptoms of infectious diseases and symptoms of underlying diseases and output them. In this case, the vital sign detection model can be trained by taking one or more vital signs and information regarding the patient's underlying disease as input, and outputting normal or abnormal symptoms of infectious diseases / underlying diseases. In this embodiment, if there are abnormalities in the symptoms of an infectious disease, non-face-to-face medical treatment may be performed according to a general home treatment process, and if there are abnormalities in the symptoms of an underlying disease, non-face-to-face medical treatment may be performed according to a multidisciplinary collaborative process.

[0071] The vital sign detection unit (530) can adjust the sensitivity for detecting abnormalities in the patient's vital signs according to the severity of the infectious disease or the risk of the infectious disease calculated by the initial evaluation unit (100). For example, if the severity of the infectious disease or the risk of the infectious disease calculated by the initial evaluation unit (100) is high, the vital sign detection unit (530) can increase the sensitivity to determine that there is a vital sign abnormality even with relatively small abnormal symptoms. Conversely, if the severity of the infectious disease or the risk of the infectious disease calculated by the initial evaluation unit (100) is low, the vital sign detection unit (530) can decrease the sensitivity to determine that there is no vital sign abnormality even with relatively high abnormal symptoms.

[0072] The vital sign detection unit (530) can adjust the sensitivity for detecting abnormalities in the patient's vital signs according to the severity of the underlying disease or the risk of infectious disease calculated by the initial evaluation unit (100). In particular, in this case, the sensitivity for detecting abnormalities in the underlying disease can be adjusted.

[0073] The power determination unit (550) inputs patient information, the underlying disease information, the biosignal, the diagnosis information, and the real-time medical history results into a trained power determination model and can output one of discontinuation of non-face-to-face treatment (visit to a medical institution), discontinuation of non-face-to-face treatment (recommendation for hospitalization), and continuation of non-face-to-face treatment.

[0074] The referral decision model can be trained by taking patient information, the underlying disease information, the biosignals, the diagnostic information, and real-time medical history results as inputs, and by taking one of discontinuation of non-face-to-face treatment (visit to a medical institution), discontinuation of non-face-to-face treatment (recommendation for hospitalization), and continuation of non-face-to-face treatment as outputs.

[0075] Discontinuation of non-face-to-face medical treatment (visit to a medical institution) is defined as a case where a patient with an infectious disease discontinues home treatment before the end of the isolation period and visits a medical institution or is admitted to a general ward; discontinuation of non-face-to-face medical treatment (recommendation for hospitalization) is defined as a case where a patient with an infectious disease discontinues home treatment before the end of the isolation period and receives oxygen therapy or dies; and continuation of non-face-to-face medical treatment may be defined as a case where a patient with an infectious disease completes home treatment by the end of the isolation period.

[0076] The power decision unit (550) may hold a non-face-to-face medical consultation if it outputs that the non-face-to-face medical consultation should be maintained, and may send a notification to a medical institution or guardian if it outputs that the non-face-to-face medical consultation should be stopped (visit to a medical institution) or that the non-face-to-face medical consultation should be stopped (recommendation for hospitalization).

[0077] The non-face-to-face medical department (700) holds non-face-to-face medical consultations for patients receiving home treatment at regular intervals, but if it is confirmed that the patient has an underlying disease through the underlying disease information above, it may hold a multidisciplinary consultation.

[0078] When the non-face-to-face medical department (700) holds a multidisciplinary consultation, it requests a non-face-to-face consultation from medical staff related to infectious diseases and medical staff related to underlying diseases, shares and displays the patient information, underlying disease information, biosignals, diagnostic information and real-time medical history results to the requested medical staff, and can receive a consultation opinion from each medical staff.

[0079] A clinical decision support system and computer program specialized for infectious diseases, which is another embodiment of the present invention, may include an initial evaluation operation, a medical guidance operation, a monitoring operation, and a non-face-to-face medical treatment operation.

[0080] The initial evaluation operation calculates the severity and risk of the infectious disease using patient information, and inputs the calculated severity and risk of the infectious disease into an artificial intelligence model or compares it with preset criteria to determine one of the treatment types: home treatment, inpatient treatment, and ICU (Intensive Care Unit). The initial evaluation operation refers to the operation performed in the aforementioned initial evaluation unit (100).

[0081] The medical guide operation can determine monitoring guidelines and medical guidelines for patients for whom home treatment has been decided based on patient information, calculated severity and risk of infectious disease, and medical history results. The medical guide operation refers to the operation performed in the aforementioned medical guide unit (300).

[0082] The monitoring operation can perform monitoring of home-treatment patients according to the monitoring guidelines determined by the medical guide operation. The monitoring operation refers to the operation performed by the aforementioned monitoring unit (500).

[0083] The non-face-to-face medical treatment operation involves holding a non-face-to-face medical treatment for a patient undergoing home treatment at regular intervals, and if it is confirmed through the above-mentioned underlying disease information that the patient has an underlying disease, a multidisciplinary consultation may be held. The non-face-to-face medical treatment operation refers to an operation performed in the aforementioned non-face-to-face medical treatment unit (700).

[0084] FIG. 9 shows a schematic diagram of a computing environment according to an embodiment of the present invention.

[0085] Although the present disclosure has been described as generally being implementable by a computing device, those skilled in the art will understand that the present disclosure may be implemented in combination with computer-executable instructions and / or other program modules that can be executed on one or more computers, and / or as a combination of hardware and software.

[0086] Generally, a program module includes routines, programs, components, data structures, etc., that perform a specific task or implement a specific abstract data type. Furthermore, those skilled in the art will be well aware that the method of the present disclosure may be implemented in other computer system configurations, including single-processor or multi-processor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. (each of which may be connected to and operated with one or more associated devices).

[0087] The embodiments described in this disclosure may also be implemented in a distributed computing environment in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0088] Computers typically include various computer-readable media. Any medium accessible by a computer may be a computer-readable medium, and such computer-readable media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media. By example, but not limiting, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and non-volatile media, transitory and non-transitory media, and removable and non-removable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD (digital video disk) or other optical disk storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other media that can be accessed by a computer and used to store desired information.

[0089] Computer-readable transmission media typically include all information transmission media that implement computer-readable instructions, data structures, program modules, or other data, etc., on a modulated data signal, such as a carrier wave or other transport mechanism. The term modulated data signal means a signal in which one or more of the characteristics of the signal are set or modified to encode information within the signal. By example, not limiting, computer-readable transmission media include wired media, such as wired networks or direct-wired connections, and wireless media, such as acoustic, RF, infrared, and other wireless media. Any combination of the media described above is also considered to be within the scope of computer-readable transmission media.

[0090] An exemplary environment for implementing various aspects of the present disclosure, including a computer (1000), is shown, wherein the computer (1000) includes a processing unit (1020), a system memory (1030), and a system bus (1010). The system bus (1010) connects system components, including the system memory (1030) (but not limited thereto), to the processing unit (1020). The processing unit (1020) may be any processor among various commercial processors. Dual processors and other multiprocessor architectures may also be used as the processing unit (1020).

[0091] The system bus (1010) may be any of several types of bus structures that can be additionally interconnected to a local bus using any of the memory bus, peripheral bus, and various commercial bus architectures. The system memory (1030) includes read-only memory (ROM) (1034) and random access memory (RAM) (1032). The basic input / output system (BIOS) is stored in non-volatile memory (1034), such as ROM, EPROM, EEPROM, etc., and this BIOS includes basic routines that help transfer information between components within the computer (1000) at times such as during startup. The RAM (1032) may also include high-speed RAM, such as static RAM, for caching data.

[0092] The computer (1000) also includes an internal hard disk drive (HDD) (1050) (e.g., EIDE, SATA)—this internal hard disk drive (1050) may also be configured for external use within a suitable chassis (not shown)—a magnetic floppy disk drive (FDD) (1060) (e.g., for reading from or writing to a removable diskette), and an optical disk drive (1070) (e.g., for reading from a CD-ROM disk or reading from or writing to other high-capacity optical media such as a DVD). The hard disk drive (1050), the magnetic disk drive (1060), and the optical disk drive (1070) may each be connected to the system bus (1010) via a hard disk drive interface, a magnetic disk drive interface, and an optical drive interface. Interfaces for implementing external drives include at least one or both of the Universal Serial Bus (USB) and IEEE 1394 interface technologies.

[0093] These drives and associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, etc. In the case of a computer (1000), the drives and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will know that other types of computer-readable media, such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc., may also be used in exemplary operating environments and that any of these media may contain computer-executable instructions for performing the methods of the present disclosure.

[0094] A number of program modules, including an operating system (1092), one or more application programs (1094), other program modules (1096), and a database (1098), may be stored in the drive and RAM (1032). All or part of the operating system, applications, modules, and / or data may also be cached in RAM (1032). It will be well known that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

[0095] The user can input commands and information into the computer (1000) through one or more wired / wireless input devices (1042), such as pointing devices like a keyboard and a mouse. Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, a touch screen, etc. These and other input devices are often connected to the processing unit (1020) via an input / output interface (1040) connected to the system bus (1010), but may also be connected via other interfaces such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc.

[0096] A monitor or other type of display device is also connected to the system bus (1010) via an interface such as a video adapter. In addition to the monitor, the computer generally includes other peripheral output devices (not shown), such as speakers, a printer, and so on.

[0097] A computer (1000) may operate in a networked environment using a logical connection to one or more remote computers, such as remote computer(s) (1082), via wired and / or wireless communication. The remote computer(s) (1082) may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other conventional network node, and may include many or all of the components generally described for the computer (1000). The logical connection includes a wired / wireless connection to a local area network (LAN) and / or a larger network, e.g., a wide area network (WAN). Such LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may be connected to a global computer network, e.g., the Internet.

[0098] When used in a LAN networking environment, the computer (1000) is connected to a local network (not shown) via a wired and / or wireless communication network interface or adapter (not shown). The adapter (not shown) may facilitate wired or wireless communication to the LAN (not shown), and the LAN (not shown) may also include a wireless access point installed therein to communicate with the wireless adapter (not shown). When used in a WAN networking environment, the computer (1000) may include a modem (not shown), be connected to a communication computing device on the WAN (not shown), or have other means to establish communication over the WAN (not shown), such as through the Internet. The modem (not shown), which may be internal or external and wired or wireless, is connected to the system bus (1010) via a serial port interface (not shown). In a networked environment, the program modules described for the computer (1000) or parts thereof may be stored in a remote memory / storage device (not shown). You will be well aware that the illustrated network connection is exemplary and that other means of establishing communication links between computers can be used.

[0099] The computer (1000) operates to communicate with any wireless device or object that is deployed and operated via wireless communication, for example, a printer, scanner, desktop and / or portable computer, PDA (portable data assistant), communication satellite, any equipment or place associated with a wireless detectable tag, and a telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network, or simply ad hoc communication between at least two devices.

[0100] Wi-Fi (Wireless Fidelity) enables connectivity to the Internet and other sources without wires. Wi-Fi is a wireless technology, similar to a cell phone, that allows devices, such as computers, to transmit and receive data indoors and outdoors—that is, anywhere within the coverage area of ​​a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band).

[0101] Those skilled in the art of the present disclosure will understand that information and signals may be represented using any various different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced in the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0102] Those skilled in the art will understand that the various exemplary logic blocks, modules, processors, means, circuits, and model steps described in connection with the embodiments disclosed herein may be implemented by electronic hardware, various forms of programs or design code (referred to herein as software for convenience), or a combination of all such. To clearly illustrate this interoperability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in relation to their functions. Whether such functions are implemented as hardware or software depends on the design constraints imposed on the specific application and the overall system. Those skilled in the art may implement the functions described in various ways for each specific application, but such implementation decisions should not be interpreted as being outside the scope of the present disclosure.

[0103] The various embodiments presented herein may be implemented as methods, devices, or articles manufactured using standard programming and / or engineering techniques. The term "article manufactured" includes a computer program, a carrier, or a medium accessible from any computer-readable storage device. For example, computer-readable storage media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical discs (e.g., CDs, DVDs, etc.), smart cards, and flash memory devices (e.g., EEPROMs, cards, sticks, key drives, etc.). Additionally, the various storage media presented herein include one or more devices and / or other machine-readable media for storing information.

[0104] It should be understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. It should be understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of this disclosure based on design priorities. The appended method claims provide elements of various steps in a sample order, but do not imply being limited to the specific order or hierarchy presented.

[0105] Description of the presented embodiments is provided so that a person skilled in the art may use or practice the present disclosure. Various modifications to these embodiments will be apparent to a person skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments presented herein, but should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein.

[0106] The embodiments of the present invention described above are not implemented only through devices and methods, but may also be implemented through a program that realizes a function corresponding to the configuration of the embodiments of the present invention, or a recording medium on which such a program is recorded. Such a recording medium may be executed not only on a server but also on a user terminal.

[0107] Although embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concept of the present invention as defined in the following claims also fall within the scope of the present invention. Explanation of the symbols

[0108] 10: Clinical Decision Support System Specialized for Infectious Diseases 100: Initial Evaluation Department 110: Receiver 130: 1st Analysis Division 150: 2nd Analysis Division 170: Treatment Type Determination Section 300: Clinical Guide Department 500: Monitoring Department 510: Receiver 530: Vital Sign Detection Unit 550: Power determination unit 700: Telemedicine Department

Claims

Claim 1 An initial evaluation unit that calculates the severity and risk of an infectious disease using patient information and underlying disease information, and determines one of the treatment types among home treatment, inpatient treatment, and ICU (Intensive Care Unit) by inputting the calculated severity and risk of the infectious disease into an artificial intelligence model or comparing it with preset criteria; a treatment guide unit that determines monitoring and treatment guidelines for patients determined to receive home treatment based on patient information, underlying disease information, the calculated severity and risk of the infectious disease, and the results of the medical history interview; A clinical decision support system specialized for infectious diseases, comprising: a monitoring unit that performs monitoring of a patient receiving home treatment according to monitoring guidelines determined by the above-mentioned medical guide unit; wherein the underlying disease information includes the presence or absence of an underlying disease and the type of underlying disease; wherein the monitoring unit analyzes a biosignal acquired in real time by one or more biosignal measuring devices to detect whether there is an abnormality in the patient's vital signs, distinguishes whether there is an underlying disease in the patient using the above-mentioned underlying disease information, receives real-time diagnostic information capable of diagnosing the underlying disease if the patient has an underlying disease, determines whether the detected abnormality in vital signs is caused by an infectious disease or by an underlying disease using the above-mentioned biosignal and the above-mentioned real-time diagnostic information, inputs the above-mentioned patient information, the above-mentioned underlying disease information, the above-mentioned biosignal, the above-mentioned real-time diagnostic information, and the real-time medical history results into a trained decision model to output one of a visit to a medical institution due to the discontinuation of non-face-to-face treatment, a recommendation for hospitalization due to the discontinuation of non-face-to-face treatment, or the continuation of non-face-to-face treatment. Claim 2 A clinical decision support system specialized for infectious diseases according to claim 1, wherein the monitoring unit performs patient monitoring at regular intervals, and the treatment guide unit determines monitoring guidelines and treatment guidelines by analyzing underlying disease information of a patient for whom home treatment has been decided, and if it is confirmed through the underlying disease information that the patient has an underlying disease, the monitoring unit transmits a monitoring guideline to perform monitoring at least twice a day. Claim 3 A clinical decision support system specialized for infectious diseases, wherein, in paragraph 1, it further includes a non-face-to-face medical department that holds non-face-to-face medical consultations for patients undergoing home treatment at regular intervals, and holds a multidisciplinary consultation if it is confirmed that the patient has an underlying disease through the underlying disease information. Claim 4 In paragraph 3, the above-mentioned telemedicine department requests telemedicine from medical staff related to infectious diseases and medical staff related to underlying diseases when holding a multidisciplinary consultation, shares and displays the patient information, the underlying disease information, the biosignals, the real-time diagnostic information, and the real-time medical history results to the requested medical staff, and receives consultation opinion letters from each medical staff member; a clinical decision support system specialized for infectious diseases. Claim 5 A computer program stored on a computer-readable storage medium and comprising instructions that cause a computer to perform the following operations, wherein the operations include: an initial assessment operation that calculates the severity and risk of an infectious disease using patient information and underlying disease information, and inputs the calculated severity and risk of the infectious disease into an artificial intelligence model or compares it with preset criteria to determine one of the treatment types among home treatment, inpatient treatment, and ICU (Intensive Care Unit); and a treatment guide operation that determines monitoring guidelines and treatment guidelines for a patient determined to receive home treatment based on patient information, underlying disease information, the calculated severity and risk of the infectious disease, and the results of a medical history interview. A computer program stored on a computer-readable storage medium, comprising: a monitoring operation for performing monitoring of a patient receiving home treatment according to monitoring guidelines determined by the above-mentioned medical guide operation; wherein the underlying disease information includes the presence or absence of an underlying disease and the type of underlying disease, and the monitoring operation includes: an operation for detecting whether there is an abnormality in the patient's vital signs by analyzing a biosignal acquired in real time by one or more biosignal measuring devices; an operation for distinguishing whether there is an underlying disease in the patient using the above-mentioned underlying disease information; an operation for receiving real-time diagnostic information capable of diagnosing the underlying disease if the patient has an underlying disease, and determining whether the detected abnormality in vital signs is caused by an infectious disease or by the underlying disease; and an operation for inputting the patient information, the above-mentioned underlying disease information, the above-mentioned biosignal, the above-mentioned real-time diagnostic information, and the real-time medical history results into a trained decision model to output one of a visit to a medical institution due to the discontinuation of non-face-to-face treatment, a recommendation for hospitalization due to the discontinuation of non-face-to-face treatment, or the continuation of non-face-to-face treatment. Claim 6 A computer program stored on a computer-readable storage medium, wherein, in paragraph 5, the monitoring operation includes an operation of performing patient monitoring at regular intervals, and the treatment guide operation analyzes the underlying disease information of a patient for whom home treatment has been decided to determine the monitoring guideline and the treatment guideline, and if it is confirmed through the underlying disease information that the patient has an underlying disease, the monitoring guideline is transmitted to perform monitoring at least twice a day. Claim 7 A computer program stored on a computer-readable storage medium, wherein the above operations further include a non-face-to-face medical consultation operation for a patient undergoing home treatment at regular intervals, and a multidisciplinary consultation operation for a patient if it is confirmed through the underlying disease information that the patient has an underlying disease. Claim 8 In claim 7, the above-mentioned non-face-to-face medical treatment operation comprises: an operation of requesting non-face-to-face medical treatment from medical staff related to infectious diseases and medical staff related to underlying diseases when holding the above-mentioned multidisciplinary consultation; an operation of sharing and displaying the above-mentioned patient information, the above-mentioned underlying disease information, the above-mentioned biosignals, the above-mentioned real-time diagnostic information and real-time medical history results to the requested medical staff; and an operation of receiving consultation opinion letters from each medical staff; a computer program stored on a computer-readable storage medium.

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