CN109473154B - Post-diagnosis reminding method based on knowledge relation analysis and terminal equipment - Google Patents
Post-diagnosis reminding method based on knowledge relation analysis and terminal equipment Download PDFInfo
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Abstract
The invention is applicable to the technical field of data processing, and provides a post-diagnosis reminding method based on knowledge relation analysis, terminal equipment and a computer readable storage medium, wherein the method comprises the following steps: patient information and diagnosis information related to a patient are obtained, and medication prompts and disease types in the diagnosis information are obtained through analysis, wherein the medication prompts comprise medication frequency; searching a post-diagnosis prompt in a preset knowledge relation library according to the patient information and the disease types, and combining the medication prompt and the post-diagnosis prompt into first reminding information, wherein the knowledge relation library stores contents according to the hierarchical directory structure of which the disease types are main directories and patient information is sub-directories, and the post-diagnosis prompt fed back by the corresponding previous patient is stored under the directory of each patient information; and sending the first reminding information to the patient according to the medication frequency. According to the invention, the reminding information is independently constructed for the single patient based on the knowledge relational library, so that the pertinence and the reminding effect of the post-diagnosis reminding are improved.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a post-diagnosis reminding method based on knowledge relation analysis, terminal equipment and a computer readable storage medium.
Background
The treatment is an unavoidable matter in daily life of people, and for a medical service organization, after the treatment of a patient is finished, reminding information is sent to the patient, on one hand, the reminding information is used for reminding the patient to carry out post-treatment care and carrying out re-treatment in time; another aspect is to increase the viscosity between the patient and the mechanism.
In the prior art, the reminding information sent after the patient is in a diagnosis is usually generated by a unified template, the content is approximately the same, and the difference is not reflected in the reminding information aiming at different patients, so that the reminding information is difficult to combine with the self situation for the patients. In conclusion, the association degree of reminding information in the prior art and the patient is low, and the help effect on the patient is poor.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a post-diagnosis reminding method, terminal equipment and a computer readable storage medium based on knowledge relation analysis, so as to solve the problems that the association degree of reminding information and a patient is low and the reminding information and the patient are not applicable to the patient in the prior art.
A first aspect of an embodiment of the present invention provides a post-diagnosis reminding method based on knowledge relation analysis, including:
acquiring patient information and diagnostic information related to a patient, and performing text analysis on the diagnostic information to obtain medication prompts and disease types in the diagnostic information, wherein the medication prompts comprise medication frequency, and the patient information is an identity characteristic of the patient;
Searching a post-diagnosis prompt in a preset knowledge relation base according to the patient information and the disease types, and combining the medication prompt and the post-diagnosis prompt into first prompting information, wherein the knowledge relation base stores contents of a hierarchical directory structure of a secondary directory according to the disease types, and stores the post-diagnosis prompt fed back by a corresponding previous patient under the directory of each patient information;
and sending the first reminding information to the patient according to the medication frequency.
A second aspect of an embodiment of the present invention provides a terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring patient information and diagnostic information related to a patient, and performing text analysis on the diagnostic information to obtain medication prompts and disease types in the diagnostic information, wherein the medication prompts comprise medication frequency, and the patient information is an identity characteristic of the patient;
searching a post-diagnosis prompt in a preset knowledge relation base according to the patient information and the disease types, and combining the medication prompt and the post-diagnosis prompt into first prompting information, wherein the knowledge relation base stores contents of a hierarchical directory structure of a secondary directory according to the disease types, and stores the post-diagnosis prompt fed back by a corresponding previous patient under the directory of each patient information;
And sending the first reminding information to the patient according to the medication frequency.
A third aspect of the embodiments of the present invention provides a computer readable storage medium storing a computer program which when executed by a processor performs the steps of:
acquiring patient information and diagnostic information related to a patient, and performing text analysis on the diagnostic information to obtain medication prompts and disease types in the diagnostic information, wherein the medication prompts comprise medication frequency, and the patient information is an identity characteristic of the patient;
searching a post-diagnosis prompt in a preset knowledge relation base according to the patient information and the disease types, and combining the medication prompt and the post-diagnosis prompt into first prompting information, wherein the knowledge relation base stores contents of a hierarchical directory structure of a secondary directory according to the disease types, and stores the post-diagnosis prompt fed back by a corresponding previous patient under the directory of each patient information;
and sending the first reminding information to the patient according to the medication frequency.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
according to the embodiment of the invention, the patient information and the diagnosis information related to the patient are obtained, the medication prompt and the disease type in the diagnosis information are obtained through analysis, then the post-diagnosis prompt is searched in the preset knowledge relational library according to the patient information and the disease type, the medication prompt and the searched post-diagnosis prompt are combined into the first reminding information, and finally the first reminding information is sent to the patient according to the medication frequency in the medication prompt.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an implementation of a post-diagnosis reminding method based on knowledge relation analysis according to an embodiment of the invention;
FIG. 2 is a flowchart of a post-diagnosis reminding method based on knowledge relation analysis according to a second embodiment of the present invention;
FIG. 3 is a flowchart of a post-diagnosis reminding method based on knowledge relation analysis according to a third embodiment of the present invention;
FIG. 4 is a flowchart of a post-diagnosis reminding method based on knowledge relation analysis according to a fourth embodiment of the invention;
FIG. 5 is a flowchart of a post-diagnosis reminding method based on knowledge relation analysis according to a fifth embodiment of the present invention;
fig. 6 is a block diagram of a terminal device according to a sixth embodiment of the present invention;
fig. 7 is a schematic diagram of a terminal device according to a seventh embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to illustrate the technical scheme of the invention, the following description is made by specific examples.
Fig. 1 shows an implementation flow of a post-diagnosis reminding method based on knowledge relation analysis according to an embodiment of the present invention, which is described in detail below:
in S101, patient information and diagnostic information related to a patient are obtained, text analysis is performed on the diagnostic information, and medication prompts and disease types in the diagnostic information are obtained, wherein the medication prompts include medication frequency, and the patient information is an identity feature of the patient.
For each patient after the treatment, firstly, patient information and diagnosis information related to the patient are acquired, wherein the patient information is an identity of the patient, and the specific format of the patient information is not limited in the embodiment of the invention, for example, the patient information can be set to be an age area of the patient or whether smoking history exists. When the patient information is acquired, searching can be performed in a database of a medical institution where the patient is at a doctor or in a third party platform according to the name of the patient, the searched result is matched according to the format of the patient information, and the content which is successfully matched is used as the patient information. Diagnostic information is delivered to the patient by the physician and is obtained by, but not limited to, the following means: the first way is offline acquisition, namely, firstly acquiring a prescription form (the format of the prescription form can be paper or electronic) issued by a doctor for a patient, then performing optical character recognition (Optical Character Recognition, OCR) on the prescription form, taking the content in the prescription form as diagnostic information, namely, for the prescription form of the paper form, performing image acquisition on the prescription form, performing OCR on the acquired image, and for the prescription form of the electronic form, directly calling OCR software of an issuing source to perform recognition; the second mode is online acquisition, aiming at the scene that the doctor issues the prescription on line in the online system, an input control is preset in the online system, the triggering condition of the input control is specifically set to be that the doctor issues an instruction of issuing the prescription in the online system (such as clicking a certain button), and after the input control is triggered, the content in the prescription is acquired as diagnosis information.
After the diagnostic information of the patient is acquired, the diagnostic information is text-processedAnalyzing to obtain medication prompts and disease types in the diagnosis information, wherein the medication prompts comprise medication frequency. Specifically, the text analysis may be keyword matching, that is, matching the diagnosis information with a database in the medical institution, where the content of the diagnosis information that is successfully matched is used as a disease category, and the database includes keywords of a plurality of disease categories. As for medication prompts, medication keywords such as "twice a day" or "three times a day" and the like may be preset for matching. For example, the medication hint in the diagnostic information is "Medicine A Three times a day, one for each time, the Disease type is Disease A The frequency of medication in the medication prompt is "three times a day". In order to facilitate the acquisition of medication prompts and disease types, a standard input mechanism can be set, namely, a doctor is limited to input corresponding contents in preset input positions, for example, the medication prompts are input in the input positions of the medication prompts, and the contents are only required to be acquired in the corresponding input positions during the acquisition, so that the process of text analysis is omitted, and the acquisition efficiency of the medication prompts and the disease types is improved.
Optionally, a task identifier is built for each patient, the task identifier is placed in a preset task queue, and when the task identifier is located at the head of the task queue, text analysis is performed on diagnostic information of the patient corresponding to the task identifier. In an actual application scenario, patient information and diagnostic information of a plurality of patients may need to be processed in a short time, so in the embodiment of the invention, a task identifier is built for each patient after the patient is treated, the newly built task identifier is placed at the tail of a preset task queue (such as a kafka task queue), when the task identifier is positioned at the head of the task queue, the task identifier is taken out from the task queue, a task corresponding to the task identifier is executed, and text analysis is performed on the diagnostic information of the patient corresponding to the task identifier, so that medication prompts and disease types in the diagnostic information are obtained. It is worth mentioning that the patient information and the diagnostic information are acquired when the task corresponding to the task identification is executed. By the method, the ordering of reminding after the diagnosis of a plurality of patients is improved.
In S102, searching for a post-diagnosis prompt in a preset knowledge relation library according to the patient information and the disease types, and combining the medication prompt and the post-diagnosis prompt into first prompting information, where the knowledge relation library is a main catalog according to the disease types, the patient information is a hierarchical catalog structure of a secondary catalog, and the post-diagnosis prompt corresponding to the previous patient feedback is stored under the catalog of each patient information.
The embodiment of the invention also comprises a preset knowledge relation library, wherein the storage format of the knowledge relation library is in a hierarchical directory structure, namely the knowledge relation library comprises a plurality of main directories, each main directory corresponds to one disease type, each main directory comprises a plurality of sub-directories, each sub-directory corresponds to one patient information, and each sub-directory stores post-diagnosis prompts fed back by a corresponding patient (a patient after a previous diagnosis), and the patient accords with the patient information corresponding to the sub-directory and the disease type corresponding to the main directory to which the sub-directory belongs. It should be noted that, the format of the knowledge relational database can be a database, and the main catalog and the secondary catalog are database indexes; the knowledge relation base can also be a hard disk, and the main catalog and the secondary catalog are file catalogs in the hard disk. After the patient information and the disease type of the patient are obtained in step S101, a corresponding main catalog is found in the knowledge relational database according to the disease type, and a corresponding sub catalog is found in the main catalog according to the patient information, so as to obtain a post-diagnosis prompt in the sub catalog. After finding out the post-diagnosis prompt, combining the medication prompt and the post-diagnosis prompt into first prompt information, wherein the combination sequence is not limited in the embodiment of the invention, and the medication prompt is "Medicine" assuming that the post-diagnosis prompt is "no wine and strenuous exercise, so as to reduce the intake of fried foods and rapeseed oil and avoid excessive satiety A Three times a day, one for each administration, the combined first reminding information can be' Medicine A Three times a day, taking one tablet each time; the drinking is forbidden and the movement is violent, so that the intake of fried foods and rapeseed oil is reduced, and excessive satiety is avoided.
In S103, the first reminding information is sent to the patient according to the medication frequency.
In the embodiment of the invention, the sending time corresponding to a plurality of preset medicine taking frequencies one by one is set, after the first reminding short message is generated, the target sending time corresponding to the medicine taking frequency in the medicine taking prompt is determined, and the first reminding short message is sent to a patient according to the target sending time. For example, the sending time corresponding to the medication frequency of "once a day" is preset to be twelve midday points, the sending time corresponding to the medication frequency of "twice a day" is set to be twelve midday points and five afternoon points, the sending time corresponding to the medication frequency of "three times a day" is set to be eight am points, twelve midday points and five afternoon points (for example only, specific sending time can be set according to the actual application scenario), if it is determined that the medication frequency in the medication prompt is "twice a day", the first reminding short message is sent to the patient at twelve midday points and five afternoon points each day. The sending mode of the first reminding information can be mail, instant communication message or mobile phone short message, for example, the sending mode is mobile phone short message, the user identification (Subscriber Identification Module, SIM) card number of the patient is obtained in advance, and when the current time is the same as the determined sending time, the first reminding short message is sent to the SIM card number. In addition, more contents, such as the review time of the patient, can be inserted into the first reminding information.
As can be seen from the embodiment shown in fig. 1, in the embodiment of the present invention, by acquiring patient information and diagnostic information related to a patient, performing text analysis on the diagnostic information to obtain a medication prompt and a disease type in the diagnostic information, where the medication prompt includes a medication frequency, searching for a post-diagnosis prompt in a preset knowledge relational library according to the patient information and the disease type, combining the medication prompt and the post-diagnosis prompt into first reminding information, and finally sending the first reminding information to the patient according to the medication frequency.
Fig. 2 shows a method of expanding the process of searching for post-diagnosis prompts in a preset knowledge relational database according to patient information and disease types, based on the first embodiment of the present invention. The embodiment of the invention provides a flow chart for realizing a post-diagnosis reminding method based on knowledge relation analysis, as shown in fig. 2, the post-diagnosis reminding method can comprise the following steps:
In S201, a plurality of preset environmental vocabularies are matched with the searched post-diagnosis prompt, the successfully matched environmental vocabularies are used as target vocabularies, and the content related to the target vocabularies in the post-diagnosis prompt is intercepted as environmental early warning information.
After finding out the post-diagnosis prompt from the knowledge relation library, matching a plurality of preset environmental vocabularies with the found post-diagnosis prompt, wherein the plurality of preset environmental vocabularies are such as ' cloudy day ', ' sunny day ', ' rainy day and the like. If the post-diagnosis prompt is successfully matched with one of the environment vocabularies, the successfully matched environment vocabularies are taken as target vocabularies, and the content related to the target vocabularies in the post-diagnosis prompt is taken as environment early warning information, and a specific intercepting mode is that sentences in which the target vocabularies in the post-diagnosis prompt are located are taken as environment early warning information (sentences are divided by periods, and other punctuation marks can be divided as well). For example, the environment vocabulary includes "rainy day", and the post-diagnosis prompt is "do aerobic exercise more, the rainy day keeps indoor dry as much as possible", the successfully matched environment vocabulary "rainy day" is taken as the target vocabulary, and the content related to the target vocabulary "rainy day keeps indoor dry as much as possible" in the post-diagnosis prompt is intercepted as the environment early warning information.
In S202, a current environment where the patient is located is obtained, and if the current environment matches the target vocabulary, the environment early warning information is sent to the patient.
In this step, the current environment in which the patient is located is obtained, specifically, the geographic location provided by the patient during the visit of the patient may be obtained, and the current environment in the geographic location is monitored, where the monitoring mode may be that the current environment is analyzed according to the weather forecast in the geographic location. And if the current environment accords with the target vocabulary, the environment early warning information is sent to the patient. In order to avoid frequently sending environment early warning information and affecting the life of the patient, the current environment of the patient can be acquired once every preset acquisition time period (such as 24 hours).
As can be seen from the embodiment shown in fig. 2, in the embodiment of the present invention, a plurality of preset environmental vocabularies are matched with the searched post-diagnosis prompt, the successfully matched environmental vocabularies are used as target vocabularies, the content related to the target vocabularies in the post-diagnosis prompt is intercepted as environmental early warning information, the current environment where the patient is located is obtained, if the current environment matches with the target vocabularies, the environmental early warning information is sent to the patient, and the embodiment of the present invention carries out post-diagnosis prompt when the current environment where the patient is located meets the target vocabularies according to the condition that the post-diagnosis prompt contains the target vocabularies, so that the accuracy and timeliness of the post-diagnosis prompt are improved.
Fig. 3 shows a method of searching for post-diagnosis prompts in a preset knowledge relation base according to patient information and disease types, and expanding a process of combining medication prompts and post-diagnosis prompts into first reminding information on the basis of the first embodiment of the invention. The embodiment of the invention provides a flow chart for realizing a post-diagnosis reminding method based on knowledge relation analysis, as shown in fig. 3, the post-diagnosis reminding method can comprise the following steps:
in S301, word segmentation is performed on the plurality of post-diagnosis prompts found in the knowledge relational library, and a common vocabulary is counted based on the plurality of post-diagnosis prompts after word segmentation, and the occurrence rate of the common vocabulary in the plurality of post-diagnosis prompts.
Since there may be a plurality of past patients who match a certain disease type and certain patient information, there may be a plurality of post-diagnosis prompts stored in the knowledge relational database in correspondence with the disease type and the patient information. In the embodiment of the invention, if a plurality of post-diagnosis prompts are found in the knowledge relational database according to the patient information and the disease types, word segmentation processing is performed on the plurality of post-diagnosis prompts, and the word segmentation processing can be realized based on an open-source word segmentation tool. For multiple diagnosis after word segmentation And after-prompting, counting the common vocabulary, and obtaining the occurrence rate of the common vocabulary in the plurality of after-diagnosis prompts, wherein the occurrence frequency of the common vocabulary in the plurality of after-diagnosis prompts is more than one time. For example, post-diagnosis prompts remixing A Is "multiple movements". Less spicy food and prompt for reminning after diagnosis B Is "keep calm". Attention to exercise, prompt remixing after diagnosis C Is "multiple movements". Calm mood, prompt reminning after diagnosis A The word segmentation processing results are that the number of the words is more, the movement is less, the eating is spicy, and the Reminding is prompted after diagnosis B The word segmentation results are 'keep, calm, pay attention to, exercise', prompt Reminding after diagnosis C The result after word segmentation is that the common words of "movement" have 3/3=100% occurrence rate in the prompts after multiple diagnosis, the common words of "calm" have 2/3≡67%, the common words of "hot eating" have 1/3≡33% occurrence rate in the prompts after multiple diagnosis, etc.
Optionally, when the post-diagnosis prompt is subjected to word segmentation, the invalid word is omitted according to a preset invalid word list. In the embodiment of the invention, an invalid word list can be preset, for example, the invalid word list comprises words such as 'more', 'less', and the like, and the words relate to the word Qi, the adverbs, the prepositions, the connective words and the like, and when the word segmentation processing is carried out, the words appearing in the invalid word list are omitted from the words segmented based on the post-diagnosis prompt, so that the word segmentation processing and the effective rate of the shared vocabulary statistics are improved.
In S302, the plurality of occurrences are ordered to generate an occurrence sequence, the occurrences higher than a preset ratio threshold are intercepted, and the intercepted occurrences and the corresponding sentences in which the shared vocabulary is located are combined to form a target prompt.
After a plurality of common words and the occurrence rate corresponding to each common word are obtained, the plurality of occurrence rates are sequenced to generate an occurrence rate sequence, the occurrence rate higher than a preset ratio threshold value in the occurrence rate sequence is intercepted, and the intercepted occurrence rate and the corresponding sentences in the post-diagnosis prompt of the common words are combined to be a target prompt according to the sequence of the occurrence rate sequence. In the embodiment of the invention, one division mode of the sentence is to divide the sentence by periods, and more division modes can be applied according to different practical application scenes. Taking the example in the step S301 as an example, assuming that the order in the occurrence sequence is from high to low, the ratio threshold is 60%, the generated occurrence sequence is 100%,67%,33%, the truncated first occurrence is 100%, the sentence where the corresponding shared vocabulary is located is "multiple motions" (assuming that one or more post-diagnosis corresponding to the shared vocabulary is presented and the sentence where the shared vocabulary is located is truncated therefrom), the truncated second occurrence is 67%, the sentence where the corresponding shared vocabulary is located is "keep calm", and the final combined target prompt is "multiple motions, 100%; keep calm, 67% ".
Optionally, after determining the common vocabulary corresponding to the intercepted occurrence rate, determining a plurality of post-diagnosis prompts corresponding to the common vocabulary, approving the post-diagnosis prompt with the largest number, and intercepting the sentence where the common vocabulary is located in the post-diagnosis prompt. When determining the sentences in which the common vocabulary is located, if the common vocabulary corresponds to only one post-diagnosis prompt, intercepting the sentences in which the common vocabulary is located in the post-diagnosis prompt; if the common vocabulary corresponds to a plurality of post-diagnosis prompts, a certain post-diagnosis prompt corresponding to the common vocabulary can be randomly selected for sentence interception, but in the embodiment of the invention, in order to improve the accuracy of interception operation, an evaluation interface can be opened for the post-diagnosis prompt in the knowledge relation library, and a patient can approve the post-diagnosis prompt through the evaluation interface. After the occurrence rate is intercepted in the occurrence rate sequence, determining a common vocabulary corresponding to the occurrence rate, determining the post-diagnosis prompt with the highest approval number in a plurality of post-diagnosis prompts corresponding to the common vocabulary, and intercepting sentences in which the common vocabulary is located in the post-diagnosis prompt. By the method, the reliability and the accuracy of statement interception are improved, and inaccurate results caused by random interception are prevented.
In S303, the medication prompt and the target prompt are combined into the first alert information.
After the target prompt is combined, the medication prompt and the target prompt are further combined into the first prompt information, and compared with the case that the first prompt information in the step S102 only comprises one post-diagnosis prompt, the embodiment of the invention synthesizes a plurality of post-diagnosis prompts, and the generated first prompt information comprises the sentences with the shared vocabulary and the occurrence rate corresponding to the shared vocabulary, so that the patient can judge the applicability of the corresponding sentences based on the occurrence rate.
As can be seen from the embodiment shown in fig. 3, in the embodiment of the present invention, word segmentation is performed on the multiple post-diagnosis prompts found in the knowledge relational library, the common vocabulary and the occurrence rate of the common vocabulary in the multiple post-diagnosis prompts are counted based on the multiple post-diagnosis prompts after word segmentation, then the multiple occurrence rates are ordered to generate an occurrence rate sequence, the occurrence rate higher than a preset ratio threshold is intercepted, the intercepted occurrence rate and the sentences where the corresponding common vocabulary are located are combined as target prompts according to the sequence of the occurrence rate sequence, and finally the medication prompts and the target prompts are combined as first reminding information.
Fig. 4 shows a method of expanding an embodiment of the present invention based on the first embodiment of the present invention and based on patient information including a plurality of identity features. The embodiment of the invention provides a flow chart for realizing a post-diagnosis reminding method based on knowledge relation analysis, as shown in fig. 4, the post-diagnosis reminding method can comprise the following steps:
in S401, a plurality of post-diagnosis prompts in the knowledge relational library are obtained at intervals of a preset time period, and the number of the post-diagnosis prompts conforming to the identity feature is counted for a plurality of post-diagnosis prompts corresponding to each disease type.
The knowledge relation library comprises a main catalog and a secondary catalog, wherein the main catalog corresponds to the disease type, the secondary catalog corresponds to the patient information, and in the embodiment of the invention, the patient information comprises a plurality of identity features, so that in order to improve the normalization and the effectiveness of information storage, and also in order to adapt to the scene that the knowledge relation library may be updated at intervals, the secondary catalog is subdivided again based on the plurality of identity features. Specifically, a plurality of post-diagnosis prompts in the knowledge relational database are obtained every preset time period, and the number of the post-diagnosis prompts conforming to the identity characteristics is counted for the plurality of post-diagnosis prompts corresponding to each disease type, wherein the preset time period can be determined according to the data updating frequency of the knowledge relational database in an actual application scene. For example, if the patient information includes a plurality of identity features including male, female, with smoking history and no smoking history (male and female belong to a class of identity features, with smoking history and no smoking history belong to a class of identity features), the number of post-diagnosis prompts fed back by the male, female, with smoking history and no smoking history, respectively, is counted for the previous patient suffering from the disease type tuberculosis.
In S402, a directory level corresponding to the identity feature is calculated according to the number of the prompts, the hierarchical directory structure of the knowledge relational library is reconstructed according to the directory levels, and the post-diagnosis prompts are restored according to the reconstructed hierarchical directory structure.
And calculating the grade of the catalogue corresponding to each identity feature according to the obtained prompting quantity, specifically calculating the balance value corresponding to each type of identity feature, carrying out catalog division by taking the identity feature with the largest balance value as the current catalogue, then continuously calculating the balance value corresponding to the identity features of the rest types according to the divided catalogues, carrying out catalog division again by taking the identity feature with the largest balance value as the current catalogue, and repeating the process until all the identity features are divided. The directory level is determined according to the sequence of directory division, and if the time of taking a certain identity as the current directory is earlier, the directory level is higher. It should be appreciated that the directory partitioning in step S402 is performed under the primary directory of the knowledge-relation library, i.e. in the secondary directory. After the catalog grade corresponding to each identity feature is calculated, if the identity feature corresponding to the catalog grade is completely consistent with the identity feature corresponding to the catalog grade in the original hierarchical catalog structure, the operation is not performed; if the identity characteristic corresponding to the directory level is inconsistent with the identity characteristic corresponding to the directory level in the original hierarchical directory structure, reconstructing the hierarchical directory structure in the knowledge relation base according to the directory level, and storing the plurality of post-diagnosis prompts again according to the reconstructed hierarchical directory structure.
For ease of understanding, it is assumed that the total Number of post-diagnosis cues under a disease type tuberculosis is Number All Wherein the Number of post-diagnosis prompts fed back by the previous patient is Number Man The Number of post-diagnosis prompts fed back by the female patient is Number Women The Number of post-diagnosis prompts fed back by the previous patient with smoking history is Number Smoke The Number of post-diagnosis prompts fed back by the previous patient without smoking history is Number No-Smoke And calculating an equilibrium value for carrying out catalog division according to each type of identity features, wherein the calculation formula is as follows:
in the above formula, value balanced-sex A first equalization Value, of a type of identity features to which men and women belong balanced-smoke For the second equalization value of the identity feature of the type with and without smoking history, L is a preset constant, so that the obtained equalization value is convenient for statistics and calculation, for example, L can be set to 10.
If the first equilibrium value is larger than the second equilibrium value, the identity features of the male and female are used as the current catalogue to be subjected to catalog division, namely the male is used as a catalogue, and the catalogue is stored with post-diagnosis prompts fed back by the previous patient of the male; women are taken as a catalogue, and post-diagnosis prompts fed back by the past patients of the women are stored under the catalogue. It should be noted that, since the male and female belong to the same identity, the corresponding catalogue of the male and the corresponding catalogue of the female belong to the same grade, and under the corresponding catalogue of the male and the corresponding catalogue of the female, the catalogue division of the identity belonging to the same identity is performed with smoking history and no smoking history.
As can be seen from the embodiment shown in fig. 4, in the embodiment of the present invention, a plurality of post-diagnosis prompts in the knowledge relational library are obtained every preset time period, for a plurality of post-diagnosis prompts corresponding to each disease category, the number of post-diagnosis prompts corresponding to the identity features is counted, the directory level corresponding to the identity features is calculated according to the number of the prompts, the hierarchical directory structure of the knowledge relational library is reconstructed according to the number of the directory levels, and the plurality of post-diagnosis prompts are restored according to the reconstructed hierarchical directory structure.
Fig. 5 shows a method of refining a process of sending first reminding information to a patient according to a medication frequency on the basis of the first embodiment of the present invention. The embodiment of the invention provides a flow chart for realizing a post-diagnosis reminding method based on knowledge relation analysis, as shown in fig. 5, the post-diagnosis reminding method can comprise the following steps:
in S501, a medical history of the patient is queried and an afflicted condition of the patient is obtained from the medical history.
Because the patient at the visit may have other conditions before, in embodiments of the present invention, a record of the patient's medical history is searched in a database of the medical institution or in a third party platform based on the patient's name or other information, and the patient's condition before the time of generation of the record of medical history is obtained from the record of medical history. In order to prevent false acquisition, the treatment result of the disease history on the disease can be identified, and if the treatment result is cure, the disease is not acquired; if the treatment results in no cure (some conditions exist today which cannot be cured), the affected condition is acquired.
In S502, the corresponding post-diagnosis prompt is found in the knowledge relation library according to the patient information and the disease, the first prompt information and the post-diagnosis prompt are combined into a second prompt information, and the second prompt information is sent to the patient according to the medication frequency.
After the suffering symptoms are acquired, corresponding post-diagnosis prompts are searched in a knowledge relation base according to the patient information and the suffering symptoms, the searching mode is the same as that in the step S102, the post-diagnosis prompts and the first reminding information are combined to form second reminding information, and the second reminding information is sent to the patient according to the medication frequency.
As can be seen from the embodiment shown in fig. 5, in the embodiment of the present invention, the medical history of the patient is queried, the patient's suffering symptoms are obtained from the medical history, then the corresponding post-diagnosis prompt is found in the knowledge relational base according to the patient information and the suffering symptoms, the first prompt information and the post-diagnosis prompt are combined to form the second prompt information, and the second prompt information is sent to the patient according to the medication frequency.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Fig. 6 shows a block diagram of a terminal device according to an embodiment of the present invention, where the terminal device includes units for performing the steps in the corresponding embodiment of fig. 1. Please refer to fig. 1 and the related description of the embodiment corresponding to fig. 1. For convenience of explanation, only the portions related to the present embodiment are shown.
Referring to fig. 6, the terminal device includes:
an analysis unit 61, configured to obtain patient information and diagnostic information related to a patient, and perform text analysis on the diagnostic information to obtain a medication prompt and a disease type in the diagnostic information, where the medication prompt includes a medication frequency, and the patient information is an identity feature of the patient;
the combination unit 62 is configured to search for a post-diagnosis prompt in a preset knowledge relation library according to the patient information and the disease type, and combine the medication prompt and the post-diagnosis prompt into first prompt information, where the knowledge relation library is a main catalog according to the disease type, the patient information is a hierarchical catalog structure of a secondary catalog, and store the post-diagnosis prompt corresponding to the previous patient feedback under the catalog of each patient information;
and a sending unit 63, configured to send the first reminding information to the patient according to the medication frequency.
Optionally, the combining unit 62 further includes:
the intercepting unit is used for matching a plurality of preset environment vocabularies with the searched post-diagnosis prompt, taking the successfully matched environment vocabularies as target vocabularies, and intercepting the content related to the target vocabularies in the post-diagnosis prompt as environment early warning information;
And the environment early warning unit is used for acquiring the current environment of the patient, and sending the environment early warning information to the patient if the current environment accords with the target vocabulary.
Optionally, the combining unit 62 includes:
the word segmentation unit is used for carrying out word segmentation processing on the plurality of the post-diagnosis prompts searched in the knowledge relational library, counting common words based on the plurality of the post-diagnosis prompts after word segmentation, and the occurrence rate of the common words in the plurality of the post-diagnosis prompts;
the sorting unit is used for sorting the plurality of occurrence ratios to generate an occurrence ratio sequence, intercepting the occurrence ratio higher than a preset ratio threshold value, and combining the intercepted occurrence ratio and the corresponding sentences in which the common vocabulary is located as target prompts;
and the combination subunit is used for combining the medication prompt and the target prompt into the first reminding information.
Optionally, the patient information includes a plurality of identity features, and the terminal device further includes:
the statistics unit is used for acquiring a plurality of post-diagnosis prompts in the knowledge relation library every preset time period, and counting the prompt number of the post-diagnosis prompts conforming to the identity characteristics according to the plurality of post-diagnosis prompts corresponding to each disease type;
And the reconstruction unit is used for calculating the catalog grade corresponding to the identity characteristic according to the prompting quantity, reconstructing the hierarchical catalog structure of the knowledge relation base according to the catalog grade, and storing the prompting after diagnosis again according to the reconstructed hierarchical catalog structure.
Alternatively, the transmitting unit 63 includes:
a query unit for querying a medical history record of the patient and obtaining an afflicted condition of the patient from the medical history record;
and the sending subunit is used for searching the corresponding post-diagnosis prompt in the knowledge relation library according to the patient information and the sick diseases, combining the first prompt information and the post-diagnosis prompt into second prompt information, and sending the second prompt information to the patient according to the medication frequency.
Therefore, the terminal equipment provided by the embodiment of the invention independently builds the reminding information based on the knowledge relation base aiming at the identity condition and the illness condition of a single patient, thereby improving the pertinence of the reminding after diagnosis and the reminding effect of the reminding information on the patient.
Fig. 7 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 7, the terminal device 7 of this embodiment includes: a processor 70, a memory 71 and a computer program 72 stored in the memory 71 and executable on the processor 70, such as a post-diagnosis reminder based on knowledge relation analysis. The processor 70, when executing the computer program 72, implements the steps of the various post-diagnosis reminding method embodiments based on knowledge relation analysis described above, such as steps S101 to S103 shown in fig. 1. Alternatively, the processor 70, when executing the computer program 72, implements the functions of the units in the embodiments of the terminal device described above, for example, the functions of the units 61 to 63 shown in fig. 6.
By way of example, the computer program 72 may be divided into one or more units, which are stored in the memory 71 and executed by the processor 70 to accomplish the present invention. The one or more units may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program 72 in the terminal device 7. For example, the computer program 72 may be divided into an analysis unit, a combination unit and a transmission unit, each unit having the following specific functions:
the analysis unit is used for acquiring patient information and diagnosis information related to a patient, and carrying out text analysis on the diagnosis information to obtain medication prompts and disease types in the diagnosis information, wherein the medication prompts comprise medication frequency, and the patient information is the identity characteristic of the patient;
the combination unit is used for searching a post-diagnosis prompt in a preset knowledge relation library according to the patient information and the disease types, and combining the medication prompt and the post-diagnosis prompt into first prompting information, wherein the knowledge relation library is used for storing contents according to the disease types as a main catalog, the patient information is in a hierarchical catalog structure of a secondary catalog, and the post-diagnosis prompt fed back by the corresponding previous patient is stored under the catalog of each patient information;
And the sending unit is used for sending the first reminding information to the patient according to the medication frequency.
The terminal device 7 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor 70, a memory 71. It will be appreciated by those skilled in the art that fig. 7 is merely an example of the terminal device 7 and does not constitute a limitation of the terminal device 7, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor 70 may be a central processing unit (Central Processing Unit, CPU), or may be another general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the terminal device 7, such as a hard disk or a memory of the terminal device 7. The memory 71 may be an external storage device of the terminal device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the terminal device 7. The memory 71 is used for storing the computer program as well as other programs and data required by the terminal device. The memory 71 may also be used for temporarily storing data that has been output or is to be output.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units is illustrated, and in practical application, the above-mentioned functional allocation may be performed by different functional units, that is, the internal structure of the terminal device is divided into different functional units, so as to perform all or part of the above-mentioned functions. The functional units in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application. The specific working process of the units in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed terminal device and method may be implemented in other manners. For example, the above-described terminal device embodiments are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
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 a plurality of 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 in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; 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, and are intended to be included in the scope of the present invention.
Claims (8)
1. The post-diagnosis reminding method based on knowledge relation analysis is characterized by comprising the following steps of:
acquiring patient information and diagnostic information related to a patient, and performing text analysis on the diagnostic information to obtain medication prompts and disease types in the diagnostic information, wherein the medication prompts comprise medication frequency, and the patient information is an identity characteristic of the patient;
searching a post-diagnosis prompt in a preset knowledge relation base according to the patient information and the disease types, and combining the medication prompt and the post-diagnosis prompt into first prompting information, wherein the knowledge relation base stores contents of a hierarchical directory structure of a secondary directory according to the disease types, and stores the post-diagnosis prompt fed back by a corresponding previous patient under the directory of each patient information;
Sending the first reminding information to the patient according to the medication frequency;
matching a plurality of preset environment vocabularies with the searched post-diagnosis prompt, taking the successfully matched environment vocabularies as target vocabularies, and intercepting the content related to the target vocabularies in the post-diagnosis prompt as environment early warning information;
and acquiring the current environment of the patient, and if the current environment accords with the target vocabulary, transmitting the environment early warning information to the patient.
2. The post-diagnosis reminding method according to claim 1, wherein searching for a post-diagnosis reminder in a preset knowledge relation base according to the patient information and the disease type, and combining the medication reminder and the post-diagnosis reminder into first reminding information comprises:
performing word segmentation processing on the plurality of post-diagnosis prompts found in the knowledge relational library, and counting common words based on the plurality of post-diagnosis prompts after word segmentation, and the occurrence rate of the common words in the plurality of post-diagnosis prompts;
sorting the plurality of occurrence ratios to generate an occurrence ratio sequence, intercepting the occurrence ratio higher than a preset ratio threshold, and combining the intercepted occurrence ratio and the corresponding statement of the shared vocabulary into a target prompt;
And combining the medication prompt and the target prompt into the first reminding information.
3. The post-diagnosis reminding method according to claim 1, wherein the patient information includes a plurality of the identity features, the post-diagnosis reminding method further comprising:
acquiring a plurality of post-diagnosis prompts in the knowledge relation base every preset time period, and counting the prompt number of the post-diagnosis prompts conforming to the identity feature according to the plurality of post-diagnosis prompts corresponding to each disease type;
and calculating the catalog grade corresponding to the identity characteristic according to the prompting quantity, reconstructing the hierarchical catalog structure of the knowledge relation base according to the catalog grade, and storing the prompting after diagnosis again according to the reconstructed hierarchical catalog structure.
4. The post-diagnosis alert method of claim 1 wherein transmitting the first alert information to the patient according to the frequency of administration comprises:
querying a medical history of the patient and obtaining an afflicted condition of the patient from the medical history;
and searching the corresponding post-diagnosis prompt in the knowledge relation base according to the patient information and the sick diseases, combining the first prompt information and the post-diagnosis prompt into second prompt information, and sending the second prompt information to the patient according to the medication frequency.
5. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring patient information and diagnostic information related to a patient, and performing text analysis on the diagnostic information to obtain medication prompts and disease types in the diagnostic information, wherein the medication prompts comprise medication frequency, and the patient information is an identity characteristic of the patient;
searching a post-diagnosis prompt in a preset knowledge relation base according to the patient information and the disease types, and combining the medication prompt and the post-diagnosis prompt into first prompting information, wherein the knowledge relation base stores contents of a hierarchical directory structure of a secondary directory according to the disease types, and stores the post-diagnosis prompt fed back by a corresponding previous patient under the directory of each patient information;
sending the first reminding information to the patient according to the medication frequency;
matching a plurality of preset environment vocabularies with the searched post-diagnosis prompt, taking the successfully matched environment vocabularies as target vocabularies, and intercepting the content related to the target vocabularies in the post-diagnosis prompt as environment early warning information;
And acquiring the current environment of the patient, and if the current environment accords with the target vocabulary, transmitting the environment early warning information to the patient.
6. The terminal device of claim 5, wherein the searching for post-diagnosis prompts in a preset knowledge relational library according to the patient information and the disease type, and combining the medication prompts and the post-diagnosis prompts into first reminder information, comprises:
performing word segmentation processing on the plurality of post-diagnosis prompts found in the knowledge relational library, and counting common words based on the plurality of post-diagnosis prompts after word segmentation, and the occurrence rate of the common words in the plurality of post-diagnosis prompts;
sorting the plurality of occurrence ratios to generate an occurrence ratio sequence, intercepting the occurrence ratio higher than a preset ratio threshold, and combining the intercepted occurrence ratio and the corresponding statement of the shared vocabulary into a target prompt;
and combining the medication prompt and the target prompt into the first reminding information.
7. The terminal device of claim 5, wherein transmitting the first reminder information to the patient according to the medication frequency comprises:
Querying a medical history of the patient and obtaining an afflicted condition of the patient from the medical history;
and searching the corresponding post-diagnosis prompt in the knowledge relation base according to the patient information and the sick diseases, combining the first prompt information and the post-diagnosis prompt into second prompt information, and sending the second prompt information to the patient according to the medication frequency.
8. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the post-diagnosis alert method according to any one of claims 1 to 4.
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Effective date of registration: 20231220 Address after: 518000 Room 201, building A, No. 1, Qian Wan Road, Qianhai Shenzhen Hong Kong cooperation zone, Shenzhen, Guangdong (Shenzhen Qianhai business secretary Co., Ltd.) Patentee after: KANGJIAN INFORMATION TECHNOLOGY (SHENZHEN) Co.,Ltd. Address before: 518000 Room 201, building A, No. 1, Qian Wan Road, Qianhai Shenzhen Hong Kong cooperation zone, Shenzhen, Guangdong (Shenzhen Qianhai business secretary Co., Ltd.) Patentee before: Ping An Wanjia Medical Management Co.,Ltd. |