CN116206774A - Method and system for automatically matching nursing treatment scheme by combining big data - Google Patents
Method and system for automatically matching nursing treatment scheme by combining big data Download PDFInfo
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Abstract
The application relates to the technical field of medical systems, in particular to a method and a system for automatically matching nursing treatment schemes by combining big data. The method comprises the following steps: dividing the combined data of the inspection report according to classification of departments and disease types to obtain standard inspection items corresponding to the disease types; acquiring data of all patients in the department, and establishing a disease rehabilitation condition model of a rehabilitation patient by combining the standard examination items; and inputting a patient examination report into the disease rehabilitation condition model, and matching to obtain a nursing treatment scheme. According to the nursing method and system, the proper nursing scheme can be automatically matched for the sign and the inspection result of each patient based on big data, the accuracy of the establishment of the nursing scheme is improved, and medical staff is assisted to treat and care the patient.
Description
Technical Field
The application relates to the technical field of medical systems, in particular to a method and a system for automatically matching nursing treatment schemes by combining big data.
Background
In the traditional medical care mode, the final care scheme is generally obtained through doctor's advice to the patient, and in the process, the doctor generally follows the physical condition of the patient further according to self experience and the subsequent surface diagnosis and bed detection modes, and adjusts the care scheme, but the physical sign, such as the physical condition and recovery speed, of each patient are different, and the doctor cannot necessarily adjust the optimal care scheme for each patient in time based on the condition, so that the accuracy of the care scheme is affected.
Disclosure of Invention
In order to solve or partially solve the problems in the related art, the application provides a method and a system for automatically matching a nursing treatment scheme by combining big data, which can automatically match a proper nursing scheme for the sign and the inspection result of each patient based on the big data, and improve the accuracy of the formulation of the nursing scheme so as to assist medical staff to treat and care the patient.
In a first aspect, an embodiment of the present application provides a method for automatically matching a care treatment plan in combination with big data, which adopts the following technical scheme:
a method of automatically matching care treatment regimens in conjunction with big data, comprising: dividing the combined data of the inspection report according to classification of departments and disease types to obtain standard inspection items corresponding to the disease types; acquiring data of all patients in the department, and establishing a disease rehabilitation condition model of a rehabilitation patient by combining the standard examination items; and inputting a patient examination report into the disease rehabilitation condition model, and matching to obtain a nursing treatment scheme.
Through adopting above-mentioned technical scheme, through classifying according to department and disease type can divide the combination data of inspection report to obtain the standard inspection item that every disease type corresponds, through the data of obtaining all patients in the department, can establish the disease rehabilitation situation model of recovered patient again in combination with standard inspection item, when importing patient's inspection report into disease rehabilitation situation model, can match automatically and obtain the nursing treatment plan, and then realize that sign and inspection result to every patient match automatically and get suitable nursing plan, improve the accuracy of nursing plan, with supplementary medical personnel to carry out treatment, nursing work to the patient.
Optionally, the step of obtaining all patient data in the department and combining the standard examination item to build a disease rehabilitation condition model of the rehabilitation patient includes: acquiring all patient data in the department, and performing data modeling on the patient data of the same disease type to obtain a disease type data model; performing line analysis on the treatment process data in the disease type data model by combining the standard examination items corresponding to each disease type to obtain a first analysis line; performing line analysis on the health standard data by combining the standard examination items corresponding to each disease type to obtain a second analysis line; calculating a deviation value of the first analysis line based on the second analysis line, and judging the patient as a rehabilitation patient when the deviation value reaches a health deviation range, so as to obtain the relationship between the initial sign condition of the rehabilitation patient and the recovery date; and matching a nursing treatment scheme adopted corresponding to the recovery date based on the relation between the initial physical sign condition of the patient and the recovery date, and obtaining the disease recovery condition model of the patient.
By adopting the technical scheme, through acquiring all patient data in a department, data modeling can be carried out on the patient data of the same disease type to obtain a disease type data model, line analysis can be carried out on treatment process data in the model by combining standard examination items corresponding to each disease type, a first analysis line can be obtained, line analysis can be carried out on health standard data by combining standard examination items corresponding to each disease type, a second analysis line can be obtained, further the deviation value of the first analysis line based on the second analysis line can be calculated, when the deviation value reaches a healthy deviation range, the patient can be judged to be a rehabilitation patient, the relationship between the initial condition of the patient to be treated and the recovery date can be obtained, the nursing treatment scheme adopted by the corresponding recovery date can be matched based on the relationship between the initial condition of the patient to be treated and the recovery date, the disease recovery condition model of the rehabilitation patient can be obtained, the subsequent nursing treatment scheme adopted by the corresponding rehabilitation patient can be matched based on the examination report of the patient, and the nursing treatment scheme adopted by the similar examination result can be used as the nursing treatment scheme of the patient, and the nursing treatment and medical care personnel can carry out treatment and medical care work on the patient.
Optionally, the inputting the patient examination report into the disease rehabilitation condition model, matching to obtain a care treatment scheme, includes: inputting the patient examination report into the data rehabilitation model, and calculating the similarity of the patient examination report and the combined data in the examination report of the rehabilitation patient; when the similarity meets the preset similarity, the nursing treatment scheme corresponding to the rehabilitation patient is combined with doctor orders to carry out correction treatment, and the nursing treatment scheme of the patient is obtained.
By adopting the technical scheme, the similarity of the combined data in the inspection report of the recovered patient is calculated by inputting the inspection report of the patient into the data recovery model, so that the nursing treatment scheme adopted by the recovered patient with high similarity can be found out, and the accurate nursing treatment scheme of the patient can be obtained by carrying out correction treatment in combination with doctor orders.
Optionally, the classifying the combined data of the inspection report according to the classification of the department and the disease type to obtain standard inspection items corresponding to each disease type includes: dividing the combined data of the inspection report according to classification of departments and disease types; and when the occurrence frequency of the examination items in the examination report aiming at the same disease type meets the preset occurrence frequency, taking the examination items as the standard examination items of the disease type.
By adopting the technical scheme, the combined data of the inspection report is divided to obtain each inspection item, and when the frequency of occurrence of the inspection items in the inspection report aiming at the same disease type meets the preset occurrence frequency, the inspection item can be used as a standard inspection item of the disease type, namely, the item which is required to be inspected and analyzed for the disease type can be accurately positioned.
Optionally, the patient profile includes: examination reports, treatment process data, and physical rehabilitation status.
By adopting the technical scheme, the whole process data from diagnosis, treatment and rehabilitation of the patient can be obtained by obtaining the examination report, the treatment process data and the physical rehabilitation state of the patient.
In a second aspect, an embodiment of the present application provides a system for automatically matching a care treatment plan in combination with big data, which adopts the following technical scheme:
a data adaptation system for use in a medical system, comprising: the classification module is used for classifying the combined data of the inspection report according to the classification of the department and the disease type to obtain a standard inspection item corresponding to the disease type; the model building module is used for obtaining all patient data in the department and building a disease rehabilitation condition model of a rehabilitation patient by combining the standard examination item; and the matching module is used for inputting the patient examination report into the disease rehabilitation condition model and matching to obtain a nursing treatment scheme.
Through adopting above-mentioned technical scheme, can divide the combination data of inspection report according to the classification of department and disease type through classification module to obtain the standard inspection item that every disease type corresponds, obtain the data of all patients in the department through model establishment module, combine standard inspection item again, can establish the disease rehabilitation situation model of recovered patient, when importing the patient inspection report into disease rehabilitation situation model through the matching module, can automatic match and obtain the nursing treatment plan, and then realize that the sign and the inspection result to every patient match automatically and get suitable nursing plan, improve the accuracy of nursing plan, with supplementary medical personnel to carry out treatment, nursing work to the patient.
Optionally, the model building module includes: the modeling unit is used for acquiring all patient data in the department, and carrying out data modeling on the patient data of the same disease type to obtain a disease type data model; the first line analysis unit is used for carrying out line analysis on the treatment process data in the disease type data model by combining the standard examination items corresponding to each disease type to obtain a first analysis line; the second line analysis unit is used for carrying out line analysis on the health standard data by combining the standard examination items corresponding to each disease type to obtain a second analysis line; the first calculating unit is used for calculating the deviation value of the first analysis line based on the second analysis line, and judging that the patient is a rehabilitation patient when the deviation value reaches a healthy deviation range, so as to obtain the relationship between the initial sign condition of the patient and the recovery date; the matching unit is used for matching the nursing treatment scheme adopted corresponding to the recovery date based on the relation between the initial physical sign condition of the patient and the recovery date, and obtaining the disease recovery condition model of the patient.
By adopting the technical scheme, all patient data in a department can be obtained through the modeling unit, data modeling can be carried out on the patient data of the same disease type to obtain a disease type data model, the first line analysis unit can carry out line analysis by combining treatment process data in the model with standard examination items corresponding to each disease type, a first analysis line can be obtained, the second line analysis unit can carry out line analysis on health standard data by combining standard examination items corresponding to each disease type, a second analysis line can be obtained, further, the deviation value of the first analysis line based on the second analysis line can be calculated through the first calculation unit, the patient can be judged to be a rehabilitation patient when the deviation value reaches a health deviation range, the relationship between the initial diagnosis physical sign condition and the recovery date of the rehabilitation patient is obtained, the nursing treatment scheme adopted by the corresponding recovery date is matched through the matching unit based on the relationship between the initial diagnosis physical sign condition and the recovery date of the rehabilitation patient, the disease rehabilitation patient model is obtained, the follow-up nursing treatment scheme corresponding to the patient is conveniently matched based on the examination report of the patient, the medical care system is adopted by the patient, and the medical care system is used as an auxiliary nursing scheme for nursing patients.
Optionally, the matching module includes: a second calculation unit for inputting the patient examination report into the data rehabilitation model, and calculating the similarity with the combined data in the examination report of the rehabilitation patient; and the correction unit is used for correcting the nursing treatment scheme corresponding to the rehabilitation patient according to the doctor's advice when the similarity meets the preset similarity, so as to obtain the nursing treatment scheme of the patient.
By adopting the technical scheme, the patient examination report is input into the data rehabilitation model through the second calculation unit, the similarity of the combined data in the examination report of the rehabilitation patient is calculated, and the nursing treatment scheme adopted by the rehabilitation patient with high similarity can be found out, so that the correction treatment is carried out by combining the doctor orders through the correction unit, and the accurate nursing treatment scheme of the patient can be obtained.
Optionally, the classification module includes: the classification unit is used for classifying the combined data of the inspection report according to classification of departments and disease types; and a standard examination item determination unit that takes an examination item in the inspection examination report as the standard examination item of the disease type when the frequency of occurrence of the examination item for the same disease type satisfies a preset occurrence frequency.
By adopting the technical scheme, the classification unit is used for dividing the combined data of the inspection report to obtain each inspection item, and the standard inspection item determining unit can be used for taking the inspection item in the inspection report as the standard inspection item of the disease type when the occurrence frequency of the inspection item in the inspection report aiming at the same disease type meets the preset occurrence frequency, so that the item which is required to be inspected and analyzed for the disease type can be accurately positioned.
Optionally, the patient profile includes: examination reports, treatment process data, and physical rehabilitation status.
By adopting the technical scheme, the whole process data from diagnosis, treatment and rehabilitation of the patient can be obtained by obtaining the examination report, the treatment process data and the physical rehabilitation state of the patient.
In summary, the present application includes at least one of the following beneficial technical effects:
the combined data of the inspection report can be divided according to classification of departments and disease types to obtain standard inspection items corresponding to each disease type, the disease recovery condition model of recovered patients can be established by acquiring data of all patients in the departments and combining the standard inspection items, and when the patient inspection report is input into the disease recovery condition model, a nursing treatment scheme can be automatically matched, so that the proper nursing scheme is automatically matched according to the physical sign and inspection result of each patient, the accuracy of the nursing scheme is improved, and medical staff is assisted to treat and care the patients.
Through obtaining all patient data in a department, data modeling can be carried out on the patient data of the same disease type to obtain a disease type data model, line analysis is carried out on treatment process data in the model by combining standard examination items corresponding to each disease type, a first analysis line can be obtained, line analysis is carried out on health standard data by combining standard examination items corresponding to each disease type, a second analysis line can be obtained, further, the deviation value of the first analysis line based on the second analysis line can be calculated, when the deviation value reaches a health deviation range, the patient can be judged to be a rehabilitation patient, the relationship between the initial physical sign condition of the patient to be diagnosed and the recovery date is obtained, the nursing treatment scheme adopted by the corresponding recovery date is matched based on the relationship between the initial physical sign condition of the patient to be diagnosed and the recovery date, and the disease rehabilitation condition model of the rehabilitation patient is obtained, so that the follow-up nursing treatment scheme corresponding to the rehabilitation patient can be directly based on the examination report of the patient is matched, and the nursing treatment scheme adopted by the patient corresponding to the similar examination result is used as the nursing treatment scheme of the patient.
By inputting the patient examination report into the data rehabilitation model, the similarity of the combined data in the examination report of the rehabilitation patient is calculated, and the nursing treatment scheme adopted by the rehabilitation patient with high similarity can be found out, so that the accurate nursing treatment scheme of the patient can be obtained by carrying out correction treatment in combination with doctor orders.
By dividing the combined data of the inspection report, each inspection item is obtained, and when the frequency of occurrence of the inspection item in the inspection report aiming at the same disease type meets the preset occurrence frequency, the inspection item can be used as a standard inspection item of the disease type, namely, the item which is required to be inspected and analyzed for the disease type can be accurately positioned.
Drawings
FIG. 1 is a flow chart of a method for automatically matching care treatment regimens in conjunction with big data according to an embodiment of the present application;
FIG. 2 is a schematic illustration of a specific flow of step S20 in the method of automatically matching a care treatment regimen with big data disclosed in FIG. 1;
FIG. 3 is a schematic representation of trends in a pre-meal blood glucose test analysis during hospitalization of a diabetic patient;
FIG. 4 is a schematic diagram showing a care regimen in a trend graph of the pre-meal blood glucose test analysis of the diabetic patient of FIG. 3 during hospitalization;
FIG. 5 is a schematic diagram of a data adapting system for a medical system according to another embodiment of the present disclosure;
fig. 6 is a schematic diagram of a specific structure of a model building module applied to the medical system data adapting system disclosed in fig. 5.
Detailed Description
The terminology used in the following embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It should also be understood that the term "and/or" as used in this application refers to and encompasses any or all possible combinations of one or more of the listed items.
The terms "first," "second," "third," and the like, are used below for descriptive purposes only and are not to be construed as implying or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", or a third "may explicitly or implicitly include one or more such feature, and in the description of embodiments of the present application, unless otherwise indicated, the meaning of" a plurality "is two or more.
In the related art, in the conventional medical care manner, a final care scheme is generally obtained through a doctor's order of a patient, and in the process, the doctor generally follows the physical condition of the patient according to his own experience and the subsequent surface diagnosis and bed detection manners and adjusts the care scheme, but the physical sign, such as the physical condition and recovery speed, of each patient are different, and based on this situation, the doctor may not necessarily adjust the optimal care scheme for each patient in time, so that the accuracy of the care scheme may be affected.
Therefore, in order to solve or partially solve the problems in the related art, the present application provides a method and a system for automatically matching a care treatment plan in combination with big data, which can automatically match a proper care plan for the sign and the inspection result of each patient based on the big data, and improve the accuracy of the formulation of the care plan, so as to assist the medical staff to perform treatment and care on the patient.
The following describes the technical scheme of the embodiments of the present application in detail with reference to the accompanying drawings.
Referring to fig. 1, a method for automatically matching care treatment regimens in conjunction with big data, comprising the steps of:
s10, dividing the combined data of the inspection report according to classification of departments and disease types to obtain standard inspection items corresponding to the disease types;
among them, hospitals generally have many different departments to divide patients to major doctors for treatment through the departments, and common departments include endocrinology, thyroid surgery, hepatobiliary surgery, gynecology, nephrology, oncology, general surgery, etc.
Specifically, when the department is endocrinology (also called endocrinology), the corresponding disease types include diabetes, hyperthyroidism (hyperthyroidism), hyperuricemia, metabolic syndrome, obesity, gout and the like, and when the disease types are diabetes, the corresponding combined data of the inspection report can include one or more data of fasting blood glucose, urine glucose, glycosylated hemoglobin, insulin release test, islet antibody and the like in different postprandial periods, and the inspection items related to the previous inspection of diabetes can be obtained by dividing the combined data of the inspection report, so that the standard inspection items of the inspection of diabetes can be determined, and the standard inspection items include, for example, fasting blood glucose, blood glucose in different postprandial periods, urine glucose; when the disease type is hyperthyroidism, the corresponding combined data of the inspection report may include one or more data of free triiodothyronine, free thyroxine, thyroid stimulating hormone and the like, and the inspection items related to the conventional inspection of hyperthyroidism can be obtained by dividing the combined data of the inspection report, so that the standard inspection items for inspecting diabetes can be determined, and the standard inspection items include, for example, free triiodothyronine, free thyroxine, thyroid stimulating hormone.
S20, acquiring data of all patients in a department, and establishing a disease rehabilitation condition model of a rehabilitation patient by combining standard examination items;
wherein the patient profile may include: examination reports, treatment process data, and physical rehabilitation status. The disease rehabilitation condition model of the rehabilitation patient can be understood as a collective model formed by summarizing treatment data of the rehabilitation patient corresponding to each disease type.
For example, all patient data in endocrinology is obtained, based on the disease type treated by endocrinology, the treatment process data of the patient corresponding to the standard examination items are obtained from the patient data by combining the standard examination items corresponding to each disease type, the treatment process data of the rehabilitation patient are screened, the treatment data comprise nursing treatment schemes, and the treatment data are collected to form a rehabilitation condition model.
S30, inputting a patient examination report into a disease rehabilitation condition model, and matching to obtain a nursing treatment scheme.
The patient examination report may include basic information (such as name, age, sex), examination room, disease type of examination, and examination item data of the patient. The patient examination report is input into the disease rehabilitation condition model to automatically match the examination item data of the rehabilitation patient similar to the patient examination report, and the corresponding nursing treatment scheme adopted by the rehabilitation patient can be obtained, so that the follow-up treatment scheme of the patient can be formulated based on the previous nursing treatment scheme, the accuracy of the formulation of the nursing scheme is improved, and the medical staff is assisted in carrying out treatment and nursing work on the patient.
Referring to fig. 2, in another embodiment, step S20 includes:
s21, acquiring all patient data in a department, and performing data modeling on the patient data of the same disease type to obtain a disease type data model;
the data modeling is performed on patient data of the same disease type, so that a disease type data model is obtained, each disease type can be understood to form a data model, for example, the data modeling is performed on diabetic patient data, a diabetes data model is obtained, and the data modeling is performed on thyroid patient data, so that a thyroid data model is obtained.
S22, carrying out line analysis on treatment process data in the disease type data model by combining standard examination items corresponding to each disease type to obtain a first analysis line;
the treatment process data can comprise treatment schemes and physical sign conditions adopted between the initial physical sign condition of the visit and the recovery date, wherein the physical sign conditions are detected data conditions.
For example, referring to fig. 3, a trend graph of a pre-meal blood glucose (i.e., fasting blood glucose) test analysis during hospitalization of a diabetic patient, the abscissa in fig. 3 represents the time axis of the hospitalization of the patient from 3 months 1 to 3 months 13, the ordinate represents the blood glucose axis, and the fold line shown within the two axes is the blood glucose profile during hospitalization of the patient, i.e., as the first analysis line.
S23, carrying out line analysis on the health standard data by combining standard examination items corresponding to each disease type to obtain a second analysis line;
based on the above example, fasting blood glucose is taken as a standard test item of diabetes, wherein the reference value of health standard data of fasting blood glucose is 3.9-6.1mmol/L, and referring to FIG. 3, blood glucose 3.9mmol/L and blood glucose 6.1mmol/L can be respectively taken as parallel reference lines based on time axis, and taken as a second analysis line.
S24, calculating a deviation value of the first analysis line based on the second analysis line, and judging that the patient is a rehabilitation patient when the deviation value reaches a healthy deviation range, so as to obtain the relationship between the initial physical sign condition of the patient and the recovery date;
the health deviation range can be determined based on the health standard data, when the patient checking data is continuously within the health deviation range, the patient can be judged to be recovered, and the relationship diagram between the initial checking data of the patient in the diagnosis and the recovery date can be obtained in turn, as shown in fig. 3.
S25, based on the relation between the initial sign condition of the patient to be treated and the recovery date, matching the nursing treatment scheme adopted by the corresponding recovery date to obtain the disease recovery condition model of the patient to be recovered.
The nursing treatment scheme can be designed around the aspects of medication type, medication times, medication amount, diet and the like. As shown in fig. 4, it can be understood that a model of the diabetic rehabilitation status of a rehabilitation patient adopts the following care treatment scheme during rehabilitation:
the treatment regimen of 3-1 (3 month No. 1) to 3-5 (3 month No. 5) is attention diet (diabetes diet), insulin injection;
3-6 is a diet of attention;
3-7 is to pay attention to diet and inject insulin;
the treatment regimen of 3-8 to 3-12 was the attention diet.
In another embodiment, step S30 includes:
s31, inputting a patient examination report into a data rehabilitation model, and calculating the similarity of the patient examination report and combined data in the examination report of a rehabilitation patient;
the nursing treatment scheme of the rehabilitation patient closest to the patient can be found through similarity comparison, and the nursing treatment scheme can be used as a reference basis for a subsequent doctor to formulate a treatment scheme for the patient.
And S32, when the similarity meets the preset similarity, correcting the nursing treatment scheme corresponding to the rehabilitation patient by combining with the doctor' S advice to obtain the nursing treatment scheme of the patient.
The correction processing combined with doctor orders can be understood as that a doctor corrects the nursing treatment scheme by acquiring nursing treatment schemes of similar rehabilitation patients and carrying out order indication based on the conditions of the patients and according to previous experience so as to obtain a more accurate nursing treatment scheme which is more suitable for the patients.
In another embodiment, step S10 includes:
s11, dividing the combined data of the inspection report according to classification of departments and disease types;
and S12, when the occurrence frequency of the examination items in the examination report aiming at the same disease type meets the preset occurrence frequency, taking the examination items as standard examination items of the disease type.
Wherein, each doctor can inform the patient to examine different items based on different conditions of the patient, and generate an examination report of the corresponding item, but the items to be examined based on the same disease type are certain, so in order to determine the items to be examined of the same disease type, the frequency of different examination items appearing in the examination report of the examination of the same disease type can be calculated, and when the preset occurrence frequency, for example 80%, is met, the frequency is used as a standard examination item of the disease type, so as to assist the doctor in judging the disease condition of the patient.
Referring to fig. 5, a data adapting system for a medical system according to another embodiment of the present application includes: a classification module 21, a model building module 22 and a matching module 23.
The classification module 21 is configured to divide the combined data of the inspection report according to classification of the department and the disease type, so as to obtain a standard inspection item corresponding to the disease type; the model building module 22 is used for obtaining all patient data in the department and building a disease rehabilitation condition model of the rehabilitation patient by combining standard examination items; the matching module 23 is used for inputting the patient examination report into the disease rehabilitation condition model, and matching to obtain the nursing treatment scheme.
Referring to fig. 6, in another embodiment, the model creation module 22 includes: a modeling unit 221, a first line analysis unit 222, a second line analysis unit 223, a first calculation unit 224, and a matching unit 225.
The modeling unit 221 is configured to obtain all patient data in the department, perform data modeling on patient data of the same disease type, and obtain a disease type data model; the first line analysis unit 222 is configured to perform line analysis on the treatment process data in the disease type data model in combination with the standard inspection item corresponding to each disease type, so as to obtain a first analysis line; the second line analysis unit 223 is configured to perform line analysis on the health standard data in combination with the standard test item corresponding to each disease type, so as to obtain a second analysis line; the first calculating unit 224 is configured to calculate a deviation value of the first analysis line based on the second analysis line, and determine that the patient is a rehabilitation patient when the deviation value reaches the health deviation range, so as to obtain a relationship between a diagnosis initial sign condition and a recovery date of the rehabilitation patient; the matching unit 225 is configured to match a care treatment plan adopted corresponding to a recovery date based on a relationship between a diagnosis initial sign condition and the recovery date of the recovery patient, and obtain a disease recovery condition model of the recovery patient.
In another embodiment, the matching module 23 includes: a second calculation unit 231 and a correction unit 232, the second calculation unit 231 being configured to input a patient examination report into the data rehabilitation model, calculate a similarity with combined data in the examination report of the rehabilitation patient; when the similarity meets the preset similarity, the correction unit 232 performs correction processing on the nursing treatment scheme corresponding to the rehabilitation patient in combination with the doctor's advice to obtain the nursing treatment scheme of the patient.
In another embodiment, the classification module 21 includes: a classification unit 211 and a standard examination item determination unit 212, the classification unit 211 dividing the combined data of the inspection examination report according to classification of departments and disease types; the standard test item determination unit 212 takes a test item in the test report as a standard test item for a disease type when the frequency of occurrence of the test item for the same disease type satisfies a preset occurrence frequency.
In another embodiment, the patient profile includes: examination reports, treatment process data, and physical rehabilitation status.
It should be noted that, in the system for automatically matching a care treatment plan with big data disclosed in this embodiment, a method for automatically matching a care treatment plan with big data is implemented, as in the above embodiment, and therefore, will not be described in detail herein. Alternatively, each module in the present embodiment and the other operations or functions described above are respectively for realizing the method in the foregoing embodiment.
Another embodiment of the present invention provides a computer-readable storage medium. The computer readable storage medium is, for example, a nonvolatile memory, which is, for example: magnetic media (e.g., hard disk, floppy disk, and magnetic strips), optical media (e.g., CDROM disks and DVDs), magneto-optical media (e.g., optical disks), and hardware systems specially constructed for storing and performing computer-executable instructions (e.g., read-only memory (ROM), random Access Memory (RAM), flash memory, etc.). Computer-readable storage medium 40 has stored thereon computer-executable instructions. The computer-readable storage medium may be executable by one or more processors or processing systems to implement the image editing method in the foregoing first embodiment.
In addition, it should be understood that the foregoing embodiments are merely exemplary illustrations of the present invention, and the technical solutions of the embodiments may be arbitrarily combined and matched without conflict in technical features, contradiction in structure, and departure from the purpose of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the partitioning of elements is merely a logical functional partitioning, and there may be additional partitioning in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not implemented. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, system or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit/module in the embodiments of the present invention may be integrated in one processing unit/module, or each unit/module may exist alone physically, or two or more units/modules may be integrated in one unit/module. The integrated units/modules may be implemented in hardware or in hardware plus software functional units/modules.
The integrated units/modules implemented in the form of software functional units/modules described above may be stored in a computer readable storage medium. The software functional units described above are stored in a storage medium and include instructions for causing one or more processors of a computer device (which may be a personal computer, a server, or a network device, etc.) to perform some steps of the methods of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for automatically matching care treatment regimens in conjunction with big data, comprising:
dividing the combined data of the inspection report according to classification of departments and disease types to obtain standard inspection items corresponding to the disease types;
acquiring data of all patients in the department, and establishing a disease rehabilitation condition model of a rehabilitation patient by combining the standard examination items;
and inputting a patient examination report into the disease rehabilitation condition model, and matching to obtain a nursing treatment scheme.
2. The method for automatically matching care treatment with big data according to claim 1, wherein said obtaining all patient data in said department and combining said standard examination items creates a disease recovery condition model of a recovered patient, comprising:
acquiring all patient data in the department, and performing data modeling on the patient data of the same disease type to obtain a disease type data model;
performing line analysis on the treatment process data in the disease type data model by combining the standard examination items corresponding to each disease type to obtain a first analysis line;
performing line analysis on the health standard data by combining the standard examination items corresponding to each disease type to obtain a second analysis line;
calculating a deviation value of the first analysis line based on the second analysis line, and judging the patient as a rehabilitation patient when the deviation value reaches a health deviation range, so as to obtain the relationship between the initial sign condition of the rehabilitation patient and the recovery date;
and matching a nursing treatment scheme adopted corresponding to the recovery date based on the relation between the initial physical sign condition of the patient and the recovery date, and obtaining the disease recovery condition model of the patient.
3. The method of automatically matching a care treatment regimen in connection with big data of claim 1, wherein the inputting of a patient exam report into the disease rehabilitation profile model, the matching resulting care treatment regimen, comprises:
inputting the patient examination report into the data rehabilitation model, and calculating the similarity of the patient examination report and the combined data in the examination report of the rehabilitation patient;
when the similarity meets the preset similarity, the nursing treatment scheme corresponding to the rehabilitation patient is combined with doctor orders to carry out correction treatment, and the nursing treatment scheme of the patient is obtained.
4. The method for automatically matching a care treatment plan with big data according to claim 1, wherein the classifying the combined data of the inspection report according to the classification of departments and disease types to obtain standard inspection items corresponding to each disease type comprises:
dividing the combined data of the inspection report according to classification of departments and disease types;
and when the occurrence frequency of the examination items in the examination report aiming at the same disease type meets the preset occurrence frequency, taking the examination items as the standard examination items of the disease type.
5. The method of automatically matching a care treatment regimen in connection with big data of claim 1, wherein the patient profile comprises: examination reports, treatment process data, and physical rehabilitation status.
6. A system for automatically matching care treatment regimens in conjunction with big data, comprising:
the classification module is used for classifying the combined data of the inspection report according to the classification of the department and the disease type to obtain a standard inspection item corresponding to the disease type;
the model building module is used for obtaining all patient data in the department and building a disease rehabilitation condition model of a rehabilitation patient by combining the standard examination item;
and the matching module is used for inputting the patient examination report into the disease rehabilitation condition model and matching to obtain a nursing treatment scheme.
7. The system for automatically matching a care treatment regimen in connection with big data as recited in claim 6, wherein the modeling module comprises:
the modeling unit is used for acquiring all patient data in the department, and carrying out data modeling on the patient data of the same disease type to obtain a disease type data model;
the first line analysis unit is used for carrying out line analysis on the treatment process data in the disease type data model by combining the standard examination items corresponding to each disease type to obtain a first analysis line;
the second line analysis unit is used for carrying out line analysis on the health standard data by combining the standard examination items corresponding to each disease type to obtain a second analysis line;
the first calculating unit is used for calculating the deviation value of the first analysis line based on the second analysis line, and judging that the patient is a rehabilitation patient when the deviation value reaches a healthy deviation range, so as to obtain the relationship between the initial sign condition of the patient and the recovery date;
the matching unit is used for matching the nursing treatment scheme adopted corresponding to the recovery date based on the relation between the initial physical sign condition of the patient and the recovery date, and obtaining the disease recovery condition model of the patient.
8. The system for automatically matching a care treatment regimen in connection with big data of claim 6, wherein the matching module comprises:
a second calculation unit for inputting the patient examination report into the data rehabilitation model, and calculating the similarity with the combined data in the examination report of the rehabilitation patient;
and the correction unit is used for correcting the nursing treatment scheme corresponding to the rehabilitation patient according to the doctor's advice when the similarity meets the preset similarity, so as to obtain the nursing treatment scheme of the patient.
9. The system for automatically matching a care treatment regimen in connection with big data as recited in claim 6, wherein the classification module comprises:
the classification unit is used for classifying the combined data of the inspection report according to classification of departments and disease types;
and a standard examination item determination unit that takes an examination item in the inspection examination report as the standard examination item of the disease type when the frequency of occurrence of the examination item for the same disease type satisfies a preset occurrence frequency.
10. The system for automatically matching a care treatment regimen in connection with big data as recited in claim 6, wherein the patient profile comprises: examination reports, treatment process data, and physical rehabilitation status.
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