CN116153519B - Medical risk identification processing system based on big data technology - Google Patents
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Abstract
The invention relates to the technical field of health monitoring, in particular to a medical risk identification processing system based on big data technology, which comprises a data acquisition module, a data processing module, a network communication module and a user client, wherein the data acquisition module is used for acquiring medical detection data, the data processing module is used for carrying out arrangement analysis on the data acquired by the data acquisition module and grading the acquired data information according to analysis results, and the network communication module is used for being connected with the user client through a wireless network and sending information to the user client periodically. According to the invention, the basic ecological signs of the user are obtained by monitoring the basic data information of the user, the bad data of the user are obtained by monitoring the supervision information of the user, the integral data score of the user is calculated, the data information of the user signs is graded according to the score, the physical condition of the user is well comprehensively considered, different reminding time periods are set for the data information of different grades, the user is specifically prompted to carry out health detection, and the timeliness of the health detection is increased.
Description
Technical Field
The invention relates to the technical field of health monitoring, in particular to a medical risk identification processing system based on big data technology.
Background
With the development of society, people's importance level of health is increasing year by year, and health monitoring is becoming a topic of concern.
Chinese patent publication No.: CN114038578A discloses a 5G-based medical supervision system, which relates to the technical field of medical services, is applied to a medical supervision platform, and comprises a medical record analysis module, a physical sign analysis module and an associated early warning module; the medical record analysis module is used for receiving and analyzing the electronic medical record information of the user and calculating to obtain a diagnosis and treatment coefficient ZL; if ZL is greater than the diagnosis and treatment coefficient threshold value, generating a diagnosis and treatment signal; after receiving the diagnosis and treatment signals, the medical supervision platform generates physical sign analysis signals; the sign analysis module is used for acquiring sign parameter information of the user according to a preset interval and analyzing the sign parameter information to evaluate the physical state of the user in response to receiving the sign analysis signal; the association early warning module is used for transmitting the sign abnormal signals and sign parameter information of the user to the mobile phone terminals of the corresponding family members for early warning.
In the existing monitoring system, key monitoring is often carried out on abnormal signals, sub-health states of monitored objects cannot be timely discovered, and hysteresis is provided for health monitoring.
Disclosure of Invention
Therefore, the invention provides a medical risk identification processing system based on a big data technology, which is used for solving the problems that in the existing monitoring system in the prior art, key monitoring is often carried out aiming at abnormal signals, sub-health states of monitored objects cannot be timely discovered, and hysteresis is caused to health monitoring.
To achieve the above object, the present invention provides a medical risk identification processing system based on big data technology, comprising,
the data acquisition module is used for acquiring medical detection data, wherein the medical detection data comprises basic data information and supervision information, the basic data information is divided into a plurality of types, and corresponding basic information scores are acquired for each basic data information;
the data processing module is connected with the data acquisition module and is used for sorting and analyzing the data acquired by the data acquisition module and grading the acquired data information according to an analysis result, a plurality of basic information parameter matrixes are arranged in the data processing module, each basic information parameter matrix comprises a plurality of basic information parameters, a plurality of basic information scores are determined according to comparison of one basic information and the corresponding plurality of basic information parameters, basic information data scores are integrally generated, the data processing module analyzes the supervision information and calculates the supervision information score of a patient in combination with the basic information data scores, and the patient condition is graded according to the supervision information score;
the network communication module is connected with the data processing module and is connected with the user client through a wireless network, and can periodically send information to the user client, and the time intervals for sending information for different grades of data information are different;
the user client is connected with the data processing module through the network communication module and is used for periodically receiving information sent by the data processing module.
Further, the data acquisition module acquires basic data information of a patient, for any patient basic data information including first basic information a, second basic information B and third basic information C, the data processing module analyzes the acquired first basic information a to acquire a first basic information score a, analyzes the second basic information B to acquire a second basic information score B, analyzes the third basic information C to acquire a third basic information score C, and calculates a patient basic information data score f1, f1=a+b+c.
Further, a first basic information parameter matrix A0, a first basic information scoring parameter matrix A0, a second basic information parameter matrix B0, a second basic information scoring parameter matrix B0, a third basic information parameter matrix C0 and a third basic information scoring parameter matrix C0 are arranged in the data processing module,
a0 = { A1, A2, A3}, A1 is a first basic information first parameter, A2 is a first basic information second parameter, A3 is a first basic information third parameter;
a0 = { a1, a2, a3, a4}, a1 is a first basic information first scoring parameter, a2 is a first basic information second scoring parameter, a3 is a first basic information third scoring parameter, a4 is a first basic information fourth scoring parameter;
b0 = { B1, B2}, B1 being the second basic information first parameter, B2 being the second basic information second parameter;
b0 = { b1, b2}, b1 is the second basic information first scoring parameter, b2 is the second basic information second scoring parameter;
c0 = { C1, C2, C3}, C1 being the third basic information first parameter, C2 being the third basic information second parameter, C3 being the third basic information third parameter;
c0 = { c1, c2, c3, c4}, c1 is a third basic information first scoring parameter, c2 is a third basic information second scoring parameter, c3 is a third basic information third scoring parameter, c4 is a third basic information fourth scoring parameter;
if A is less than or equal to A1, the data processing module selects a first scoring parameter A1 of the first basic information as a first basic information score;
if A1 is more than A and less than or equal to A2, the data processing module selects a first basic information second scoring parameter A2 as a first basic information score;
if A2 is more than A and less than or equal to A3, the data processing module selects a third grading parameter A3 of the first basic information as a first basic information grading;
if A is more than A3, the data processing module selects a fourth grading parameter a4 of the first basic information as a first basic information grading;
if b=b1, the data processing module selects the first scoring parameter B1 of the second basic information as the second basic information score;
if b=b2, the data processing module selects a second scoring parameter B2 of the second basic information as a second basic information score;
if C is less than or equal to C1, the data processing module selects a first grading parameter C1 of the third basic information as a third basic information grading;
if C1 is more than C and less than or equal to C2, the data processing module selects a second grading parameter C2 of the third basic information as a third basic information grading;
if C2 is more than C and less than or equal to C3, the data processing module selects a third grading parameter C3 of the third basic information as a third basic information grading;
if C is larger than C3, the data processing module selects a fourth grading parameter C4 of the third basic information as a third basic information grading.
Further, a supervision information matrix D0 and a supervision information scoring parameter matrix D0, d0= { D1, D2, & gt, dn }, wherein D1 is first supervision information, D2 is second supervision information, dn is nth supervision information, D1 is first supervision information scoring parameter, D2 is second supervision information scoring parameter, dn is nth supervision information scoring parameter, N is a positive integer, are arranged in the data processing module;
the data acquisition module acquires the supervision information of the patient, corrects the basic information score according to the supervision information, and acquires the supervision information score F2 of the patient;
if the obtained patient data contains the j-th supervision information Dj, the data processing module corrects the basic information score according to the j-th supervision information score parameter Dj, wherein j=1, 2;
when a plurality of supervision messages are stored in the acquired patient data, the data processing module marks the stored supervision messages, the stored supervision messages are respectively recorded as j 1-th supervision messages Dj1 and j 2-th supervision messages Dj2, and the jk-th supervision messages Djk are the number of the supervision messages stored in the patient data;
set f2=f1+And xE×H, wherein E is a supervision information scoring fundamental correction parameter, H is a supervision information scoring combined correction parameter, the supervision information scoring fundamental correction parameter is determined by the quantity of supervision information stored in patient data, and the supervision information scoring combined correction parameter is determined by the relevance among the supervision information stored in the patient data.
Further, the data processing module classifies the supervision information into key supervision information and general supervision information, and the data processing module selects the supervision information scoring joint correction parameters according to the number of the key supervision information stored in the patient data;
the data processing module is provided with a first evaluation parameter R1 for the quantity of key supervision information, a second evaluation parameter R2 for the quantity of key supervision information, a first parameter value E1 for the supervision information scoring joint correction parameter, a second parameter value E2 for the supervision information scoring joint correction parameter and a third parameter value E3 for the supervision information scoring joint correction parameter, wherein E1 is smaller than E2 and smaller than E3;
the data processing module acquires the quantity R of key supervision information stored in patient data;
if R is less than or equal to R1, the data processing module selects a first parameter value E1 of the supervision information scoring joint correction parameter as a supervision information scoring basic correction parameter E;
if R1 is more than R and less than or equal to R2, the data processing module selects a second parameter value E2 of the supervision information scoring combined correction parameter as a supervision information scoring basic correction parameter E;
and if R is more than R2, the data processing module selects a third parameter value E3 of the supervision information scoring combined correction parameter as a supervision information scoring basic correction parameter E.
Further, the data processing module acquires the number S of supervision information stored in the patient data, and sets e=e+s×z, where E is a basic value of a supervision information scoring basic correction parameter, and Z is a calculated compensation value of the number of supervision information to the supervision information scoring basic correction parameter.
Further, the data processing module carries out grading on the acquired patient information according to the grading of the supervision information F2, a first evaluation parameter F01 for grading the supervision information is arranged in the data processing module, a second evaluation parameter F02 for grading the supervision information is arranged in the data processing module,
if F2 is less than or equal to F01, the data processing module judges that the score of the acquired patient information is first-level;
if F01 is more than F2 and less than or equal to F02, the data processing module judges that the score of the acquired patient information is of a second level;
if F2 > F02, the data processing module determines that the score of the acquired patient information is three-level.
Further, for the scores of different grades, the data processing module is internally provided with different information reminding interval durations,
the information reminding interval duration of the first grade of the patient information is T1;
the information reminding interval duration of the patient information with the score of two levels is T2;
the information reminding interval duration of the patient information with the score of three levels is T3;
and the data processing module sends information reminding to the user client through the network communication module every time the information reminding interval duration passes, and prompts the relevant user to carry out health monitoring.
Further, the data acquisition module re-acquires the data information of the patient after each time of health monitoring, the data processing module re-calculates the supervision information score F2', re-grades the acquired patient information, and re-determines the information reminding interval duration according to the grading.
Further, if the grading of the patient information is reduced and is lower than the grading of the patient information when the grading of the supervision information is carried out three times continuously, the data processing module prolongs the information reminding interval duration;
and if the score of the patient information is increased in a grading manner, the data processing module shortens the information reminding interval duration.
Compared with the prior art, the method has the beneficial effects that the basic ecological signs of the user are obtained by monitoring the basic data information of the user, the bad data of the user are obtained by monitoring the supervision information of the user, the overall data score of the user is calculated by integrating the basic data information and the supervision information, the data information of the user signs is graded according to the score, the physical condition of the user is well comprehensively considered, the user is intuitively fed back to the user, different reminding time periods are set for different graded data information, the user is specifically prompted to carry out health detection, and the timeliness of the health detection is improved.
Further, basic ecological signs of the user are obtained by monitoring basic data information of the user, basic information scores are calculated, basic physical quality of the user is intuitively reflected, physical conditions of the user are reasonably reminded, different basic information scores are given to different data information, and the authenticity and reliability of data calculation are improved.
Further, by supervising the bad physical information and calculating the scores of the supervising information, the user can know the physical state of the user in real time, the authenticity and reliability of data calculation are improved, the basic physical quality of the user is intuitively reflected, and the timeliness of health detection is improved.
Further, for some supervision information, certain relevance (such as three highs) exists in the supervision information, when certain problems occur at the same time, a certain problem may occur in the body, so that key supervision information is set, and the scoring and joint correction parameters of the supervision information are adjusted according to the quantity of the key supervision information, so that the authenticity and reliability of data calculation are further improved, the basic physical quality of a user is intuitively reflected, and the timeliness of health detection is improved.
Further, the larger the quantity of the supervision information is, the worse the physical quality is, so that the supervision information scoring basic correction parameters are positively correlated with the supervision information quantity, the authenticity and reliability of data calculation are further improved, the basic physical quality of a user is intuitively reflected, and the timeliness of health detection is improved.
Further, the periodic detection and regulation of the monitoring information scores, the timeliness of data is guaranteed, meanwhile, when the scores are increased, the risk that the body is poor in development is indicated, the information reminding interval duration is shortened immediately, the timeliness of safety monitoring health is guaranteed, when the scores are reduced in a grading manner, the information reminding interval duration is prolonged only by the fact that the information reminding interval duration is needed to be continuously and repeatedly appearing, and the phenomenon that the health state is misestimated due to good physical state in a certain detection is prevented.
Drawings
Fig. 1 is a schematic structural diagram of a medical risk identification processing system based on big data technology in an embodiment.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a medical risk identification processing system based on big data technology in an embodiment.
The invention provides a medical risk identification processing system based on big data technology, which comprises,
the data acquisition module is used for acquiring medical detection data, wherein the medical detection data comprises basic data information and supervision information, the basic data information is divided into a plurality of types, and corresponding basic information scores are acquired for each basic data information;
the data processing module is connected with the data acquisition module and is used for sorting and analyzing the data acquired by the data acquisition module and grading the acquired data information according to an analysis result, a plurality of basic information parameter matrixes are arranged in the data processing module, each basic information parameter matrix comprises a plurality of basic information parameters, a plurality of basic information scores are determined according to comparison of one basic information and the corresponding plurality of basic information parameters, basic information data scores are integrally generated, the data processing module analyzes the supervision information and calculates the supervision information score of a patient in combination with the basic information data scores, and the patient condition is graded according to the supervision information score;
the network communication module is connected with the data processing module and is connected with the user client through a wireless network, and can periodically send information to the user client, and the time intervals for sending information for different grades of data information are different;
the user client is connected with the data processing module through the network communication module and is used for periodically receiving information sent by the data processing module.
According to the invention, basic ecological signs of the user are obtained by monitoring basic data information of the user, bad data of the user are obtained by monitoring supervision information of the user, and the overall data scoring of the user is calculated by integrating the basic data information and the supervision information, the data information of the user signs is graded according to the scoring, the physical condition of the user is comprehensively considered well and intuitively fed back to the user, and meanwhile, different reminding time periods are set for different graded data information, so that the user is prompted to perform health detection in a targeted manner, and the timeliness of health detection is improved.
Specifically, the data acquisition module acquires basic data information of a patient, for any patient basic data information including first basic information a, second basic information B and third basic information C, the data processing module analyzes the acquired first basic information a to acquire a first basic information score a, analyzes the second basic information B to acquire a second basic information score B, analyzes the third basic information C to acquire a third basic information score C, and calculates a patient basic information data score f1, f1=a+b+c. In this embodiment, the first basic information is age information, the second basic information is sex information, and the third basic information is body fat rate information.
Specifically, a first basic information parameter matrix A0, a first basic information scoring parameter matrix A0, a second basic information parameter matrix B0, a second basic information scoring parameter matrix B0, a third basic information parameter matrix C0 and a third basic information scoring parameter matrix C0 are arranged in the data processing module,
a0 = { A1, A2, A3}, A1 is a first basic information first parameter, A2 is a first basic information second parameter, A3 is a first basic information third parameter;
a0 = { a1, a2, a3, a4}, a1 is a first basic information first scoring parameter, a2 is a first basic information second scoring parameter, a3 is a first basic information third scoring parameter, a4 is a first basic information fourth scoring parameter;
b0 = { B1, B2}, B1 being the second basic information first parameter, B2 being the second basic information second parameter;
b0 = { b1, b2}, b1 is the second basic information first scoring parameter, b2 is the second basic information second scoring parameter;
c0 = { C1, C2, C3}, C1 being the third basic information first parameter, C2 being the third basic information second parameter, C3 being the third basic information third parameter;
c0 = { c1, c2, c3, c4}, c1 is a third basic information first scoring parameter, c2 is a third basic information second scoring parameter, c3 is a third basic information third scoring parameter, c4 is a third basic information fourth scoring parameter;
if A is less than or equal to A1, the data processing module selects a first scoring parameter A1 of the first basic information as a first basic information score;
if A1 is more than A and less than or equal to A2, the data processing module selects a first basic information second scoring parameter A2 as a first basic information score;
if A2 is more than A and less than or equal to A3, the data processing module selects a third grading parameter A3 of the first basic information as a first basic information grading;
if A is more than A3, the data processing module selects a fourth grading parameter a4 of the first basic information as a first basic information grading;
if b=b1, the data processing module selects the first scoring parameter B1 of the second basic information as the second basic information score;
if b=b2, the data processing module selects a second scoring parameter B2 of the second basic information as a second basic information score;
if C is less than or equal to C1, the data processing module selects a first grading parameter C1 of the third basic information as a third basic information grading;
if C1 is more than C and less than or equal to C2, the data processing module selects a second grading parameter C2 of the third basic information as a third basic information grading;
if C2 is more than C and less than or equal to C3, the data processing module selects a third grading parameter C3 of the third basic information as a third basic information grading;
if C is larger than C3, the data processing module selects a fourth grading parameter C4 of the third basic information as a third basic information grading.
Basic ecological signs of the user are obtained by monitoring basic data information of the user, basic information scores are calculated, basic physical quality of the user is intuitively reflected, physical conditions of the user are reasonably reminded, different basic information scores are given to different data information, and the authenticity and reliability of data calculation are improved.
Specifically, a supervision information matrix D0 and a supervision information scoring parameter matrix D0, d0= { D1, D2, & gt, dn }, wherein D1 is first supervision information, D2 is second supervision information, dn is nth supervision information, D1 is first supervision information scoring parameter, D2 is second supervision information scoring parameter, dn is nth supervision information scoring parameter, N is a positive integer, are arranged in the data processing module;
the data acquisition module acquires the supervision information of the patient, corrects the basic information score according to the supervision information, and acquires the supervision information score F2 of the patient;
if the obtained patient data contains the j-th supervision information Dj, the data processing module corrects the basic information score according to the j-th supervision information score parameter Dj, wherein j=1, 2;
when a plurality of supervision messages are stored in the acquired patient data, the data processing module marks the stored supervision messages, the stored supervision messages are respectively recorded as j 1-th supervision messages Dj1 and j 2-th supervision messages Dj2, and the jk-th supervision messages Djk are the number of the supervision messages stored in the patient data;
set f2=f1+And xE×H, wherein E is a supervision information scoring fundamental correction parameter, H is a supervision information scoring combined correction parameter, the supervision information scoring fundamental correction parameter is determined by the quantity of supervision information stored in patient data, and the supervision information scoring combined correction parameter is determined by the relevance among the supervision information stored in the patient data.
By supervising the bad physical information and calculating the scores of the supervising information, the user can know the physical state of the user in real time, the authenticity and reliability of data calculation are improved, the basic physical quality of the user is intuitively reflected, and the timeliness of health detection is improved.
Specifically, the data processing module classifies the supervision information into key supervision information and general supervision information, and the data processing module selects the supervision information scoring joint correction parameters according to the number of the key supervision information stored in the patient data;
the data processing module is provided with a first evaluation parameter R1 for the quantity of key supervision information, a second evaluation parameter R2 for the quantity of key supervision information, a first parameter value E1 for the supervision information scoring joint correction parameter, a second parameter value E2 for the supervision information scoring joint correction parameter and a third parameter value E3 for the supervision information scoring joint correction parameter, wherein E1 is smaller than E2 and smaller than E3;
the data processing module acquires the quantity R of key supervision information stored in patient data;
if R is less than or equal to R1, the data processing module selects a first parameter value E1 of the supervision information scoring joint correction parameter as a supervision information scoring basic correction parameter E;
if R1 is more than R and less than or equal to R2, the data processing module selects a second parameter value E2 of the supervision information scoring combined correction parameter as a supervision information scoring basic correction parameter E;
and if R is more than R2, the data processing module selects a third parameter value E3 of the supervision information scoring combined correction parameter as a supervision information scoring basic correction parameter E.
For certain supervision information, certain relevance (such as three highs) exists in the supervision information, and when certain problems occur at the same time, a certain body possibly occurs, so that key supervision information is set, and the scoring and joint correction parameters of the supervision information are adjusted according to the quantity of the key supervision information, so that the authenticity and reliability of data calculation are further improved, the basic physical quality of a user is intuitively reflected, and the timeliness of health detection is improved.
Specifically, the data processing module acquires the number S of supervision information stored in patient data, and sets e=e+s×z, where E is a basic value of a supervision information scoring basic correction parameter, and Z is a calculated compensation value of the number of supervision information to the supervision information scoring basic correction parameter.
The larger the quantity of the supervision information is, the worse the physical quality is, so that the supervision information scoring basic correction parameters are positively correlated with the quantity of the supervision information, the authenticity and reliability of data calculation are further improved, the basic physical quality of a user is intuitively reflected, and the timeliness of health detection is improved.
Specifically, the data processing module carries out grading on the acquired patient information according to the grading of the supervision information F2, a first evaluation parameter F01 for grading the supervision information and a second evaluation parameter F02 for grading the supervision information are arranged in the data processing module,
if F2 is less than or equal to F01, the data processing module judges that the score of the acquired patient information is first-level;
if F01 is more than F2 and less than or equal to F02, the data processing module judges that the score of the acquired patient information is of a second level;
if F2 > F02, the data processing module determines that the score of the acquired patient information is three-level.
In particular, for scores of different grades, different information reminding interval durations are arranged in the data processing module,
the information reminding interval duration of the first grade of the patient information is T1;
the information reminding interval duration of the patient information with the score of two levels is T2;
the information reminding interval duration of the patient information with the score of three levels is T3;
and the data processing module sends information reminding to the user client through the network communication module every time the information reminding interval duration passes, and prompts the relevant user to carry out health monitoring. Grading the data information of the user sign according to the grading, comprehensively considering the physical condition of the user, intuitively feeding back the data information to the user, setting different reminding time periods for the data information of different grades, pertinently prompting the user to perform health detection, and improving the timeliness of the health detection
Specifically, the data acquisition module re-acquires the data information of the patient after each time of health monitoring, the data processing module re-calculates the supervision information score F2', re-grades the acquired patient information, and re-determines the information reminding interval duration according to the grading.
Specifically, if the grading of the patient information is reduced, and the grading of the patient information is continuously lower than the grading of the supervision information F2 three times, the data processing module prolongs the information reminding interval duration;
and if the score of the patient information is increased in a grading manner, the data processing module shortens the information reminding interval duration.
The periodic detection adjusts the monitoring information score, ensures the timeliness of data, simultaneously when the score rises, indicates that the body has the risk of going forward to development, immediately shortens the information reminding interval duration at the moment, ensures the timeliness of safety monitoring health, and when the score is reduced in a grading manner, the information reminding interval duration is prolonged only by needing to appear continuously for multiple times, so that the body state is better during certain detection, and the phenomenon of misestimating the health state is prevented.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (2)
1. A medical risk identification processing system based on big data technology is characterized by comprising,
the data acquisition module is used for acquiring medical detection data, wherein the medical detection data comprises basic data information and supervision information, the basic data information is divided into a plurality of types, and corresponding basic information scores are acquired for each basic data information;
the data processing module is connected with the data acquisition module, is used for sorting and analyzing the data acquired by the data acquisition module and grading the acquired data information according to an analysis result, a plurality of basic information parameter matrixes are arranged in the data processing module, each basic information parameter matrix comprises a plurality of basic information parameters, a plurality of basic information scores are determined according to comparison of one basic information and a plurality of corresponding basic information parameters, basic information data scores are integrally generated, the data processing module analyzes the supervision information and calculates the supervision information score of a patient in combination with the basic information data scores, the patient condition is graded according to the supervision information score, the supervision information is classified into key supervision information and general supervision information, and the data processing module calculates and corrects the supervision information score according to the supervision information quantity and key supervision information quantity contained in the acquired medical detection data;
the network communication module is connected with the data processing module and is connected with the user client through a wireless network, and can periodically send information to the user client, and the time intervals for sending information for different grades of data information are different;
the data acquisition module acquires basic data information of a patient, wherein for any patient basic data information comprising first basic information A, second basic information B and third basic information C, the data processing module analyzes the acquired first basic information A to acquire a first basic information score a, analyzes the second basic information B to acquire a second basic information score B, analyzes the third basic information C to acquire a third basic information score C, and calculates a patient basic information data score F1, F1=a+b+c;
the data processing module is internally provided with a first basic information parameter matrix A0, a first basic information grading parameter matrix A0, a second basic information parameter matrix B0, a second basic information grading parameter matrix B0, a third basic information parameter matrix C0 and a third basic information grading parameter matrix C0, wherein,
a0 = { A1, A2, A3}, A1 is a first basic information first parameter, A2 is a first basic information second parameter, A3 is a first basic information third parameter;
a0 = { a1, a2, a3, a4}, a1 is a first basic information first scoring parameter, a2 is a first basic information second scoring parameter, a3 is a first basic information third scoring parameter, a4 is a first basic information fourth scoring parameter;
b0 = { B1, B2}, B1 being the second basic information first parameter, B2 being the second basic information second parameter;
b0 = { b1, b2}, b1 is the second basic information first scoring parameter, b2 is the second basic information second scoring parameter;
c0 = { C1, C2, C3}, C1 being the third basic information first parameter, C2 being the third basic information second parameter, C3 being the third basic information third parameter;
c0 = { c1, c2, c3, c4}, c1 is a third basic information first scoring parameter, c2 is a third basic information second scoring parameter, c3 is a third basic information third scoring parameter, c4 is a third basic information fourth scoring parameter;
if A is less than or equal to A1, the data processing module selects a first scoring parameter A1 of the first basic information as a first basic information score;
if A1 is more than A and less than or equal to A2, the data processing module selects a first basic information second scoring parameter A2 as a first basic information score;
if A2 is more than A and less than or equal to A3, the data processing module selects a third grading parameter A3 of the first basic information as a first basic information grading;
if A is more than A3, the data processing module selects a fourth grading parameter a4 of the first basic information as a first basic information grading;
if b=b1, the data processing module selects the first scoring parameter B1 of the second basic information as the second basic information score;
if b=b2, the data processing module selects a second scoring parameter B2 of the second basic information as a second basic information score;
if C is less than or equal to C1, the data processing module selects a first grading parameter C1 of the third basic information as a third basic information grading;
if C1 is more than C and less than or equal to C2, the data processing module selects a second grading parameter C2 of the third basic information as a third basic information grading;
if C2 is more than C and less than or equal to C3, the data processing module selects a third grading parameter C3 of the third basic information as a third basic information grading;
if C is more than C3, the data processing module selects a fourth grading parameter C4 of the third basic information as a third basic information grading;
the data processing module is internally provided with a supervision information matrix D0 and a supervision information scoring parameter matrix D0, d0= { D1, D2, & gt, dn }, wherein D1 is first supervision information, D2 is second supervision information, & gt, dn is nth supervision information, D1 is first supervision information scoring parameter, D2 is second supervision information scoring parameter, & gt, dn is nth supervision information scoring parameter, and N is a positive integer;
the data acquisition module acquires the supervision information of the patient, corrects the basic information score according to the supervision information, and acquires the supervision information score F2 of the patient;
if the obtained patient data contains the j-th supervision information Dj, the data processing module corrects the basic information score according to the j-th supervision information score parameter Dj, wherein j=1, 2;
when a plurality of supervision messages are stored in the acquired patient data, the data processing module marks the stored supervision messages, the stored supervision messages are respectively recorded as j 1-th supervision messages Dj1 and j 2-th supervision messages Dj2, and the jk-th supervision messages Djk are the number of the supervision messages stored in the patient data;
set f2=f1+X E x H, wherein E is a supervision information scoring fundamental correction parameter, H is a supervision information scoring combined correction parameter, the supervision information scoring fundamental correction parameter is determined by the quantity of supervision information stored in patient data, and the supervision information scoring combined correction parameter is determined by the relevance among supervision information stored in the patient data;
the data processing module classifies the supervision information into key supervision information and general supervision information, and selects the scoring joint correction parameters of the supervision information according to the number of the key supervision information stored in the patient data;
the data processing module is provided with a first evaluation parameter R1 for the quantity of key supervision information, a second evaluation parameter R2 for the quantity of key supervision information, a first parameter value E1 for the supervision information scoring joint correction parameter, a second parameter value E2 for the supervision information scoring joint correction parameter and a third parameter value E3 for the supervision information scoring joint correction parameter, wherein E1 is smaller than E2 and smaller than E3;
the data processing module acquires the quantity R of key supervision information stored in patient data;
if R is less than or equal to R1, the data processing module selects a first parameter value E1 of the supervision information scoring joint correction parameter as a supervision information scoring basic correction parameter E;
if R1 is more than R and less than or equal to R2, the data processing module selects a second parameter value E2 of the supervision information scoring combined correction parameter as a supervision information scoring basic correction parameter E;
if R is more than R2, the data processing module selects a third parameter value E3 of the supervision information scoring combined correction parameter as a supervision information scoring basic correction parameter E;
the data processing module acquires the quantity S of supervision information stored in patient data, and sets E=e+S×Z, wherein E is a basic value of a supervision information scoring basic correction parameter, and Z is a calculated compensation value of the supervision information quantity to the supervision information scoring basic correction parameter;
the data processing module carries out grading on the acquired patient information according to the grading F2 of the supervision information, the data processing module is internally provided with a first evaluation parameter F01 of grading of the supervision information and a second evaluation parameter F02 of grading of the supervision information,
if F2 is less than or equal to F01, the data processing module judges that the score of the acquired patient information is first-level;
if F01 is more than F2 and less than or equal to F02, the data processing module judges that the score of the acquired patient information is of a second level;
if F2 > F02, the data processing module determines that the score of the acquired patient information is three-level
For the grading of different grades, the data processing module is internally provided with different information reminding interval duration,
the information reminding interval duration of the first grade of the patient information is T1;
the information reminding interval duration of the patient information with the score of two levels is T2;
the information reminding interval duration of the patient information with the score of three levels is T3;
t1 is more than T2 is more than T3, and the data processing module sends information reminding to the user client through the network communication module every time the information reminding interval duration passes, so that relevant users are reminded to carry out health monitoring;
and the data acquisition module re-acquires the data information of the patient after each time of health monitoring, the data processing module re-calculates the supervision information score F2', re-grades the acquired patient information, and re-determines the information reminding interval duration according to the grading.
2. The medical risk identification processing system based on big data technology as set forth in claim 1, wherein,
if the grading of the patient information is reduced and is lower than the grading of the patient information when the grading of the patient information is regulated to be F2 three times continuously, the data processing module prolongs the information reminding interval duration;
and if the score of the patient information is increased in a grading manner, the data processing module shortens the information reminding interval duration.
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| CN110634573A (en) * | 2019-09-27 | 2019-12-31 | 南昌大学第一附属医院 | Clinical cerebral infarction patient recurrence risk early warning scoring visualization model system and evaluation method thereof |
| CN111145908A (en) * | 2019-12-26 | 2020-05-12 | 糖医生健康管理南京有限公司 | Health assessment comprehensive information service platform |
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| CN110634573A (en) * | 2019-09-27 | 2019-12-31 | 南昌大学第一附属医院 | Clinical cerebral infarction patient recurrence risk early warning scoring visualization model system and evaluation method thereof |
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