CN117219247B - Intelligent management system for patient treatment - Google Patents
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- 238000007781 pre-processing Methods 0.000 claims abstract description 44
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- 238000005314 correlation function Methods 0.000 claims 2
- 238000000034 method Methods 0.000 abstract description 7
- 208000017667 Chronic Disease Diseases 0.000 description 8
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Abstract
The application relates to an intelligent management system for patient treatment, the field is intelligent medical technology field, the system includes: the data acquisition module is used for acquiring relevant data of a patient, and the relevant data at least comprises: patient visit data, lifestyle survey data; the data preprocessing module is used for preprocessing the related data of the patient and generating a first output result based on the preprocessing result; the prediction module is used for inputting the first output result into a pre-constructed state prediction model to obtain a second output result; the correction module is used for correcting the second output result to obtain a third output result; and the early warning module is used for sending out early warning prompt according to the third output result. According to the method and the device, related diseases are predicted in advance by combining multidimensional data, so that middle-high risk patients can be screened out, early warning prompt is made for the related patients, unnecessary loss can be effectively avoided, and the intelligent management level of medical information is effectively improved.
Description
Technical Field
The application relates to the technical field of intelligent medical treatment, in particular to an intelligent management system for patient treatment.
Background
With the development of scientific technology, the information technology is increasingly popularized, so that the intelligent management requirement of hospitals on information is continuously improved.
In recent years, as the population ages, chronic diseases are seriously endangering the life and health of human beings, and become a serious public health problem in the global scope. The chronic disease is a general term that chronic disease is not a specific disease, but a type of disease is hidden, the etiology is complex, the course of the disease is long, the disease is not prolonged, and some chronic disease is not completely known, so that the treatment information of a chronic disease patient has a longer period and is complex, and the chronic disease treatment method has important significance for the information tracking management of the chronic disease patient.
The tracking management mode in the prior art mainly depends on the follow-up doctor to carry out manual periodical tracking follow-up or is triggered under the condition that serious symptoms appear in patients, so that the manual workload is huge, time and labor are consumed, related diseases cannot be accurately predicted in advance, intervention treatment is carried out when the serious symptoms appear, and the mode cannot be called as an effective chronic disease management mode.
Therefore, it is highly desirable to provide an intelligent management system for patient care that can improve the level of intelligent management of medical information.
Disclosure of Invention
Based on this, there is a need to provide an intelligent management system for patient care that improves the level of intelligent management of medical information, the system comprising:
the data acquisition module is used for acquiring relevant data of a patient, and the relevant data at least comprises: patient visit data, lifestyle survey data;
the data preprocessing module is used for preprocessing the patient-related data and generating a first output result based on a preprocessing result;
the prediction module is configured to input the first output result into a pre-constructed state prediction model, to obtain a second output result, where the state prediction model includes:
wherein,representing a second output result,/->Represents the initial time +_>Indicates the current time, ++>Indicating the proportion of disease occurrence,/->Representing the state quantity in the target period +.>Corresponding regulatory function, +.>、/>All represent weight coefficients, ">Representing a first associated value,/->Representing a second association value;
the correction module is used for correcting the second output result to obtain a third output result;
and the early warning module is used for sending out early warning prompt according to the third output result.
Optionally, the patient visit data includes at least one of: image data, diagnostic data, patient basic information, the data preprocessing module comprising a first preprocessing unit for:
performing format conversion, smoothing and noise reduction on the first image data to obtain second image data in a unified format;
comparing the second image data with standard image data to determine abnormal image data;
decomposing the abnormal image data according to a preset size to obtain a decomposition quantity value;
determining a first standard value based on the decomposition number value and a first mapping table;
extracting key data in the diagnosis data and the basic patient information, and determining a second standard value and a third standard value based on a second mapping table and a third mapping table respectively;
and calculating the association coefficients of the first standard value, the second standard value and the third standard value by using the first association function to obtain a first association value.
Optionally, the lifestyle survey data includes at least one of: sleep data, diet data and physical exercise data, the data preprocessing module further comprises a second preprocessing unit for:
extracting key data in the sleep data, the diet data and the physical exercise data, and determining a fourth standard value, a fifth standard value and a sixth standard value based on a fourth mapping table, a fifth mapping table and a sixth mapping table respectively;
and calculating the association coefficients of the fourth standard value, the fifth standard value and the sixth standard value by using the second association function to obtain a second association value.
Optionally, the first association function includes:
wherein,representing a first associated value,/->Representing the association function +_>Representing the first correction factor, ">Representing a first standard value, +_>Representing a second standard value, +_>Representing a third standard value, ++>Representing the first association coefficient,/->Representing a first amount of time.
Optionally, the second association function includes:
wherein,representing a second associated value,/->Representing the association function +_>Representing a second correction factor, ">Represents a fourth standard value, ++>Representing a fifth standard value, +_>Represents a sixth standard value, ++>Representing a second association coefficient,/->Representing a second amount of time.
Optionally, the data preprocessing module further includes a first output result generating unit, where the first output result generating unit is configured to:
and generating a data set corresponding to the target patient based on the first association value and the second association value, namely the first output result.
Optionally, the prediction module includes a state prediction model generating unit, where the state prediction model generating unit is configured to:
preprocessing a plurality of patient related data in a historical database based on a data preprocessing module to obtain a plurality of preprocessing results, and generating a corresponding data set;
dividing the data set into a training set, a testing set and a verification set according to a preset proportion;
training, testing and verifying the initial state prediction model based on the training set, the testing set and the verification set respectively;
when the precision of the initial state prediction model is greater than a first preset value, training is completed to obtain a final state prediction model, and the method comprises the following steps:
wherein,representing a second output result,/->Represents the initial time +_>Indicates the current time, ++>Indicating the proportion of disease occurrence,/->Representing the state quantity in the target period +.>Corresponding regulatory function, +.>、/>All represent weight coefficients.
Optionally, the prediction module further includes a prediction unit, where the prediction unit is configured to:
and inputting the first output result into a state prediction model to obtain the second output result.
Optionally, correcting the second output result to obtain a third output result includes:
when the absolute value of the second output result is smaller than a second preset value, correcting the second output result by using a correction function to obtain a third output result, wherein the correction function comprises:
wherein,representing a third output result, ">Indicating correction value->Representing a second output result.
Optionally, according to the third output result, sending the early warning prompt includes:
and when the absolute value of the third output result is larger than a third preset value, sending an early warning prompt to the terminal.
The above-described intelligent management system for patient visits, the system comprising: the data acquisition module is used for acquiring relevant data of a patient, and the relevant data at least comprises: patient visit data, lifestyle survey data; the data preprocessing module is used for preprocessing the patient-related data and generating a first output result based on a preprocessing result; the prediction module is used for inputting the first output result into a pre-constructed state prediction model to obtain a second output result; the correction module is used for correcting the second output result to obtain a third output result; the early warning module is used for sending an early warning prompt according to the third output result, and the early warning prompt is used for predicting related diseases in advance by combining multidimensional data, so that middle and high risk patients can be screened out, the early warning prompt is made for the related patients, unnecessary loss can be effectively avoided, and the intelligent management level of medical information is effectively improved.
Drawings
FIG. 1 is a block diagram of an intelligent management system for patient visits in one embodiment.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
It should be understood that throughout this description, unless the context clearly requires otherwise, the words "comprise," "comprising," and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, it is the meaning of "including but not limited to".
It should also be appreciated that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
It should be noted that the terms "S1", "S2", and the like are used for the purpose of describing steps only, and are not intended to be limited to the order or sequence of steps or to limit the present application, but are merely used for convenience in describing the method of the present application and are not to be construed as indicating the sequence of steps. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.
In one embodiment, as shown in FIG. 1, there is provided an intelligent management system for patient visits, comprising:
the data acquisition module is used for acquiring relevant data of a patient, and the relevant data at least comprises: patient visit data, lifestyle survey data.
The patient visit data includes at least one of the following: image data, diagnosis data and patient basic information, wherein the image data can comprise CT, nuclear magnetic resonance or other imaging examination, the diagnosis data can comprise a preliminary diagnosis result of a doctor on a patient written on a medical record sheet or a clinical diagnosis result on a test report sheet, and the patient basic information can comprise information of age, gender, past medical history, physical signs and the like; the lifestyle survey data includes at least one of: sleep data, diet data and physical exercise data, wherein the sleep data can comprise sleep time, sleep time and the like, the diet data can comprise water intake, three-meal collocation and three-meal time, and the physical exercise data can comprise exercise interval time, exercise quantity and the like; the data acquisition can be obtained in the forms of oral inquiry, questionnaire survey and the like, and finally uploaded to the electronic system terminal.
And the data preprocessing module is used for preprocessing the patient-related data and generating a first output result based on the preprocessing result.
It should be noted that, the data preprocessing module includes a first preprocessing unit, where the first preprocessing unit is configured to:
performing format conversion, smoothing and noise reduction on the first image data to obtain second image data in a unified format;
comparing the second image data with standard image data to determine abnormal image data, wherein the standard image data is normal and healthy related part image data, and determining the corresponding image data as abnormal data when the parameter value difference value of the second image data and the standard image data is larger than a preset value through comparing parameters such as pixel value and the like, and storing the image data in a preset range of the abnormal image data, namely the determined abnormal image data, so as to be used for obtaining a decomposition number value later, wherein the preset value and the preset range value can be set according to actual requirements;
decomposing the abnormal image data according to a preset size to obtain a decomposition number value, wherein the preset size can be set according to actual requirements, and the abnormal image data is decomposed according to the size of a rectangular block with the preset size of 0.5mm multiplied by 0.3mm, wherein the n value is the decomposition number value;
determining a first standard value based on the decomposition quantity value and a first mapping table, wherein the content of the first mapping table comprises the decomposition quantity value and a corresponding expert score, and the first standard value is the expert score;
extracting key data in the diagnosis data and the basic patient information, and determining a second standard value and a third standard value based on a second mapping table and a third mapping table respectively, wherein the contents of the second mapping table and the third mapping table are text data and expert scores corresponding to the text data, the second standard value and the third standard value are expert score values, and the second standard value and the third standard value can be determined by comparing the similarity of the key data and the text data in the mapping table;
calculating association coefficients of the first standard value, the second standard value and the third standard value by using a first association function to obtain a first association value, wherein the first association function comprises:
wherein,representing a first associated value,/->Representing the association function +_>Representing the first correction factor, ">Representing a first standard value, +_>Representing a second standard value, +_>Representing a third standard value, ++>Representing the first association coefficient,/->Representing a first amount of time;
further, the data preprocessing module further comprises a second preprocessing unit, and the second preprocessing unit is used for:
extracting key data in the sleep data, the diet data and the physical exercise data, and determining a fourth standard value, a fifth standard value and a sixth standard value based on the fourth mapping table, the fifth mapping table and the sixth mapping table respectively, wherein the content of the fifth mapping table is text data and expert scores corresponding to the text data, the content of the fourth mapping table and the sixth mapping table is numerical value or text data and expert scores corresponding to the text data, the fourth standard value, the fifth standard value and the sixth standard value are expert score values, and the fourth standard value, the fifth standard value and the sixth standard value can be determined by comparing the similarity of the key data and the text data or the numerical value in the mapping table;
calculating association coefficients of the fourth standard value, the fifth standard value and the sixth standard value by using a second association function to obtain a second association value, wherein the second association function comprises:
wherein,representing a second associated value,/->Representing the association function +_>Representing a second correction factor, ">Represents a fourth standard value, ++>Representing a fifth standard value, +_>Represents a sixth standard value, ++>Representing a second association coefficient,/->Representing a second amount of time.
The above-mentioned time amount may be doctor inquiry time or life habit data investigation time.
Still further, the data preprocessing module further includes a first output result generating unit, where the first output result generating unit is configured to:
based on the first association value and the second association value, a data set corresponding to the target patient is generated, that is, the first output result, and the data set may beWherein->Representing the target patient code.
And the prediction module is used for inputting the first output result into a pre-constructed state prediction model to obtain a second output result.
The prediction module includes a state prediction model generating unit, where the state prediction model generating unit is configured to:
preprocessing a plurality of patient related data in a historical database based on a data preprocessing module to obtain a plurality of preprocessing results, and generating a corresponding data set;
dividing the data set into a training set, a testing set and a verification set according to a preset proportion;
training, testing and verifying the initial state prediction model based on the training set, the testing set and the verification set respectively;
when the precision of the initial state prediction model is greater than a first preset value, wherein the first preset value can be set according to actual requirements, training is completed, and the final state prediction model is obtained, and the method comprises the following steps:
wherein,representing a second output result,/->Represents the initial time +_>Indicates the current time, ++>Indicating the proportion of disease occurrence,/->Representing state quantity in target time period/>Corresponding regulatory function, +.>、/>The weight coefficients are represented, wherein the initial time is the initial consultation time, and the current time is the time for real-time management by adopting the system.
Further, the prediction module further includes a prediction unit, where the prediction unit is configured to:
and inputting the first output result into a state prediction model to obtain the second output result.
And the correction module is used for correcting the second output result to obtain a third output result.
It should be noted that this step specifically includes:
when the absolute value of the second output result is smaller than a second preset value, wherein the second preset value can be set according to actual requirements, the second output result is corrected by using a correction function to obtain a third output result, and the correction function comprises:
wherein,representing a third output result, ">Indicating correction value->Representing a second output result.
And the early warning module is used for sending out early warning prompt according to the third output result.
It should be noted that this step specifically includes:
when the absolute value of the third output result is larger than a third preset value, an early warning prompt is sent to the terminal, wherein the third preset value can be set according to actual requirements, the third preset value is larger than the second preset value, the terminal can comprise a doctor terminal and a patient terminal, the doctor terminal can judge whether active intervention is needed according to the early warning prompt result, and the patient can judge whether deep examination and treatment are needed according to prompt information so as to prevent the deterioration of illness state.
In the above-mentioned intelligent management system for patient visits, the system includes: the data acquisition module is used for acquiring relevant data of a patient, and the relevant data at least comprises: patient visit data, lifestyle survey data; the data preprocessing module is used for preprocessing the patient-related data and generating a first output result based on a preprocessing result; the prediction module is used for inputting the first output result into a pre-constructed state prediction model to obtain a second output result; the correction module is used for correcting the second output result to obtain a third output result; the early warning module is used for sending an early warning prompt according to the third output result, and the early warning prompt is used for predicting related diseases in advance by combining multidimensional data, so that middle and high risk patients can be screened out, the early warning prompt is made for the related patients, unnecessary loss can be effectively avoided, and the intelligent management level of medical information is effectively improved.
The various modules in the intelligent management system for patient visits described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (6)
1. An intelligent management system for patient visits, comprising:
the data acquisition module is used for acquiring relevant data of a patient, and the relevant data at least comprises: patient visit data, lifestyle survey data;
the data preprocessing module is used for preprocessing the patient-related data and generating a first output result based on a preprocessing result;
the prediction module is configured to input the first output result into a pre-constructed state prediction model, to obtain a second output result, where the state prediction model includes:
wherein,representing a second output result,/->Represents the initial time +_>Indicates the current time, ++>The occurrence ratio of the disease is represented,representing the state quantity in the target period +.>Corresponding regulatory function, +.>、/>All represent weight coefficients, ">Representing a first associated value,/->Representing a second association value;
the correction module is used for correcting the second output result to obtain a third output result;
the early warning module is used for sending out early warning prompt according to the third output result;
wherein the patient visit data includes at least one of: image data, diagnostic data, patient basic information, the data preprocessing module comprising a first preprocessing unit for:
performing format conversion, smoothing and noise reduction on the first image data to obtain second image data in a unified format;
comparing the second image data with standard image data to determine abnormal image data;
decomposing the abnormal image data according to a preset size to obtain a decomposition quantity value;
determining a first standard value based on the decomposition number value and a first mapping table;
extracting key data in the diagnosis data and the basic patient information, and determining a second standard value and a third standard value based on a second mapping table and a third mapping table respectively;
calculating association coefficients of the first standard value, the second standard value and the third standard value by using a first association function to obtain a first association value;
the lifestyle survey data includes at least one of: sleep data, diet data and physical exercise data, the data preprocessing module further comprises a second preprocessing unit for:
extracting key data in the sleep data, the diet data and the physical exercise data, and determining a fourth standard value, a fifth standard value and a sixth standard value based on a fourth mapping table, a fifth mapping table and a sixth mapping table respectively;
calculating association coefficients of the fourth standard value, the fifth standard value and the sixth standard value by using a second association function to obtain a second association value;
the first correlation function includes:
wherein,representing a first associated value,/->Representing the association function +_>Representing the first correction factor, ">Representing a first standard value, +_>Representing a second standard value, +_>Representing a third standard value, ++>Representing the first association coefficient,/->Representing a first amount of time;
the second correlation function includes:
wherein,representing a second associated value,/->Representing the association function +_>Representing a second correction factor, ">Represents a fourth standard value, ++>Representing a fifth standard value, +_>Represents a sixth standard value, ++>Representing a second association coefficient,/->Representing a second amount of time.
2. The intelligent management system for patient visits of claim 1, wherein the data preprocessing module further includes a first output result generation unit for:
and generating a data set corresponding to the target patient based on the first association value and the second association value, namely the first output result.
3. The intelligent management system for patient visits of claim 2, wherein the prediction module includes a state prediction model generation unit for:
preprocessing a plurality of patient related data in a historical database based on a data preprocessing module to obtain a plurality of preprocessing results, and generating a corresponding data set;
dividing the data set into a training set, a testing set and a verification set according to a preset proportion;
training, testing and verifying the initial state prediction model based on the training set, the testing set and the verification set respectively;
and when the precision of the initial state prediction model is larger than a first preset value, training is completed, and the final state prediction model is obtained.
4. The intelligent management system for patient visits of claim 3, wherein the prediction module further comprises a prediction unit for:
and inputting the first output result into a state prediction model to obtain the second output result.
5. The intelligent management system for patient visits of claim 4, wherein correcting the second output results to obtain a third output result includes:
when the absolute value of the second output result is smaller than a second preset value, correcting the second output result by using a correction function to obtain a third output result, wherein the correction function comprises:
wherein,representing a third output result, ">Indicating correction value->Representing a second output result.
6. The intelligent management system for patient visits of claim 5, wherein issuing an early warning prompt based on the third output result includes:
and when the absolute value of the third output result is larger than a third preset value, sending an early warning prompt to the terminal.
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| 基于人工智能的新型冠状病毒肺炎放射科预警系统研究;张凯;刘秀民;陈玉环;谭佳;宋婷妮;李真林;;中国医疗设备;20200610(第06期);全文 * |
| 市域脑卒中疾病与气象因素的关系及预测;程学伟;韩兆洲;;气象;20180621(第06期);全文 * |
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