CN114464286A - Visualized case data importing and reporting system and method based on man-machine interaction - Google Patents
Visualized case data importing and reporting system and method based on man-machine interaction Download PDFInfo
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Abstract
The invention provides a system and a method for importing and reporting visual case data based on human-computer interaction, and belongs to the technical field of evidence-based medicine and visualization. The method is applied to the server and comprises the following steps: s100, determining a case inclusion standard; s200, extracting a candidate data set meeting the conditions from the general case database; s300, randomly grouping to obtain a plurality of candidate grouped data sets; s400, generating a plurality of corresponding visual maps; s500 distributing to a plurality of clinical patients associated with the evidence-based objectives; s600, receiving case data fed back by the clinical patient; and S700, after reliability verification is carried out on the fed back case data, the visual map is updated based on the case data passing the reliability verification, and the step S500 is returned. According to the technical scheme, the patient is guided to report the self case data through the human-computer interaction terminal and is visually displayed, and the interest and the enthusiasm of user participation are improved.
Description
Technical Field
The invention provides a system and a method for importing and reporting visual case data based on human-computer interaction, and belongs to the technical field of evidence-based medicine and visualization.
Background
Evidence-based medicine (EBM) means "medicine following Evidence", which is also called Evidence medicine and Evidence medicine. Based on evidence-based medicine of the target symptoms, multi-dimensional information related to the target symptoms can be shown in multiple directions by determining the evidence-based targets of cases.
Three basic elements for clinical decision making using evidence-based medicine are: best clinical study evidence, physician personal experience, and patient basic value view and willingness. Only if the clinician really understands the 3 essential elements of evidence-based medicine to make evidence-based clinical decisions can over-diagnosis and over-treatment be reduced.
Evidence-based medicine is clinical medicine that follows evidence, and its core idea is that any medical intervention should be based on the results of recent best scientific research, aiming at the scientification of clinical medical decision-making. It combines the personal clinical practice experience of doctors with scientific evidence, and gives consideration to the resource amount, the needs and the value orientation of patients to carry out the science and the art of clinical practice and health decision, so that the patients can be treated optimally. Therefore, how to obtain the latest and best scientific research results and obtain the latest clinical data of the patient in time becomes the key point that evidence-based medicine can play a role.
The Chinese patent application CN113643821A discloses a multi-center knowledge map joint decision support method and system, which utilizes medical knowledge map technology and block chain technology to realize local semantic reasoning and chain result summarization of clinical data in a mode of combining a local knowledge map and a chain synchronous map, thereby synthesizing fragmented cross-institution medical data of patients by means of knowledge map technology under the condition that original medical data does not go out of a hospital, and providing interpretable clinical decision support containing complete clinical evidence of patients based on deductive reasoning and evidence-based medicine.
The Chinese patent application CN113113136A provides a medical aid decision-making system based on mixed reality, which comprises AR glasses, a PAD terminal, an intelligent bracelet, a server and a configuration system arranged in the server, wherein the configuration system comprises a form type illness state task configuration module, an AR aid decision-making module, a team task allocation module and a critical value real-time early warning module to realize intelligent diagnosis decision-making support based on evidence-based medicine.
However, the inventors have found that the above prior art focuses on data processing itself and neglects real-time interaction with clinical patients during evidence-based medical applications. Because the clinical data relate to the privacy of patients, the positivity of the patients participating in the evidence-based medicine is not strong, so that the latest clinical data of the patients are seriously lagged to acquire, and the requirements of the evidence-based medicine cannot be met only by adopting a mode of summarizing, summarizing or extracting afterwards; meanwhile, the prior art is used for acquiring data in a small range, so that the diversity of the data is insufficient.
Disclosure of Invention
In order to solve the technical problems, the invention provides a system and a method for importing and reporting visual case data based on human-computer interaction, computer equipment for implementing the method and a storage medium.
In a first aspect of the invention, a visualized case data importing method based on human-computer interaction is provided, and the method is applied to a server.
In a specific implementation, the method comprises the following steps:
s100: determining a case evidence-based target;
s200: extracting a candidate data set meeting the conditions from a general case database based on a case evidence-based target;
s300: after random decentralized grouping is carried out on the candidate data sets, a plurality of candidate grouped data sets are obtained;
specifically, after random grouping is performed on the candidate data sets, a plurality of random grouping data sets are obtained;
after the candidate data sets are subjected to decentralized grouping, a plurality of candidate grouped data sets are obtained;
s400: generating a corresponding plurality of visual maps based on each candidate grouped data set;
s500: distributing the plurality of visualization maps to a plurality of clinical patients associated with the evidence-based objective;
s600: receiving case data fed back by the clinical patient;
s700: after the reliability verification is performed on the fed back case data, updating the visual map based on the case data passing the reliability verification, and returning to the step S500;
each clinical patient is provided with a handheld terminal, and the handheld terminal has a human-computer interaction interface and multiple input functions;
the handheld terminal is communicated with the server, receives the visual map distributed by the server and displays the visual map on the human-computer interaction interface.
Specifically, the step S100 of determining the evidence-based target of the case includes determining a target disease state, an evidence-based time scale and an evidence-based space scale.
As a specific embodiment of the visualization, the visualization map includes a plurality of graph nodes of different levels, a top-level node is the target condition, a middle-level node includes other conditions associated with the target condition, and a last-level node includes corresponding clinical data, which includes numerical values and pictures.
In a second aspect of the present invention, a visualized case data report method is provided, where after receiving case data fed back by the clinical patient, the method uses the visualized case data importing method based on human-computer interaction according to the first aspect, a case data report of the target condition is generated, and the case data report is displayed on the human-computer interaction interface.
The case data report comprises a visualization graph with dynamically variable graph nodes, and the change trend of other symptoms related to the target symptoms on the set evidence-following time scale and evidence-following space scale is displayed based on the dynamic change of the graph nodes.
The technical scheme of the invention can be automatically realized by computer equipment based on computer program instructions.
Therefore, in a third aspect of the present invention, a visualized case data importing system based on human-computer interaction is provided, the system includes a human-computer interaction terminal, the human-computer interaction terminal includes a processor, and the processor is configured to execute the following instructions:
setting a target disease state, a evidence-following time scale and a evidence-following space scale on a visual interface of the human-computer interaction terminal;
extracting a candidate data set meeting the conditions from a general case database based on the target disease state, the evidence-based time scale and the evidence-based space scale;
after random grouping is carried out on the candidate data sets, a plurality of candidate grouped data sets are obtained;
generating a corresponding plurality of visual maps based on each candidate grouped data set;
distributing the plurality of visualization maps to a plurality of clinical patients associated with evidence-based goals;
receiving case data fed back by the clinical patient;
and after reliability verification is carried out on the fed back case data, the visual map is updated based on the case data passing the reliability verification.
The human-computer interaction terminal is in wireless communication with handheld terminals of a plurality of clinical patients;
transmitting the updated visualization map to the handheld terminals of a plurality of clinical patients associated with the evidence-based objective.
More specifically, the visualization graph includes at least one top level node, a plurality of intermediate nodes, and a plurality of last level nodes;
the updating of the visualization map based on the case data passing the reliability verification specifically includes:
updating a display mode of the intermediate node and/or the final node based on the case data;
the display mode comprises highlight display, gray scale display or non-display.
The invention can be realized as a computer medium, wherein computer program instructions are stored on the computer medium, and the visualized case data importing and reporting method based on human-computer interaction in the first aspect is realized by executing the program instructions.
Similarly, in a fourth aspect of the present invention, the present invention may also be embodied as a computer program product, which is loaded into a computer storage medium and executed by a processor of a server, so as to implement all or part of the steps of the above-mentioned method for importing and reporting case data based on human-computer interaction.
According to the technical scheme, the patient is guided to report the self case data through the man-machine interaction terminal, the knowledge spectrogram in the evidence-following process is displayed in a visualized mode and is dynamically updated, and the updated dynamic change is displayed to the user in real time, so that the user can obtain feedback feeling and acquisition feeling after submitting the data, and the interest and the enthusiasm of user participation are improved. Meanwhile, the technical scheme creatively establishes the general case database based on the medical insurance big database, and can ensure the diversity and comprehensiveness of evidence-based process data.
Further advantages of the invention will be apparent in the detailed description section in conjunction with the drawings attached hereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic main flow diagram of a visualized case data importing method based on human-computer interaction according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a server and terminal connections implementing the method of FIG. 1;
3(a) -3 (c) are schematic diagrams of dynamic representations of a plurality of visualization atlases resulting from the implementation of the method of FIG. 1;
FIG. 4 is a schematic diagram of a human-computer interaction based visualization case data import system implementing the method of FIG. 1;
fig. 5 is a schematic diagram of a computer device, a storage medium, and a computer program implementing the method of fig. 1.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Referring to fig. 1, fig. 1 is a schematic main flow diagram of a visualized case data importing method based on human-computer interaction according to an embodiment of the present invention;
in fig. 1, the method comprises the steps of:
s100: determining a case evidence-based target;
s200: extracting a candidate data set meeting the conditions from a general case database based on a case evidence-based target;
s300: after random grouping is carried out on the candidate data sets, a plurality of candidate grouped data sets are obtained;
s400: generating a plurality of corresponding visual maps based on each candidate grouped data set;
s500: distributing the plurality of visualization maps to a plurality of clinical patients associated with the evidence-based objective; s600: receiving case data fed back by the clinical patient;
s700: and after the reliability verification is performed on the fed back case data, updating the visual map based on the case data passing the reliability verification, and returning to the step S500.
Specifically, the step S100 determines the evidence-based target of the case, including determining the target disease condition, the evidence-based time scale and the evidence-based space scale.
The target disease is one or more diseases to be researched in the evidence-based medical process, and the disease pathogenesis, the existing clinical treatment means, the treatment effect, the doctor feedback and the patient feedback are mainly researched;
the evidence-based time scale is used for limiting the occurrence time starting point of data which is required to be acquired in the evidence-based medical process;
the evidence-based spatial scale limits the spatial position range of data to be acquired in the evidence-based medical process, and the spatial position range can be limited by a region range.
In step S200, the candidate data set meeting the condition is extracted from the general case database, and the candidate data set may specifically be:
and extracting qualified candidate data sets from the universal case database based on the target disease symptoms, the evidence-based time scale and the evidence-based space scale.
As a more specific improvement, the general case database is established based on the existing medical insurance database, clinical medication details related to a plurality of cases in the medical insurance database are classified according to the cases and patient information, anonymization processing is carried out, and clinical medication (representing clinical treatment means), patient attribute segmentation (age, sex and the like after anonymization), doctor prescriptions and medication periods (representing treatment effects, doctor feedback and patient feedback) of each disease are obtained, and the general case database can be established by combining the information.
In the prior art, the process of clinical research on electronic data collection is mostly a small range of theoretical exploration. The embodiment of the invention creatively establishes the general case database based on the medical insurance big database, and can ensure the diversity and comprehensiveness of data collected in the research process.
Extracting eligible candidate data sets from the universal case database, including extracting data sets that are eligible for the target condition, evidence-based timescales, and evidence-based spatial scales.
In order to avoid adverse effects caused by data repetition and dimension loss caused by data omission, as a further improvement of the above embodiment, after performing random grouping on the candidate data sets, multiple candidate grouped data sets need to be obtained;
after random grouping, the same candidate grouping data set does not have repeated data sets, and meanwhile random decentralized processing is carried out, so that the data dimension diversity of the original candidate grouping data set is kept to the maximum extent.
Next, generating a plurality of corresponding visual maps based on each candidate grouped data set;
distributing the plurality of visualization maps to a plurality of clinical patients associated with evidence-based objectives.
Specifically, in the distribution, based on the attributes (age group, sex, etc.) of the existing clinical patients and the names of the patient disorders, the visual map generated based on the candidate group data set selected based on the names of the patient disorders corresponding to the attribute information may be distributed to the clinical patients.
In this way, the distribution process can ensure that the node information of the visual map received by each patient is related to the node information of the visual map, so that the patients are attracted to pay positive attention to the interest of the visual map to the maximum extent and participate actively.
On this basis, receiving case data fed back by the clinical patient;
and after reliability verification is carried out on the fed back case data, the visual map is updated based on the case data passing the reliability verification.
Data reliability verification belongs to the prior art, and the invention does not expand the data reliability verification.
The method described in fig. 1 is applied to a server, and in particular, see fig. 2.
Each clinical patient is provided with a handheld terminal, and the handheld terminal has a human-computer interaction interface and a plurality of input functions;
the handheld terminal is communicated with the server, receives the visual map distributed by the server and displays the visual map on the human-computer interaction interface.
The multiple input functions include one or any combination of voice input, touch input, and video recognition input.
Next, fig. 3(a) -3 (c) are schematic diagrams of performing dynamic rendering of the plurality of visualization atlases obtained as described in fig. 1.
The visualization graph comprises a plurality of graph nodes of different levels, wherein the top level nodes are the target symptoms, the middle level nodes comprise other symptoms related to the target symptoms, and the last level nodes comprise corresponding clinical data, wherein the clinical data comprise numerical values and pictures.
The method illustrated in figure 1 is a continuous loop process, and after each loop or a predetermined number of loops, a case data report may be generated.
More specific embodiments provide a visualized case data report method, which uses the visualized case data importing method based on human-computer interaction described in fig. 1 to receive case data fed back by the clinical patient, continuously and dynamically generate a case data report of the target disease state, and display the case data report on the human-computer interaction interface.
The case data report comprises a visualization graph with dynamically variable graph nodes, and the change trend of other symptoms related to the target symptoms on the set evidence-following time scale and evidence-following space scale is displayed based on the dynamic change of the graph nodes.
Meanwhile, the method continuously updates the visual map based on the case data passing the reliability verification, and specifically comprises the following steps:
updating a display mode of the intermediate node and/or the final node based on the case data;
the display mode comprises highlight display, gray scale display or non-display.
Take fig. 3(a) -3 (c) as an example.
In fig. 3(a), the visualization graph comprises a plurality of graph nodes at different levels;
the top level node is the target condition a,
the intermediate node comprises other disorders (A) associated with said target disorder1/A2/A3/A4)；
The final node includes corresponding clinical data (a1/a2/a3/a 4).
Fig. 3(a) is a visualization map in an original state, that is, a visualization map generated based on each candidate group data set after performing steps S100 to S400.
In the original visualization map, all nodes are normally displayed, the lengths (representing the strength of relevance) of connecting lines between the nodes are uniformly represented as a uniform standard unit length STL, and the STL can be adaptively adjusted based on the scale size and the resolution size of the visualization terminal and the display module of the handheld terminal.
After performing steps S500-S600 described in fig. 1, the visualization map is updated based on the case data that passes the reliability verification, see fig. 3 (b).
At this time, the display mode of the nodes is not changed, but the length of a connecting line between the nodes (representing the strength of the relevance) is changed;
as a specific algorithm implementation form, the connection length between nodes is calculated in two ways:
(1) the node length Li between the intermediate node Ai and the final node Ai depends on the magnitude of the amount of clinical data (Ai) corresponding to the associated condition (Ai);
let the size of the clinical data currently acquired by the final node ai be datacuriIncluding picture data size imagcuriAnd numerical data size numcuri；
Let Datapre be the size of the clinical data that last node ai acquirediIncluding picture data size imagpreiAnd numerical data size numprei；
If it is
The node length between the intermediate node Ai and the final node Ai is updated
(2) The node length Mi of the top level node a and the intermediate node Ai depends on whether the number of associated conditions (Ai) is updated;
if the number of the relevant symptoms corresponding to the top-level node A is not updated, the node length Mi is not updated;
if the number of the relevant symptoms corresponding to the top-level node A is Numcur at this time and Numpre at the previous time, the length of the node is
After continuing to perform steps S500-S600 described in fig. 1, updating the visualization map based on the case data that passes the reliability verification, see fig. 3 (c);
at this time, the display mode of the node and the connection length (indicating the strength of the association) between the nodes are changed.
More specifically, a case data report for the target condition may be generated by dynamically visualizing the changing differences between maps (not shown in the figures) and presented on the human-computer interface.
The case data report comprises a visualization graph with dynamically variable graph nodes, and the change trend of other symptoms related to the target symptoms on the set evidence-following time scale and evidence-following space scale is displayed based on the dynamic change of the graph nodes.
Based on the introduction of fig. 1-3, and with reference next to fig. 4, a human-computer interaction based visualized case data importing system is provided, the system comprising a human-computer interaction terminal including a processor configured to execute the following instructions:
setting a target disease state, a evidence-following time scale and a evidence-following space scale on a visual interface of the human-computer interaction terminal;
extracting a candidate data set meeting the conditions from a general case database based on the target disease state, the evidence-based time scale and the evidence-based space scale;
after random grouping is carried out on the candidate data sets, a plurality of random grouped data sets are obtained;
after the candidate data sets are subjected to decentralized grouping, a plurality of candidate grouped data sets are obtained;
generating a corresponding plurality of visual maps based on each candidate grouped data set;
distributing the plurality of visualization maps to a plurality of clinical patients associated with evidence-based goals;
receiving case data fed back by the clinical patient;
and after reliability verification is carried out on the fed back case data, the visual map is updated based on the case data passing the reliability verification.
The human-computer interaction terminal is in wireless communication with handheld terminals of a plurality of clinical patients;
transmitting the updated visualization map to the handheld terminals of a plurality of clinical patients associated with the evidence-based objective.
The visualization graph comprises at least one top level node, a plurality of intermediate nodes and a plurality of last level nodes;
the updating of the visualization map based on the case data passing the reliability verification specifically includes:
updating a display mode of the intermediate node and/or the final node based on the case data;
the display mode includes highlight display, gray scale display or non-display.
The technical scheme of the invention can be automatically realized by computer equipment based on computer program instructions. Similarly, the present invention can also be embodied as a computer program product, which is loaded on a computer storage medium and executed by a processor to implement the above technical solution.
In particular, further embodiments include a server comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions comprising instructions for performing the method of fig. 1.
In particular, referring to fig. 5, further embodiments include a computer device comprising a memory storing a computer executable program and a processor configured to perform the various steps S100-S700 of the above method.
The technical scheme of the invention visually displays the knowledge spectrogram in the evidence-following process, dynamically updates the knowledge spectrogram, and displays the updated dynamic change to the user in real time, so that the user can obtain feedback and acquisition after submitting data, and the interest and enthusiasm of user participation are improved; meanwhile, the technical scheme creatively establishes the general case database based on the medical insurance big database, and can ensure the diversity and comprehensiveness of the data in the test process.
As a more specific follow-up improvement, in practical application, a data collection system similar to the existing EDC system is developed for entering collected case data; using a central stochastic system for the randomization and distribution of case data; the existing CTMS system is used for tracking management and updating of the test project; the pre-existing epro system can be used for self-reporting data of patients during the test.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
The present invention is not limited to the specific module structure described in the prior art. The prior art mentioned in the background section can be used as part of the invention to understand the meaning of some technical features or parameters. The scope of the present invention is defined by the claims.
Claims (10)
1. A visualized case data importing method based on human-computer interaction is applied to a server and is characterized by comprising the following steps:
s100: determining a case evidence-based target;
s200: extracting a candidate data set meeting the conditions from a general case database based on a case evidence-based target;
s300: after random grouping is carried out on the candidate data sets, a plurality of candidate grouped data sets are obtained;
s400: generating a corresponding plurality of visual maps based on each candidate grouped data set;
s500: distributing the plurality of visualization atlases to the user
A plurality of clinical patients associated with evidence-based goals;
s600: receiving case data fed back by the clinical patient;
s700: after the reliability verification is performed on the fed back case data, updating the visual map based on the case data passing the reliability verification, and returning to the step S500;
each clinical patient is provided with a handheld terminal, and the handheld terminal has a human-computer interaction interface and multiple input functions;
the handheld terminal is communicated with the server, receives the visual map distributed by the server and displays the visual map on the human-computer interaction interface.
2. The visualized case data importing method based on human-computer interaction as claimed in claim 1, wherein:
the multiple input functions include one or any combination of voice input, touch input, and video recognition input.
3. The visualized case data importing method based on human-computer interaction as claimed in claim 1, wherein:
the step S100 of determining the evidence-based target of the case includes determining a target condition, an evidence-based time scale and an evidence-based space scale.
4. The visual case data importing method based on human-computer interaction as claimed in claim 3, wherein:
the visualization graph comprises a plurality of graph nodes of different levels, wherein the top level nodes are the target symptoms, the middle level nodes comprise other symptoms related to the target symptoms, and the last level nodes comprise corresponding clinical data, wherein the clinical data comprise numerical values and pictures.
5. A visualized case data report method, which uses the visualized case data importing method based on human-computer interaction according to any one of claims 1-4 to receive the case data fed back by the clinical patient, and then generates a case data report of the target disease state, and displays the case data report on the human-computer interaction interface.
6. The method of claim 5, wherein the step of displaying the data includes the steps of:
the case data report comprises a visualization graph with dynamically variable graph nodes, and the change trend of other symptoms related to the target symptoms on the set evidence-following time scale and evidence-following space scale is displayed based on the dynamic change of the graph nodes.
7. A visualized case data importing system based on human-computer interaction comprises a human-computer interaction terminal, wherein the human-computer interaction terminal comprises a processor, and the processor is configured to execute the following instructions:
setting a target disease state, a evidence-following time scale and a evidence-following space scale on a visual interface of the human-computer interaction terminal;
extracting a candidate data set meeting the conditions from a general case database based on the target disease state, the evidence-based time scale and the evidence-based space scale;
after random grouping is carried out on the candidate data sets, a plurality of candidate grouped data sets are obtained;
generating a corresponding plurality of visual maps based on each candidate grouped data set;
distributing the plurality of visualization maps to a plurality of clinical patients associated with evidence-based goals;
receiving case data fed back by the clinical patient;
and after reliability verification is carried out on the fed back case data, the visual map is updated based on the case data passing the reliability verification.
8. The visualized case data importing system based on human-computer interaction as claimed in claim 7, wherein:
the human-computer interaction terminal is in wireless communication with handheld terminals of a plurality of clinical patients;
transmitting the updated visualization map to the handheld terminals of a plurality of clinical patients associated with the evidence-based objective.
9. The visualized case data importing system based on human-computer interaction as claimed in claim 7, wherein:
the visualization graph comprises at least one top level node, a plurality of intermediate nodes and a plurality of last level nodes;
the updating of the visualization map based on the case data passing the reliability verification specifically comprises:
updating a display mode of the intermediate node and/or the final node based on the case data;
the display mode comprises highlight display, gray scale display or non-display.
10. A server, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions comprising instructions for performing the method of any of claims 1-6.
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