CN110827951A - Clinical intelligent decision platform - Google Patents
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- CN110827951A CN110827951A CN201911348291.2A CN201911348291A CN110827951A CN 110827951 A CN110827951 A CN 110827951A CN 201911348291 A CN201911348291 A CN 201911348291A CN 110827951 A CN110827951 A CN 110827951A
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- 239000003814 drug Substances 0.000 claims abstract description 194
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- 238000007689 inspection Methods 0.000 claims description 15
- 201000010099 disease Diseases 0.000 claims description 14
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 14
- 238000004088 simulation Methods 0.000 claims description 13
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- 230000036760 body temperature Effects 0.000 description 1
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
The invention discloses a clinical intelligent decision platform, which comprises: the target parameter acquisition module is used for acquiring the medical history, the medication history, the current examination report, the current physical state, the medication allergy history and the details of the currently taken medicines of the patient; the treatment curve drawing module is used for drawing a treatment curve according to the medical history and the medication history of the patient so as to obtain effective medicinal components; the drug recommendation module is used for realizing acquisition of recommended drugs based on a BP neural network model according to the current examination report of the patient; and the medicine taking guidance module is used for outputting corresponding medicine taking guidance suggestions according to the current examination report of the patient and the medicines recommended by the medicine recommendation module. The invention can output more accurate medication guidance according to different conditions and requirements of the patient, realizes targeted medication while reducing the workload of doctors, can reduce the damage to the body of the patient caused by medication errors, and can improve the efficacy of the medicine.
Description
Technical Field
The invention relates to the field of medication auxiliary systems, in particular to a clinical intelligent decision platform.
Background
At present, the existing medicine decision system is implemented based on the knowledge characteristics of medicines, the taboo and the reasonability of a doctor prescription are analyzed in a regular form, personalized and all-around medicine guidance is realized from different angles in a reasonable range according to the condition of a patient, and the guidance effect on the doctor is very limited.
Meanwhile, no system for guiding the decision of the traditional Chinese medicine medication based on the clinical diagnosis and treatment medication rules of various old traditional Chinese medicines exists.
Disclosure of Invention
In order to solve the problems, the invention provides a clinical intelligent decision platform which can output more accurate medication guidance according to different conditions and requirements of patients, reduce the workload of doctors, realize targeted medication, reduce the damage to the bodies of the patients caused by medication errors and improve the drug effect.
In order to achieve the purpose, the invention adopts the technical scheme that:
a clinical intelligent decision platform comprising:
the target parameter acquisition module is used for acquiring the medical history, the medication history, the current examination report, the current physical state, the medication allergy history and the details of the currently taken medicines of the patient;
the treatment curve drawing module is used for drawing a treatment curve according to the medical history and the medication history of the patient so as to obtain effective medicinal components;
the drug recommendation module is used for realizing acquisition of recommended drugs based on a BP neural network model according to the current examination report of the patient;
and the medicine taking guidance module is used for outputting corresponding medicine taking guidance suggestions according to the current examination report of the patient and the medicines recommended by the medicine recommendation module.
Furthermore, when the treatment curve drawing module draws, firstly, the disease types in the medical history and the medication history need to be classified in a corresponding manner, and then, the treatment curve corresponding to each disease type is drawn according to the classification result.
Further, the medicine details include the name of the medicine, the amount used, and the time taken.
Further, the medicine recommending module finishes acquiring recommended medicines through the following steps;
s1, mining the inspection result based on the current inspection report, and then obtaining a corresponding recommended drug table based on the inspection result and the BP neural network model;
s2, based on the details of the medicines which are taken currently, eliminating the medicines which correspond to the illness state being treated and the medicines which are in conflict with the medicines;
s3, acquiring drug allergy components based on the drug allergy history, and then excavating drugs containing the drug allergy components in the drug recommendation table obtained in the step S2 and removing the drugs;
s4, obtaining the current body bearing capacity of the patient based on the current examination report and the current body state of the patient, and eliminating the medicines with side effects outside the body bearing capacity range;
s5, based on the effective medicine components, selecting medicines containing the effective medicine components from the recommended medicine table obtained in the step S4;
s6, based on the least cost principle/the best efficacy principle, selecting the final medicine from the recommended medicine table obtained in the step S5.
Further, still include:
the medicine changing reminding module is used for obtaining the taken time of the currently taken medicine according to details of the currently taken medicine, then comparing the time with the medicine taking time limit corresponding to the medicine, if the time limit is exceeded, starting the early warning module to remind in a mode of popping up a dialog box, simultaneously starting the medicine recommending module to output the corresponding replaced medicine, and starting the medicine taking guiding module to output the corresponding medicine taking guiding suggestion, wherein the medicine taking guiding suggestion comprises the name, the dosage, the taking time, the time limit capable of being continuously taken and the taking attention.
Further, still include:
and the short message editing and sending module is used for filling the results output by the medicine recommending module and the medicine taking guiding module into a preset template and sending the result to the corresponding patient mobile terminal.
Further, still include:
the simulation analysis model building module is used for building a patient body simulation model according to the physical condition of the patient and the current examination report;
and the simulation analysis module is used for driving the calculation analysis module to calculate and solve different parameters by taking the input medicines, the dosage, the taking time and the continuous taking period as actuation conditions.
The invention has the following beneficial effects:
1) can output comparatively accurate medication guidance according to different conditions and requirements of the patient, realize targeted medication while lightening the workload of doctors, reduce the damage to the body of the patient caused by medication errors and improve the drug effect.
2) The system has the functions of medicine-taking time recording and calculating, so that the medicine-changing reminding can be carried out in time, and the damage to the body of a patient caused by long-term taking of certain medicine is avoided.
3) The system is provided with a simulation analysis function, and can perform simulation analysis according to the condition of a patient, so that a doctor can intuitively know the expected treatment effect of each medication guide.
Drawings
Fig. 1 is a system block diagram of a clinical intelligent decision platform according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a clinical intelligent decision platform, including:
the target parameter acquisition module is used for acquiring the medical history, the medication history, the current examination report, the current physical state, the medication allergy history and the details of the currently taken medicine of the patient, wherein the details of the medicine comprise the name, the dosage and the time of the medicine; the body state at least comprises the current body temperature, blood pressure and whether the body has external damage; during data acquisition, acquiring in a blank filling mode, mining target data in diagnosis and treatment historical data of a patient through a data mining module, and filling in and inputting by the patient in a questionnaire mode if the target data is not acquired;
the treatment curve drawing module is used for drawing a treatment curve according to the medical history and the medication history of the patient so as to obtain effective medicinal components; during drawing, firstly, classifying the disease species in the medical history and the medication history in a mutually corresponding mode, and then respectively drawing a treatment curve corresponding to each disease species according to the classification result; the effective component is a main component contained in a medicament with the best treatment effect in the process of medication; the treatment effect is reflected by the medical history, and the medical history is reflected by the corresponding index change condition in each inspection report; the treatment curve takes the index change as the ordinate and the drug name as the abscissa;
the drug recommendation module is used for realizing acquisition of recommended drugs based on a BP neural network model according to the current examination report of the patient;
and the medicine taking guidance module is used for outputting corresponding medicine taking guidance suggestions according to the current examination report of the patient and the medicines recommended by the medicine recommendation module.
The medicine changing reminding module is used for obtaining the taken time of the currently taken medicine according to details of the currently taken medicine, then comparing the time with the medicine taking time limit corresponding to the medicine, if the time limit is exceeded, starting the early warning module to remind in a mode of popping up a dialog box, simultaneously starting the medicine recommending module to output the corresponding replaced medicine, and starting the medicine taking guiding module to output the corresponding medicine taking guiding suggestion, wherein the medicine taking guiding suggestion comprises the name, the dosage, the taking time, the time limit capable of being continuously taken and the taking attention.
The short message editing and sending module is used for filling the results output by the medicine recommending module and the medicine taking guiding module into a preset template and sending the result to the corresponding patient mobile terminal;
the simulation analysis model building module builds a patient body simulation model based on SIMULINK according to the body condition of the patient and the current examination report;
the simulation analysis module is used for driving the calculation analysis module to calculate and solve different parameters by taking the input medicines, the dosage, the taking time and the continuous taking period as actuation conditions; after the relationship between the medicine, the dosage, the taking time and the continuous taking period and each element in the simulation analysis model building module is established, the parameters are changed in a specified range, so that various calculation analysis modules are driven to calculate and solve different parameters;
and the central processing unit is used for coordinating the work of the modules.
In this embodiment, the medicine recommending module completes acquisition of recommended medicines through the following steps;
s1, mining the inspection result based on the current inspection report, and then obtaining a corresponding recommended drug table based on the inspection result and the BP neural network model; specifically, firstly, mining an inspection result in a current inspection report through a data mining module, then inputting the mined inspection result into a BP neural network model as an input item to obtain a corresponding medicine data set, and then filling the obtained medicine data into a prefabricated EXCEL table one by one to obtain a recommended medicine table;
s2, based on the details of the medicines which are taken currently, eliminating the medicines which correspond to the illness state being treated and the medicines which are in conflict with the medicines; specifically, firstly, a medicine variety is obtained from details of medicines which are taken currently through a data mining module, then a corresponding disease name is output according to the medicine variety, similarity comparison is carried out on the disease name and the disease name output in a current inspection report, if the similarity is larger than 90%, the same disease is considered, and medicines corresponding to the disease in a recommended medicine table are removed; then, acquiring medicine component information from the details of the currently taken medicines through a data mining module, mining medicine components in a gram with the medicine component information in a database, outputting corresponding medicines according to the gram medicine components, and then rejecting corresponding medicines in a recommended medicine table;
s3, acquiring drug allergy components based on the drug allergy history, and then excavating drugs containing the drug allergy components in the drug recommendation table obtained in the step S2 and removing the drugs; specifically, drug allergy components are mined in the drug allergy history through a data mining module, then drugs containing the drug allergy components are mined in the drug recommendation table obtained in the step S2 and removed; preferably, allergy detection reports may also be included;
s4, obtaining the current body bearing capacity of the patient based on the current examination report and the current body state of the patient, and eliminating the medicines with side effects outside the body bearing capacity range; specifically, a patient body simulation model is constructed according to the physical condition of the patient and the current examination report; taking the medicines in the medicine recommendation table obtained in the step S3 as used medicines, taking the standard dosage of the used medicines as an actuating condition to drive the calculation analysis module to calculate and solve different parameters, and if the solution result is not tolerable, proposing the corresponding medicines;
s5, based on the effective medicine components, selecting medicines containing the effective medicine components from the recommended medicine table obtained in the step S4; firstly, based on the disease to be treated at present, namely the disease which is not treated by the drug at present, calling a corresponding treatment curve to obtain a corresponding drug active ingredient, and then selecting the drug containing the active drug ingredient from the recommended drug list obtained in the step S4;
s6, based on the least cost principle/the best efficacy principle, selecting the final medicine from the recommended medicine table obtained in the step S5. When the step is used, the final medicine can be selected according to the selected condition of the patient.
In the embodiment, the crawler module crawls clinical diagnosis and treatment medication laws of various old traditional Chinese medicines on the internet to acquire data such as usage amount, taboo, interaction, medication of special population with taboo and the like of various medicines to construct a corresponding BP neural network model, and then current inspection report data of a patient is input into the BP neural network model to acquire recommended medicines. The invention can inherit the academic experience and thought of the famous old traditional Chinese medicine and search the medical treatment law, thereby realizing the cross-regional and different-time traditional Chinese medicine diagnosis and treatment of primary physicians.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (7)
1. A clinical intelligent decision platform, characterized by: the method comprises the following steps:
the target parameter acquisition module is used for acquiring the medical history, the medication history, the current examination report, the current physical state, the medication allergy history and the details of the currently taken medicines of the patient;
the treatment curve drawing module is used for drawing a treatment curve according to the medical history and the medication history of the patient so as to obtain effective medicinal components;
the drug recommendation module is used for realizing acquisition of recommended drugs based on a BP neural network model according to the current examination report of the patient;
and the medicine taking guidance module is used for outputting corresponding medicine taking guidance suggestions according to the current examination report of the patient and the medicines recommended by the medicine recommendation module.
2. A clinical intelligence decision platform according to claim 1, wherein: when the treatment curve drawing module draws, firstly, the disease types in the disease history and the medication history need to be classified in a mutually corresponding mode, and then, the treatment curve corresponding to each disease type is drawn respectively according to the classification result.
3. A clinical intelligence decision platform according to claim 1, wherein: the medication details include the name of the medication, the amount used, and the time taken.
4. A clinical intelligence decision platform according to claim 1, wherein: the drug recommending module finishes acquiring recommended drugs through the following steps;
s1, mining the inspection result based on the current inspection report, and then obtaining a corresponding recommended drug table based on the inspection result and the BP neural network model;
s2, based on the details of the medicines which are taken currently, eliminating the medicines which correspond to the illness state being treated and the medicines which are in conflict with the medicines;
s3, acquiring drug allergy components based on the drug allergy history, and then excavating drugs containing the drug allergy components in the drug recommendation table obtained in the step S2 and removing the drugs;
s4, obtaining the current body bearing capacity of the patient based on the current examination report and the current body state of the patient, and eliminating the medicines with side effects outside the body bearing capacity range;
s5, based on the effective medicine components, selecting medicines containing the effective medicine components from the recommended medicine table obtained in the step S4;
s6, based on the least cost principle/the best efficacy principle, selecting the final medicine from the recommended medicine table obtained in the step S5.
5. A clinical intelligence decision platform according to claim 1, wherein: further comprising:
the medicine changing reminding module is used for obtaining the taken time of the currently taken medicine according to details of the currently taken medicine, then comparing the time with the medicine taking time limit corresponding to the medicine, if the time limit is exceeded, starting the early warning module to remind in a mode of popping up a dialog box, simultaneously starting the medicine recommending module to output the corresponding replaced medicine, and starting the medicine taking guiding module to output the corresponding medicine taking guiding suggestion, wherein the medicine taking guiding suggestion comprises the name, the dosage, the taking time, the time limit capable of being continuously taken and the taking attention.
6. A clinical intelligence decision platform according to claim 1, wherein: further comprising:
and the short message editing and sending module is used for filling the results output by the medicine recommending module and the medicine taking guiding module into a preset template and sending the result to the corresponding patient mobile terminal.
7. A clinical intelligence decision platform according to claim 1, wherein: further comprising:
the simulation analysis model building module is used for building a patient body simulation model according to the physical condition of the patient and the current examination report;
and the simulation analysis module is used for driving the calculation analysis module to calculate and solve different parameters by taking the input medicines, the dosage, the taking time and the continuous taking period as actuation conditions.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111696678A (en) * | 2020-06-15 | 2020-09-22 | 中南大学 | Deep learning-based medication decision method and system |
| CN112017745A (en) * | 2020-09-08 | 2020-12-01 | 平安科技（深圳）有限公司 | Decision information recommendation method, decision information recommendation device, medicine information recommendation method, medicine information recommendation device, equipment and medium |
| CN112233752A (en) * | 2020-12-14 | 2021-01-15 | 强基(上海)医疗器械有限公司 | Information processing method and device for endoscope examination report |
| CN112309519A (en) * | 2020-10-26 | 2021-02-02 | 浙江大学 | Electronic medical record medication structured processing system based on multiple models |
| CN112349371A (en) * | 2020-11-18 | 2021-02-09 | 南通市第一人民医院 | Chemotherapy patient drug record evaluation method and device |
| CN112568879A (en) * | 2020-12-09 | 2021-03-30 | 中国人民解放军海军军医大学第一附属医院 | Monitoring of blood flow mechanics and instruction system of using medicine |
| CN112885487A (en) * | 2021-03-18 | 2021-06-01 | 宁夏医科大学总医院 | Drug gene detection project management system |
| CN113657970A (en) * | 2021-08-30 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Artificial intelligence based medicine recommendation method, device, equipment and storage medium |
| CN118280512A (en) * | 2024-04-05 | 2024-07-02 | 泰昊乐生物科技有限公司 | Personalized treatment scheme recommendation method and system based on artificial intelligence |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111696678A (en) * | 2020-06-15 | 2020-09-22 | 中南大学 | Deep learning-based medication decision method and system |
| CN112017745A (en) * | 2020-09-08 | 2020-12-01 | 平安科技（深圳）有限公司 | Decision information recommendation method, decision information recommendation device, medicine information recommendation method, medicine information recommendation device, equipment and medium |
| WO2021151357A1 (en) * | 2020-09-08 | 2021-08-05 | 平安科技（深圳）有限公司 | Decision information recommendation method and apparatus, medicine information recommendation method and apparatus, and device and medium |
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| CN112309519A (en) * | 2020-10-26 | 2021-02-02 | 浙江大学 | Electronic medical record medication structured processing system based on multiple models |
| CN112349371A (en) * | 2020-11-18 | 2021-02-09 | 南通市第一人民医院 | Chemotherapy patient drug record evaluation method and device |
| CN112568879A (en) * | 2020-12-09 | 2021-03-30 | 中国人民解放军海军军医大学第一附属医院 | Monitoring of blood flow mechanics and instruction system of using medicine |
| CN112233752A (en) * | 2020-12-14 | 2021-01-15 | 强基(上海)医疗器械有限公司 | Information processing method and device for endoscope examination report |
| CN112885487A (en) * | 2021-03-18 | 2021-06-01 | 宁夏医科大学总医院 | Drug gene detection project management system |
| CN113657970A (en) * | 2021-08-30 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Artificial intelligence based medicine recommendation method, device, equipment and storage medium |
| CN113657970B (en) * | 2021-08-30 | 2024-06-28 | 深圳平安医疗健康科技服务有限公司 | Medicine recommendation method, device, equipment and storage medium based on artificial intelligence |
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