WO2022044131A1 - 分析装置 - Google Patents

分析装置 Download PDF

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Publication number
WO2022044131A1
WO2022044131A1 PCT/JP2020/032059 JP2020032059W WO2022044131A1 WO 2022044131 A1 WO2022044131 A1 WO 2022044131A1 JP 2020032059 W JP2020032059 W JP 2020032059W WO 2022044131 A1 WO2022044131 A1 WO 2022044131A1
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WO
WIPO (PCT)
Prior art keywords
patient
severity
lung sound
unit
instruction
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2020/032059
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English (en)
French (fr)
Japanese (ja)
Inventor
昌夫 樋口
充 野間
玲史 近藤
友督 荒井
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NEC Corp
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NEC Corp
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Publication date
Application filed by NEC Corp filed Critical NEC Corp
Priority to JP2022544943A priority Critical patent/JP7563459B2/ja
Priority to US18/021,017 priority patent/US12569192B2/en
Priority to PCT/JP2020/032059 priority patent/WO2022044131A1/ja
Publication of WO2022044131A1 publication Critical patent/WO2022044131A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4842—Monitoring progression or stage of a disease
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/003—Detecting lung or respiration noise
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • A61B7/02—Stethoscopes
    • A61B7/04—Electric stethoscopes
    • 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
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • 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
    • G16H40/60—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 operation of medical equipment or devices
    • G16H40/63—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 operation of medical equipment or devices for local operation
    • 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
    • G16H40/60—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 operation of medical equipment or devices
    • G16H40/67—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 operation of medical equipment or devices for remote operation
    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases

Definitions

  • the present invention relates to an analyzer, an analysis method, and a recording medium.
  • Heart failure is some form of cardiac dysfunction, that is, dyspnea, malaise, and edema appear as a result of organic and / or functional abnormalities in the heart that disrupt the compensatory mechanism of cardiac pump function, resulting in exercise tolerance.
  • a clinical syndrome in which the ability is reduced.
  • Patients with heart failure are always at risk of exacerbation, even if they are in remission with treatment. If an acute exacerbation occurs in a patient due to excessive intake of water or salt, forgetting to take medicine, excessive exercise, etc., the patient will be forced to be re-hospitalized. Therefore, it is important to prevent acute exacerbations by detecting the exacerbation of heart failure in discharged patients at an early stage and intervening in treatment.
  • One of the methods for diagnosing heart failure is auscultation of lung sounds.
  • Such a medical examination is one of the safe and easy methods for diagnosing the health condition of the lungs and, by extension, heart failure.
  • it is difficult to obtain detailed and accurate diagnostic results without a trained specialist. Therefore, it was not possible to make a detailed diagnosis at sites such as rounds and home-visit care by general nurses and long-term care workers.
  • Patent Document 7 describes a server device provided with an advice sentence example setting means for automatically creating advice information based on a measurement number condition and an abnormal value range received from a doctor's terminal.
  • Japanese Unexamined Patent Publication No. 2014-4018 Special Table 2002-538921 Publication No. Special Table 2017-536905 WO2010 / 0444552 Japanese Unexamined Patent Publication No. 2008-11936 Patent No. 48494424 Japanese Unexamined Patent Publication No. 2006-259827 Japanese Unexamined Patent Publication No. 2007-19081
  • an object of the present invention is to provide an analyzer, an analysis method, and a recording medium that solve the problem that it may be difficult to take appropriate measures.
  • the analyzer which is one embodiment of the present invention, is A detection unit that detects abnormal lung sound at each auscultation position based on a time-series acoustic signal including lung sound at each auscultation position.
  • a determination unit for determining the severity of heart failure of the patient based on the detection result of the lung sound abnormality for each auscultation position detected by the detection unit and the state information indicating the patient's condition.
  • a judgment unit that determines an instruction to be output to the patient based on the result of the determination by the determination unit. It takes the configuration of having.
  • the analysis method which is another embodiment of the present invention is: Information processing equipment Based on the time-series acoustic signal including the lung sound at each auscultation position, the lung sound abnormality is detected at each auscultation position. The severity of heart failure in the patient is determined based on the detected result of detecting the abnormal lung sound for each auscultation position and the condition information indicating the condition of the patient. Based on the judgment result, the instruction to be output to the patient is judged.
  • the recording medium which is another embodiment of the present invention is For information processing equipment
  • a detection unit that detects abnormal lung sound at each auscultation position based on a time-series acoustic signal including lung sound at each auscultation position.
  • a determination unit for determining the severity of heart failure of the patient based on the detection result of the lung sound abnormality for each auscultation position detected by the detection unit and the state information indicating the patient's condition.
  • a judgment unit that determines an instruction to be output to the patient based on the result of the determination by the determination unit. It is a computer-readable recording medium on which a program for realizing the above is recorded.
  • the present invention can provide an analyzer, an analysis method, and a recording medium capable of causing a patient to take an appropriate response by having the above-mentioned configuration.
  • FIG. 1 is a block diagram showing a configuration example of the analyzer 100.
  • FIG. 2 is a diagram showing an example of lung sound data 151.
  • 3 and 4 are diagrams for explaining an example of auscultation positions.
  • FIG. 5 is a diagram showing an example of analysis result information 152.
  • FIG. 6 is a diagram showing an example of information for determining severity 153.
  • FIG. 7 is a diagram showing an example of severity information 154.
  • FIG. 8 is a diagram showing an example of the instruction determination information 155.
  • FIG. 9 is a diagram showing an example of information included in the personal status information 156.
  • FIG. 10 is a flowchart showing an operation example of the analyzer 100.
  • FIG. 11 is a block diagram showing another configuration example of the analyzer.
  • the analyzer 100 that outputs an instruction according to the result of analyzing the lung sound will be described.
  • the analyzer 100 acquires lung sounds from a plurality of locations on the chest and back, and detects abnormalities at each location based on the acquired lung sounds. Further, the analyzer 100 determines the severity of heart failure based on the detected result. After that, the analyzer 100 determines the instruction to the user based on the determined severity. Then, the analyzer 100 outputs the determined instruction to the user or the like.
  • the analyzer 100 has personal state information 156 indicating the personal state.
  • the analyzer 100 can utilize the personal condition information 156 when determining the severity of heart failure or when determining an instruction according to the severity.
  • the analysis device 100 is an information processing device that analyzes lung sounds acquired from a patient and outputs instructions according to the analysis results.
  • the analyzer 100 is, for example, a smartphone, a tablet terminal, a PDA (Personal Digital Assistant), a notebook computer, or the like.
  • the analyzer 100 may be other than those illustrated above.
  • FIG. 1 shows a configuration example of the analyzer 100.
  • the analyzer 100 has, for example, an electronic stethoscope 110, an operation input unit 120, a screen display unit 130, a communication I / F unit 140, and a storage unit 150 as main components. It has an arithmetic processing unit 160 and.
  • the electronic stethoscope 110 acquires the patient's lung sound.
  • the electronic stethoscope 110 converts the patient's lung sound into a digital signal by applying the chest piece of the stethoscope to the chest or back of the patient, and transfers it to the arithmetic processing unit 160 wirelessly or by wire.
  • the operation input unit 120 includes an operation input device such as a keyboard and a mouse.
  • the operation input unit 1120 detects the operation of the user who uses the analyzer 100 and outputs it to the arithmetic processing unit 160.
  • the user may include a medical worker such as a doctor or a nurse, a care worker such as a certified care worker, or a patient's family.
  • the screen display unit 130 is composed of a screen display device such as an LCD (Liquid Crystal Display).
  • the screen display unit 130 can display various information such as analysis results on the screen in response to an instruction from the arithmetic processing unit 160.
  • the communication I / F unit 140 is composed of a data communication circuit.
  • the communication I / F unit 140 performs data communication with various external devices such as a server device connected via wire or wireless.
  • the storage unit 150 is a storage device such as a hard disk or a memory.
  • the storage unit 150 stores processing information and a program 157 necessary for various processes in the arithmetic processing unit 160.
  • the program 157 realizes various processing units by being read and executed by the arithmetic processing unit 160.
  • the program 157 is read in advance from an external device or a recording medium via a data input / output function such as the communication I / F unit 140, and is stored in the storage unit 150.
  • the main information stored in the storage unit 150 includes, for example, lung sound data 151, analysis result information 152, severity determination information 153, severity information 154, instruction determination information 155, personal condition information 156, and the like. ..
  • Lung sound data 151 shows lung sound data for each auscultation position.
  • FIG. 2 shows an example of the information contained in the lung sound data 151.
  • the lung sound data 151 includes, for example, lung sound data for each auscultation position.
  • the information included in the lung sound data 151 as illustrated in FIG. 2 is created at the timing of, for example, every time an analysis is performed using the analyzer 100, or every time an auscultation is performed using the electronic stethoscope 110.
  • the lung sound data 151 may be a combination of data identification information according to the date and time of auscultation and the information illustrated in FIG.
  • the item of auscultation position refers to the approximate location of the patient's body to which the chest piece of the electronic stethoscope 110 is applied to hear the lung sound. That is, the auscultation position is the acquisition site of the lung sound. For example, in the example of FIG. 2, a total of 12 auscultation positions from the auscultation position (1) to the auscultation position (12) are set (in FIG. 2, the auscultation positions (3) to (11) are omitted. Yes).
  • FIG. 3 is a schematic diagram for explaining an example from the auscultation positions (1) to (6)
  • FIG. 4 is a schematic diagram for explaining an example from the auscultation positions (7) to (12). be.
  • the auscultation positions (1) and (2) are set, for example, to the left and right of the upper lung field of the precordium.
  • the auscultation positions (3) and (4) are set, for example, to the left and right of the precordial midlung field.
  • the auscultation positions (5) and (6) are set, for example, to the left and right of the lower lung field of the precordium.
  • the auscultation positions (7) and (8) are set to the left and right of the upper lung field on the back, for example.
  • the auscultation positions (9) and (10) are set, for example, to the left and right of the middle lung field on the back.
  • the auscultation positions (11) and (12) are set, for example, to the left and right of the lower lung field on the back.
  • the auscultation position is preset.
  • the auscultation position is not limited to the number and location mentioned above.
  • auscultation positions may be set not only in the precordium and the back but also in the upper lung field, the middle lung field, and the lower lung field of the left and right lateral chests, and a total of 18 auscultation positions may be set.
  • some of the above auscultation positions may be excluded.
  • the auscultation positions (3) to (6), (9), and (10) are excluded, and the total of the auscultation positions (1), (2), (7), (8), (11), and (12). It may be limited to 6 places.
  • a digital time-series acoustic signal including the lung sound acquired by the electronic stethoscope 110 at the auscultation position is recorded.
  • the posture of the patient at the time of auscultation is roughly divided into the recumbent position and the sitting position, but the precordial and back auscultation is usually performed in the sitting position.
  • the signal length of one lung sound data (for example, data 1) is arbitrary.
  • One lung sound data may be a signal of a patient's continuous N breaths.
  • N is a positive integer of 1 or more.
  • the lung sound data is a signal obtained by processing the time-series acoustic signal acquired from the electronic stethoscope 110, such as removal of the time-series acoustic signal during the resting phase, noise removal, and addition of respiratory timing. It may be there.
  • the analysis result information 152 shows the result of the abnormality detection unit 162, which will be described later, detecting an abnormality based on the lung sound data 151.
  • FIG. 5 shows an example of the information included in the analysis result information 152.
  • the analysis result information 152 includes, for example, an analysis result for each auscultation position.
  • the information included in the analysis result information 152 as illustrated in FIG. 5 is, for example, timing such as every time an analysis is performed using the analyzer 100, or every time an abnormality detection unit 162 analyzes the lung sound data 151. Created with.
  • the analysis result information 152 may be a combination of the analysis result identification information according to the date and time when the abnormality detection unit 162 performed the analysis and the information as illustrated in FIG.
  • the analysis result item records the result of mechanical analysis of the lung sound data by the abnormality detection unit 162, which will be described later.
  • a numerical value indicating whether or not the lung sound data is abnormal lung sound data is recorded.
  • two values of a value 0 indicating that the lung sound is normal and a value 1 indicating that the lung sound is abnormal may be recorded.
  • a numerical value indicating the degree of abnormality of the lung sound data may be recorded.
  • an abnormality degree below a preset threshold value indicates that the lung sound data is a normal lung sound
  • an abnormality degree exceeding the threshold value indicates that the lung sound data is an abnormal lung sound.
  • the severity determination information 153 is information used when the severity determination unit 163, which will be described later, determines the severity.
  • the severity determination information 153 is read from an external device, a recording medium, or the like via a data input / output function such as the communication I / F unit 140, or the user operates the operation input unit 120 to input the information. It is acquired in advance by the method of the above and stored in the storage unit 150.
  • the severity determination information 153 may be created at a timing such as when the patient is discharged from the hospital. In other words, the severity determination information 153 may be created according to the patient's condition at the time of discharge. In general, many hospitalized patients with heart failure are discharged after receiving treatment for heart failure and in remission.
  • the lung sounds of many patients at the time of discharge are normal.
  • the patient may be discharged from the hospital in a mild condition.
  • the severity determination information 153 may be created / updated at the timing of going to the hospital or the like.
  • FIG. 6 shows an example of the severity determination information 153.
  • the severity determination information 153 has, for example, a column corresponding to one-to-one correspondence between auscultation positions (1) to (12) and a row corresponding to one-to-one correspondence to severity. It is a table to set a + symbol indicating that there is an abnormality in lung sound and a-symbol indicating that there is no abnormality in lung sound at the intersection of rows and columns. For example, in the case of FIG. 6, the table shown by the severity determination information 153 shows that if there is no abnormality in lung sound at any auscultation position, the severity is determined to be 0.
  • the severity determination information 153 is a class of N + 1 from 0 to N in severity of heart failure depending on the combination of the presence or absence of abnormal lung sound at the auscultation positions (1) to (12). It is classified into.
  • the severity 0 is a state in which no abnormal lung sound is heard, and thus it can be said that the heart failure is in remission.
  • severity 1 is a state in which abnormal lung sound can be heard only in the lower lung field of the back, it cannot be said that heart failure is in remission, but it is mild and some patients are discharged in such a state. It is in a state of doing.
  • Severity 2 can be said to be more severe than severity 1 because abnormal lung sounds are produced in one of the lower lung fields in the precordium in addition to the lower lung field in the back. However, since it still belongs to mild illness, it can be said that there is a high probability that readmission can be prevented if appropriate measures are taken at this point.
  • the information shown by the severity determination information 153 is not limited to the case illustrated in FIG.
  • the severity determination information 153 at least one of the auscultation positions (11) and (12) set in the lower lung field of the back has an abnormal lung sound, and the other auscultation positions (1) to (10).
  • Example in FIG. 6 shows that there is an abnormality in lung sound only in the case where there is no abnormality in lung sound in other auscultation positions (1) to (4) and (7) to (10), and the severity is 1 in both cases.
  • Information other than the above may be shown.
  • the number of columns of the severity determination information 153 may correspond to the number of auscultation positions preset in the lung sound data 151.
  • the ra sound is mild when it is heard only at the end of inspiration, and it is severe when it is heard immediately after the start of inspiration. Therefore, in addition to the presence or absence of abnormal lung sound for each auscultation position, the timing at which abnormal lung sound is heard is set in the judgment table, and the combination of the auscultation position, the presence or absence of abnormal lung sound, and the timing at which abnormal lung sound is heard causes heart failure.
  • the severity may be determined. Further, the severity determination information 153 may set the severity according to the type and number of abnormal sounds such as rales having different properties (rough crackles, fine crackles).
  • the severity determination information 153 may be set according to the information that can be included in the personal condition information 156, which will be described later, such as the amount of weight gain of the patient. Further, the severity determination information 153 may be, for example, information in which the number of auscultation positions that have become abnormal lung sounds and the severity of heart failure of the patient are associated with each other. For example, the severity determination information 153 includes severity 0, 1, and when the number of auscultatory positions resulting in abnormal lung sounds is 0, 1 or more and 2 or less, 3 or more and 4 or less, 5 or more and 8 or less, and 9 or more, respectively. It may be 2, 3, 4 (maximum).
  • the severity information 154 shows the result of determination by the severity determination unit 163, which will be described later, using the analysis result information 152 and the severity determination information 153.
  • FIG. 7 shows an example of the analysis result information 152. Referring to FIG. 7, in the severity information 154, for example, the severity identification information according to the date and time when the severity determination unit 163 made the determination is associated with the severity.
  • the result of determination by the severity determination unit 163 using the analysis result information 152 and the severity determination information 153 is recorded. That is, information indicating the above-mentioned severity such as severity 0, severity 1, severity 2, ..., Etc. is recorded.
  • the instruction determination information 155 is information used by the instruction determination unit 164, which will be described later, to determine an instruction according to the severity.
  • the instruction determination information 155 is read from an external device, a recording medium, or the like via a data input / output function such as the communication I / F unit 140, or the user operates the operation input unit 120 to input the information. It is acquired in advance by the method and stored in the storage unit 150.
  • the instruction determination information 155 may be created at a timing such as when the patient is discharged from the hospital.
  • the instruction judgment information 155 By creating the instruction judgment information 155 according to the condition at the time of discharge, it is possible to make a judgment according to the condition of the patient at the time of discharge, such as a patient discharged in remission or a patient discharged in a mild condition. Is possible.
  • the instruction determination information 155 may be created / updated at the timing of going to the hospital or the like.
  • FIG. 8 shows an example of the instruction determination information 155.
  • the severity determination information 153 indicates, for example, the instruction content for each severity. For example, in the first line of FIG. 8, when the severity is 0, 1, or 2, the next inspection instruction indicating that the date and time for the next inspection is indicated is shown.
  • the severity 0 to 2 and the instruction content "next inspection instruction” are associated with each other.
  • the severity 3 to 4 and the instruction content "medication instruction” are associated with each other.
  • the severity 5 to N and the instruction content "consultation instruction” are associated with each other.
  • the next inspection instruction for example, the next inspection after 3 hours, the next inspection one day later, and the like, the inspection using the analyzer 100, the date and time of the analysis, and the like are instructed.
  • the time until the next inspection is, for example, predetermined.
  • the medication instruction it is instructed to take a preset medicine.
  • the consultation instruction the consultation is instructed.
  • the date and time of consultation may be specified, such as consultation today or tomorrow.
  • the instruction content may include other than those illustrated above.
  • the instruction content may be a combination of the above-exemplified content such as the next test instruction after taking the drug.
  • the content of the instruction may be further subdivided, for example, the time and date until the next examination differ depending on whether the severity is 0 or 1.
  • the instruction determination information 155 may be set according to the information that can be included in the personal status information 156, such as subdividing the instruction content according to the medication status.
  • Personal condition information 156 is information indicating the condition of the patient.
  • the information included in the personal condition information 156 can be used when the severity determination unit 163 determines the severity, or when the instruction determination unit 164 determines the instruction.
  • the personal status information 156 is read from an external device, a recording medium, or the like via a data input / output function such as the communication I / F unit 140, or the user operates the operation input unit 120 to input the information. It has been acquired in advance by the storage unit 150 and stored in the storage unit 150.
  • the personal state information 156 may be updated as appropriate by performing an operation on the operation input unit 120 each time the analysis is performed using the analysis device 100.
  • FIG. 9 shows an example of the information included in the personal status information 156.
  • the personal state information 156 for example, the state identification information according to the date and time when the state information is input and the state information are associated with each other.
  • the item of the state information in the personal state information 156 may include information indicating the weight of the patient, the medication status, and the like.
  • the information indicating the medication status may include, for example, information such as the date and time of the previous medication, the type of the medication last taken, the frequency of medication, and the like.
  • the item of state information includes information indicating blood pressure, pulse, subjective symptoms (shortness of breath when going out, swelling, coughing, loss of appetite, etc.), water intake, percutaneous arterial oxygen saturation (SPO2), etc. It may be included.
  • the item of the status information may include matters to be contacted by the doctor at the time of discharge or visit.
  • the above is an example of the main information stored in the storage unit 150.
  • Various identification information such as data identification information, analysis result identification information, severity identification information, and state identification information may be different from each other, and for example, information according to the date and time of the entire analysis is common. It doesn't matter if it is a thing.
  • the arithmetic processing unit 160 has a microprocessor such as a CPU and its peripheral circuits, and by reading and executing the program 157 from the storage unit 150, the hardware and the program 157 are linked to realize various processing units. do.
  • the main processing units realized by the arithmetic processing unit 160 include a lung sound acquisition unit 161, an abnormality detection unit 162, a severity determination unit 163, an instruction determination unit 164, an output unit 165, and the like.
  • the lung sound acquisition unit 161 acquires digital time-series acoustic signals including the patient's lung sound and other information.
  • the lung sound acquisition unit 161 acquires a digital time-series acoustic signal including the patient's lung sound from the electronic stethoscope 110 according to a user's instruction input from the operation input unit 120 or the like. Further, the lung sound acquisition unit 161 can acquire information indicating the date and time together with the digital time-series acoustic signal. Then, the lung sound acquisition unit 161 uses the acquired digital time-series acoustic signal and other information to generate lung sound data 151 as illustrated in FIG. 2, and stores it in the storage unit 150. As described above, the lung sound acquisition unit 161 may combine the data identification information and the information as illustrated in FIG. 2.
  • the method of acquiring the lung sound for each auscultation position of the patient with an electronic stethoscope and recording it in association with the auscultation position is arbitrary.
  • the lung sound acquisition unit 161 may instruct the patient to breathe timing by a method as described in Patent Document 8.
  • the lung sound acquisition unit 161 may be configured to calculate an index value of lung sound quality and issue a warning based on the calculated index value to the screen display unit 130 or the like. By giving such a warning, the patient, the user, or the like can take measures to reduce the background noise and / or increase the lung sound, and then acquire the lung sound again.
  • the index value calculation process for lung sound quality is performed, for example, by applying a predetermined filter and then calculating and comparing the signal intensities.
  • the lung sound acquisition unit 161 uses a band-passing filter to obtain a time-series acoustic signal in a frequency band of 100 Hz to about 2 kHz including the patient's lung sound from the time-series acoustic signal output from the electronic stethoscope 110. To extract.
  • the lung sound acquisition unit 161 calculates the intensity of the lung sound and the intensity of the background noise in the extracted time-series acoustic signal, and calculates the degree of difference between them as an index value of the quality of the lung sound.
  • the lung sound acquisition unit 161 detects an inspiratory phase, an expiratory phase, and a resting phase from a time-series acoustic signal including lung sounds. Then, the lung sound acquisition unit 161 calculates the intensity of the time-series acoustic signal in the rest phase as the intensity of the background noise.
  • the intensity of the time-series acoustic signal can be, for example, the root mean square of the amplitude value, but is not limited to this, and may be an amplitude or the like. Further, the lung sound acquisition unit 161 calculates a value obtained by subtracting the background noise intensity from the time-series acoustic signal intensity in the inspiratory phase and / or the expiratory phase as the lung sound intensity.
  • the lung sound acquisition unit 161 uses the ratio of the intensity of the lung sound to the calculated intensity of the background noise as an index value of the quality of the lung sound.
  • the index value of the quality of the lung sound is not limited to the above, and the S / N ratio calculated from the intensity of the lung sound and the intensity of the background noise may be used as the index value.
  • the application of the filter may be omitted.
  • the lung sound acquisition unit 161 can detect the expiratory phase and the inspiratory phase by comparing the time-series acoustic signal with a predetermined threshold value. Further, the lung sound acquisition unit 161 can detect a predetermined period immediately before the start of the detected inspiration as a resting phase. The lung sound acquisition unit 161 may detect the inspiratory phase, the expiratory phase, and the resting phase by using a method other than those exemplified above. For example, the lung sound acquisition unit 161 performed machine learning to estimate which section of the time-series acoustic signal including the lung sound output from the electronic stethoscope 110 is the inspiratory phase, the expiratory phase, and the resting phase.
  • the learning model can be pre-generated by machine learning using a machine learning algorithm such as a neural network, for example, using a time-series acoustic signal including various lung sounds as training data.
  • the lung sound acquisition unit 161 removes the period of the resting phase and the background noise from the digital time-series acoustic signal including the lung sound, and listens to the digital time-series acoustic signal after the period of the resting phase and the background noise are removed. It may be configured to be recorded in the lung sound data 151 in association with.
  • the lung sound acquisition unit 161 transmits a digital time-series acoustic signal including lung sound to a section consisting of an inspiratory phase and an expiratory phase immediately after that (hereinafter referred to as an inspiratory / expiratory section) and a resting phase section (hereinafter referred to as pause). It is divided into two parts (referred to as a section).
  • the lung sound acquisition unit 161 calculates the frequency spectra of the inspiratory / expiratory section and the resting section by performing a fast Fourier transform (FFT) on the digital time-series acoustic signals of the inspiratory / expiring section and the resting section, respectively.
  • FFT fast Fourier transform
  • the lung sound acquisition unit 161 subtracts the frequency spectrum of the rest section from the frequency spectrum of the inspiratory / expiratory section. This subtraction suppresses background noise contained in the inspiratory and expiratory phases.
  • the lung sound acquisition unit 161 generates a digital time-series acoustic signal after noise removal in the inspiratory / expiratory section by inversely converting the frequency spectrum of the inspiratory / expiratory section after the subtraction.
  • the lung sound acquisition unit 161 records the generated digital time-series acoustic signal after noise removal in the inspiratory / expiratory section in the lung sound data 151 in association with the auscultation position.
  • the lung sound acquisition unit 161 may remove the period of the rest phase from the digital time-series acoustic signal including the lung sound at the auscultation position, and may not remove the background noise.
  • the lung sound acquisition unit 161 divides the digital time-series acoustic signal including the lung sound at the auscultation position of interest into an inspiratory / expiratory section and a rest section, and the digital time-series acoustic signal in the inspiratory / expiratory section. Is recorded in the lung sound data 151 in association with the auscultation position.
  • the abnormality detection unit 162 detects an abnormality from the lung sound data of each auscultation position included in the lung sound data 151, associates the detection result with the auscultation position, and records it in the analysis result information 152.
  • the abnormality detection unit 162 inputs lung sound data into an abnormality detection model that is generated and stored in advance, and acquires the probability that the lung sound data is an abnormal lung sound from the abnormality detection model.
  • the abnormality detection unit 162 compares the probability of abnormal lung sound with a preset threshold value. Then, when the probability exceeds the threshold value, the abnormality detection unit 162 determines that it is an abnormal lung sound. That is, the abnormality detection unit 162 detects the abnormality. On the other hand, when it is equal to or less than the threshold value, the abnormality detection unit 162 determines that the sound is not an abnormal lung sound. After that, the abnormality detection unit 162 records the detection result in the analysis result information 152.
  • teacher data is generated using a database that collects abnormal sounds, and deep learning is used to learn the characteristics and discrimination criteria of the input sound data (input data).
  • deep learning is used to learn the characteristics and discrimination criteria of the input sound data (input data).
  • the anomaly detection unit 162 uses a spectrogram in which voices are arranged in chronological order by FFT (Fast Fourier Transform) or log-FFT for learning and input data at regular intervals, and RNN (recurrent neural network) is used for deep learning.
  • FFT Fast Fourier Transform
  • log-FFT log-FFT
  • RNN recurrent neural network
  • a network) or CNN convolutive neural network
  • the abnormality detection unit 162 may use a method of converting the lung sound wave type into a short-time feature amount such as a zero crossing coefficient or an MFCC (mel frequency cepstrum coefficient) and detecting an abnormal sound by machine learning.
  • the anomaly detection unit 162 may model with a GMM (mixed Gaussian distribution) at the time of learning and check whether or not it fits the model at the time of detection.
  • the abnormality detection unit 162 may learn the identification surface of a classifier such as an SVM (support vector machine) and use the identification surface to identify whether the input data corresponds to an abnormal sound. ..
  • the anomaly detection unit 162 generates features using the data itself, such as NMF (non-negative matrix factorization) and PCA (principal component analysis), in addition to the method of directly obtaining such features as described above. You may try to do it.
  • the abnormality detection unit 162 detects an abnormal sound by a decision tree or the like by using statistical characteristics of the input waveform such as a long-term power distribution of the input signal and a distribution of the component amount / component ratio in a specific frequency bin range. You may. In that case, the abnormality detection unit 162 has, as an item of the decision tree, a direct value (for example, when the power exceeds 20 mW for 3 consecutive frames) and a statistical feature (for example, a process that approximates Gauss and is larger than 3 ⁇ ). When a frame occurs) may be used.
  • a direct value for example, when the power exceeds 20 mW for 3 consecutive frames
  • a statistical feature for example, a process that approximates Gauss and is larger than 3 ⁇ .
  • the abnormality detection unit 162 may detect not the input signal itself but the abnormal sound by modeling it in an AR (autoregressive) process or the like and some of its model parameters exceed a threshold value. Although these methods may not include the learning process, they are included in the supervised learning for convenience because they include the observation of the abnormal sound which is the target signal in the determination of the decision tree and the threshold value.
  • the abnormality detection unit 162 may be configured to learn the abnormality detection model by using the lung sound data of the past patient and the auscultatory findings, for example, when the patient is discharged from the hospital.
  • the lung sound data used when learning the abnormality detection model in addition to the lung sound data at the time of discharge of the patient, the normal lung sound data of the patient before that may be used, or a person other than the patient. You may use the normal lung sound data of.
  • the abnormality detection model may be generated for each auscultation position, or may be common to a plurality of auscultation positions. Further, the anomaly detection model may be a plurality of models machine-learned from different viewpoints. For example, in the abnormality detection model, the lung sound at the same auscultation position is divided into the lung sound part of the inspiratory phase, the lung sound part of the expiratory phase, and the rest (that is, the resting phase) based on the breathing timing, and the inspiratory phase is taken. A model trained using the lung sound portion of the phase and a model trained using the lung sound portion of the expiratory phase may be included.
  • the severity determination unit 163 determines the severity based on the analysis result for each auscultation position indicated by the analysis result information 152 and the severity determination information 153. Then, the severity determination unit 163 stores the determined severity in the storage unit 150 as the severity information 154 in association with the severity identification information.
  • the severity determination unit 163 identifies the auscultation position where the abnormality is detected with reference to the analysis result information 152. Then, the severity determination unit 163 determines the severity corresponding to the specified result with reference to the severity determination information 153.
  • the severity determination information 153 can be created according to the patient's condition at the time of discharge.
  • the severity determination unit 163 refers to the severity determination information 153 according to the condition of the patient at the time of discharge, whereby the patient at the time of discharge is referred to. It can also be said that the severity can be determined according to the condition.
  • the severity determination unit 163 can be configured to correct the determined severity by referring to the personal condition information 156 when determining the severity. For example, there is a finding that when heart failure is aggravated, water accumulates in the lungs, resulting in weight gain. Therefore, the severity determination unit 163 can correct the determined severity based on the weight of the patient included in the personal condition information 156. For example, when a predetermined condition such as an increase in body weight of 3 kg in one week is satisfied, the severity determination unit 163 can make a correction for a set value such as increasing the determined severity by one. The severity determination unit 163 may make corrections other than those exemplified above, such as increasing the severity by 2 when the weight increases by 5 kg in one week.
  • the severity determination unit 163 makes corrections using personal condition information 156 other than those exemplified above, such as increasing the severity when the blood pressure drops by a predetermined value or more or when the pulse exceeds a predetermined number. You can go.
  • the severity determination unit 163 directly determines the severity in consideration of the personal condition information 156 by referring to the severity determination information 153 in which the severity is set according to the information that can be included in the personal condition information 156. May be good.
  • the instruction determination unit 164 determines the instruction based on the severity indicated by the severity information 154 and the instruction determination information 155. For example, the instruction determination unit 164 determines the instruction by specifying the instruction content corresponding to the severity indicated by the severity information 154 in the instruction determination information 155.
  • the instruction determination information 155 can be created according to the patient's condition at the time of discharge.
  • the instruction determination unit 164 responds to the patient's condition at the time of discharge by referring to the instruction determination information 155 according to the patient's condition at the time of discharge. It can also be said that the instructions can be judged.
  • the instruction determination unit 164 can be configured to correct the determined instruction by referring to the personal state information 156 when determining the instruction. For example, based on the information indicating the medication status included in the personal condition information 156, it is determined that a predetermined condition is satisfied, such as taking medication at a predetermined interval. In such a case, it is assumed that the severity of the drug is worsening despite the conditions being met, and the efficacy of the drug is worsening. Therefore, the instruction determination unit 164 can correct the instruction according to a predetermined correction policy, such as correcting the instruction when the severity is increased by one or instructing the patient to receive a medical examination regardless of the severity.
  • the instruction determination unit 164 may directly determine the instruction in consideration of the personal state information 156 by referring to the instruction determination information 155 in which the instruction corresponding to the information that can be included in the personal state information 156 is set.
  • the output unit 165 displays the instruction determined by the instruction determination unit 164 on the screen display unit 130. Further, the output unit 165 transmits the instruction determined by the instruction determination unit 164, the lung sound data 151, the analysis result information 152, the severity information 154, the information input in advance, and the like to the set external device. Can be configured. For example, when the severity exceeds the transmission threshold value, the instruction determination unit 164 sets the lung sound data 151, the analysis result information 152, the severity information 154, the preset treatment policy information, and the like. May be configured to send to.
  • the external device to be transmitted by the output unit 165 may be, for example, a portable information terminal owned by a patient, a portable information terminal owned by a doctor such as a family doctor, a mobile information terminal owned by a medical institution, or an information processing device.
  • the input treatment policy information may include information indicating the presence / absence and type of undesired treatment.
  • the output to the external device by the output unit 165 may be realized by using at least one of any communication methods such as mail, a message function of groupware, and business chat.
  • the lung sound acquisition unit 161 acquires a digital time-series acoustic signal including the patient's lung sound for each auscultation position (step S101). Then, the lung sound acquisition unit 161 uses the acquired digital time-series acoustic signal and other information to generate lung sound data 151 as illustrated in FIG. 2, and stores it in the storage unit 150. As described above, the lung sound acquisition unit 161 may combine the data identification information and the information as illustrated in FIG. 2.
  • the lung sound acquisition unit 161 When the lung sound acquisition unit 161 acquires a digital time-series acoustic signal or the like, the lung sound quality index value may be calculated and a warning based on the calculated index value may be given to the screen display unit 130 or the like. Further, the lung sound acquisition unit 161 performs processing such as removal of time-series acoustic signals, noise removal, and addition of respiratory timing during the resting phase, and then generates lung sound data 151 using the processed data. And may be stored in the storage unit 150.
  • the abnormality detection unit 162 detects an abnormality from the lung sound data of each auscultation position included in the lung sound data 151, associates the detection result with the auscultation position, and records it in the analysis result information 152 (step S102). For example, the abnormality detection unit 162 inputs lung sound data into an abnormality detection model that is generated and stored in advance, and acquires the probability that the lung sound data is an abnormal lung sound from the abnormality detection model. Next, the abnormality detection unit 162 compares the probability of abnormal lung sound with a preset threshold value. Then, when the probability exceeds the threshold value, the abnormality detection unit 162 determines that it is an abnormal lung sound. That is, the abnormality detection unit 162 detects the abnormality. On the other hand, when it is equal to or less than the threshold value, the abnormality detection unit 162 determines that the sound is not an abnormal lung sound. After that, the abnormality detection unit 162 records the detection result in the analysis result information 152.
  • the severity determination unit 163 determines the severity based on the analysis result for each auscultation position indicated by the analysis result information 152 and the severity determination information 153 (step S103). Then, the severity determination unit 163 stores the determined severity in the storage unit 150 as the severity information 154 in association with the severity identification information.
  • the severity determination unit 163 may determine the severity according to the patient's condition at the time of discharge by referring to the severity determination information 153 according to the condition of the patient at the time of discharge. Further, the severity determination unit 163 may correct the determined severity by referring to the personal condition information 156 when determining the severity. The severity determination unit 163 directly determines the severity in consideration of the personal condition information 156 by referring to the severity determination information 153 in which the severity is set according to the information that can be included in the personal condition information 156. May be good.
  • the instruction determination unit 164 determines the instruction based on the severity indicated by the severity information 154 and the instruction determination information 155 (step S104). For example, the instruction determination unit 164 determines the instruction by specifying the instruction content corresponding to the severity in the instruction determination information 155.
  • the instruction determination unit 164 may determine the instruction according to the patient's condition at the time of discharge by referring to the instruction determination information 155 according to the condition of the patient at the time of discharge. Further, the instruction determination unit 164 may refer to the personal state information 156 when determining the instruction and correct the determined instruction. The instruction determination unit 164 may directly determine the instruction in consideration of the personal state information 156 by referring to the instruction determination information 155 in which the instruction corresponding to the information that can be included in the personal state information 156 is set.
  • the output unit 165 displays the instruction determined by the instruction determination unit 164 on the screen display unit 130 (step S105). Further, the output unit 165 can be configured to transmit the instruction determined by the instruction determination unit 164, the lung sound data 151, the analysis result information 152, the severity information 154, and the like to a preset external device. ..
  • the instruction determination unit 164 is configured to transmit lung sound data 151, analysis result information 152, severity information 154, and the like to a preset external device when the severity exceeds the transmission threshold value. You may.
  • the external device to be transmitted by the output unit 165 may be, for example, a portable information terminal owned by a patient, a portable information terminal owned by a doctor such as a family doctor, a mobile information terminal owned by a medical institution, or an information processing device.
  • the analyzer 100 has an abnormality detection unit 162, a severity determination unit 163, an instruction determination unit 164, and an output unit 165.
  • the instruction determination unit 164 can determine the instruction using the severity determined by the severity determination unit 163 based on the detection result by the abnormality detection unit 162.
  • the output unit 165 can output an instruction according to the severity. This makes it possible to output appropriate instructions according to the severity.
  • the severity determination unit 163 and the instruction determination unit 164 can determine the severity and determine the instruction according to the patient's condition at the time of discharge. As a result, the severity determination unit 163 and the instruction determination unit 164 can make a more appropriate determination and determination according to the patient's condition.
  • the severity determination unit 163 and the instruction determination unit 164 can determine the severity and determine the instruction in consideration of the personal condition information 156. As a result, the severity determination unit 163 and the instruction determination unit 164 can make a more appropriate determination and determination according to the patient's condition.
  • the abnormality detection unit 162 only performs the analysis corresponding to the auscultation position where the lung sound data is acquired.
  • the severity determination unit 163 may be configured to determine whether or not to determine the severity based on the number of auscultation positions analyzed by the abnormality detection unit 162. For example, if the number of auscultatory positions for which lung sound data has not been acquired and whether or not it is abnormal lung sound has not been analyzed is less than a preset threshold value, the severity determination unit 163 calculates the severity. Instead, it is possible to display on the screen display unit 130 that the analysis has ended with an error.
  • the severity determination unit 163 assumes that no abnormal lung sound is detected at the auscultatory positions where the analysis of whether or not the lung sound is abnormal has not been performed. Then, the severity can be calculated. In this case, the severity determination unit 163 may keep the calculated severity as the most optimistic value. That is, when the calculated severity is severity 1, it can be retained as "severity 1 or higher” or "at least severity 1" instead of "severity 1".
  • the threshold value may be set arbitrarily.
  • the analyzer 100 may be configured to determine which auscultation position to focus on based on the auscultation position where an abnormality has been detected in the patient's past.
  • the lung sound acquisition unit 161 of the analyzer 100 can calculate the abnormality detection frequency for each auscultation position based on the past analysis result information 152. Further, the lung sound acquisition unit 161 can guide the lung sound data to be acquired in descending order of the calculated abnormality detection frequency.
  • the severity determination unit 163 determines whether or not to calculate the severity, as compared with the case where the lung sound data is not acquired in descending order of abnormality detection frequency. The threshold used may be reduced.
  • the arithmetic processing unit 160 may have an emergency call unit 166 in addition to each of the above-mentioned processing units.
  • the emergency call unit 166 makes an emergency call to a doctor such as a family doctor or an information processing terminal owned by a medical institution when a predetermined report condition is satisfied.
  • the reporting conditions may be set arbitrarily.
  • the report condition is output by the output unit 165, such as that the medical examination is not received within the set period after the medical examination instruction is output, or that the next examination instruction is output but there is no re-examination within the set period.
  • the conditions may be according to the later situation.
  • the reporting conditions may differ depending on the severity.
  • the reporting conditions may be set according to the condition at the time of discharge of the patient.
  • various information can be included in the emergency call.
  • the emergency call can include at least one of a report condition that triggered the emergency call, lung sound data 151, analysis result information 152, severity information 154, treatment policy information, and the like.
  • FIG. 12 is a block diagram of the analysis system 200 according to the second embodiment of the present invention.
  • the analysis system 200 includes a plurality of analysis devices 210 and a server device 220. Further, the plurality of analyzers 210 and the server device 220 are connected to each other so as to be able to communicate with each other through a network 230 such as the Internet.
  • the analyzer 210 is an information processing device that outputs an instruction according to the result of analyzing the lung sound.
  • the analyzer 210 may be, but is not limited to, a smartphone, a tablet terminal, a PDA, a notebook computer, or the like.
  • the analyzer 210 includes an electronic stethoscope (not shown), a communication I / F unit, an operation input unit, a screen display unit, a storage unit, and an arithmetic processing unit.
  • the server device 220 is a computer that provides various services necessary for lung sound analysis to a plurality of analyzers 210 through the network 230.
  • the server device 220 has at least lung sound data 151, analysis result information 152, severity determination information 153, severity information 154, instruction determination information 155, personal condition information 156, and program 157 shown in FIG. Some are stored and they are provided to the analyzer 210 through the network 230. Therefore, as compared with the analyzer 100 of FIG. 1, the analyzer 210 has the lung sound data 151, the analysis result information 152, the severity determination information 153, the severity information 154, and the instruction determination information 155 in the storage unit 150. It is not necessary to store at least a part of the personal state information 156 and the program 157, and the storage capacity can be reduced.
  • the server device 220 analyzes at least a part of the functions of the lung sound acquisition unit 161, the abnormality detection unit 162, the severity determination unit 163, the instruction determination unit 164, and the output unit 165 shown in FIG. 1 through the network 230. It can be provided to the device 210. That is, the server device 220 executes at least a part of each process shown in FIG. 10 on behalf of the analyzer 210. Therefore, the analyzer 210 can simplify the configuration of the arithmetic processing unit 160 as compared with the analyzer 100 of FIG.
  • FIG. 13 shows a hardware configuration example of the analyzer 300.
  • the analyzer 300 has the following hardware configuration as an example.
  • -CPU Central Processing Unit
  • 301 Arimetic unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • 303 storage device
  • -Program group 304 loaded in RAM 303 -Storage device 305 that stores the program group 304 Drive device 306 that reads and writes the recording medium 310 external to the information processing device.
  • -Communication interface 307 that connects to the communication network 311 outside the information processing device.
  • the analyzer 300 can realize the functions as the detection unit 321 and the determination unit 322 and the determination unit 323 shown in FIG. 14 by the CPU 301 acquiring the program group 304 and executing the program group 304.
  • the program group 304 is stored in the storage device 305 or the ROM 302 in advance, for example, and the CPU 301 loads the program group 304 into the RAM 303 or the like and executes the program group 304 as needed.
  • the program group 304 may be supplied to the CPU 301 via the communication network 311 or may be stored in the recording medium 310 in advance, and the drive device 306 may read the program and supply the program to the CPU 301.
  • FIG. 13 shows an example of the hardware configuration of the analyzer 300.
  • the hardware configuration of the analyzer 300 is not limited to the above case.
  • the analyzer 300 may be configured from a part of the above-mentioned configuration, such as not having the drive device 306.
  • the detection unit 321 detects the lung sound abnormality at each auscultation position based on the time-series acoustic signal including the lung sound at each auscultation position.
  • the determination unit 322 determines the severity of heart failure of the patient based on the detection result of the lung sound abnormality for each auscultation position detected by the detection unit 321 and the state information indicating the patient's condition.
  • the judgment unit 323 determines an instruction to be output to the patient based on the result of the determination by the determination unit 322.
  • the analyzer 300 has a detection unit 321, a determination unit 322, and a determination unit 323.
  • the determination unit 323 can determine the instruction to be output to the patient by using the severity determined by the determination unit 322 based on the detection result by the detection unit 321. As a result, it becomes possible to output appropriate instructions according to the severity. Further, since the determination unit 322 makes a determination based on the state information when determining the severity, it is possible to make a more accurate determination according to the patient's condition. As a result, it becomes possible to output more appropriate instructions.
  • the above-mentioned analyzer 300 can be realized by incorporating a predetermined program into an information processing device such as the analyzer 300.
  • the analyzer has a detection unit for detecting lung sound abnormality at each auscultation position and a detection unit based on a time-series acoustic signal including lung sound at each auscultation position. Based on the detection result of lung sound abnormality for each auscultation position detected by the department and the condition information indicating the patient's condition, the judgment unit for determining the severity of the patient's heart failure and the result determined by the determination unit. It is a program to realize a judgment unit that judges the instruction to be output to the patient.
  • the information processing device detects the lung sound abnormality at each auscultation position based on the time-series acoustic signal including the lung sound at each auscultation position. Then, the severity of the patient's heart failure is determined based on the detection result of the lung sound abnormality for each auscultation position detected and the condition information indicating the patient's condition, and based on the determined result, the patient is treated. It is a method of judging the instruction to be output.
  • the present invention can be used for a device or system for analyzing human lung sound, and in particular, for a device or system for early detection of exacerbation of heart failure in a patient discharged from the hospital after receiving treatment for heart failure and preventing readmission.
  • a detection unit that detects abnormal lung sound at each auscultation position based on a time-series acoustic signal including lung sound at each auscultation position.
  • a determination unit for determining the severity of heart failure of the patient based on the detection result of the lung sound abnormality for each auscultation position detected by the detection unit and the state information indicating the patient's condition.
  • a judgment unit that determines an instruction to be output to the patient based on the result of the determination by the determination unit.
  • An analyzer with.
  • Appendix 2 The analysis according to Appendix 1 in which the determination unit determines the severity based on the detection result of the lung sound abnormality for each auscultation position detected by the detection unit and the state information indicating the weight of the patient.
  • Device. The determination unit determines the severity based on the detection result of the lung sound abnormality for each auscultation position detected by the detection unit, and then corrects the severity determined based on the state information.
  • the analyzer according to Appendix 2. (Appendix 4) The determination unit determines the severity according to the condition of the patient at the time of discharge by referring to the information created according to the condition of the patient at the time of discharge. The analyzer according to item 1.
  • the judgment unit makes a judgment of the instruction according to the condition of the patient at the time of discharge by referring to the information created according to the condition of the patient at the time of discharge.
  • the analyzer according to item 1. (Appendix 9) The analyzer according to any one of Supplementary note 1 to Supplementary note 8, wherein the determination unit determines a medication instruction and a next examination instruction for the patient based on the result of the determination by the determination unit. (Appendix 10) The analyzer according to any one of Supplementary note 1 to Supplementary note 9, which has an output unit for outputting the instruction determined by the determination unit. (Appendix 11) The analyzer according to Appendix 10, which has an emergency call unit that makes an emergency call according to the situation after the instruction is output by the output unit.
  • the state information includes at least one of the patient's weight, medication status, blood pressure, pulse, subjective symptoms, water intake, and percutaneous arterial oxygen saturation. Any one of Supplements 1 to 11.
  • the analyzer according to item 1.
  • Information processing equipment Based on the time-series acoustic signal including the lung sound at each auscultation position, the lung sound abnormality is detected at each auscultation position. The severity of heart failure in the patient is determined based on the detected result of detecting the abnormal lung sound for each auscultation position and the condition information indicating the condition of the patient. An analytical method for determining an instruction to be output to the patient based on the determined result.
  • a detection unit that detects abnormal lung sound at each auscultation position based on a time-series acoustic signal including lung sound at each auscultation position.
  • a determination unit for determining the severity of heart failure of the patient based on the detection result of the lung sound abnormality for each auscultation position detected by the detection unit and the state information indicating the patient's condition.
  • a judgment unit that determines an instruction to be output to the patient based on the result of the determination by the determination unit.
  • a computer-readable recording medium that records programs to achieve this.
  • Analyzer 110 Electronic stethoscope 120 Operation input unit 130 Screen display unit 140 Communication I / F unit 150 Storage unit 151 Lung sound data 152 Analysis result information 153 Severity determination information 154 Severity information 155 Instruction judgment information 156 Personal status Information 157 Program 160 Calculation processing unit 161 Lung sound acquisition unit 162 Abnormality detection unit 163 Severity determination unit 164 Instruction judgment unit 165 Output unit 166 Emergency notification unit 200 Analysis system 210 Analysis device 220 Server device 230 Network 300 Analysis device 301 CPU 302 ROM 303 RAM 304 Program group 305 Storage device 306 Drive device 307 Communication interface 308 Input / output interface 309 Bus 310 Recording medium 311 Communication network 321 Detection unit 322 Judgment unit 323 Judgment unit

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  • General Business, Economics & Management (AREA)
  • Chemical & Material Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Medicinal Chemistry (AREA)
  • Physiology (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
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