CN112489802A - Intelligent decision making system and method based on big data - Google Patents
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Abstract
The invention provides an intelligent decision-making system and method based on big data, wherein the system comprises an inquiry sequence acquisition module, a sequence optimization module and an inquiry module; the sequence optimization module comprises a reported sequence acquisition sub-module, an obstetrical examination sequence acquisition sub-module and a doctor inquiry sequence acquisition sub-module. The system and the method are based on the unique characteristics of the obstetrical outpatient service, the correction decision of the inquiry sequence is determined according to the actual conditions of the pregnant women in the multiple examination processes, and the inquiry sequence is optimized; and training a risk prediction model based on a machine learning algorithm and a big data technology to obtain the risk probability of the pregnant woman, and providing diagnosis assistance for a doctor. Therefore, the diagnosis efficiency of the obstetrical department outpatient service is improved by the multi-level means fusion, and the experience of the pregnant woman in seeing a doctor is improved.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to an intelligent decision making system and method based on big data.
Background
Prenatal care refers to the provision of a series of medical and nursing advice and measures for pregnant women in order to reduce the adverse effects of complications through early prevention and discovery of complications for the monitoring of pregnant women and fetuses, during which the provision of proper examination and medical advice is critical to reduce maternal and perinatal mortality. The obstetrical examination includes a plurality of examination items, which are different based on the difference of the pregnant weeks of the pregnant women, the obstetrical clinic has unique characteristics different from other department clinics, the specific examination content includes not only general examination but also obstetrical examination, and also some other targeted auxiliary examinations, and the items such as height/body weight index detection, blood pressure detection, uterine height and abdominal circumference detection, fetal heart sound and the like are programs which need to be considered in each obstetrical examination. The inquiry efficiency is very low due to various procedures involved in the one-time obstetrical examination process, the number of pregnant women and the number of birth population in China are greatly increased along with the release of the national second-birth policy, great diagnosis pressure is brought to obstetrical outpatient service, particularly obstetrical outpatient service of special hospitals such as maternal and child health care hospitals, and the obstetrical examination experience of the pregnant women with inconvenient actions is reduced.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent decision-making system and method based on big data, which are based on the unique characteristics of obstetrical outpatient service, determine the correction decision of an inquiry sequence according to the actual conditions in a plurality of examination processes of a pregnant woman and optimize the inquiry sequence; and training a risk prediction model based on a machine learning algorithm and a big data technology to obtain the risk probability of the pregnant woman, and providing diagnosis assistance for a doctor. The diagnosis efficiency of the obstetrical department outpatient service is improved through the multi-level means fusion, and the experience of the pregnant woman in seeing a doctor is improved.
An intelligent decision-making system based on big data comprises an inquiry sequence acquisition module, a sequence optimization module and an inquiry module. The sequence optimization module comprises a reported sequence acquisition sub-module, an obstetrical examination sequence acquisition sub-module and a doctor inquiry sequence acquisition sub-module. In particular, the method comprises the following steps of,
the inquiry sequence acquisition module is used for acquiring an inquiry sequence based on the registration record of the pregnant woman.
The reported sequence acquisition sub-module is used for inserting the currently-visited pregnant woman into a reported sequence based on the registration record of the currently-visited pregnant woman and the reported attributes of other pregnant women in the inquiry sequence acquired by the inquiry sequence acquisition module when the pregnant woman reports.
The obstetrical examination sequence acquisition submodule is used for acquiring a general examination index of the current visiting pregnant woman based on the reported sequence acquired by the reported sequence acquisition submodule, and correcting the reported sequence based on a comparison result of the general examination index and a standard value to generate the obstetrical examination sequence.
The doctor inquiry sequence acquisition submodule is used for acquiring obstetrical examination indexes of a current pregnant woman to be diagnosed based on the obstetrical examination sequence acquired by the obstetrical examination sequence acquisition submodule, inputting the general examination indexes and the obstetrical examination indexes into a risk prediction model based on a machine learning algorithm and a big data technology for risk decision, correcting the obstetrical examination sequence based on a risk probability value, and generating the doctor inquiry sequence.
The inquiry module is used for determining the inquiry sequence of the pregnant woman based on the inquiry sequence of the doctor acquired by the inquiry sequence acquisition submodule of the doctor and carrying out inquiry based on the risk decision result acquired by the inquiry sequence acquisition submodule of the doctor.
Preferably, the step of correcting the reported sequence specifically includes that when the difference value between the current general examination index of the pregnant woman in diagnosis and the standard value is smaller than a certain threshold value, the index is judged to be normal, otherwise, the index is judged to be abnormal. If the general examination indexes are judged to be normal, adding the current pregnant woman to the tail end of the obstetrical examination sequence; when the general examination index judges to be abnormal, the current pregnant woman to be diagnosed is reinserted into the last three sequence positions of the sequence reported in the sequence for reexamination. And if the results of the current three-time general examination indexes of the pregnant woman are abnormal, carrying out abnormal marking on the results in the system, finishing the detection of the current general examination indexes and adding the current general examination indexes into the tail end of the current obstetrical examination sequence.
Preferably, the general examination index comprises a BMI index and a blood pressure detection index.
Preferably, the acquiring process of the risk prediction model comprises the steps of acquiring historical pregnant woman clinical data for normative labor detection and delivery in advance, and training the risk prediction model by using an XGboost machine learning algorithm and a big data technology.
Preferably, the correcting the obstetrical examination sequence based on the risk probability value specifically includes adding the current pregnant woman to the end of the doctor inquiry sequence when the risk probability value is lower than a certain threshold; otherwise, carrying out risk early warning marking on the currently-visited pregnant woman in the system, and inserting the risk early warning marking into the first position of the inquiry sequence of the doctor.
Correspondingly, the invention also discloses an intelligent decision method based on big data, which specifically comprises the following steps:
s10: an interrogation sequence is obtained based on the registration record of the pregnant woman.
S20: when a pregnant woman reports, inserting the currently arriving pregnant woman into a reported sequence based on the registration record of the currently arriving pregnant woman and the reported attributes of other pregnant women in the inquiry sequence.
S30: and acquiring a general examination index of the current visiting pregnant woman based on the reported sequence, and correcting the reported sequence based on a comparison result of the general examination index and a standard value to generate an obstetrical examination sequence.
S40: acquiring obstetrical examination indexes of the current pregnant woman to be diagnosed based on the obstetrical examination sequence, inputting the general examination indexes and the obstetrical examination indexes into a risk prediction model based on a machine learning algorithm and a big data technology for risk decision, and correcting the obstetrical examination sequence based on a risk probability value to generate a doctor inquiry sequence.
S50: and determining the inquiry sequence of the pregnant woman based on the inquiry sequence of the doctor, and performing inquiry based on the risk decision result.
Preferably, the step of correcting the reported sequence specifically includes that when the difference value between the current general examination index of the pregnant woman in diagnosis and the standard value is smaller than a certain threshold value, the index is judged to be normal, otherwise, the index is judged to be abnormal. If the general examination indexes are judged to be normal, adding the current pregnant woman to the tail end of the obstetrical examination sequence; when the general examination index judges to be abnormal, the current pregnant woman to be diagnosed is reinserted into the last three sequence positions of the sequence reported in the sequence for reexamination. And if the results of the current three-time general examination indexes of the pregnant woman are abnormal, carrying out abnormal marking on the results in the system, finishing the detection of the current general examination indexes and adding the current general examination indexes into the tail end of the current obstetrical examination sequence.
Preferably, the general examination index comprises a BMI index and a blood pressure detection index.
Preferably, the acquiring process of the risk prediction model comprises the steps of acquiring historical pregnant woman clinical data for normative labor detection and delivery in advance, and training the risk prediction model by using an XGboost machine learning algorithm and a big data technology.
Preferably, the correcting the obstetrical examination sequence based on the risk probability value specifically includes adding the current pregnant woman to the end of the doctor inquiry sequence when the risk probability value is lower than a certain threshold; otherwise, carrying out risk early warning marking on the currently-visited pregnant woman in the system, and inserting the risk early warning marking into the first position of the inquiry sequence of the doctor.
Has the advantages that: the invention provides an intelligent decision making system and method based on big data, the system and method are based on the unique characteristics of obstetrical outpatient service, the correction decision of an inquiry sequence is determined according to the actual conditions of pregnant women in a plurality of examination processes, and the inquiry sequence is optimized, so that high-risk pregnant women can make an inquiry as early as possible; and training a risk prediction model based on a machine learning algorithm and a big data technology to obtain the risk probability of the pregnant woman, and providing diagnosis assistance for a doctor. Therefore, the diagnosis efficiency of the obstetrical department outpatient service is improved by the multi-level means fusion, and the experience of the pregnant woman in seeing a doctor is improved.
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Fig. 1 is a schematic structural diagram of an intelligent decision system based on big data according to the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the embodiment of the present invention includes an intelligent decision making system based on big data, which includes an inquiry sequence obtaining module, a sequence optimizing module, and an inquiry module. The sequence optimization module comprises a reported sequence acquisition sub-module, an obstetrical examination sequence acquisition sub-module and a doctor inquiry sequence acquisition sub-module. In particular, the method comprises the following steps of,
the inquiry sequence acquisition module is used for acquiring an inquiry sequence based on the registration record of the pregnant woman.
The obstetrical department outpatient service generally places numbers in the hospital public number and other places in hours, and the pregnant women can select corresponding inquiry doctors and time periods according to the needs of the pregnant women. And determining the doctor and the time period of inquiry of the pregnant woman based on the registration record of the pregnant woman, and acquiring the inquiry sequence of the pregnant woman.
The reported sequence acquisition sub-module is used for inserting the currently-visited pregnant woman into a reported sequence based on the registration record of the currently-visited pregnant woman and the reported attributes of other pregnant women in the inquiry sequence acquired by the inquiry sequence acquisition module when the pregnant woman reports.
When the pregnant woman reports later than the registration period due to unexpected factors, the pregnant woman can still be inserted into the corresponding patient on the basis of the registration sequence to form a reported sequence for ensuring the inquiry experience, so that the time fairness of the registration inquiry of the pregnant woman is ensured, the experience of the patient is improved, and the diagnosis efficiency of a hospital and the benefits of other pregnant women are not influenced. In one embodiment, specifically, if the registered time period of the pregnant woman a is 1 month, 1 day, 2 pm to 3 pm, and all registered pregnant women in the registered time period are BCADEF in the registered sequence, the registered pregnant woman a should be inserted between the pregnant women C and D after being registered.
The obstetrical examination sequence acquisition submodule is used for acquiring a general examination index of the current visiting pregnant woman based on the reported sequence acquired by the reported sequence acquisition submodule, and correcting the reported sequence based on a comparison result of the general examination index and a standard value to generate the obstetrical examination sequence.
The general examination indexes comprise BMI index indexes and blood pressure detection indexes, and the BMI index indexes and the blood pressure indexes of normal pregnant women are analyzed based on a big data technology to obtain pregnant woman standard values of corresponding indexes. When the difference value between the general examination index and the standard value of the current pregnant woman is smaller than a certain threshold value, judging that the index is normal, otherwise, judging that the index is abnormal. If the general examination indexes are judged to be normal, adding the current pregnant woman to the tail end of the obstetrical examination sequence; when the general examination index judges to be abnormal, the current pregnant woman to be diagnosed is reinserted into the last three sequence positions of the sequence reported in the sequence for reexamination. And if the results of the current three-time general examination indexes of the pregnant woman are abnormal, carrying out abnormal marking on the results in the system, finishing the detection of the current general examination indexes and adding the current general examination indexes into the tail end of the current obstetrical examination sequence.
The doctor inquiry sequence acquisition submodule is used for acquiring obstetrical examination indexes of a current pregnant woman to be diagnosed based on the obstetrical examination sequence acquired by the obstetrical examination sequence acquisition submodule, inputting the general examination indexes and the obstetrical examination indexes into a risk prediction model based on a machine learning algorithm and a big data technology for risk decision, correcting the obstetrical examination sequence based on a risk probability value, and generating the doctor inquiry sequence.
The method comprises the steps of obtaining historical pregnant woman clinical data for standardizing obstetrical examination and delivery in advance, training a risk prediction model by using an XGboost machine learning algorithm and a big data technology, obtaining obstetrical examination indexes of a current pregnant woman in diagnosis, inputting the general examination indexes and the obstetrical examination indexes into a risk prediction model based on the machine learning algorithm for risk decision, and correcting an obstetrical examination sequence based on risk values. When the risk probability value is lower than a certain threshold value, adding the current pregnant woman to the tail end of the inquiry sequence of the doctor; otherwise, carrying out risk early warning marking on the currently-visited pregnant woman in the system, and inserting the risk early warning marking into the first position of the inquiry sequence of the doctor. The risk prediction model comprehensively considers various examination indexes of the pregnant woman and can provide auxiliary diagnosis for doctors; and the abnormal comprehensive indexes of the pregnant women can bring risks such as abortion and the like to the pregnant women at any time, and in order to avoid the risks, the pregnant women marked with risk early warning marks are inserted to the head of an inquiry sequence of doctors, so that the high-risk pregnant women can see medical information as early as possible and obtain the emergency treatment effect to a certain extent.
The inquiry module is used for determining the inquiry sequence of the pregnant woman based on the inquiry sequence of the doctor acquired by the inquiry sequence acquisition submodule of the doctor and carrying out inquiry based on the risk decision result acquired by the inquiry sequence acquisition submodule of the doctor.
Another embodiment of the present invention further provides an intelligent decision method based on big data, which specifically includes the following steps:
s10: an interrogation sequence is obtained based on the registration record of the pregnant woman.
The obstetrical department outpatient service generally places numbers in the hospital public number and other places in hours, and the pregnant women can select corresponding inquiry doctors and time periods according to the needs of the pregnant women. And determining the doctor and the time period of inquiry of the pregnant woman based on the registration record of the pregnant woman, and acquiring the inquiry sequence of the pregnant woman.
S20: when a pregnant woman reports, inserting the currently arriving pregnant woman into a reported sequence based on the registration record of the currently arriving pregnant woman and the reported attributes of other pregnant women in the inquiry sequence.
When the pregnant woman reports later than the registration period due to unexpected factors, the pregnant woman can still be inserted into the front and back of the corresponding patient based on the registration sequence to form a reported sequence for ensuring the inquiry experience, so that the inquiry sequence is corrected, the time fairness of the registration inquiry of the pregnant woman is ensured, the patient experience is improved, and the hospital diagnosis efficiency and the benefits of other pregnant women are not influenced. In one embodiment, specifically, if the registered time period of the pregnant woman a is 1 month, 1 day, 2 pm to 3 pm, and all registered pregnant women in the registered time period are BCADEF in the registered sequence, the registered pregnant woman a should be inserted between the pregnant women C and D after being registered.
S30: and acquiring a general examination index of the current visiting pregnant woman based on the reported sequence, and correcting the reported sequence based on a comparison result of the general examination index and a standard value to generate an obstetrical examination sequence.
The general examination indexes comprise BMI index indexes and blood pressure detection indexes, and the BMI index indexes and the blood pressure indexes of normal pregnant women are analyzed based on a big data technology to obtain pregnant woman standard values of corresponding indexes. When the difference value between the general examination index and the standard value of the current pregnant woman is smaller than a certain threshold value, judging that the index is normal, otherwise, judging that the index is abnormal. If the general examination indexes are judged to be normal, adding the current pregnant woman to the tail end of the obstetrical examination sequence; when the general examination index judges to be abnormal, the current pregnant woman to be diagnosed is reinserted into the last three sequence positions of the sequence reported in the sequence for reexamination. And if the results of the current three-time general examination indexes of the pregnant woman are abnormal, carrying out abnormal marking on the results in the system, finishing the detection of the current general examination indexes and adding the current general examination indexes into the tail end of the current obstetrical examination sequence.
S40: acquiring obstetrical examination indexes of the current pregnant woman, inputting the general examination indexes and the obstetrical examination indexes into a risk prediction model based on a machine learning algorithm for risk decision, correcting the obstetrical examination sequence based on a risk probability value, and generating a doctor inquiry sequence.
The method comprises the steps of obtaining historical pregnant woman clinical data for standardizing obstetrical examination and delivery in advance, training a risk prediction model by using an XGboost machine learning algorithm and a big data technology, obtaining obstetrical examination indexes of a current pregnant woman in diagnosis, inputting the general examination indexes and the obstetrical examination indexes into a risk prediction model based on the machine learning algorithm for risk decision, and correcting an obstetrical examination sequence based on risk values. When the risk probability value is lower than a certain threshold value, adding the current pregnant woman to the tail end of the inquiry sequence of the doctor; otherwise, carrying out risk early warning marking on the currently-visited pregnant woman in the system, and inserting the risk early warning marking into the first position of the inquiry sequence of the doctor. The risk prediction model comprehensively considers various examination indexes of the pregnant woman and can provide auxiliary diagnosis for doctors; and the abnormal comprehensive indexes of the pregnant women can bring risks such as abortion and the like to the pregnant women at any time, and in order to avoid the risks, the pregnant women marked with risk early warning marks are inserted to the head of an inquiry sequence of doctors, so that the high-risk pregnant women can see medical information as early as possible, and the effect similar to emergency treatment priority is obtained to a certain extent.
S50: and determining the inquiry sequence of the pregnant woman based on the inquiry sequence of the doctor, and performing inquiry based on the risk decision result.
The above-mentioned embodiments are only for convenience of description, and are not intended to limit the present invention in any way, and those skilled in the art will understand that the technical features of the present invention can be modified or changed by other equivalent embodiments without departing from the scope of the present invention.
Claims (10)
1. An intelligent big data-based decision making system, comprising: the system comprises an inquiry sequence acquisition module, a sequence optimization module and an inquiry module; the sequence optimization module comprises a reported sequence acquisition sub-module, an obstetrical examination sequence acquisition sub-module and a doctor inquiry sequence acquisition sub-module; in particular, the method comprises the following steps of,
the inquiry sequence acquisition module is used for acquiring an inquiry sequence based on the registration record of the pregnant woman;
the reported sequence acquisition sub-module is used for inserting the currently-visited pregnant woman into a reported sequence based on the registration record of the currently-visited pregnant woman and the reported attributes of other pregnant women in the inquiry sequence acquired by the inquiry sequence acquisition module when the pregnant woman reports to the time;
the obstetrical examination sequence acquisition submodule is used for acquiring a general examination index of the current visiting pregnant woman based on the reported sequence acquired by the reported sequence acquisition submodule, and correcting the reported sequence based on a comparison result of the general examination index and a standard value to generate an obstetrical examination sequence;
the doctor inquiry sequence acquisition submodule is used for acquiring obstetrical examination indexes of a current pregnant woman to be diagnosed based on the obstetrical examination sequence acquired by the obstetrical examination sequence acquisition submodule, inputting the general examination indexes and the obstetrical examination indexes into a risk prediction model based on a machine learning algorithm and a big data technology for risk decision making, correcting the obstetrical examination sequence based on a risk probability value and generating a doctor inquiry sequence;
the inquiry module is used for determining the inquiry sequence of the pregnant woman based on the inquiry sequence of the doctor acquired by the inquiry sequence acquisition submodule of the doctor and carrying out inquiry based on the risk decision result acquired by the inquiry sequence acquisition submodule of the doctor.
2. The intelligent big-data-based decision making system according to claim 1, wherein the modifying the reported sequence specifically comprises determining that the index is normal when the difference between the current physical examination index of the pregnant woman and the standard value is less than a certain threshold, or determining that the index is abnormal otherwise; if the general examination indexes are judged to be normal, adding the current pregnant woman to the tail end of the obstetrical examination sequence; and when the general examination index is judged to be abnormal, reinserting the current pregnant woman to the last three sequence positions in the sequence reported to the current pregnant woman for reexamination, if the results of the three general examination indexes of the current pregnant woman are abnormal, carrying out abnormal marking on the current pregnant woman in the system, ending the detection of the current general examination index and adding the current general examination index to the tail end of the current obstetrical examination sequence.
3. The big-data based intelligent decision making system according to claim 2, wherein the general examination index comprises BMI index and blood pressure detection index.
4. The big-data-based intelligent decision making system according to claim 2, wherein the risk prediction model is obtained by pre-obtaining historical maternal clinical data for normative labor detection and delivery, and training the risk prediction model by using the XGboost machine learning algorithm and big data technology.
5. The intelligent big data-based decision making system according to claim 2, wherein the modifying the obstetrical examination sequence based on the risk probability value specifically comprises adding the currently-visited pregnant woman to the end of the doctor consultation sequence when the risk probability value is lower than a certain threshold; otherwise, carrying out risk early warning marking on the currently-visited pregnant woman in the system, and inserting the risk early warning marking into the first position of the inquiry sequence of the doctor.
6. An intelligent decision-making method based on big data is characterized by comprising the following steps:
s10: acquiring an inquiry sequence based on the registration record of the pregnant woman;
s20: when a pregnant woman reports, inserting the currently arriving pregnant woman into a reported sequence based on the registration record of the currently arriving pregnant woman and the reported attributes of other pregnant women in the inquiry sequence;
s30: acquiring a general examination index of the current pregnant woman, and correcting the reported sequence based on the comparison result of the general examination index and a standard value to generate an obstetrical examination sequence;
s40: acquiring obstetrical examination indexes of the current pregnant woman to be diagnosed based on the obstetrical examination sequence, inputting the general examination indexes and the obstetrical examination indexes into a risk prediction model based on a machine learning algorithm and a big data technology for risk decision, and correcting the obstetrical examination sequence based on a risk probability value to generate a doctor inquiry sequence;
s50: and determining the inquiry sequence of the pregnant woman based on the inquiry sequence of the doctor, and performing inquiry based on the risk decision result.
7. The method as claimed in claim 6, wherein the step S30 of correcting the reported sequence includes that when the difference between the current general examination index and the standard value is less than a certain threshold, the index is determined to be normal, otherwise, the index is determined to be abnormal; if the general examination indexes are judged to be normal, adding the current pregnant woman to the tail end of the obstetrical examination sequence; and when the general examination index is judged to be abnormal, reinserting the current pregnant woman to the last three sequence positions in the sequence reported to the current pregnant woman for reexamination, if the results of the three general examination indexes of the current pregnant woman are abnormal, carrying out abnormal marking on the current pregnant woman in the system, ending the detection of the current general examination index and adding the current general examination index to the tail end of the current obstetrical examination sequence.
8. The intelligent big-data-based decision making method according to claim 7, wherein the general examination index comprises a BMI index and a blood pressure detection index.
9. The big-data-based intelligent decision-making method according to claim 7, wherein the obtaining process of the risk prediction model in step S40 includes obtaining historical maternal clinical data for normative labor test and delivery in advance, and training the risk prediction model by using the XGBoost machine learning algorithm and the big data technology.
10. The intelligent big-data-based decision making method according to claim 7, wherein the step S40 of modifying the obstetrical examination sequence based on the risk probability value specifically includes adding the current pregnant woman to the end of the physician consultation sequence when the risk probability value is lower than a certain threshold; otherwise, carrying out risk early warning marking on the currently-visited pregnant woman in the system, and inserting the risk early warning marking into the first position of the inquiry sequence of the doctor.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110111878A (en) * | 2019-04-19 | 2019-08-09 | 苏州智康信息科技股份有限公司 | Medical treatment preliminary hearing method, apparatus calculates equipment and storage medium |
| CN110148469A (en) * | 2019-05-23 | 2019-08-20 | 梁国强 | A kind of intelligence production check system |
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