CN117577334B - Health monitoring system based on medical instrument equipment - Google Patents
Health monitoring system based on medical instrument equipment Download PDFInfo
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Abstract
The invention discloses a health monitoring system based on medical equipment, which relates to the technical field of medical equipment and comprises the following components: a medical instrument device identification module that identifies a type of medical instrument device that is accessed to the health monitoring system; monitoring an access module; the monitoring index acquisition module is used for identifying indexes corresponding to the data according to the identification sequence; the index grading module grades the monitored index according to the importance degree; an index standard evaluation module; a monitoring data set processing module; the monitoring data set analysis module is used for judging whether the evaluation data set is abnormal or not; and an early warning module. The method comprises the steps of setting a monitoring index acquisition module and a monitoring data set analysis module, intelligently identifying indexes corresponding to each data in a data set, and judging the health condition deterioration or improvement of a patient through trend analysis of past data of the patient.
Description
Technical Field
The invention relates to the technical field of medical equipment, in particular to a health monitoring system based on medical equipment.
Background
Medical devices refer to instruments, devices, appliances, materials or other items used in the human body, either alone or in combination, and also include the software required. During use, the following intended purposes are intended to be achieved: prevention, diagnosis, treatment, monitoring and alleviation of diseases.
The medical device classification method has three main categories, namely diagnosis device category, treatment device category and auxiliary device category. While diagnostic equipment for health monitoring can be divided into eight categories: x-ray diagnosis apparatus, ultrasonic diagnosis apparatus, functional examination apparatus, endoscopy apparatus, nuclear medicine apparatus, experimental diagnosis apparatus, and pathological diagnosis apparatus.
The data transmitted by the medical equipment of different types are different, and the data indexes transmitted by the medical equipment of the same type are different in sequence due to the difference of models, but the indexes corresponding to the data cannot be identified by the existing monitoring method, so that the judgment of data abnormality is puzzled, and in addition, the judgment of the health abnormality is single in the prior art, so that the health condition of a patient cannot be known deeply.
Disclosure of Invention
In order to solve the technical problems, the technical scheme provides a health monitoring system based on medical equipment, which solves the problems that the data transmitted by different medical equipment in the background technology are different, the data indexes transmitted by the same type of medical equipment are different in sequence due to the difference of models, but the indexes corresponding to the data cannot be identified by the existing monitoring method, so that the judgment of abnormal data is puzzled, and in addition, the judgment of abnormal health condition is single and the health condition of a patient cannot be known deeply in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme:
A medical instrument device-based health monitoring system, comprising:
a medical instrument device identification module that identifies a type of medical instrument device that is accessed to the health monitoring system;
The monitoring access module is used for docking the medical equipment with the health monitoring system;
the monitoring index acquisition module is used for pre-identifying the test data set transmitted by the medical equipment to obtain the identification sequence of the data set transmitted by the medical equipment;
The monitoring index acquisition module pre-identifies the test data set transmitted by the medical equipment, and the identification sequence of the data set transmitted by the medical equipment is obtained, and comprises the following steps:
the monitoring index acquisition module acquires at least one index of the medical instrument equipment according to the identified type of the medical instrument equipment;
Acquiring the value range of each index;
acquiring at least one test data set transmitted by medical equipment, wherein the number of data of the test data set is equal to the index number of the medical equipment, and the number of data of the test data set is n;
acquiring the ith data of each test data group, and acquiring a first index, wherein the ith data of each test data group is in the value range of the first index;
Identifying the ith data in the test data set transmitted by the medical equipment as data of a first index;
when i is taken from 1 to n, obtaining an index corresponding to the data of each bit in the test data set;
Replacing the data of each bit in the test data set with a corresponding index to obtain the identification sequence of the data set transmitted by the medical equipment;
The monitoring index acquisition module acquires a data set transmitted by medical equipment, and identifies indexes corresponding to the data according to an identification sequence;
The index corresponding to the identification data comprises the following steps according to the identification sequence:
Acquiring an identification sequence of a data set transmitted by medical equipment, and correspondingly identifying the data of the data set with indexes of the same rank in the identification sequence;
The index grading module grades the monitored indexes according to the importance degree to obtain key monitoring indexes and non-key monitoring indexes;
the index standard evaluation module is used for making an abnormal judgment standard of data corresponding to the index;
The index standard evaluation module establishes an abnormality judgment standard of data corresponding to the index and comprises the following steps:
The monitoring index acquisition module acquires at least one datum of the healthy crowd and acquires an index corresponding to the datum;
summarizing the reference data corresponding to the same index to obtain a reference data set;
Searching a reference data maximum value and a reference data minimum value in the reference data set as a normal value range of the index;
judging that the data is abnormal when the data is not in the normal value range of the index, otherwise, judging that the data is normal;
when the data is abnormal, the index standard evaluation module judges whether the data is larger than the maximum value of the reference data, if so, the difference value between the data and the maximum value of the reference data is divided by the maximum value of the reference data to obtain an abnormal duty ratio;
If not, dividing the value of the difference between the minimum value of the reference data and the data by the minimum value of the reference data to obtain an abnormal duty ratio;
Acquiring a value range of an abnormal duty ratio according to the existing data, and equally dividing the value range of the abnormal duty ratio into at least one abnormal degree range;
According to the value of the middle value of the abnormal degree range, the abnormal degree level is distributed, and the greater the value of the middle point of the abnormal degree range is, the higher the abnormal degree level is;
the monitoring data set processing module is used for carrying out mean value processing on the existing monitoring data set of the patient to obtain an evaluation data set of the patient, and updating the evaluation data set of the patient every day;
The monitoring data set analysis module is used for judging whether the evaluation data set is abnormal or not, if not, performing no processing, and if so, judging the degree of abnormality of the evaluation data set;
The judging and evaluating the degree of abnormality of the data set includes the steps of:
Calculating an abnormal duty ratio of each evaluation data in the evaluation data set, and selecting the abnormal duty ratio with the largest numerical value as a pre-abnormal duty ratio;
judging the degree of abnormality of the evaluation data set according to the level of abnormality degree of the pre-abnormality duty ratio;
Analyzing the updated evaluation data set of the patient every day to judge the deterioration or improvement of the health condition of the patient;
The daily analysis of the updated patient assessment data set to determine patient health deterioration or improvement comprises the steps of:
Acquiring an abnormal duty ratio of the evaluation data with the abnormality in the evaluation data set, averaging the abnormal duty ratio of the evaluation data corresponding to the key monitoring index to obtain an average abnormal duty ratio;
drawing an average abnormal proportion trend image according to the average abnormal proportion of each day, judging the trend of the average abnormal proportion according to the trend in the image, if the trend is upward, deteriorating the health condition of the patient, if the trend is downward, improving the health condition of the patient, and if the trend is uniform, keeping the health condition of the patient unchanged;
the early warning module sends out early warning when the health condition of the patient is worsened, and the early warning module sends out early warning when the estimated data are abnormal.
Preferably, the index grading module grades the monitored index according to the importance degree, including the following steps:
according to the neural network model, the influence degree of the index on health is obtained, the influence factor of the index on health is constructed, and the larger the influence factor is, the larger the importance degree is;
sequencing the indexes according to the influence factors of the indexes on health;
The indexes of the preset duty ratio are arranged in front and used as key monitoring indexes, and the rest indexes are used as non-key monitoring indexes.
Preferably, the monitoring data set processing module performs mean value processing on an existing monitoring data set of the patient to obtain an evaluation data set of the patient, and the method includes the following steps:
The monitoring data set processing module acquires a daily monitoring data set of the patient from the time of the monitoring day;
And superposing and averaging the data of the corresponding positions in the monitoring data set, and summarizing to obtain an evaluation data set of the patient.
Preferably, the monitoring data analysis module judges whether the evaluation data set has an abnormality or not, including the steps of:
Judging whether the evaluation data in the evaluation data set are in the normal value range of the corresponding index or not, if not, judging that the evaluation data are abnormal, otherwise, judging that the evaluation data are normal;
If the evaluation data in the evaluation data set are all normal, the evaluation data set is normal, and if not, the evaluation data set is abnormal.
Compared with the prior art, the invention has the beneficial effects that:
Through setting up medical instrument equipment identification module, monitoring index acquisition module, index classification module and monitoring data set analysis module, can obtain the index set that corresponds with medical instrument equipment according to the type difference of medical instrument equipment, and according to the data set of medical instrument equipment transmission, the index that every data corresponds in the intelligent recognition data set, and then can judge whether data have the abnormality according to the normal scope of index, and, still through classifying the index, judge the degree of abnormality of data, and then finer understanding patient's health condition, and through analyzing the trend of patient's past data, judge that patient's health condition worsens or improves, thereby can monitor the health condition comprehensively.
Drawings
FIG. 1 is a schematic diagram of a medical device based health monitoring system of the present invention;
FIG. 2 is a schematic flow chart of a recognition sequence of a test data set transmitted by a medical device obtained by pre-recognizing the test data set transmitted by the medical device by the monitoring index acquisition module;
FIG. 3 is a schematic diagram of a process flow of classifying monitored indicators according to importance by the indicator classification module of the present invention;
FIG. 4 is a flow chart of an abnormality judgment criterion of data corresponding to an index formulated by the index criterion evaluation module of the present invention;
FIG. 5 is a schematic flow chart of the process of the monitoring data set processing module of the present invention for performing mean value processing on the existing monitoring data set of the patient to obtain the evaluation data set of the patient;
FIG. 6 is a schematic diagram of a process for determining whether an abnormal flow exists in an evaluation data set by the monitoring data set analysis module according to the present invention;
FIG. 7 is a flow chart of determining the degree of abnormality of the evaluation data set according to the present invention;
FIG. 8 is a schematic flow chart of daily analysis of updated patient assessment data set to determine patient health deterioration or improvement according to the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
Referring to fig. 1, a health monitoring system based on medical equipment, comprising:
a medical instrument device identification module that identifies a type of medical instrument device that is accessed to the health monitoring system;
The monitoring access module is used for docking the medical equipment with the health monitoring system;
The monitoring index acquisition module is used for pre-identifying the test data set transmitted by the medical equipment to obtain the identification sequence of the data set transmitted by the medical equipment, and acquiring the data set transmitted by the medical equipment according to the identification sequence to identify the index corresponding to the data;
The index grading module grades the monitored indexes according to the importance degree to obtain key monitoring indexes and non-key monitoring indexes;
the index standard evaluation module is used for making an abnormal judgment standard of data corresponding to the index;
the monitoring data set processing module is used for carrying out mean value processing on the existing monitoring data set of the patient to obtain an evaluation data set of the patient, and updating the evaluation data set of the patient every day;
The monitoring data set analysis module is used for judging whether the evaluation data set is abnormal or not, if not, the monitoring data set analysis module does not perform any treatment, if so, the abnormality degree of the evaluation data set is judged, the updated evaluation data set of the patient is analyzed every day, and the health condition of the patient is judged to be worsened or improved;
the early warning module sends out early warning when the health condition of the patient is worsened, and the early warning module sends out early warning when the estimated data are abnormal.
The working process of the health monitoring system based on the medical equipment is as follows:
step one: the monitoring access module is used for docking the medical equipment with the health monitoring system;
Step two: the medical instrument equipment identification module identifies the type of medical instrument equipment connected to the health monitoring system;
Step three: the monitoring index acquisition module pre-identifies the test data set transmitted by the medical equipment to obtain an identification sequence of the data set transmitted by the medical equipment, and acquires the data set transmitted by the medical equipment to identify indexes corresponding to the data according to the identification sequence;
Step four: the index grading module grades the monitored indexes according to the importance degree to obtain key monitoring indexes and non-key monitoring indexes;
step five: the index standard evaluation module formulates an abnormality judgment standard of data corresponding to the index;
step six: the monitoring data set processing module carries out mean value processing on the existing monitoring data set of the patient to obtain an evaluation data set of the patient, and updates the evaluation data set of the patient every day;
Step seven: the monitoring data set analysis module judges whether the evaluation data set has abnormality according to the abnormality judgment standard, if not, the early warning module sends out early warning if not, the monitoring data set analysis module judges the abnormality degree of the evaluation data set according to the abnormality judgment standard, the updated evaluation data set of the patient is analyzed every day to judge the health condition deterioration or improvement of the patient, and the early warning module sends out early warning when the health condition of the patient is deteriorated.
Referring to fig. 2, the monitoring index obtaining module pre-identifies the test data set transmitted by the medical equipment, and obtains the identification sequence of the data set transmitted by the medical equipment, which includes the following steps:
the monitoring index acquisition module acquires at least one index of the medical instrument equipment according to the identified type of the medical instrument equipment;
Acquiring the value range of each index;
acquiring at least one test data set transmitted by medical equipment, wherein the number of data of the test data set is equal to the index number of the medical equipment, and the number of data of the test data set is n;
acquiring the ith data of each test data group, and acquiring a first index, wherein the ith data of each test data group is in the value range of the first index;
Identifying the ith data in the test data set transmitted by the medical equipment as data of a first index;
when i is taken from 1 to n, obtaining an index corresponding to the data of each bit in the test data set;
Replacing the data of each bit in the test data set with a corresponding index to obtain the identification sequence of the data set transmitted by the medical equipment;
The data in the data sets transmitted by the medical equipment of the same type and different types corresponds to one index, but the sequence of the transmitted data is different, so that index identification cannot be carried out on the data according to the same sequence, therefore, pre-identification is needed, the sequence of the indexes corresponding to the data transmitted by the medical equipment connected to the monitoring system is obtained, the indexes corresponding to the data are identified according to the sequence of the indexes, and whether the data are abnormal or not is judged according to the normal range of the indexes;
the pre-identification method is characterized in that because the value ranges of the data corresponding to different indexes are different, whether one data corresponds to a certain index is judged, only the data with the same rank in the data set is required to be obtained, whether the data with the same rank is within the value range of the index is judged, if so, the index corresponds to the data, and the data with the same rank in the data set transmitted by the subsequent medical equipment corresponds to the index.
According to the identification sequence, the index corresponding to the identification data comprises the following steps:
Acquiring an identification sequence of a data set transmitted by medical equipment, and correspondingly identifying the data of the data set with indexes of the same rank in the identification sequence;
Because the sequence of the indexes corresponding to each data in the data group is determined by the identification sequence, the indexes corresponding to the data of the data group can be acquired according to the identification sequence, and whether the data is abnormal or not can be judged according to the normal value range of the indexes.
Referring to fig. 3, the index ranking module ranks the monitored index according to importance, including the steps of:
according to the neural network model, the influence degree of the index on health is obtained, the influence factor of the index on health is constructed, and the larger the influence factor is, the larger the importance degree is;
sequencing the indexes according to the influence factors of the indexes on health;
the indexes of the preset duty ratio are arranged in front and used as key monitoring indexes, and the rest indexes are used as non-key monitoring indexes;
the preset duty ratio is a percentage, for example, 10%, and the index of the previous preset duty ratio is the index of the previous 10%;
The importance degree grading is mainly used as a basis for judging whether the health condition of a patient is worsened, and because the abnormal indexes of the patient are possibly too many, but the influence on judging whether the patient is worsened is weaker, and the change condition of the abnormal indexes is negligible, so that the change condition of the key monitoring indexes with large influence factors is used as the basis for judging, the indexes participating in judgment can be reduced, and the running speed of the whole system is faster.
Referring to fig. 4, the index criterion evaluation module formulates an abnormality judgment criterion of data corresponding to an index, including the steps of:
The monitoring index acquisition module acquires at least one datum of the healthy crowd and acquires an index corresponding to the datum;
summarizing the reference data corresponding to the same index to obtain a reference data set;
Searching a reference data maximum value and a reference data minimum value in the reference data set as a normal value range of the index;
judging that the data is abnormal when the data is not in the normal value range of the index, otherwise, judging that the data is normal;
when the data is abnormal, the index standard evaluation module judges whether the data is larger than the maximum value of the reference data, if so, the difference value between the data and the maximum value of the reference data is divided by the maximum value of the reference data to obtain an abnormal duty ratio;
If not, dividing the value of the difference between the minimum value of the reference data and the data by the minimum value of the reference data to obtain an abnormal duty ratio;
Acquiring a value range of an abnormal duty ratio according to the existing data, and equally dividing the value range of the abnormal duty ratio into at least one abnormal degree range;
According to the value of the middle value of the abnormal degree range, the abnormal degree level is distributed, and the greater the value of the middle point of the abnormal degree range is, the higher the abnormal degree level is;
the middle value of the abnormality degree range is the numerical value of the middle point of the abnormality degree range, and the abnormality degree ranges are not intersected with each other, so that the corresponding abnormality degree level with larger middle value of the abnormality degree range is higher;
the abnormality judgment criteria include not only criteria for judging whether or not an abnormality is present, but also criteria for judging the degree of abnormality.
Referring to fig. 5, the monitoring data set processing module performs mean processing on an existing monitoring data set of a patient to obtain an evaluation data set of the patient, which includes the following steps:
The monitoring data set processing module acquires a daily monitoring data set of the patient from the time of the monitoring day;
Superposing and averaging the data of the corresponding positions in the monitoring data set, and summarizing to obtain an evaluation data set of the patient;
the monitoring data set processing module uses the average value of the patient data as an evaluation data set, so that judgment errors caused by single data fluctuation are avoided.
Referring to fig. 6, the monitoring data set analysis module determines whether an abnormality exists in the evaluation data set, including the steps of:
Judging whether the evaluation data in the evaluation data set are in the normal value range of the corresponding index or not, if not, judging that the evaluation data are abnormal, otherwise, judging that the evaluation data are normal;
If the evaluation data in the evaluation data set are all normal, the evaluation data set is normal, and if not, the evaluation data set is abnormal.
Referring to fig. 7, judging the degree of abnormality of the evaluation data group includes the steps of:
Calculating an abnormal duty ratio of each evaluation data in the evaluation data set, and selecting the abnormal duty ratio with the largest numerical value as a pre-abnormal duty ratio;
judging the degree of abnormality of the evaluation data set according to the level of abnormality degree of the pre-abnormality duty ratio;
and carrying out targeted health recovery on the patient according to the degree of abnormality of the evaluation data set.
Referring to fig. 8, the daily analysis of the updated patient's assessment data set to determine patient health deterioration or improvement includes the steps of:
Acquiring an abnormal duty ratio of the evaluation data with the abnormality in the evaluation data set, averaging the abnormal duty ratio of the evaluation data corresponding to the key monitoring index to obtain an average abnormal duty ratio;
drawing an average abnormal proportion trend image according to the average abnormal proportion of each day, judging the trend of the average abnormal proportion according to the trend in the image, if the trend is upward, deteriorating the health condition of the patient, if the trend is downward, improving the health condition of the patient, and if the trend is uniform, keeping the health condition of the patient unchanged;
the health trend of the patient is visually represented by using a chart, and the treatment dosage is determined to be increased or decreased according to the health trend.
Still further, the present disclosure provides a storage medium having a computer readable program stored thereon, the computer readable program when invoked running the health monitoring system based on medical equipment as described above.
It is understood that the storage medium may be a magnetic medium, e.g., floppy disk, hard disk, magnetic tape; optical media such as DVD; or a semiconductor medium such as a solid state disk SolidStateDisk, SSD, etc.
In summary, the invention has the advantages that: through setting up medical instrument equipment identification module, monitoring index acquisition module, index classification module and monitoring data set analysis module, can obtain the index set that corresponds with medical instrument equipment according to the type difference of medical instrument equipment, and according to the data set of medical instrument equipment transmission, the index that every data corresponds in the intelligent recognition data set, and then can judge whether data have the abnormality according to the normal scope of index, and, still through classifying the index, judge the degree of abnormality of data, and then finer understanding patient's health condition, and through analyzing the trend of patient's past data, judge that patient's health condition worsens or improves, thereby can monitor the health condition comprehensively.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A medical device-based health monitoring system, comprising:
a medical instrument device identification module that identifies a type of medical instrument device that is accessed to the health monitoring system;
The monitoring access module is used for docking the medical equipment with the health monitoring system;
the monitoring index acquisition module is used for pre-identifying the test data set transmitted by the medical equipment to obtain the identification sequence of the data set transmitted by the medical equipment;
The monitoring index acquisition module pre-identifies the test data set transmitted by the medical equipment, and the identification sequence of the data set transmitted by the medical equipment is obtained, and comprises the following steps:
the monitoring index acquisition module acquires at least one index of the medical instrument equipment according to the identified type of the medical instrument equipment;
Acquiring the value range of each index;
acquiring at least one test data set transmitted by medical equipment, wherein the number of data of the test data set is equal to the index number of the medical equipment, and the number of data of the test data set is n;
acquiring the ith data of each test data group, and acquiring a first index, wherein the ith data of each test data group is in the value range of the first index;
Identifying the ith data in the test data set transmitted by the medical equipment as data of a first index;
when i is taken from 1 to n, obtaining an index corresponding to the data of each bit in the test data set;
Replacing the data of each bit in the test data set with a corresponding index to obtain the identification sequence of the data set transmitted by the medical equipment;
The monitoring index acquisition module acquires a data set transmitted by medical equipment, and identifies indexes corresponding to the data according to an identification sequence;
The index corresponding to the identification data comprises the following steps according to the identification sequence:
Acquiring an identification sequence of a data set transmitted by medical equipment, and correspondingly identifying the data of the data set with indexes of the same rank in the identification sequence;
The index grading module grades the monitored indexes according to the importance degree to obtain key monitoring indexes and non-key monitoring indexes;
the index standard evaluation module is used for making an abnormal judgment standard of data corresponding to the index;
The index standard evaluation module establishes an abnormality judgment standard of data corresponding to the index and comprises the following steps:
The monitoring index acquisition module acquires at least one datum of the healthy crowd and acquires an index corresponding to the datum;
summarizing the reference data corresponding to the same index to obtain a reference data set;
Searching a reference data maximum value and a reference data minimum value in the reference data set as a normal value range of the index;
judging that the data is abnormal when the data is not in the normal value range of the index, otherwise, judging that the data is normal;
when the data is abnormal, the index standard evaluation module judges whether the data is larger than the maximum value of the reference data, if so, the difference value between the data and the maximum value of the reference data is divided by the maximum value of the reference data to obtain an abnormal duty ratio;
If not, dividing the value of the difference between the minimum value of the reference data and the data by the minimum value of the reference data to obtain an abnormal duty ratio;
Acquiring a value range of an abnormal duty ratio according to the existing data, and equally dividing the value range of the abnormal duty ratio into at least one abnormal degree range;
According to the value of the middle value of the abnormal degree range, the abnormal degree level is distributed, and the greater the value of the middle point of the abnormal degree range is, the higher the abnormal degree level is;
the monitoring data set processing module is used for carrying out mean value processing on the existing monitoring data set of the patient to obtain an evaluation data set of the patient, and updating the evaluation data set of the patient every day;
The monitoring data set analysis module is used for judging whether the evaluation data set is abnormal or not, if not, performing no processing, and if so, judging the degree of abnormality of the evaluation data set;
The judging and evaluating the degree of abnormality of the data set includes the steps of:
Calculating an abnormal duty ratio of each evaluation data in the evaluation data set, and selecting the abnormal duty ratio with the largest numerical value as a pre-abnormal duty ratio;
judging the degree of abnormality of the evaluation data set according to the level of abnormality degree of the pre-abnormality duty ratio;
Analyzing the updated evaluation data set of the patient every day to judge the deterioration or improvement of the health condition of the patient;
The daily analysis of the updated patient assessment data set to determine patient health deterioration or improvement comprises the steps of:
Acquiring an abnormal duty ratio of the evaluation data with the abnormality in the evaluation data set, averaging the abnormal duty ratio of the evaluation data corresponding to the key monitoring index to obtain an average abnormal duty ratio;
drawing an average abnormal proportion trend image according to the average abnormal proportion of each day, judging the trend of the average abnormal proportion according to the trend in the image, if the trend is upward, deteriorating the health condition of the patient, if the trend is downward, improving the health condition of the patient, and if the trend is uniform, keeping the health condition of the patient unchanged;
the early warning module sends out early warning when the health condition of the patient is worsened, and the early warning module sends out early warning when the estimated data are abnormal.
2. The medical device-based health monitoring system of claim 1, wherein the index ranking module ranks the monitored indices according to importance level comprising the steps of:
according to the neural network model, the influence degree of the index on health is obtained, the influence factor of the index on health is constructed, and the larger the influence factor is, the larger the importance degree is;
sequencing the indexes according to the influence factors of the indexes on health;
The indexes of the preset duty ratio are arranged in front and used as key monitoring indexes, and the rest indexes are used as non-key monitoring indexes.
3. The medical device-based health monitoring system of claim 2, wherein the monitoring data set processing module performs a mean value processing on an existing monitoring data set of the patient to obtain an evaluation data set of the patient, and the method comprises the following steps:
The monitoring data set processing module acquires a daily monitoring data set of the patient from the time of the monitoring day;
And superposing and averaging the data of the corresponding positions in the monitoring data set, and summarizing to obtain an evaluation data set of the patient.
4. A health monitoring system based on medical equipment according to claim 3, wherein the monitoring data analysis module determines whether there is an abnormality in the evaluation data set comprises the steps of:
Judging whether the evaluation data in the evaluation data set are in the normal value range of the corresponding index or not, if not, judging that the evaluation data are abnormal, otherwise, judging that the evaluation data are normal;
If the evaluation data in the evaluation data set are all normal, the evaluation data set is normal, and if not, the evaluation data set is abnormal.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102637209A (en) * | 2012-03-28 | 2012-08-15 | 深圳市理邦精密仪器股份有限公司 | Data classifying and processing method and system of medical equipment |
| CN107341357A (en) * | 2017-07-17 | 2017-11-10 | 成都嘉逸科技有限公司 | A kind of data collecting system for medical monitoring equipment |
| CN113627754A (en) * | 2021-07-27 | 2021-11-09 | 北京达佳互联信息技术有限公司 | Operation control method and device for index detection, electronic equipment and storage medium |
| CN116110577A (en) * | 2022-11-16 | 2023-05-12 | 荣科科技股份有限公司 | Health monitoring analysis method and system based on big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102637209A (en) * | 2012-03-28 | 2012-08-15 | 深圳市理邦精密仪器股份有限公司 | Data classifying and processing method and system of medical equipment |
| CN107341357A (en) * | 2017-07-17 | 2017-11-10 | 成都嘉逸科技有限公司 | A kind of data collecting system for medical monitoring equipment |
| CN113627754A (en) * | 2021-07-27 | 2021-11-09 | 北京达佳互联信息技术有限公司 | Operation control method and device for index detection, electronic equipment and storage medium |
| CN116110577A (en) * | 2022-11-16 | 2023-05-12 | 荣科科技股份有限公司 | Health monitoring analysis method and system based on big data |
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