WO2017084559A1 - 一种云平台 - Google Patents

一种云平台 Download PDF

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Publication number
WO2017084559A1
WO2017084559A1 PCT/CN2016/105925 CN2016105925W WO2017084559A1 WO 2017084559 A1 WO2017084559 A1 WO 2017084559A1 CN 2016105925 W CN2016105925 W CN 2016105925W WO 2017084559 A1 WO2017084559 A1 WO 2017084559A1
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WIPO (PCT)
Prior art keywords
user
steady state
state value
ventilator
data set
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Ceased
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PCT/CN2016/105925
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English (en)
French (fr)
Inventor
曹志新
庄志
孟庆凯
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BMC Medical Co Ltd
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BMC Medical Co Ltd
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Priority to EP16865736.9A priority Critical patent/EP3378394A4/en
Priority to US15/744,798 priority patent/US11464424B2/en
Publication of WO2017084559A1 publication Critical patent/WO2017084559A1/zh
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
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. ventilators; Tracheal tubes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/18—General characteristics of the apparatus with alarm
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/35—Communication
    • A61M2205/3546—Range
    • A61M2205/3553—Range remote, e.g. between patient's home and doctor's office
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/35—Communication
    • A61M2205/3576—Communication with non implanted data transmission devices, e.g. using external transmitter or receiver
    • A61M2205/3584—Communication with non implanted data transmission devices, e.g. using external transmitter or receiver using modem, internet or Bluetooth®
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/84—General characteristics of the apparatus for treating several patients simultaneously

Definitions

  • the present application relates to the technical field of online data analysis, and more particularly to a cloud platform.
  • a ventilator is a medical device that can replace, control or change a person's normal physiological breathing and increase lung ventilation. It can improve respiratory function, prevent and treat respiratory failure, and is used for Chronic Obstructive Pulmonary Diseases (COPD).
  • COPD Chronic Obstructive Pulmonary Diseases
  • COPD chronic obstructive pulmonary disease
  • Bi-level positive airway pressure (BiPAP) ventilation is a non-invasive ventilation technique that is ventilated through a nose mask or nasal mask. It can effectively improve ventilation and ventilation, effectively improve diffusion function and oxygenation function, and help correct patient circulation.
  • Abnormal function it can quickly improve hypoxemia and hypercapnia, improve clinical symptoms, reduce the occurrence of tracheal intubation; avoid a series of complications such as breathing caused by intubation of invasive mechanical ventilation Machine-related pneumonia is conducive to disease observation and respiratory management, improving the infection of patients with Acute Exacerbation of Chronic Obstructive Pulmonary Diseases (AECOPD).
  • AECOPD Chronic Obstructive Pulmonary Diseases
  • the patient is less painful, easy to tolerate, and avoids or reduces sedatives.
  • the use can also shorten the number of hospital stays, improve the quality of life of patients, and reduce hospitalization costs and patient mortality.
  • the current use of BiPAP ventilator assisted therapy has become a recognized and effective method and is widely used in intensive care units, general wards and homes.
  • AECOPD Acute exacerbation of COPD
  • AECOPD Acute exacerbation of COPD
  • AECOPD has a serious negative impact on patients' quality of life, disease progression and socio-economic burden, and can accelerate the decline of lung function in patients, which is related to the increased mortality of hospitalized patients.
  • AECOPD is an acute onset process in which acute respiratory symptoms are exacerbated in COPD patients.
  • Prevention and treatment of AECOPD is central to COPD management.
  • AECOPD symptoms and lung function vary widely, and are related to basic lung function, aggravation and individual sensitivity. Under normal circumstances, when the patient cough increases, cough increases, wheezing increases; activity decline significantly affects basic life (feeding and falling asleep); self-regulating drugs can not be alleviated, emergency or outpatient treatment should be the basic standard of AECOPD.
  • AECOPD patients basically require a physician to adjust the treatment plan, so an accurate assessment of the severity of the condition must be made. There is no effective assessment method for AECOPD patients in the prior art, which often leads to difficulty in treatment after the onset of the disease.
  • One of the technical problems solved by the embodiments of the present application is how to early detect the possible aggravation of the patient's respiratory diseases through early monitoring, such as the AECOPD state that may occur in patients with COPD.
  • this may give an alarm or prompt for a situation in which a patient may enter the AECOPD state by using various respiratory related data uploaded by the patient's used ventilator recorded by the cloud server and performing statistical analysis of the data. . After receiving such an alarm or prompt (or receiving such an alarm or prompt from the doctor and notifying the patient), the patient may go to the doctor's office for a diagnosis and determine whether the AECOPD status has been entered.
  • the inventors have also contemplated that embodiments of the present application can be used for early detection of severe symptoms of other respiratory diseases.
  • a cloud platform includes a receiving unit and a processing unit.
  • the receiving unit is adapted to receive the at least one user breathing data set transmitted by the at least one ventilator device during the at least one upload period.
  • the processing unit is adapted to acquire a first set of user breath data transmitted by a user of one of the at least one ventilator device during an upload cycle, and based on the set of steady state values of the user
  • a user breathing data set performs statistical analysis, and when it is determined that the result of the statistical analysis is that the first user breathing data set satisfies the data abnormality criterion, an alarm signal is issued.
  • a method of alerting an abnormality of user breathing data includes receiving at least one user breath data set transmitted by at least one ventilator device during at least one upload period; acquiring a first user sent by the ventilator device of the at least one ventilator device during an upload cycle a user breathing data set, and performing statistical analysis on the first user breathing data set based on the set of steady state values of the user, when determining that the result of the statistical analysis is that the first user breathing data set meets a data abnormality criterion , an alarm signal is issued.
  • an alarm for abnormality of user breathing data device of includes: receiving means for receiving at least one user breathing data set transmitted by the at least one ventilator device in at least one uploading cycle; and obtaining means for acquiring one of the at least one ventilator device a first user breath data set sent by the user in an upload period, and an alarm device for performing statistical analysis on the first user breath data set based on the set of steady state values of the user, determining the statistical analysis
  • the result is that when the first user breathing data set satisfies the data abnormality criterion, an alarm signal is issued.
  • a program comprising readable code that, when executed on a device, causes the device to perform an alerting of an abnormality of user breathing data in accordance with an embodiment of the present application. method.
  • a readable medium in which a program as described in the embodiments of the present application is stored.
  • a set of online user breathing data analysis protocols in combination with offline respiratory disease monitoring is provided so that patients may be detected in time in the early stages of aggravation.
  • Figure 1 illustrates a schematic diagram of a system in which embodiments of the present application can be implemented
  • FIG. 2 illustrates a block diagram of a cloud server in accordance with an embodiment of the present application
  • FIG. 3 illustrates a flowchart of a method capable of alerting an abnormality of a user's breathing data set in accordance with an embodiment of the present application
  • FIG. 4 illustrates a flow diagram of an apparatus capable of alerting an abnormality of user breathing data in accordance with an embodiment of the present application
  • FIG. 5 shows a block diagram of a computing device for performing a method of alerting an abnormality of user breathing data in accordance with the present application
  • FIG. 6 illustrates a storage unit for maintaining or carrying program code that implements a method of alerting an abnormality of user breathing data in accordance with the present application.
  • FIG. 1 shows a schematic diagram of a system 100 in which various embodiments of the present application can be implemented.
  • system 100 can include ventilators 12, 14 and 16, a cloud server 20, and a database 22 coupled to cloud server 20, wherein ventilators 12, 14 and 16 are each coupled to the Internet 18 via a data link. It is in turn connected to the cloud server 20 via a communication link.
  • ventilators 12 and 14 can be located in the home of different patients, and ventilator 16 can be a ventilator located in a hospital.
  • the data link in Figure 1 can be any type of wired or wireless connection, including but not limited to telephone lines, cable lines, power lines, TV broadcasts, remote wireless connections, short-range wireless connections, and the like.
  • the network connecting the ventilators 12, 14 and 16 and the cloud server 20 is shown as the Internet in FIG. 1, the embodiments of the present application are also applicable to other network forms including, but not limited to, mobile. Telephone network, Wireless Local Area Networks (W LAN), self-organizing network, Ethernet LAN, Token Ring LAN, wide area network, and any combination of these network forms and the Internet.
  • WLAN Wireless Local Area Networks
  • Ethernet LAN Ethernet LAN
  • Token Ring LAN Token Ring LAN
  • wide area network wide area network
  • Communication technologies or communication standards applicable to communication between devices may include, but are not limited to, Code Division Multiple Access (CDMA), Global System for Mobile communication (GSM), and Universal Mobile Telecommunications System ( Universal Mobile Telecommunications System (UMTS), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Transmission Control Protocol/Internet Protocol (Transmission Control Protocol/Internet Protocol, TCP/IP) ), Short Message Service (SMS), Multimedia Message Service (MMS), Email, Instant Messaging Service (IMS), Bluetooth, IEEE 802.11, etc.
  • CDMA Code Division Multiple Access
  • GSM Global System for Mobile communication
  • UMTS Universal Mobile Telecommunications System
  • TDMA Time Division Multiple Access
  • FDMA Frequency Division Multiple Access
  • TCP/IP Transmission Control Protocol/Internet Protocol
  • SMS Short Message Service
  • MMS Multimedia Message Service
  • Email Instant Messaging Service
  • Bluetooth IEEE 802.11, etc.
  • Communication devices involved in implementing various embodiments of the present application can communicate using a variety of media including, but not limited to, radio, in
  • the ventilators 12, 14 and 16 can be any type of ventilator, including a single-level ventilator, a dual-level ventilator, etc., or a hospital-invasive ventilator that uses the ventilator.
  • the generated user breathing data collection is uploaded to the cloud server.
  • the user breathing data uploaded by the ventilator is data relating to the breathing state of the user, which is a collection of values of various types of breathing state indicators (in real time or at a particular time).
  • the ventilators 12, 14 and 16 may also include various related monitoring devices capable of recording user breathing data, such as monitoring devices that can monitor blood oxygenation data, chest strap data, brain electrical data, heart rate data, and the like.
  • the ventilator 12 or 14 that the user can use at home uploads the set of user breathing data generated by the user when using the ventilator to the cloud server 20.
  • the user's breathing data set generated by the user using the ventilator 16 at the hospital is also uploaded to the cloud server 20.
  • the cloud server 20 can analyze the uploaded user breath data set to determine the Whether the user's current breathing state is abnormal.
  • the user breath data may be stored in the database 22 connected to the cloud server 20 or may be stored in a memory in the cloud server 20.
  • the cloud server 200 can include a receiving unit 42, a storage unit 44, and a processing unit 46 that are interconnected via a system bus 50.
  • the cloud server 200 as a computer system may further include other units not shown in the figure, such as RAM memory, ROM memory, various hardware controllers (such as a hard disk controller, a keyboard controller, a serial interface controller, Parallel interface controllers, display controllers), hard disks, keyboards, serial interface devices, parallel interface devices, displays, and the like.
  • the receiving unit 42 may include a transmitter and/or a receiver, which may be implemented as an RF interface, a Bluetooth interface, and/or an IrDA interface for providing communication services.
  • Processing unit 46 may be implemented using any commercially available CPU, digital signal processor (DSP), or any other electronic programmable logic device.
  • the storage unit 44 can be implemented as a RAM memory, a ROM memory, an EEPROM memory, a flash memory, a hard disk, or any combination thereof. It is to be understood that the structural block diagrams illustrated in FIG. 2 are shown for purposes of illustration only and are not a limitation of the application. In some cases, some of these devices can be added or removed as needed.
  • receiving unit 42, the storage unit 44, and the processing unit 46 are all implemented in the cloud server 200 in FIG. 2, the functions to be implemented by the cloud server on the cloud platform may also be distributed to different under the cloud platform. Entity to achieve. For example, what is to be implemented by the receiving unit 42 and the storage unit 44 can be implemented at a central server, and the functions to be implemented by the processing unit are implemented at the client.
  • the ventilator can be used at home to continuously monitor the physical condition.
  • Embodiments of the present application are based on a home non-invasive ventilator 12, 14 that can be networked and uploaded in real time, or a therapeutic ventilator 16 for a medical unit, and a cloud platform that supports real-time uploading of user respiratory data to establish an online data analysis System for real-time monitoring of the state of development of the patient's respiratory condition.
  • the cloud platform is constructed, for example, by the ventilators 12, 14, 16 shown in FIG. 1, the cloud server 20, and an optional database 22.
  • the patient begins to use the non-invasive ventilator 12 or 14 in his or her home, or before returning home after a stable discharge from the hospital to continue using the non-invasive ventilator, it is necessary for the doctor to first analyze the various systems of the system.
  • a new online system monitoring cycle for the patient is initiated using data determination to set or reset. After the start of a monitoring cycle, patients should use a non-invasive ventilator daily and network to upload user breathing data to the cloud platform.
  • the patient may need to have a regular follow-up visit, and after the referral, the doctor will adjust the online system's judgment criteria for the user's respiratory data based on all the monitoring data of the patient between the return visit and the last referral.
  • the online analysis system determines an abnormality in the patient's breathing data for the patient, an alert is issued and the doctor and/or patient can receive an early warning signal on the cloud platform.
  • the patient should see the doctor promptly after seeing the warning, and the doctor further diagnoses the condition to determine whether the patient needs hospitalization, or just needs to adjust the online system's judgment criteria.
  • the physician can determine the criteria for the online analysis system to determine the condition of the patient.
  • the online judgment criteria include the criteria for determining the patient's steady state and the criteria for the patient's mild abnormal state. Standards and criteria for the severity of abnormal conditions in patients.
  • the cloud platform performs a statistical analysis of patient usage data every 24 hours as the current patient data statistics.
  • the validity of the 24-hour data is determined before the statistics. For example, if the usage time is at least 4 hours on the day and the air leakage does not exceed 30 LPM, the current data is judged as valid data; the invalid data is not counted. , as if there is no use data within 24 hours.
  • the 24-hour start and end points are adjustable, for example, from 19:00 on the current day to 19:00 on the next day, or from 9:00 on the current day to 9:00 on this day.
  • the basis for determining the state of the patient's condition as a steady state or an abnormal state is statistical data of user breathing data uploaded by the ventilator for the patient every 24 hours on the cloud platform, such as respiratory rate (RR), moisture. Quantity (Vt), respiratory rate to tidal volume ratio (RR/Vt), user-initiated ventilator percentage, user-switched ventilator percentage, on-board time in an upload cycle, blood oxygen saturation (SpO2), etc. It should be understood that, based on the recorded data of the non-invasive ventilator, the judgment of the condition may further include more data of the user's respiratory state index or other user's vital state indicator.
  • Vt For the tidal volume Vt, if (Vt - Vt steady state value) ⁇ -1 ⁇ (Vt steady state value ⁇ 30%), or (Vt - Vt steady state value) > (Vt steady state value ⁇ 100%), it is judged Is Vt severely abnormal; if -1 ⁇ (Vt steady state value ⁇ 30%) ⁇ (Vt - Vt steady state value) ⁇ -1 ⁇ (Vt steady state value ⁇ 20%), or (Vt steady state value ⁇ 70%) When ⁇ (Vt-Vt steady state value) ⁇ (Vt steady state value x 100%), it is determined that Vt is slightly abnormal.
  • the machine time in each upload cycle if it is in 3 consecutive upload cycles,
  • the time stable state value is ⁇ 50%, it is judged that the upper machine time is abnormally abnormal in one uploading period; if it is in three consecutive uploading cycles, the stable time value of the running time in one uploading period is ⁇ 30% ⁇
  • RR, Vt, and SpO2 are three statistical values, RR and Vt are simultaneously determined to be severe abnormalities, or SpO2 is determined to be severely abnormal, or the three statistical values are simultaneously If it is a severe abnormality, the data of this day is judged to be a severe abnormality. If RR and Vt are judged to be mild abnormalities at the same time, or SpO2 is judged to be a mild abnormality, or three statistical values are mild abnormalities at the same time, the current data is judged to be a mild abnormality. .
  • the cloud platform if the statistical data of a certain day is determined to be a severe abnormality, the cloud platform is required to issue an early warning to the daily data and notify the doctor and/or the patient. If the statistics of a certain day are judged to be mildly abnormal, the cloud platform needs to view the statistics of the last 5 days: if in the last 5 days, at least 3 days of statistics have been judged as mild abnormalities, or the last 3 days If the statistical data is continuously judged as mild abnormality, it is necessary to issue an early warning to the daily data and notify the doctor and/or the patient.
  • the operation of the cloud platform to judge the abnormality of the user's breathing data and issue an early warning may be performed by the cloud server.
  • the operation of the cloud platform to judge the abnormality of the user's breathing data and issue an early warning may be performed by a dedicated client included in the cloud platform.
  • the patient should contact the doctor in time to diagnose the patient's condition according to the symptoms seen by the doctor; or the doctor actively initiates a face-to-face diagnosis with the patient. If the doctor judges that the patient AECOPD may be attacked and needs hospitalization, the current monitoring period of the online system is terminated by the doctor; after the patient is discharged from the hospital, the doctor adjusts the steady state judgment standard of the statistical values of the respiratory data of the online system, and then Start the next online system monitoring cycle. If the doctor judges that the patient is only in the period of fluctuation of the COPD steady state, the current monitoring cycle of the online system continues, but the doctor needs to adjust the steady state judgment standard of each statistical value of the online system.
  • the data of the set of steady state values may be fluctuating for the same user, or the initial period of the next cycle of the user may be set according to a weighted average of the parameters of the user during the previous period that is determined to be a steady state.
  • the set of steady state values and optionally, is further fine-tuned by the physician based on this.
  • the receiving unit 42 of the cloud server 20 is adapted to receive user breathing data from the ventilator 12, 14 or 16, and may store these data in the storage unit 44 of the cloud server 20 or in a database 22 connected thereto.
  • the processing unit 46 of the cloud server 20 is adapted to acquire from the storage unit 44 or the database 22 a set of user breathing data transmitted by the user of the ventilator device within 24 hours and based on the stability of the user
  • the set of state values performs statistical analysis on the set of user breathing data, and when it is determined that the result of the statistical analysis is that the user breathing data set satisfies the data abnormality criterion, an alarm signal is issued.
  • the alarm signal can be sent to the patient terminal device or the computer at the doctor's end, the form of the alarm signal including, but not limited to, a short message sent to the patient terminal device, a warning indication on the doctor's computer display, or an electronic message sent to the doctor Mail, etc.
  • the received user breath data set is statistically analyzed every 24 hours in the above embodiment, in other embodiments, 10 hours per night (corresponding to the patient's default sleep time period), For example, from 20:00 to 6:00 am, or every 48 hours, as a user's breathing data upload cycle or analysis cycle.
  • the cloud platform needs to check whether the statistical data of at least 3 days in the statistics of the last 5 days is judged as a slight abnormality, or The 3-day statistical data was continuously judged as a mild abnormality. It should be understood that the selection of the above 5 days and 3 days is only an example, and the parameter can be reconfigured by the doctor according to the specific use of the patient.
  • the uploading of the user's breathing data by the ventilator 12, 14 or 16 may be sent in real time, or may be sent periodically, for example, once per upload cycle.
  • the processing unit 46 in the cloud server 20 may also be adapted to perform a validity analysis on the set of user breathing data transmitted by the ventilator device during an upload cycle, and to breathe the user who does not meet the validity criteria. Data collection without statistical analysis.
  • a pre-treatment step can also be implemented at the ventilator 12, 14 or 16.
  • the aforementioned criteria for data severity anomaly criteria, data mild anomaly criteria, or effectiveness analysis may be based on empirical knowledge of a physician, which may be obtained by a physician based on a large number of experiments with different symptoms of the patient.
  • the user breathing data set uploaded by the ventilator may include a respiratory rate (RR), a tidal volume (Vt), a ratio of respiratory rate to tidal volume (RR/Vt), a user-triggered ventilator percentage, and a user switching.
  • RR respiratory rate
  • Vt tidal volume
  • RR/Vt ratio of respiratory rate to tidal volume
  • SpO 2 oxygen saturation
  • the set of steady state values for a user's time is also based on these breathing metrics.
  • the online user breathing data analysis method of the cloud platform of the present application may also be applied to other Analysis of data on respiratory diseases (eg, asthma, bronchitis, pulmonary heart disease, etc.) is used in conjunction with offline diagnostic systems for other diseases to diagnose this type of disease.
  • respiratory diseases eg, asthma, bronchitis, pulmonary heart disease, etc.
  • the cloud server 20 may send an alarm signal to the user of the ventilator 12, 14, or may send an alarm signal to the doctor, or may send an alarm signal to the attendant at the cloud server 20.
  • the form of the alarm signal may include various forms of notifications such as audio, video, or text, for example, sending a text message or a multimedia message to a registered mobile phone or instant messaging tool of the user.
  • cloud server 20 may be implemented in software, hardware, or a combination of software and hardware.
  • the hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor, personal computer (PC), or mainframe.
  • a suitable instruction execution system such as a microprocessor, personal computer (PC), or mainframe.
  • the application is implemented as software including, but not limited to, firmware, resident software, microcode, and the like.
  • FIG. 3 illustrates a flow diagram of a method 300 of alerting an abnormality of user breathing data in accordance with an embodiment of the present application.
  • At step 310 at least one user breathing data set transmitted by at least one ventilator device during at least one upload period is received.
  • the received at least one user breathing data set is stored in a memory or an external database.
  • a first set of user breath data transmitted by a user of one of the at least one ventilator device during an upload cycle is obtained.
  • the first user breathing data is based on the set of steady state values for the first user
  • the set performs statistical analysis, and when it is determined that the result of the statistical analysis is that the first user respiratory data set satisfies the data abnormality criterion, an alarm signal is issued.
  • the device 400 includes: a receiving device 410, configured to receive at least one user breathing data set sent by at least one ventilator device in at least one uploading period; and a storage device 420, configured to store the received at least one user breathing data set in a memory or An external database; an obtaining means 430, configured to acquire a first user breathing data set transmitted by a user of one of the at least one ventilator device in an uploading cycle; and an alarm device 440 for using the first
  • the set of steady state values of the user performs statistical analysis on the first user respiratory data set, and when it is determined that the result of the statistical analysis is that the first user respiratory data set satisfies the data abnormality criterion, an alarm signal is issued.
  • the teachings of the present application can also be implemented as a computer program product of a computer readable storage medium, including computer program code, which, when executed by a processor, enables the processor to be implemented in accordance with the methods of the embodiments of the present application.
  • the computer storage medium can be any tangible medium such as a floppy disk, CD-ROM, DVD, hard drive, or even network media.
  • FIG. 5 illustrates a computing device that can implement a method of alerting an abnormality of user breathing data in accordance with the present invention, such as a computing device including a cloud platform device, a server, a ventilator device, and the like.
  • the computing device conventionally includes a processor 510 and a program product or readable medium in the form of a memory 520.
  • the processor can include a processing unit of the cloud platform.
  • the memory 520 may be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), an EPROM, or a ROM, such as a storage device of a cloud platform.
  • Memory 520 has a memory space 530 for program code 531 for performing any of the method steps described above.
  • Storage space 530 can include various program code 531 for implementing various steps in the above methods, respectively.
  • These program codes can be read from or written to one or more program products.
  • These program products include program code carriers such as memory cards.
  • Such a program product is typically a portable or fixed storage unit as described with reference to FIG.
  • the storage unit may have storage segments, storage spaces, and the like that are similarly arranged to memory 520 in the computing device of FIG.
  • the program code can be compressed, for example, in an appropriate form.
  • the storage unit includes readable code 531', ie, code that can be read by a processor, such as 510, that when executed by a computing device causes the computing device to perform various steps in the methods described above .
  • FIG. 3 the block diagram in FIG. 4, illustrates the functionality and operation of a possible implementation of a method, or computer program product, in accordance with various embodiments of the present application, wherein Operational representations are optional features and operations.
  • Each block of the flowcharts and block diagrams can represent a module, a program segment, or a portion of code that comprises one or more executable instructions for implementing a predetermined logical function.
  • the functions noted in the blocks may also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • steps S310 and S320 and steps S330 and S340 may be different execution sequences.
  • the ventilator uploads user breathing data in real time
  • the data receiving and storing functions of steps S310 and S320 are performed in real time; only in one data uploading period.
  • steps S330 and S340 are performed to perform data analysis on the data received in the upload cycle period.
  • each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented in a dedicated hardware-based system that performs the specified function or operation. Or it can be implemented by a combination of dedicated hardware and computer instructions.
  • the statistical data that needs to be analyzed can be automatically uploaded to the cloud platform by the household non-invasive ventilator used by the patient, and automatically analyzed by the cloud platform to display the analysis.
  • the results or reports do not include steps that require the patient to complete, so the procedure is very easy for the patient and the error rate is low.
  • the online analysis program of various embodiments of the present application needs to combine and work with a set of offline delay judgment system to confirm whether the patient has AECOPD.
  • attention is paid to the real-time breathing data of the patient, and through comprehensive analysis of a large amount of data, when the abnormality of the respiratory data is judged, the warning of the medical treatment is issued to the patient.
  • This not only utilizes the advantages of the cloud platform to process data in real time, but also combines the traditional offline diagnostic method with a large amount of data diagnosis, which greatly improves the timeliness and accuracy of patient diagnosis and treatment, and reduces The pain and burden of the patient also reduces the consumption and cost of doctors and hospitals.

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Abstract

一种云平台，其包括接收单元（42）和处理单元（46）。接收单元（42）被适配为接收至少一个呼吸机设备（12、14、16）在至少一个上传周期内发送的至少一个用户呼吸数据集合。处理单元（46）被适配为获取至少一个呼吸机设备（12、14、16）中的一个呼吸机设备的用户在一个上传周期内发送的第一用户呼吸数据集合，并且基于所述用户的稳定状态值集合对所述第一用户呼吸数据集合进行统计分析，在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据异常标准时，则发出报警信号。所述云平台提供了与线下呼吸系统疾病诊断相结合的线上用户呼吸数据分析方案，使得患者的病情加重有可能被及时发现。

Description

一种云平台 技术领域
本申请涉及在线数据分析的技术领域，更具体地，涉及一种云平台。
背景技术
随着互联网技术的发展，各行各业都在纷纷推出自己的云产品和基于云的各种服务。在医疗行业里，互联网医疗正在成为行业发展的新方向之一。另一方面，结合数据分析技术，使得有可能在云平台的云服务器端或云客户端对各种医疗器械终端、例如呼吸机上传的大量用户呼吸数据进行综合分析和评估，以便为患者的居家康复和慢性病管理提供远程监控，降低患者治疗成本。
呼吸机是一种能代替、控制或改变人的正常生理呼吸、增加肺通气量的医疗设备，能够改善呼吸功能，预防和治疗呼吸衰竭，用于慢性阻塞性肺病(Chronic Obstructive Pulmonary Diseases，COPD)、支气管扩张、肺结核、肺部职业病、神经肌肉疾病、过度肥胖、胸廓畸形等疾病并发呼吸衰竭的治疗，其中COPD因发病人数众多，无有效药物治疗，成为导致呼吸衰竭的常见病种，造成严重疾病负担。
慢性阻塞性肺病(COPD)为一种以不完全可逆气流受限为标志性特征的肺部疾病，且多数患者病情呈进行性加重，发展到晚期除肺脏外，还可累及心脑肾等机体多个重要脏器。近年来COPD发病率和病死率不断升高。据世界银行预计，至2020年COPD的世界疾病经济负担将从1990年的第12位升至第5位，全球死因顺位将从1990年的第6位升至第3位。
COPD是因呼吸单位解剖结构改变，气体弥散障碍，缺氧与CO2潴留使 中枢反应低下，加之呼吸肌疲劳，所以正规药物治疗难以取得满意效果。双水平气道内正压(BiPAP)通气是经口鼻面罩或鼻面罩进行通气的一种无创通气技术，能有效改善通气和换气，有效改善弥散功能和氧合功能，有助于纠正患者循环功能的异常，故可很快改善低氧血症和高碳酸血症，改善临床症状，降低气管插管的发生；避免了有创机械通气需要气管插管所产生的一系列并发症，如呼吸机相关性肺炎，有利于病情观察和呼吸道管理，改善了COPD急性加重期(Acute Exacerbation of Chronic Obstructive Pulmonary Diseases，AECOPD)患者感染不易控制的情况；患者痛苦小、易耐受，避免或减少了镇静剂的使用，还可缩短患者住院天数，提高了患者的生活质量，降低了住院费用及患者病死率。目前使用BiPAP呼吸机辅助治疗已经成为公认的有效方式，并在重症监护病房、普通病房及家庭中广泛使用。
COPD患者每年约发生0.5-3.5次的急性加重。COPD急性加重期(AECOPD)由于呼吸道感染、气道阻塞、呼吸肌疲劳等，患者很容易出II型呼吸功能衰竭，导致其病死率与治疗难度增加。AECOPD对患者的生活质量、疾病进程和社会经济负担产生严重的负面影响，可加快患者肺功能下降速度，与住院患者的病死率增加相关。AECOPD是一种急性起病的过程，COPD患者呼吸系统症状出现急性加重。在COPD管理中，预防和治疗AECOPD居中心地位。
然而，AECOPD症状、肺功能个体差异很大，与基础肺功能、加重诱因及个体敏感性都有关。通常情况下，当患者咳嗽加重、咳痰增多，喘息加重；活动能力明显下降影响基本生活(进食与入睡)；经自行调解药物不能缓解，需要急诊或门诊治疗者应为AECOPD的基本标准。AECOPD患者基本上需要医师调整治疗方案，因此，必须对其病情严重程度进行准确评估。现有技术中未有针对AECOPD患者的有效评估方法，常会导致患者发病后难于治疗。
发明内容
本申请实施方式解决的技术问题之一在于如何通过早期监测，及早发现患者的呼吸系统疾病有可能出现的加重状态，比如COPD患者有可能出现的AECOPD状态。
根据本申请的实施方式，这可以通过利用云服务器记录的患者使用过的呼吸机上传的各种呼吸相关的数据，并进行数据统计分析，来对于患者可能进入AECOPD状态的情形给出告警或提示。患者在接收到这样的告警或提示后(或者由医生接收到这样的告警或提示，并通知患者)，可以前去医生处会诊，由医生来进行诊断并确定是否进入了AECOPD状态。然而，应当注意，发明人也设想到了本申请的实施方式可以用于其他的呼吸系统疾病的重度症状的及早发现。
根据本申请的一个方面，提供一种云平台，其包括接收单元和处理单元。接收单元被适配为接收至少一个呼吸机设备在至少一个上传周期内发送的至少一个用户呼吸数据集合。处理单元被适配为获取所述至少一个呼吸机设备中的一个呼吸机设备的用户在一个上传周期内发送的第一用户呼吸数据集合，并且基于所述用户的稳定状态值集合对所述第一用户呼吸数据集合进行统计分析，在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据异常标准时，则发出报警信号。
根据本申请的另一个方面，提供一种对用户呼吸数据的异常进行报警的方法。所述方法包括：接收至少一个呼吸机设备在至少一个上传周期内发送的至少一个用户呼吸数据集合；获取所述至少一个呼吸机设备中的一个呼吸机设备的用户在一个上传周期内发送的第一用户呼吸数据集合，以及基于所述用户的稳定状态值集合对所述第一用户呼吸数据集合进行统计分析，在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据异常标准时，则发出报警信号。
根据本申请的又一个方面，提供一种对用户呼吸数据的异常进行报警 的设备。所述设备包括：接收装置，用于接收至少一个呼吸机设备在至少一个上传周期内发送的至少一个用户呼吸数据集合；获取装置，用于获取所述至少一个呼吸机设备中的一个呼吸机设备的用户在一个上传周期内发送的第一用户呼吸数据集合，以及报警装置，用于基于所述用户的稳定状态值集合对所述第一用户呼吸数据集合进行统计分析，在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据异常标准时，则发出报警信号。
根据本申请的又一个方面，提供一种程序，包括可读代码，当所述可读代码在设备上运行时，导致设备执行根据本申请实施例所述的对用户呼吸数据的异常进行报警的方法。
根据本申请的又一个方面，提供一种可读介质，其中存储了如本申请实施例所述的程序。
通过本申请的各种实施方式，提供了一套与线下呼吸系统疾病监测相结合的线上用户呼吸数据分析方案，使得患者在病情加重前期有可能被及时发现。
在结合COPD给出的实施方式中，能够通过分析用户的当前周期的呼吸数据集合、和/或历史呼吸数据集合来对用户出现AECOPD发病的可能性给出告警，以便于患者可以积极就医，或者医生可以在AECOPD发病前提前介入。
从下文结合附图所做出的详细描述中，本申请的这些和其他优点和特征，连同其操作的组织和方式将变得明显，其中在整个下文描述的若干附图中，类似的元件将具有类似的编号。
附图说明
图1图示本申请的实施方式能够在其中实现的系统的示意图；
图2图示根据本申请一种实施方式的云服务器的框图；
图3图示根据本申请一种实施方式的能够对用户呼吸数据集合的异常进行报警的方法的流程图；以及
图4示出了根据本申请一种实施方式的能够对用户呼吸数据的异常进行报警的设备的流程图；
图5示出了用于执行根据本申请的对用户呼吸数据的异常进行报警的方法的计算设备的框图；
图6示出了用于保持或者携带实现根据本申请的对用户呼吸数据的异常进行报警的方法的程序代码的存储单元。
具体实施方式
下文将参考附图更完整地描述本公开内容，其中在附图中显示了本公开内容的实施方式。但是这些实施方式可以用许多不同形式来实现并且不应该被解释为限于本文所述的实施方式。相反地，提供这些实例以使得本公开内容将是透彻和完整的，并且将全面地向本领域的熟练技术人员表达本公开内容的范围。
下面结合附图以示例的方式详细描述本申请的各种实施方式。
首先参考图1，其示出了本申请的各种实施方式能够在其中实现的系统100的示意图。如图1所示，系统100可以包括呼吸机12、14和16，云服务器20，以及与云服务器20连接的数据库22，其中呼吸机12、14和16分别通过数据链路与互联网18相连，进而通过通信链路连接到云服务器20。在图1中，呼吸机12和14可以位于不同患者的家中，呼吸机16可以是位于医院的呼吸机。
图1中数据链路可以是任何类型有线或无线连接方式，包括但不限于电话线、电缆线路、电力线、TV广播、远程无线连接、短程无线连接等。虽然图1中将连接呼吸机12、14和16和云服务器20的网络示出为互联网，但是本申请的实施方式也可以适用于其他的网络形式，包括但不限于移动 电话网络、无线局域网(Wireless Local Area Networks，W LAN)、自组织网络、以太网LAN、令牌环LAN、广域网、以及这些网络形式与互联网的任意组合。
各个设备间进行通信所适用的通信技术或通信标准可以包括但不限于码分多址(Code Division Multiple Access，CDMA)、全球移动通信系统(Global System for Mobile communication，GSM)、通用移动通信系统(Universal Mobile Telecommunications System，UMTS)、时分多址(Time Division Multiple Access，TDMA)、频分多址(Frequency Division Multiple Access，FDMA)、传输控制协议/互联网协议(Transmission Control Protocol/Internet Protocol，TCP/IP)、短消息传递服务(Short Message Service，SMS)、多媒体消息传递服务(Multimedia Message Service，MMS)、电子邮件、即时消息传递服务(Instant Messaging Service，IMS)、蓝牙、IEEE 802.11等。在实现本申请的各种实施方式中所涉及的通信设备可以使用各种介质进行通信，包括但不限于无线电、红外、激光、线缆连接等。
呼吸机12、14和16可以是任何类型的呼吸机，包括单水平呼吸机、双水平呼吸机等家用无创呼吸机，也可以是医院进行治疗用的有创呼吸机，其将用户使用呼吸机时产生的用户呼吸数据集合上传至云服务器。呼吸机上传的用户呼吸数据是与用户的呼吸状态有关的数据，用户呼吸数据集合是(实时或特定时刻的)各种类型的呼吸状态指标的值的集合。呼吸机12、14和16还可以包括能够记录用户呼吸数据的各种相关监测设备，例如可监测血氧数据、胸腹带数据、脑电数据、心率数据等的监测设备。
用户可在家中使用的呼吸机12或者14将用户使用呼吸机时产生的用户呼吸数据集合上传至云服务器20。备选地或附加地，用户在医院使用呼吸机16产生的该用户的呼吸数据集合也被上传至云服务器20。
云服务器20可以对上传的某个用户呼吸数据集合进行分析，以确定该 用户的当前呼吸状态是否存在异常。用户呼吸数据可以被存储在与云服务器20相连的数据库22中，也可以被存储在云服务器20内的存储器中。
图2是根据本申请一种实施方式的云服务器200的框图。云服务器200可以包括经由系统总线50互连的接收单元42、存储单元44和处理单元46。云服务器200作为一种计算机系统，还可以包括图中未示出的其他单元，例如，RAM存储器、ROM存储器、各种硬件控制器(比如硬盘控制器、键盘控制器、串行接口控制器、并行接口控制器、显示控制器)、硬盘、键盘、串行接口设备、并行接口设备、显示器等。
接收单元42可以包括发射器和/或接收器，可以被实现为RF接口、蓝牙接口和/或IrDA接口，用于提供通信服务。处理单元46可以利用任何商业可得CPU、数字信号处理器(DSP)或任何其他电子可编程逻辑器件实现。存储单元44可以被实现为RAM存储器、ROM存储器、EEPROM存储器、闪存、硬盘或其任何组合形式。应当理解，图2所述的结构框图仅仅为了示例的目的而示出的，而不是对本申请的限制。在某些情况下，可以根据需要增加或者减少其中的一些设备。应当理解，虽然图2中将接收单元42、存储单元44和处理单元46都实现在云服务器200处，但是云平台上的云服务器所要实现的功能，也可以被分布到云平台下的不同的实体来实现。例如，将接收单元42和存储单元44所要实现的能够实现在一个中央服务器处，而将处理单元所要实现的功能实现在客户端处。
COPD患者经由医生诊断，确认其病情进入稳定状态后，可在家中使用呼吸机持续监测体征状态。本申请的实施方式基于可联网并实时上传数据的家用无创呼吸机12、14，或者医疗单位用的治疗呼吸机16，以及一个支持实时上传用户呼吸数据的云平台，建立一个线上的数据分析系统，用于实时监测患者呼吸系统病情的发展状态。所述云平台例如由图1所示的呼吸机12、14、16，云服务器20和可选的数据库22所构建。
临床经验表明，COPD患者的病情发展并不完全能够通过呼吸机产生 的用户数据体现，并且AECOPD发作期间患者的最主要症状仍需医生当面诊断，即患者是否出现AECOPD仍然需要医生来进行诊断，而不能直接或单独地依赖于呼吸机等医疗器械的数据。另外，线上分析系统对于患者病情发展中各种状态的判断分析，也需要医生根据患者以往的大量监测数据、包括用户呼吸状态数据，随着患者病情的发展而调整判断的标准，例如调整COPD患者的稳定状态的呼吸机指标范围。因此，相对于上述的线上医疗分析系统仅是提供了参考依据，对于病情的最终诊断还需要传统的线下延迟诊断系统协同工作，以达到及时、准确的诊断和治疗效果。
因此，要想实现COPD患者是否进入AECOPD状态的确诊，仅靠根据本申请实施方式的云服务器20处进行的线上数据分析是不够的，还需要一个线下诊断系统协同工作，以实现对患者及时、准确的诊断。
根据本申请的一个实施方式，患者开始在自己家中使用无创呼吸机12或14，或在病情稳定出院后回到家中继续使用无创呼吸机之前，需要先由医生对其线上分析系统的各种使用数据判断进行设定或重新设定，以此开始患者的一个新的线上系统监测周期。在一个监测周期开始后，患者应每日使用无创呼吸机，并联网以上传用户呼吸数据至云平台。
在病情处于稳定状态期间，患者可能需要定期复诊，并由医生在复诊后，根据此次复诊和上一次复诊之间患者的所有监测数据，为患者调整线上系统对用户呼吸数据的判断标准。
根据本申请的各种实施方式，一旦线上分析系统对患者的用户呼吸数据判断出异常，则发出预警，医生和/或患者可以在云平台上接到预警信号。患者应在看到预警后及时就诊，由医生进一步诊断病情，以判断需要患者住院治疗，或者只是需要调整线上系统的判断标准。
根据本申请的一个具体实施例，在一个线上监测和分析周期的开始，可以由医生确定线上分析系统对于患者各个病情状态的判断标准。线上判断标准包括对患者稳定状态的判断标准、对患者轻度异常状态的判断标 准、和对患者重度异常状态的判断标准。云平台每24小时对患者使用数据进行一次统计，作为本日的患者数据统计值。可选地，在统计前会对这24小时的数据进行有效性判断，例如，若当日使用时间至少4小时，且漏气量不超过30LPM，则本日数据判断为有效数据；无效数据不进行统计，视为这24小时内无使用数据。应当理解，该24小时的起点和终点是可调整的，例如从当日19:00开始至次日19:00，或者从当日9:00开始至此时9:00。
在该实施例中，对于患者病情状态判断为稳定状态或是异常状态的依据是云平台上每24小时对于患者使用的呼吸机上传的用户呼吸数据的统计数据，例如呼吸频率(RR)、潮气量(Vt)、呼吸频率与潮气量比值(RR/Vt)，用户触发呼吸机百分比、用户切换呼吸机百分比、一个上传周期内的上机时间、血氧饱和度(SpO2)等。应当理解，根据无创呼吸机的记录数据，对病情的判断依据还可以包含更多的用户呼吸状态指标或其他用户体征状态指标的数据。
对于各种统计数据的正常状态与异常状态的判断，可以以上述例举出的几种统计数据为例，举例说明对于这几种判断标准的设定。
对于呼吸频率RR，若|RR-RR稳定状态值|＞RR稳定状态值×30％，则判断为RR重度异常；若RR稳定状态值×20％＜|RR-RR稳定状态值|＞RR稳定状态值×30％，则判断为RR轻度异常。
对于潮气量Vt，若(Vt-Vt稳定状态值)＜-1×(Vt稳定状态值×30％)，或者(Vt-Vt稳定状态值)＞(Vt稳定状态值×100％)，则判断为Vt重度异常；若-1×(Vt稳定状态值×30％)＜(Vt-Vt稳定状态值)＜-1×(Vt稳定状态值×20％)，或者(Vt稳定状态值×70％)＜(Vt-Vt稳定状态值)＜(Vt稳定状态值×100％)，则判断为Vt轻度异常。
对于用户触发呼吸机百分比，若(用户触发呼吸机百分比-用户触发呼吸机百分比稳定状态值)＜-1×(用户触发呼吸机百分比稳定状态值× 30％)，则判断为用户触发呼吸机百分比重度异常；若-1×(用户触发呼吸机百分比稳定状态值×30％)＜(用户触发呼吸机百分比-用户触发呼吸机百分比稳定状态值)＜-1×(用户触发呼吸机百分比稳定状态值×20％)，则判断为用户触发呼吸机百分比轻度异常。
对于用户切换呼吸机百分比，若(用户切换呼吸机百分比-用户切换呼吸机百分比稳定状态值)＜-1×(用户切换呼吸机百分比稳定状态值×30％)，则判断为用户切换呼吸机百分比重度异常；若-1×(用户切换呼吸机百分比稳定状态值×30％)＜(用户切换呼吸机百分比-用户切换呼吸机百分比稳定状态值)＜-1×(用户切换呼吸机百分比稳定状态值×20％)，则判断为用户切换呼吸机百分比轻度异常。
对于一个上传周期内的上机时间，若在连续3个上传周期内，|每个上传周期内的上机时间-一个上传周期内的上机时间稳定状态值|＞一个上传周期内的上机时间稳定状态值×50％，则判断为一个上传周期内的上机时间重度异常；若在连续3个上传周期内，一个上传周期内的上机时间稳定状态值×30％＜|每个上传周期内的上机时间-一个上传周期内的上机时间稳定状态值|＞一个上传周期内的上机时间稳定状态值×50％，则判断为一个上传周期内的上机时间轻度异常。
对于血氧饱和度SpO2，若(SpO2-SpO2稳定状态值)＜-1×(SpO2稳定状态值×5％)，则判断为SpO2稳定状态值重度异常；若-1×(SpO2稳定状态值×5％)＜(SpO2-SpO2稳定状态值)＜-1×(SpO2稳定状态值×3％)，则判断为SpO2轻度异常。
在另一个实施方式中，对于每日的统计数据，若RR、Vt和SpO2这3个统计值中，RR与Vt同时判断为重度异常，或SpO2判断为重度异常，或这3个统计值同时为重度异常，则本日数据判断为重度异常；若RR与Vt同时判断为轻度异常，或SpO2判断为轻度异常，或3个统计值同时为轻度异常，则本日数据判断为轻度异常。
在该实施例中，如果某日的统计数据判断为重度异常，则需要云平台对该日数据发出预警，并通知医生和/或患者。如果某日的统计数据判断为轻度异常，则云平台需要查看最近5天的统计数据：若在最近5天中，已有至少3天的统计数据被判断为轻度异常，或最近3天的统计数据连续判断为轻度异常，则需要对该日数据发出预警，并通知医生和/或患者。
根据本申请的一个实施方式，云平台对用户呼吸数据的异常进行判断并发出预警的操作可以由云服务器执行。根据本申请的另一个实施方式，云平台对用户呼吸数据的异常进行判断并发出预警的操作可以由云平台所包括的专用客户端来执行。
对于线下的诊断系统而言，云平台发出预警后，患者应及时联系医生就诊，由医生根据面见的症状诊断患者的病情；或由医生主动与患者当面诊断。如医生判断患者AECOPD可能将发作，需住院治疗，则线上系统的本次监测周期由医生终止；待患者出院后，由医生调整线上系统各呼吸数据统计值的稳定状态判断标准，然后可以开始下一次线上系统的监测周期。如果医生判断患者只是处于COPD稳定状态的波动期，则线上系统的本次监测周期继续，但是需要医生对线上系统的各统计值的稳定状态判断标准作出调整。附加地，稳定状态值集合的数据针对同一用户可以是波动的，也可以根据该用户在上一周期被认定为是稳定状态期间的各项参数的加权平均来设置该用户的下一周期的初始的稳定状态值集合，并且可选地，再由医生基于此进行微调。
在云平台上每24小时对于患者使用的呼吸机上传的用户呼吸数据集合的分析，可以在图2所示的云服务器20处完成。云服务器20的接收单元42被适配为接收来自呼吸机12、14或16的用户呼吸数据，并且可以将这些数据存储在云服务器20的存储单元44中或者与之相连的数据库22中。云服务器20的处理单元46被适配为从存储单元44或者数据库22获取呼吸机设备的用户在24小时内发送的用户呼吸数据集合，并且基于该用户的稳 定状态值集合对所述用户呼吸数据集合进行统计分析，在确定所述统计分析的结果为所述用户呼吸数据集合满足数据异常标准时，则发出报警信号。作为示例，报警信号可以被发送到患者终端设备或医生端的计算机，报警信号的形式包括但不限于，发送给患者终端设备的短消息、医生端的计算机显示器上的警告指示、或者发送给医生的电子邮件等。
应当理解，虽然在上述实施例中每24小时对接收的用户呼吸数据集合进行统计分析，但是在其他实施方式中，也可以将每个夜晚的10小时(对应于患者的默认睡眠时间段)、比如晚上20:00到次日凌晨6:00，或者每48小时，作为一个用户呼吸数据的上传周期或分析周期。
在上述实施例中，在某日的统计数据被判断为轻度异常的情况下，云平台需要查看是否在最近5天的统计数据有至少3天的统计数据被判断为轻度异常，或最近3天的统计数据连续判断为轻度异常。应当理解，以上的5天、3天的选取仅是示例，该参数可以由医生根据患者的具体使用情况进行重新配置。
根据本申请的实施方式，呼吸机12、14或16对用户呼吸数据的上传可以是实时发送的，也可以是定期发送的，例如，每个上传周期发送一次。
根据本申请的实施方式，云服务器20中的处理单元46还可以被适配为对呼吸机设备在一个上传周期内发送的用户呼吸数据集合进行有效性分析，将不符合有效性标准的用户呼吸数据集合，而不对其进行统计分析。可选地，这样的预处理步骤也可以在呼吸机12、14或16处实现。
根据本申请的实施方式，前述的数据重度异常标准、数据轻度异常标准或者有效性分析的准则可以基于医生的经验知识，该经验知识可以由医生根据患者不同症状的大量实验而获得。
根据本申请的实施方式，呼吸机上传的用户呼吸数据集合可以包括呼吸频率(RR)、潮气量(Vt)、呼吸频率与潮气量的比值(RR/Vt)、用户触发呼吸机百分比、用户切换呼吸机百分比、一个上传周期内的上机时 间、和血氧饱和度(SpO2)中的一个或多个呼吸指标的数据。类似地，用户某段时间的稳定状态值集合也是基于这些呼吸指标的。虽然上述实施方式描述了基于这些类型的呼吸数据对COPD患者的可能的AECOPD发病进行的数据分析和报警过程，但是本申请实施方式的在云平台的在线用户呼吸数据分析方法也可以应用于其他的呼吸系统疾病(例如，哮喘、气管炎、肺心病等)数据的分析，与其他疾病的线下诊断系统结合使用，进行该类型疾病的诊断。
根据本申请的实施方式，云服务器20可以向呼吸机12、14的用户发送报警信号，也可以向医生发送报警信号，也可以在云服务器20处向值守人员发出报警信号。报警信号的形式可以包括音频、视频、或者文本等各种形式的通知，例如，向用户的已注册的手机或即时通信工具发送文本消息或多媒体消息。
应当理解，本文描述在云服务器20处执行的前述功能，可以以软件、硬件、或软件和硬件的结合来实现。硬件部分可以利用专用逻辑来实现；软件部分可以存储在存储器中，由适当的指令执行系统，例如微处理器、个人计算机(PC)或大型机来执行。在一些实施方式中，本申请实现为软件，其包括但不限于固件、驻留软件、微代码等。
图3示出了根据本申请实施方式的能够对用户呼吸数据的异常进行报警的方法300的流程图。
在步骤310，接收至少一个呼吸机设备在至少一个上传周期内发送的至少一个用户呼吸数据集合。
在步骤320，将接收的至少一个用户呼吸数据集合存储在存储器或者外部数据库中。
在步骤330，获取至少一个呼吸机设备中的一个呼吸机设备的用户在一个上传周期内发送的第一用户呼吸数据集合。
在步骤340，基于用于第一用户的稳定状态值集合对第一用户呼吸数据 集合进行统计分析，在确定所述统计分析的结果为第一用户呼吸数据集合满足数据异常标准时，则发出报警信号。
图4示出了根据本申请实施方式的能够对用户呼吸数据的异常进行报警的设备400的流程图。设备400包括：接收装置410，用于接收至少一个呼吸机设备在至少一个上传周期内发送的至少一个用户呼吸数据集合；存储装置420，用于将接收的至少一个用户呼吸数据集合存储在存储器或者外部数据库中；获取装置430，用于获取至少一个呼吸机设备中的一个呼吸机设备的用户在一个上传周期内发送的第一用户呼吸数据集合；以及报警装置440，用于基于用于第一用户的稳定状态值集合对第一用户呼吸数据集合进行统计分析，在确定所述统计分析的结果为第一用户呼吸数据集合满足数据异常标准时，则发出报警信号。
应当理解，虽然在此可以使用术语第一、第二、第N个等来描述各种元素，但是这些元素不应该受到这些术语的限制，因为这些术语仅用于将一个元素与另一个元素区别开来。
本申请的教导还可以实现为一种计算机可读存储介质的计算机程序产品，包括计算机程序代码，当计算机程序代码由处理器执行时，其使得处理器能够按照本申请实施方式的方法来实现如本文实施方式所述的对用户呼吸数据集合异常的报警。计算机存储介质可以为任何有形媒介，例如软盘、CD-ROM、DVD、硬盘驱动器、甚至网络介质等。
例如，图5示出了可以实现根据本发明的对用户呼吸数据的异常进行报警的方法的计算设备，例如计算设备包括云平台的设备、服务器、呼吸机设备等。该计算设备传统上包括处理器510和以存储器520形式的程序产品或者可读介质。例如处理器可以包括云平台的处理单元。存储器520可以是诸如闪存、EEPROM(电可擦除可编程只读存储器)、EPROM或者ROM之类的电子存储器，如云平台的存储设备。存储器520具有用于执行上述方法中的任何方法步骤的程序代码531的存储空间530。例如，用于程序代码的 存储空间530可以包括分别用于实现上面的方法中的各种步骤的各个程序代码531。这些程序代码可以从一个或者多个程序产品中读出或者写入到这一个或者多个程序产品中。这些程序产品包括诸如存储卡之类的程序代码载体。这样的程序产品通常为如参考图6所述的便携式或者固定存储单元。该存储单元可以具有与图5的计算设备中的存储器520类似布置的存储段、存储空间等。程序代码可以例如以适当形式进行压缩。通常，存储单元包括可读代码531’，即可以由例如诸如510之类的处理器读取的代码，这些代码当由计算设备运行时，导致该计算设备执行上面所描述的方法中的各个步骤。
还应当理解，图3中的流程图、图4中的框图，图示了按照本申请各种实施例的方法、或计算机程序产品的可能实现的功能和操作，其中以虚线示出的功能和操作表示是可选的功能和操作。流程图和框图的每个方框可以代表一个模块、程序段、或代码的一部分，所述模块、程序段、或代码的一部分包含一个或多个用于实现预定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。例如，步骤S310和S320与步骤S330和S340可以是不同的执行序列，在呼吸机实时地上传用户呼吸数据时，实时地执行步骤S310和S320的数据接收和存储功能；而只有在一个数据上传周期结束时，才执行步骤S330和S340而对该上传周期周期接收的数据进行一次数据分析。
也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
根据本申请的各种实施方式，需要分析的统计数据均可由患者所使用的家用无创呼吸机自动上传至云平台，并由云平台自动分析，显示出分析 结果或报告，全过程不包含需要患者完成的步骤，因此对于患者而言操作非常容易，出错率也较低。
同时，本申请的各种实施方式的线上分析方案需要结合一套线下延迟判断系统并与其协同工作，才能确诊患者是否AECOPD发病。根据本申请的实施方式，关注患者的实时呼吸数据，通过大量数据的综合分析，在判断出呼吸数据出现异常时，才向患者发出就医的警告。这既利用了云平台可以实时处理数据的优点，又融合了传统的线下诊断方式的需要较大数据量的诊断方式，使得患者诊断和治疗的及时性和准确性都大大提升，既减小患者的痛苦和负担，同时也减轻了医生和医院的消耗和成本。
已经出于示出和描述的目的给出了本申请的说明书，但是其并不意在是穷举的或者限制于所公开形式的发明。本领域技术人员在阅读了本公开内容后，还可以想到很多修改和变体。上文描述的各种实施方式可以单独使用或者在各种组合中使用，除非上下文明确指出。
因此，实施方式是为了更好地说明本申请的原理、实际应用以及使本领域技术人员中的其他人员能够理解以下内容而选择和描述的，即，在不脱离本申请精神的前提下，做出的所有修改和替换都将落入所附权利要求定义的本申请保护范围内。

Claims (15)

  1. 一种云平台，其特征在于，包括：
    接收单元，被适配为接收至少一个呼吸机设备在至少一个上传周期内发送的至少一个用户呼吸数据集合；以及
    处理单元，被适配为获取所述至少一个呼吸机设备中的一个呼吸机设备的用户在一个上传周期内发送的第一用户呼吸数据集合；并且，基于所述用户的稳定状态值集合对所述第一用户呼吸数据集合进行统计分析，在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据异常标准时，则发出报警信号。
  2. 根据权利要求1所述的云平台，其特征在于，还包括：
    存储单元，被适配为存储所述至少一个呼吸机设备发送的所述至少一个用户呼吸数据集合，
    其中所述接收单元，还被适配为将所述至少一个用户呼吸数据集合存储在所述存储单元中，并且
    所述处理单元与所述存储单元通信，并且所述处理单元，还被适配为从所述存储单元获取用户呼吸数据集合。
  3. 根据权利要求1所述的云平台，其特征在于，
    所述处理单元，被适配为在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据重度异常标准时，则发出第一报警信号；或者，
    在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据轻度异常标准时，继续获取所述用户在所述上传周期的前N-1个上传周期内发送的前N-1个用户呼吸数据集合中，并且确定所述第一用户呼吸数据集合和所述前N-1个用户呼吸数据集合中是否有M个用户呼吸数据集合满足所述数据轻度异常标准，若是，则发出第一报警信号，其中，N是大于2的整数，M是小于N的正整数。
  4. 根据权利要求3所述的云平台，其特征在于，所述处理单元，还被适配为在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据轻度异常标准时，发出第二报警信号。
  5. 根据权利要求1-4中任一项所述的云平台，其特征在于，所述上传周期是24小时。
  6. 根据权利要求2所述的云平台，其特征在于，所述处理单元，还被适配为对存储在存储器中的所述至少一个呼吸机设备在一个上传周期内发送的用户呼吸数据集合进行有效性分析，并且更新所述用户呼吸数据集合。
  7. 根据权利要求6所述的云平台，其特征在于，有效性标准是一个呼吸机设备在一个上传周期内发送的用户呼吸数据集合包括用户使用呼吸机设备至少4小时的用户呼吸数据，并且所述用户使用呼吸机设备时的漏气量不超过30升每分钟。
  8. 根据权利要求1-4中任一项所述的云平台，其特征在于，所述稳定状态值集合的数据针对同一用户是波动的。
  9. 根据权利要求1-4中任一项所述的云平台，其特征在于，所述用户呼吸数据集合包括以下各项中的一项或多项：呼吸频率、潮气量、呼吸频率与潮气量的比值、用户触发呼吸机百分比、用户切换呼吸机百分比、一个上传周期内的上机时间和血氧饱和度。
  10. 根据权利要求9所述的云平台，其特征在于，所述数据重度异常标准包括以下各项中的一项或多项：
    a.呼吸频率偏离呼吸频率稳定状态值的偏离量大于呼吸频率稳定状态值的30％；
    b.潮气量偏离潮气量稳定状态值，并且相对于潮气量稳定状态值的减小量大于潮气量稳定状态值的30％、或者相对于潮气量稳定状态值的增加量大于潮气量稳定状态值的100％；
    c.用户触发呼吸机百分比偏离用户触发呼吸机百分比稳定状态值，并且相对于用户触发呼吸机百分比稳定状态值的减少量大于用户触发呼吸机百分比稳定状态值的30％；
    d.用户切换呼吸机百分比偏离用户切换呼吸机百分比稳定状态值，并且相对于用户切换呼吸机百分比稳定状态值的减少量大于用户切换呼吸机百分比稳定状态值的30％；
    e.连续3个上传周期内的上机时间偏离一个上传周期内的上机时间稳定状态值的偏离量大于一个上传周期内的上机时间稳定状态值的50％；或者；
    f.血氧饱和度偏离血氧饱和度稳定状态值，并且相对于血氧饱和度稳定状态值的减少量大于血氧饱和度稳定状态值的5％。
  11. 根据权利要求9所述的云平台，其特征在于，所述数据轻度异常标准包括以下各项中的一项或多项：
    a.呼吸频率偏离呼吸频率稳定状态值的偏离量大于呼吸频率稳定状态值的20％且小于呼吸频率稳定状态值的30％；
    b.潮气量偏离潮气量稳定状态值，并且相对于潮气量稳定状态值的减小量大于潮气量稳定状态值的20％且小于潮气量稳定状态值的30％、或者相对于潮气量稳定状态值的增加量大于潮气量稳定状态值的70％且小于潮气量稳定状态值的100％；
    c.用户触发呼吸机百分比偏离患者触发呼吸机百分比稳定状态值，并且相对于用户触发呼吸机百分比稳定状态值的减少量大于用户触发呼吸机百分比稳定状态值的20％且小于用户触发呼吸机百分比稳定状态值的30％；
    d.用于切换呼吸机百分比偏离用户切换呼吸机百分比稳定状态值，并且相对于用户切换呼吸机百分比稳定状态值的减少量大于用户切换呼吸机百分比稳定状态值的20％且小于用户切换呼吸机百分比稳定状态值的 30％；
    e.连续3个上传周期内的上机时间偏离一个上传周期内的上机时间稳定状态值的偏离量大于一个上传周期内的上机时间稳定状态值的30％且小于一个上传周期内的上机时间稳定状态值的50％；
    f.血氧饱和度偏离血氧饱和度稳定状态值，并且相对于血氧饱和度稳定状态值的减少量大于血氧饱和度稳定状态值的3％且小于血氧饱和度稳定状态值的5％。
  12. 一种对用户呼吸数据的异常进行报警的方法，包括：
    接收至少一个呼吸机设备在至少一个上传周期内发送的至少一个用户呼吸数据集合；
    获取所述至少一个呼吸机设备中的一个呼吸机设备的用户在一个上传周期内发送的第一用户呼吸数据集合，以及
    基于所述用户的稳定状态值集合对所述第一用户呼吸数据集合进行统计分析，在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据异常标准时，则发出报警信号。
  13. 一种对用户呼吸数据的异常进行报警的设备，包括：
    接收装置，用于接收至少一个呼吸机设备在至少一个上传周期内发送的至少一个用户呼吸数据集合；
    获取装置，用于获取所述至少一个呼吸机设备中的一个呼吸机设备的用户在一个上传周期内发送的第一用户呼吸数据集合，以及
    报警装置，用于基于所述用户的稳定状态值集合对所述第一用户呼吸数据集合进行统计分析，在确定所述统计分析的结果为所述第一用户呼吸数据集合满足数据异常标准时，则发出报警信号。
  14. 一种程序，包括可读代码，当所述可读代码在设备上运行时，导致设备执行根据权利要求12所述的对用户呼吸数据的异常进行报警的方法。
  15. 一种可读介质，其中存储了如权利要求14所述的程序。
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