CN109215773B - Daily detection method based on big data - Google Patents
Daily detection method based on big data Download PDFInfo
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- CN109215773B CN109215773B CN201811363467.7A CN201811363467A CN109215773B CN 109215773 B CN109215773 B CN 109215773B CN 201811363467 A CN201811363467 A CN 201811363467A CN 109215773 B CN109215773 B CN 109215773B
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—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
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Abstract
The invention discloses a daily detection method based on big data, which belongs to the technical field of big data and comprises a terminal device, a remote medical center and an emergency center, wherein the remote medical center is provided with a remote medical server; the emergency center deploys the emergency server, and the technical problems of realizing real-time health monitoring on the patient through remote networking and timely processing emergency events are solved.
Description
Technical Field
The invention belongs to the technical field of big data, and particularly relates to a daily detection method based on big data.
Background
In the development of medical health industry and the construction of medical service systems, one of the most critical measures is to firstly strengthen the primary medical health service system and then develop the medium-grade medical service system as much as possible in order to improve the accessibility of medical health services and the efficiency of medical health investment to the maximum extent. Through long-term development, China has already established a medical health service system covering cities and countryside, which is composed of hospitals, public health institutions, basic medical health institutions and the like, but the problems of insufficient total medical health resources, low quality, unreasonable structure and layout, system fragmentation, overlarge scale of public hospitals and the like are still outstanding. How to meet the increasing medical health care requirements of the people on the premise of aging population, increasing chronic diseases and continuously rising medical cost, and solve the problems of difficult and expensive medical care, which is the problem that the development of the medical health care industry of China has to face at present.
Disclosure of Invention
The invention aims to provide a daily detection method based on big data, and solves the technical problems of realizing real-time health monitoring of patients and timely processing emergency events through remote networking.
In order to achieve the purpose, the invention adopts the following technical scheme:
a daily detection method based on big data comprises the following steps:
step 1: establishing a big data daily detection system which comprises a terminal device, a remote medical center and an emergency center, wherein the remote medical center is deployed with a remote medical server; the first-aid center deploys a first-aid server;
the terminal device is used for collecting human physiological data of a user; the terminal device is in data communication with the remote medical server and the first-aid server through a GPRS network;
step 2: the patient inputs the symptom expression of the patient into a terminal device, and the terminal device stores the complaint input by the patient in a TXT format;
a patient measures physiological data of a human body by using a terminal device;
step 3, the terminal device judges whether the GPRS module is connected with a remote medical center in a communication way: if yes, the terminal device uploads the acquired physiological data and the chief complaint to a remote medical center, the uploaded data is stored in a remote medical server of the remote medical center, and step 4 is executed;
if not, the terminal device stores the acquired physiological data and the chief complaint in a local storage module and executes the step 2;
and 4, step 4: the big data analysis of the data uploaded by the terminal device is carried out by the remote medical center, and the big data analysis method comprises the following steps:
step A: the remote medical server firstly intercepts key words of patient complaints to find out key words of symptoms and physical signs;
and B: the remote medical server searches the keywords of the symptoms and the physical signs, finds out all cases with the same symptoms and physical signs through a case database in the remote medical server, and stores the cases into a reference case set;
and 5: the remote medical server extracts the physiological data recorded in the case set and compares the physiological data with the physiological data uploaded by the terminal device: when the similarity reaches a preset degree, the remote medical server takes the reference case as a suspected disease, displays the suspected case, the human physiological data and the pathological description uploaded by the terminal device to a doctor of a remote medical center through a data table and a text format, and executes the step 6;
when the similarity does not reach the preset degree, the remote medical server marks the reference case, does not retrieve the reference case, and re-executes the step 4;
step 6: the remote medical server sends the suspected disease table to the patient reference through the terminal; meanwhile, the physiological data, the patient chief complaints, the reference disease case library, the suspected disease case library and the suspected disease table are sent to a doctor of a remote medical center;
and 7: a doctor in the remote medical center makes a preliminary diagnosis on the illness state of the patient according to the information sent by the remote medical server, and inputs a treatment suggestion to the remote medical server, and the remote medical server classifies, stores and processes the treatment suggestion;
and 8: the remote medical server sends the chronic disease treatment suggestion to the terminal device through the GPRS network, and the terminal device displays the chronic disease treatment suggestion to the patient in a text format;
the patient regularly utilizes the terminal device to measure the required human physiological data according to the doctor's advice; meanwhile, the patient inputs the pathological expression of the patient to the terminal device periodically, and the terminal device stores the pathological description input by the patient in a TXT format;
the terminal device sends the human physiological data and the pathological expression of the patient to a remote medical center regularly according to the method in the step 3;
and step 9: a doctor in the remote medical center checks daily human physiological data of a patient regularly and carries out remote double-diagnosis in an audio and video conference mode with the patient through a terminal device according to a checking result;
step 10: the doctor updates the medical advice according to the result of the re-diagnosis, including the physiological data to be monitored, the monitoring frequency and the doctor suggestion; the doctor orders are input into a remote medical server by the doctor for storage, and the remote medical server sends the doctor orders to a terminal device through a GPRS network;
step 11: the patient checks the latest medical advice and the recent physiological data change trend on the terminal device;
step 12: when the emergency health condition of a patient occurs, the family members of the patient send out emergency alarm through the terminal device, and the terminal device sends out alarm to an emergency server of an emergency center; meanwhile, the terminal device sends an emergency request to a remote medical server of a remote medical center, and the remote medical server sends the stored daily physiological data of the patient to the emergency server after receiving the emergency request;
step 13: the emergency server establishes an audio and video channel with the terminal device, and simultaneously establishes an audio and video channel between the emergency ambulance and the emergency server, medical personnel in an emergency center or on the emergency ambulance guide the family members of the patient to carry out rescue at the first time and human body physiological data measurement through audio and video communication with the patient and the family members of the patient before the emergency ambulance arrives at the site;
step 14: the doctor in the first aid center makes the rescue plan and the treatment plan according to the rescue condition of the patient, the real-time physiological characteristics of the human body and the usual physiological indexes.
The human physiological data comprises pulse, heart rate, blood pressure and electrocardiogram.
The terminal device comprises a communication DTU and medical equipment, wherein the communication DTU comprises a main controller, a Bluetooth module, a USB module, a key set, an LCD display screen, a FLASH memory, a GPRS module, a power amplifier chip, a loudspeaker and a microphone, the Bluetooth module, the USB module, the key set, the LCD display screen, the FLASH memory and the GPRS module are all electrically connected with the main controller, the loudspeaker is connected with the power amplifier chip, the power amplifier chip is connected with the GPRS module, and the microphone is connected with the GPRS module; the terminal device is communicated with the remote medical server and the first-aid server through the GPRS module.
The model of the main controller is STM32F103RCT 6; the model of the FLASH memory is W23Q 64; the type of the Bluetooth module is HCO5 Bluetooth module; the type of the USB module is CH 340G; the LCD display screen is a 4.3 inch capacitive touch screen; the GPRS module is M35 in model number; the model of the power amplifier chip is TDA 2003.
The medical equipment comprises a pulse wave portable blood pressure measuring instrument, the model of which is BP-37B, and the pulse wave portable blood pressure measuring instrument is communicated with the terminal device through Bluetooth.
The daily detection method based on big data solves the technical problems of realizing real-time health monitoring of patients and timely processing emergency events through remote networking, can monitor daily disease symptoms of the patients in real time, can enable the patients to actively upload complaints, enhances the interaction between the patients and doctors, and is convenient for the doctors to master the development trend of the disease symptoms of the patients.
Drawings
Fig. 1 is a hardware block diagram of a communication DTU of the present invention.
Detailed Description
The daily detection method based on big data as shown in fig. 1 comprises the following steps:
step 1: establishing a big data daily detection system which comprises a terminal device, a remote medical center and an emergency center, wherein the remote medical center is deployed with a remote medical server; the first-aid center deploys a first-aid server;
the terminal device is used for collecting human physiological data of a user; the terminal device is in data communication with the remote medical server and the first-aid server through a GPRS network;
the human physiological data comprises pulse, heart rate, blood pressure and electrocardiogram.
The terminal device comprises a communication DTU and medical equipment, wherein the communication DTU comprises a main controller, a Bluetooth module, a USB module, a key set, an LCD display screen, a FLASH memory, a GPRS module, a power amplifier chip, a loudspeaker and a microphone, the Bluetooth module, the USB module, the key set, the LCD display screen, the FLASH memory and the GPRS module are all electrically connected with the main controller, the loudspeaker is connected with the power amplifier chip, the power amplifier chip is connected with the GPRS module, and the microphone is connected with the GPRS module; the terminal device is communicated with the remote medical server and the first-aid server through the GPRS module.
The model of the main controller is STM32F103RCT 6; the model of the FLASH memory is W23Q 64; the type of the Bluetooth module is HCO5 Bluetooth module; the type of the USB module is CH 340G; the LCD display screen is a 4.3 inch capacitive touch screen; the GPRS module is M35 in model number; the model of the power amplifier chip is TDA 2003.
W23Q64 and STM32F103RCT6 are connected in a parallel port mode through IO ports, CH340G and HCO5 are respectively connected with two groups of IO ports of STM32F103RCT6, the connection of a 4.3-inch capacitive touch screen and STM32F103RCT6 is the prior art, so detailed description is omitted, TXD/RXD/RTS/CCTS/DTR/DCD/RING ends of M35 are respectively connected with one group of IO ports of STM32F103RCT6, MIC1P/MIC1N ends of M35 are connected with a microphone, SPK1P/SPK1N ends of M35 are connected with a power amplifier chip TDA2003, and TDA2003 is the prior art, so detailed description is omitted.
STM32F103RCT6, HCO5, W23Q64, CH340G, M35 all adopt 3.3V steady voltage chip power supply, and TDA2003 adopts 5V steady voltage chip power supply, and 3.3V steady voltage chip and 5V steady voltage chip are the prior art, so the detailed description is omitted.
The medical equipment comprises a pulse wave portable blood pressure measuring instrument, the model of which is BP-37B, and the pulse wave portable blood pressure measuring instrument is communicated with the terminal device through Bluetooth.
And the BP-37B adopts Bluetooth communication and communication DTU for data interaction.
When the terminal device collects human physiological data, the human physiological data are firstly stored in a FLASH memory as local storage data; the terminal device activates data link between the GPRS module and the remote medical server periodically, when the GPRS module is successfully linked with the remote medical server, the terminal device sends the local storage data to the remote medical server, and the remote medical server performs classification storage processing on the data sent by the terminal device.
Step 2: the patient inputs the symptom expression of the patient into a terminal device, and the terminal device stores the complaint input by the patient in a TXT format;
step 3, the terminal device judges whether the GPRS module is connected with a remote medical center in a communication way: if yes, the terminal device uploads the acquired physiological data and the chief complaint to a remote medical center, the uploaded data is stored in a remote medical server of the remote medical center, and step 4 is executed;
if not, the terminal device stores the acquired physiological data and the chief complaint in a local storage module and executes the step 2;
if not, the terminal device stores the acquired human physiological data and pathological description in a local storage module, and executes the step 2;
the big data analysis of the data uploaded by the terminal device is carried out by the remote medical center, and the big data analysis method comprises the following steps:
step A: the remote medical server firstly intercepts key words of patient complaints to find out key words of symptoms and physical signs;
and B: the remote medical server searches the keywords of the symptoms and the physical signs, finds out all cases with the same symptoms and physical signs through a case database in the remote medical server, and stores the cases into a reference case set;
and 5: the remote medical server extracts the physiological data recorded in the case set and compares the physiological data with the physiological data uploaded by the terminal device: when the similarity reaches a preset degree, the remote medical server takes the reference case as a suspected disease, displays the suspected case, the human physiological data and the pathological description uploaded by the terminal device to a doctor of a remote medical center through a data table and a text format, and executes the step 6;
when the similarity does not reach the preset degree, the remote medical server marks the reference case, does not retrieve the reference case, and re-executes the step 4;
step 6: the remote medical server sends the suspected disease table to the patient reference through the terminal; meanwhile, the physiological data, the patient chief complaints, the reference disease case library, the suspected disease case library and the suspected disease table are sent to a doctor of a remote medical center;
and 7: a doctor in the remote medical center makes a preliminary diagnosis on the illness state of the patient according to the information sent by the remote medical server, and inputs a treatment suggestion to the remote medical server, and the remote medical server classifies, stores and processes the treatment suggestion;
and 8: the remote medical server sends the chronic disease treatment suggestion to the terminal device through the GPRS network, and the terminal device displays the chronic disease treatment suggestion to the patient in a text format;
the patient regularly utilizes the terminal device to measure the required human physiological data according to the doctor's advice; meanwhile, the patient inputs the pathological expression of the patient to the terminal device periodically, and the terminal device stores the pathological description input by the patient in a TXT format;
the terminal device sends the human physiological data and the pathological expression of the patient to a remote medical center regularly according to the method in the step 3;
and step 9: a doctor in the remote medical center checks daily human physiological data of a patient regularly and carries out remote double-diagnosis in an audio and video conference mode with the patient through a terminal device according to a checking result;
step 10: the doctor updates the medical advice according to the result of the re-diagnosis, including the physiological data to be monitored, the monitoring frequency and the doctor suggestion; the doctor orders are input into a remote medical server by the doctor for storage, and the remote medical server sends the doctor orders to a terminal device through a GPRS network;
step 11: the patient checks the latest medical advice and the recent physiological data change trend on the terminal device;
step 12: when the emergency health condition of a patient occurs, the family members of the patient send out emergency alarm through the terminal device, and the terminal device sends out alarm to an emergency server of an emergency center; meanwhile, the terminal device sends an emergency request to a remote medical server of a remote medical center, and the remote medical server sends the stored daily physiological data of the patient to the emergency server after receiving the emergency request;
step 13: the emergency server establishes an audio and video channel with the terminal device, and simultaneously establishes an audio and video channel between the emergency ambulance and the emergency server, medical personnel in an emergency center or on the emergency ambulance guide the family members of the patient to carry out rescue at the first time and human body physiological data measurement through audio and video communication with the patient and the family members of the patient before the emergency ambulance arrives at the site;
step 14: the doctor in the first aid center makes the rescue plan and the treatment plan according to the rescue condition of the patient, the real-time physiological characteristics of the human body and the usual physiological indexes.
The daily detection method based on big data solves the technical problems of realizing real-time health monitoring of patients and timely processing emergency events through remote networking, can monitor daily disease symptoms of the patients in real time, can enable the patients to actively upload complaints, enhances the interaction between the patients and doctors, and is convenient for the doctors to master the development trend of the disease symptoms of the patients.
Claims (5)
1. A daily detection method based on big data is characterized in that: the method comprises the following steps:
step 1: establishing a big data daily detection system which comprises a terminal device, a remote medical center and an emergency center, wherein the remote medical center is deployed with a remote medical server; the first-aid center deploys a first-aid server;
the terminal device is used for collecting human physiological data of a user; the terminal device is in data communication with the remote medical server and the first-aid server through a GPRS network;
step 2: the patient inputs the symptom expression of the patient into a terminal device, and the terminal device stores the complaint input by the patient in a TXT format;
a patient measures physiological data of a human body by using a terminal device;
step 3, the terminal device judges whether the GPRS module is connected with a remote medical center in a communication way: if yes, the terminal device uploads the acquired physiological data and the chief complaint to a remote medical center, the uploaded data is stored in a remote medical server of the remote medical center, and step 4 is executed;
if not, the terminal device stores the acquired physiological data and the chief complaint in a local storage module and executes the step 2;
and 4, step 4: the big data analysis of the data uploaded by the terminal device is carried out by the remote medical center, and the big data analysis method comprises the following steps:
step A: the remote medical server firstly intercepts key words of patient complaints to find out key words of symptoms and physical signs;
and B: the remote medical server searches the keywords of the symptoms and the physical signs, finds out all cases with the same symptoms and physical signs through a case database in the remote medical server, and stores the cases into a reference case set;
and 5: the remote medical server extracts the physiological data recorded in the case set and compares the physiological data with the physiological data uploaded by the terminal device: when the similarity reaches a preset degree, the remote medical server takes the reference case as a suspected disease, displays the suspected case, the human physiological data and the pathological description uploaded by the terminal device to a doctor of a remote medical center through a data table and a text format, and executes the step 6;
when the similarity does not reach the preset degree, the remote medical server marks the reference case, does not retrieve the reference case, and re-executes the step 4;
step 6: the remote medical server sends the suspected disease table to the patient reference through the terminal; meanwhile, the physiological data, the patient chief complaints, the reference disease case library, the suspected disease case library and the suspected disease table are sent to a doctor of a remote medical center;
and 7: a doctor in the remote medical center makes a preliminary diagnosis on the illness state of the patient according to the information sent by the remote medical server, and inputs a treatment suggestion to the remote medical server, and the remote medical server classifies, stores and processes the treatment suggestion;
and 8: the remote medical server sends the chronic disease treatment suggestion to the terminal device through the GPRS network, and the terminal device displays the chronic disease treatment suggestion to the patient in a text format;
the patient regularly utilizes the terminal device to measure the required human physiological data according to the doctor's advice; meanwhile, the patient inputs the pathological expression of the patient to the terminal device periodically, and the terminal device stores the pathological description input by the patient in a TXT format;
the terminal device sends the human physiological data and the pathological expression of the patient to a remote medical center regularly according to the method in the step 3;
and step 9: a doctor in the remote medical center checks daily human physiological data of a patient regularly and carries out remote double-diagnosis in an audio and video conference mode with the patient through a terminal device according to a checking result;
step 10: the doctor updates the medical advice according to the result of the re-diagnosis, including the physiological data to be monitored, the monitoring frequency and the doctor suggestion; the doctor orders are input into a remote medical server by the doctor for storage, and the remote medical server sends the doctor orders to a terminal device through a GPRS network;
step 11: the patient checks the latest medical advice and the recent physiological data change trend on the terminal device;
step 12: when the emergency health condition of a patient occurs, the family members of the patient send out emergency alarm through the terminal device, and the terminal device sends out alarm to an emergency server of an emergency center; meanwhile, the terminal device sends an emergency request to a remote medical server of a remote medical center, and the remote medical server sends the stored daily physiological data of the patient to the emergency server after receiving the emergency request;
step 13: the emergency server establishes an audio and video channel with the terminal device, and simultaneously establishes an audio and video channel between the emergency ambulance and the emergency server, medical personnel in an emergency center or on the emergency ambulance guide the family members of the patient to carry out rescue at the first time and human body physiological data measurement through audio and video communication with the patient and the family members of the patient before the emergency ambulance arrives at the site;
step 14: the doctor in the first aid center makes the rescue plan and the treatment plan according to the rescue condition of the patient, the real-time physiological characteristics of the human body and the usual physiological indexes.
2. The big-data-based daily detection method as claimed in claim 1, wherein: the human physiological data comprises pulse, heart rate, blood pressure and electrocardiogram.
3. The big-data-based daily detection method as claimed in claim 1, wherein: the terminal device comprises a communication DTU and medical equipment, wherein the communication DTU comprises a main controller, a Bluetooth module, a USB module, a key set, an LCD display screen, a FLASH memory, a GPRS module, a power amplifier chip, a loudspeaker and a microphone, the Bluetooth module, the USB module, the key set, the LCD display screen, the FLASH memory and the GPRS module are all electrically connected with the main controller, the loudspeaker is connected with the power amplifier chip, the power amplifier chip is connected with the GPRS module, and the microphone is connected with the GPRS module; the terminal device is communicated with the remote medical server and the first-aid server through the GPRS module.
4. The big-data-based daily detection method as claimed in claim 3, wherein: the model of the main controller is STM32F103RCT 6; the model of the FLASH memory is W23Q 64; the type of the Bluetooth module is HCO5 Bluetooth module; the type of the USB module is CH 340G; the LCD display screen is a 4.3 inch capacitive touch screen; the GPRS module is M35 in model number; the model of the power amplifier chip is TDA 2003.
5. The big-data-based daily detection method as claimed in claim 3, wherein: the medical equipment comprises a pulse wave portable blood pressure measuring instrument, the model of which is BP-37B, and the pulse wave portable blood pressure measuring instrument is communicated with the terminal device through Bluetooth.
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