CN111324788A - Data visualization method, system, electronic equipment and storage medium - Google Patents
Data visualization method, system, electronic equipment and storage medium Download PDFInfo
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Abstract
The present disclosure provides a data visualization method, system, electronic device and storage medium, the method comprising: collecting detection data; converting the detection data into a filling area corresponding to a result grade of the detection data according to the reference range; and filling color values in the filling area in the visual graph to form the visual graph of the detection data. According to the data visualization method, the visualization graph is formed, so that the identification difficulty of the whole detection result can be directly reduced; the visual graph of the disease is formed by simultaneously filling the physical sign and index data of the patient on the visual graph, so that an efficient and simple method is provided for clinical research; the data visualization method is simple and convenient to operate and easy to implement.
Description
Technical Field
The present disclosure relates to the field of data visualization technologies, and in particular, to a data visualization method and system, an electronic device, and a storage medium.
Background
With the development of medical technology, various detection devices and detection items related to medical detection are more and more, meanwhile, the reference ranges of various detection devices, detection targets, detection methods and detection results thereof are different, the difference of the reference ranges increases the difficulty in identifying the detection results, doctors or patients not only need to compare the detection results one by one according to indexes, but also are difficult to perform common correlation of batch data.
Disclosure of Invention
In order to solve the technical problem that the identification difficulty of the detection result is large, the embodiment of the present disclosure provides the following technical solutions:
the embodiment of the present disclosure provides a data visualization method, including: collecting detection data; converting the detection data into a filling area corresponding to a result grade of the detection data according to a reference range; and filling color values in the filling area in the visual graph to form the visual graph of the detection data.
In some embodiments, the inspection data includes at least inspection items and inspection values.
In some embodiments, converting the detection data into a filled region corresponding to a result level of the detection data according to a reference range includes: preprocessing the detection data to obtain an identification interval of the detection item; dividing the reference range into at least one reference interval corresponding to a result grade; determining result levels to which the detection values belong in all the reference intervals; determining a fill area of the detection data based on the identification interval and the result level.
In some embodiments, the preprocessing the detection data to obtain the identification interval of the detection item includes: and identifying the detection items based on a preset identification rule so as to determine the identification intervals of the detection items.
In some embodiments, said determining a fill area of said detection data based on said identification interval and said result level comprises: determining a first position of the detection data within a visualization graph based on the identification interval; determining a second location of the inspection data within a visualization graph based on the result rating; determining a fill area of the inspection data within a visualization graph based on the first location and the second location.
The present disclosure also provides a data visualization system, comprising: the data acquisition module is used for acquiring detection data; the data conversion module is used for converting the detection data into a filling area corresponding to the result grade of the detection data according to a reference range; and the data visualization module is used for filling the color values in the filling area in the visualization graph to form the visualization graph of the detection data.
In some embodiments, the inspection data includes at least inspection items and inspection values.
In some embodiments, the data conversion module is specifically configured to: preprocessing the detection data to obtain an identification interval of the detection item; dividing the reference range into at least one reference interval corresponding to a result grade; determining result levels to which the detection values belong in all the reference intervals; determining a fill area of the detection data based on the identification interval and the result level.
In some embodiments, the data conversion module is specifically configured to: and identifying the detection items based on a preset identification rule so as to determine the identification intervals of the detection items.
In some embodiments, the filling area determining unit is specifically configured to: determining a first position of the detection data within a visualization graph based on the identification interval; determining a second location of the inspection data within a visualization graph based on the result rating; determining a fill area of the inspection data within a visualization graph based on the first location and the second location.
The present disclosure also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of the above when executing the computer program.
The present disclosure also provides a storage medium storing a computer program which, when executed by a processor, causes the processor to perform the method of any of the above.
This disclosed embodiment is in order to reduce the degree of difficulty of discerning of detection data such as patient's detection index and sign, will detect the project content name and convert unified sign into, will detect the testing result of project and convert specific result grade into according to its reference range and detected value, then carry out the colour to the region that corresponding sign, result grade correspond and fill, form a visual figure that has different color values, can discern the whole detection conditions of patient fast through this visual figure, directly reduce the patient and discern the degree of difficulty.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a data visualization method provided in a first embodiment of the present disclosure;
fig. 2 is a schematic flowchart illustrating a process of converting the detection data into a filling area corresponding to a result level of the detection data according to a reference range according to a first embodiment of the disclosure;
fig. 3 is a graphical illustration of an unfilled visualization provided by a first embodiment of the present disclosure;
fig. 4 is a graphical illustration of a visualization after filling provided by the first embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a data visualization system provided in a second embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to a third embodiment of the present disclosure.
Detailed Description
Specific embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings, but the present disclosure is not limited thereto.
It will be understood that various modifications may be made to the embodiments disclosed herein. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Other modifications will occur to those skilled in the art within the scope and spirit of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present disclosure will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present disclosure has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of the disclosure, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure that may be embodied in various forms. Well-known and/or repeated functions and structures have not been described in detail so as not to obscure the present disclosure with unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
A first embodiment of the present disclosure proposes a data visualization method, as shown in fig. 1, the method including the steps of:
s1, collecting detection data;
s2, converting the sensing data into a filled region corresponding to a result level of the sensing data according to the reference range;
and S3, filling color values in the filling area in the visual graph to form the visual graph of the detection data.
The detection data in this embodiment is data formed after various detections or examinations performed by the patient, and mainly includes detection items performed by the patient and corresponding detection values, and may further include detection categories to which the detection items belong, targets of the detection, specific detection methods used, and the like. The detection data of the patient can be acquired by manual collection and entry or automatic acquisition from various detection devices, it should be noted that each patient has a unique ID code, and only the data corresponding to the current patient ID is acquired when the detection data is acquired.
After the detection data is acquired, converting the detection data into a filling area corresponding to the result registration of the detection data according to the reference range, and specifically processing according to the following steps, as shown in fig. 2:
and S21, preprocessing the detection data to obtain the identification interval of the detection item.
In some embodiments, first, the detection items corresponding to the detection data are identified according to a preset identification rule, so that each detection item has a unique identification. Wherein, predetermine the rule that the identification rule is a predetermined identification detection item, make it form a fixed format, be convenient for carry on subsequent contrast and reference, in this embodiment, the fixed format after predetermineeing the identification rule sign can be:
detecting classification, detecting target, detecting item and detecting method;
the detection target includes a detection part and a detection specimen, and the description of the detection classification, the detection target, the detection item and the detection method needs to be unified when the identification is performed, and the description is preferably performed by using terms which are general or standard in the field and meet the index and the physical sign, for example, the following detection data are identified by a preset identification rule to form the following identification:
the markers of urine proteins in the urine routine are: clinical test-urine protein-chemistry; the gallbladder in the abdominal B-ultrasound is marked as follows: medical technical examination-abdomen-gall bladder-doppler; the identification of height is: general examination-patient-height-V; the indication that the left eye corrects vision is: specialist examination-eye-left eye corrected vision-V; v in the above example is a detection method that cannot be classified, and V is used instead in the present embodiment as a whole.
After the detection item is identified, the identification interval of the detection item is determined according to the corresponding identification. For example: the identification of the detection item corresponding to the detection data is as follows: the clinical examination-urine protein-chemical method, the marked interval corresponding to the phonetic letter head is: [ L, C, J, Y, N, Y, N, D, B, H, X, F ]. The method for extracting the pinyin word heads to determine the identification intervals in the embodiment is only a preferred embodiment, and the determination method can be adjusted according to actual conditions in actual use.
S22, dividing the reference range into at least one reference interval corresponding to the result level.
The detection value of each detection item has different corresponding reference intervals, which are generally used for representing the value range of the detection value under the normal condition, and in order to make the final correspondence more intuitive and simple, in this embodiment, the reference range of each detection item is firstly converted into an integer value, so as to facilitate subsequent comparison and reference. For example, assuming that the reference range of the normal detection value of a certain detection item a is [ 30-80 ], in this embodiment, the result levels to which the detection values may belong are divided into 10 levels, for example, ten numbers of-5, -4, -3, -2, -1, 1, 2,3,4,5 are used to respectively represent each result level, each result level represents a corresponding reference interval, and specifically, the reference interval corresponding to each number is described as follows:
(1) -1 and 1 are used for indicating that the detection value is in a normal reference range or the detection result is not abnormal and normal, taking the detection item A as an example, -1 and 1 are used for indicating the reference interval [ 30-80 ];
(2)2 to 5 are used for representing the condition that the detection value exceeds the upper limit of the normal reference range, and are divided into four result grades of 2,3,4 and 5 according to the degree that the detection value exceeds the upper limit, taking the detection item A as an example, 2 can represent a reference interval [ 81-100 ], 3 represents a reference interval [ 101-120 ], 4 represents a reference interval [ 121-140 ] and 5 represents a reference interval [ 161-180 ];
(3) -5 to-2 for indicating the detection value is lower than the lower limit of the normal reference range, and-2, -3, -4, -5 for indicating the detection value exceeds the upper limit, and-2, -3, -4, -5 for indicating the detection item a, -2 for indicating the reference interval [10 to 29], -3 for indicating the reference interval [ -10 to 9], -4 for indicating the reference interval [ -30 to-9 ], -5 for indicating the reference interval [ -50 to-29 ].
It should be understood that the correspondence between the reference intervals and the numbers is only an example, and when the detection items are different, the division of the reference intervals may be different, so that the adjustment and the correspondence may be performed in an actual situation.
S23, the result level to which the detection value of the detection data belongs is determined in all the reference sections.
When the detection method is specifically executed, the detection value of the detection data is compared with each reference interval to obtain the result grade of the attribution of the detection value.
Further, if the detection value is correspondingly in the normal reference range, the dichotomy is adopted to compare the detection value with the middle data of the reference range, and if the detection value is greater than or equal to the middle data, the result grade of the detection value is 1; the resulting level of the detection value is-1 if the detection value is less than the intermediate data. Taking the detection item A as an example, if the current detection value is 51, the result level of the detection value is-1; if the detection value is 70, the result level of the detection value is 1.
If the detection value exceeds the upper limit of the reference range, calculating by adopting a further method, calculating the ratio of the detection value to the upper limit of the reference range, taking an integer of the ratio, then adding 1, and the maximum is 5, for example, when the detection value is 170, the corresponding result grade is 3.
If the detection value is lower than the lower limit of the reference range, the ratio of the lower limit of the reference range to the detection value is calculated by adopting the same method, the result grade is obtained by taking the negative absolute value of the integral ratio and then subtracting 1, the minimum value is-5, for example, if the detection value is 20, the result grade of the corresponding detection result is-2.
The result level of the detection result may also be converted in some embodiments by:
for example, the identification type detection value can be determined according to keywords, and corresponds to a result grade 1 when the keywords are 'normal', and determines result grades 2 to 5 according to weights when the keywords are 'abnormal'; the descriptive detection value can be determined according to the descriptive result, the result grade is-1 when the description of 'no abnormality' appears, and the result grade is-5 to-2 according to the specific content of the description when the specific lesion description exists; for other types of detection values, the result level is-1 when there is no abnormal keyword, and the result levels-5 to-2 are determined according to the weight when there is an abnormal keyword.
S24, a filling area of the detection data is determined based on the identification interval and the result level.
In this embodiment, a plan chart shown in fig. 3 is used as a visual graph for representation, where the chart shown in fig. 3 is a schematic diagram when no data is filled, a horizontal axis of the chart is an identification interval of a detection item, and a vertical axis of the chart is a result level, and based on the identification interval and the result level of the detection item determined in the foregoing steps, an area that needs to be filled can be determined in the plan chart.
Specifically, a first position, i.e., a position on the horizontal axis (X axis), is first determined from the mark section of the preprocessed detection item. For example: the mark of the detection item A is a clinical examination-urine protein-chemical method, and the mark interval corresponding to the pinyin prefix of the mark is as follows: [ L, C, J, Y, N, Y, N, D, B, H, X, F ], i.e., the [ L, C, J, Y, N, Y, N, D, B, H, X, F ] interval on the X-axis.
Further, the value of the result level corresponding to the detection value is taken as the second position, i.e., the position on the vertical axis (Y-axis). For example, the detection value is 70, the reference range is 30-80, the grade of the conversion result according to the detection value is 1, and the corresponding value section of the Y axis [0,1 ].
According to the first position and the second position, the coordinates of the area to be filled in the visual graph of the detection data can be obtained as follows: [ L,1], [ C,1], [ J,1], [ Y,1], [ N,1], [ D,1], [ B,1], [ H,1], [ X,1] and [ F,1 ]; wherein the first position corresponds to the X-axis coordinate and the second position corresponds to the Y-axis coordinate.
And after the filling area is determined, filling color values in the corresponding filling area in the visual graph to form the visual graph of the detection data. The color value filling can be performed as follows:
each filled area may have been subjected to color filling of other test items before this filling, so that an original color value of each filled area may be obtained before this filling, the original color value being in RGB format and being a first color value RGB1(R1, G1, B1); and then respectively reducing the values of R1, G1 and B1 by a set step S to obtain a second color value RBG2(R1-S, G1-S and B1-S), filling the original filling area with the second color value, and calculating as 0 if R1-S, G1-S or B1-S is less than 0. As shown in fig. 4, each filled area in the visual graph is filled with a different color value, thereby forming a visual graph of the inspection data.
This disclosed embodiment is in order to reduce the degree of difficulty of discerning of detection data such as patient's detection index and sign, will detect the project content name and convert unified sign into, will detect the testing result of project and convert specific result grade into according to its reference range and detected value, then carry out the colour to the region that corresponding sign, result grade correspond and fill, form a visual figure that has different color values, can discern the whole detection conditions of patient fast through this visual figure, directly reduce the patient and discern the degree of difficulty. Meanwhile, by simultaneously filling the physical signs and indexes of a plurality of patients with the same disease into one picture, a unique graph of the disease can be formed, and an idea is provided for clinical research. In addition, the method provided by the embodiment has the reverse drilling capability, can click and select the area to drill the corresponding specific data, and is simple and convenient to operate and easy to implement.
A second embodiment of the present disclosure provides a data visualization system, a schematic structural diagram of which is shown in fig. 5, including: the data acquisition module 10 is used for acquiring detection data; a data conversion module 20, coupled to the data acquisition module 10, for converting the detection data into a filling area corresponding to a result level of the detection data according to the reference range; and the data visualization module 30 is coupled with the data conversion module 20 and is used for performing color value filling on the filling area in the visualization graph to form the visualization graph of the detection data.
Each module is described in detail below with reference to the disclosed embodiments.
The detection data in this embodiment is data formed after various detections or examinations performed by the patient, and mainly includes detection items performed by the patient and corresponding detection values, and may further include detection categories to which the detection items belong, targets of the detection, specific detection methods used, and the like. The data acquisition module 10 may acquire the detection data in a manner of manual collection and entry or automatic acquisition from various detection devices, and it should be noted that each patient has a unique ID code, and only data corresponding to the current patient ID is acquired when the detection data is acquired.
After the detection data is acquired, the data conversion module 20 converts the detection data into a filling area corresponding to the result registration of the detection data according to the reference range. Specifically, in some embodiments, the data conversion module 20 first identifies the detection items corresponding to the detection data according to a preset identification rule, so that each detection item has a unique identifier. Wherein, predetermine the rule that the identification rule is a predetermined identification detection item, make it form a fixed format, be convenient for carry on subsequent contrast and reference, in this embodiment, the fixed format after predetermineeing the identification rule sign can be:
detecting classification, detecting target, detecting item and detecting method;
the detection target includes a detection part and a detection specimen, and the description of the detection classification, the detection target, the detection item and the detection method needs to be unified when the identification is performed, and the description is preferably performed by using terms which are general or standard in the field and meet the index and the physical sign, for example, the following detection data are identified by a preset identification rule to form the following identification:
the markers of urine proteins in the urine routine are: clinical test-urine protein-chemistry; the gallbladder in the abdominal B-ultrasound is marked as follows: medical technical examination-abdomen-gall bladder-doppler; the identification of height is: general examination-patient-height-V; the indication that the left eye corrects vision is: specialist examination-eye-left eye corrected vision-V; v in the above example is a detection method that cannot be classified, and V is used instead in the present embodiment as a whole.
After identifying the detection item, the data conversion module 20 determines an identification interval of the detection item according to the corresponding identification, in this embodiment, the pinyin prefix of the identification may be extracted to determine the identification interval of the detection item. For example: the identification of the detection item corresponding to the detection data is as follows: the clinical examination-urine protein-chemical method, the marked interval corresponding to the phonetic letter head is: [ L, C, J, Y, N, Y, N, D, B, H, X, F ]. The method for extracting the pinyin word heads to determine the identification intervals in the embodiment is only a preferred embodiment, and the determination method can be adjusted according to actual conditions in actual use.
The detection value of each detection item has different corresponding reference intervals, which are generally used to indicate a value range of the detection value under a normal condition, and in order to make the final correspondence more intuitive and simple, in this embodiment, the data conversion module 20 first converts the reference range of each detection item into an integer value, so as to facilitate subsequent reference. For example, assuming that the reference range of the normal detection value of a certain detection item a is [ 30-80 ], in this embodiment, the result levels to which the detection values may belong are divided into 10 levels, for example, ten numbers of-5, -4, -3, -2, -1, 1, 2,3,4,5 are used to respectively represent each result level, each result level represents a corresponding reference interval, and specifically, the reference interval corresponding to each number is described as follows:
(1) -1 and 1 are used for indicating that the detection value is in a normal reference range or the detection result is not abnormal and normal, taking the detection item A as an example, -1 and 1 are used for indicating the reference interval [ 30-80 ];
(2)2 to 5 are used for representing the condition that the detection value exceeds the upper limit of the normal reference range, and are divided into four result grades of 2,3,4 and 5 according to the degree that the detection value exceeds the upper limit, taking the detection item A as an example, 2 can represent a reference interval [ 81-100 ], 3 represents a reference interval [ 101-120 ], 4 represents a reference interval [ 121-140 ] and 5 represents a reference interval [ 161-180 ];
(3) -5 to-2 for indicating the detection value is lower than the lower limit of the normal reference range, and-2, -3, -4, -5 for indicating the detection value exceeds the upper limit, and-2, -3, -4, -5 for indicating the detection item a, -2 for indicating the reference interval [10 to 29], -3 for indicating the reference interval [ -10 to 9], -4 for indicating the reference interval [ -30 to-9 ], -5 for indicating the reference interval [ -50 to-29 ].
It should be understood that the correspondence between the reference intervals and the numbers is only an example, and when the detection items are different, the division of the reference intervals may be different, so that the adjustment and the correspondence may be performed in an actual situation.
Then, the data conversion module 20 compares the detection value of the detection data with each reference interval to obtain the result grade to which the detection value belongs. Further, if the detection value is correspondingly in the normal reference range, the dichotomy is adopted to compare the detection value with the middle data of the reference range, and if the detection value is greater than or equal to the middle data, the result grade of the detection value is 1; the resulting level of the detection value is-1 if the detection value is less than the intermediate data. Taking the detection item A as an example, if the current detection value is 51, the result level of the detection value is-1; if the detection value is 70, the result level of the detection value is 1.
If the detection value exceeds the upper limit of the reference range, calculating by adopting a further method, calculating the ratio of the detection value to the upper limit of the reference range, taking an integer of the ratio, then adding 1, and the maximum is 5, for example, when the detection value is 170, the corresponding result grade is 3.
If the detection value is lower than the lower limit of the reference range, the ratio of the lower limit of the reference range to the detection value is calculated by adopting the same method, the result grade is obtained by taking the negative absolute value of the integral ratio and then subtracting 1, the minimum value is-5, for example, if the detection value is 20, the result grade of the corresponding detection result is-2.
The result level of the detection result may also be converted in some embodiments by:
for example, the identification type detection value can be determined according to keywords, and corresponds to a result grade 1 when the keywords are 'normal', and determines result grades 2 to 5 according to weights when the keywords are 'abnormal'; the descriptive detection value can be determined according to the descriptive result, the result grade is-1 when the description of 'no abnormality' appears, and the result grade is-5 to-2 according to the specific content of the description when the specific lesion description exists; for other types of detection values, the result level is-1 when there is no abnormal keyword, and the result levels-5 to-2 are determined according to the weight when there is an abnormal keyword.
In this embodiment, a plane chart shown in fig. 3 is used as a visual graph for representation, where the chart shown in fig. 3 is a schematic diagram when no data is filled, a horizontal axis of the chart is an identification interval of a detection item, and a vertical axis of the chart is a result level, and the data conversion module 20 can determine an area to be filled in the plane chart based on the determined identification interval and result level of the detection item.
Specifically, a first position, i.e., a position on the X-axis, is first determined according to the preprocessed identification section of the detection item. For example: the mark of the detection item A is a clinical examination-urine protein-chemical method, and the mark interval corresponding to the pinyin prefix of the mark is as follows: [ L, C, J, Y, N, Y, N, D, B, H, X, F ], i.e., the [ L, C, J, Y, N, Y, N, D, B, H, X, F ] interval on the X-axis.
Further, the value of the result level corresponding to the detection value is taken as the second position, i.e., the position on the Y axis. For example, the detection value is 70, the reference range is 30-80, the grade of the conversion result according to the detection value is 1, and the corresponding value section of the Y axis [0,1 ].
According to the first position and the second position, the coordinates of the area to be filled in the visual graph of the detection data can be obtained as follows: [ L,1], [ C,1], [ J,1], [ Y,1], [ N,1], [ D,1], [ B,1], [ H,1], [ X,1] and [ F,1 ]; wherein the first position corresponds to the X-axis coordinate and the second position corresponds to the Y-axis coordinate.
After the data conversion module 20 determines the filled region, the data visualization module 30 fills the color value of the corresponding filled region in the visual graph to form the visual graph of the detected data. Data visualization module 30 may perform the color value fill as follows:
each filled area may have been subjected to color filling of other test items before this filling, so that an original color value of each filled area may be obtained before this filling, the original color value being in RGB format and being a first color value RGB1(R1, G1, B1); and then respectively reducing the values of R1, G1 and B1 by a set step S to obtain a second color value RBG2(R1-S, G1-S and B1-S), filling the original filling area with the second color value, and calculating as 0 if R1-S, G1-S or B1-S is less than 0. As shown in fig. 4, each filled area in the visual graph is filled with a different color value, thereby forming a visual graph of the inspection data.
This disclosed embodiment is in order to reduce the degree of difficulty of discerning of detection data such as patient's detection index and sign, will detect the project content name and convert unified sign into, will detect the testing result of project and convert specific result grade into according to its reference range and detected value, then carry out the colour to the region that corresponding sign, result grade correspond and fill, form a visual figure that has different color values, can discern the whole detection conditions of patient fast through this visual figure, directly reduce the patient and discern the degree of difficulty. Meanwhile, by simultaneously filling the physical signs and indexes of a plurality of patients with the same disease into one picture, a unique graph of the disease can be formed, and an idea is provided for clinical research.
A third embodiment of the present disclosure provides an electronic device, a schematic structural diagram of which may be as shown in fig. 6, and the electronic device at least includes a memory 100 and a processor 200, where the memory 100 stores a computer program, and the processor 200 implements the method provided in any embodiment of the present disclosure when executing the computer program on the memory 100. Illustratively, the electronic device executes the computer program steps S101 to S103 as follows:
s101, collecting detection data;
s102, converting the detection data into a filling area corresponding to the result grade of the detection data according to the reference range;
and S103, filling color values in the filling area in the visual graph to form the visual graph of the detection data.
When the processor executes the collected detection data stored in the memory, the detection data at least comprises detection items and detection values.
The processor, when executing a filled area stored on the memory that converts the detection data into a result level corresponding to the detection data according to the reference range, specifically executes the following computer program: preprocessing the detection data to obtain an identification interval of a detection item; dividing the reference range into at least one reference interval corresponding to the result level; determining result levels to which the detection values belong in all reference intervals; a fill area of the detection data is determined based on the identification interval and the result level.
When the processor executes the preprocessing on the detection data stored in the memory to obtain the identification interval of the detection item, the following computer program is specifically executed: and identifying the detection items based on a preset identification rule so as to determine the identification intervals of the detection items.
When the processor determines the filling area of the detection data based on the identification interval and the result level, which are stored in the memory, the following computer program is specifically executed: determining a first position of the detection data within the visualization graph based on the identification interval; determining a second location of the inspection data within the visualization based on the result level; a fill area of the inspection data within the visualization graph is determined based on the first location and the second location.
This disclosed embodiment is in order to reduce the degree of difficulty of discerning of detection data such as patient's detection index and sign, will detect the project content name and convert unified sign into, will detect the testing result of project and convert specific result grade into according to its reference range and detected value, then carry out the colour to the region that corresponding sign, result grade correspond and fill, form a visual figure that has different color values, can discern the whole detection conditions of patient fast through this visual figure, directly reduce the patient and discern the degree of difficulty. Meanwhile, by simultaneously filling the physical signs and indexes of a plurality of patients with the same disease into one picture, a unique graph of the disease can be formed, and an idea is provided for clinical research.
A fourth embodiment of the present disclosure proposes a storage medium storing a computer program which, when executed by a processor, implements the method provided by any embodiment of the present disclosure, including the following steps S201 to S203:
s201, collecting detection data;
s202, converting the detection data into a filling area corresponding to the result grade of the detection data according to the reference range;
and S203, filling color values in the filling area in the visual graph to form the visual graph of the detection data.
The computer program is executed by the processor in the step of collecting the test data, the test data comprising at least test items and test values.
When the computer program is executed by the processor to convert the detection data into the filled region corresponding to the result level of the detection data according to the reference range, the processor specifically executes the following steps: preprocessing the detection data to obtain an identification interval of a detection item; dividing the reference range into at least one reference interval corresponding to the result level; determining result levels to which the detection values belong in all reference intervals; a fill area of the detection data is determined based on the identification interval and the result level.
When the computer program is executed by the processor to perform the step of preprocessing the detection data to obtain the identification interval of the detection item, the following steps are specifically executed by the processor: and identifying the detection items based on a preset identification rule so as to determine the identification intervals of the detection items.
When the computer program is executed by the processor to determine the filling area of the detection data based on the identification interval and the result level, the processor specifically executes the following steps: determining a first position of the detection data within the visualization graph based on the identification interval; determining a second location of the inspection data within the visualization based on the result level; a fill area of the inspection data within the visualization graph is determined based on the first location and the second location.
This disclosed embodiment is in order to reduce the degree of difficulty of discerning of detection data such as patient's detection index and sign, will detect the project content name and convert unified sign into, will detect the testing result of project and convert specific result grade into according to its reference range and detected value, then carry out the colour to the region that corresponding sign, result grade correspond and fill, form a visual figure that has different color values, can discern the whole detection conditions of patient fast through this visual figure, directly reduce the patient and discern the degree of difficulty. Meanwhile, by simultaneously filling the physical signs and indexes of a plurality of patients with the same disease into one picture, a unique graph of the disease can be formed, and an idea is provided for clinical research.
The above embodiments are merely exemplary embodiments of the present disclosure, which is not intended to limit the present disclosure, and the scope of the present disclosure is defined by the claims. Various modifications and equivalents of the disclosure may occur to those skilled in the art within the spirit and scope of the disclosure, and such modifications and equivalents are considered to be within the scope of the disclosure.
Claims (12)
1. A method of data visualization, comprising:
collecting detection data;
converting the detection data into a filling area corresponding to a result grade of the detection data according to a reference range;
and filling color values in the filling area in the visual graph to form the visual graph of the detection data.
2. The data visualization method according to claim 1, wherein the detection data includes at least a detection item and a detection value.
3. The data visualization method according to claim 2, wherein converting the inspection data into a filled region corresponding to a result level of the inspection data according to a reference range includes:
preprocessing the detection data to obtain an identification interval of the detection item;
dividing the reference range into at least one reference interval corresponding to a result grade;
determining result levels to which the detection values belong in all the reference intervals;
determining a fill area of the detection data based on the identification interval and the result level.
4. The data visualization method according to claim 3, wherein the preprocessing the detection data to obtain the identification interval of the detection item includes:
and identifying the detection items based on a preset identification rule so as to determine the identification intervals of the detection items.
5. The data visualization method of claim 3, wherein the determining a filled region of the detection data based on the identification interval and the result level comprises:
determining a first position of the detection data within a visualization graph based on the identification interval;
determining a second location of the inspection data within a visualization graph based on the result rating;
determining a fill area of the inspection data within a visualization graph based on the first location and the second location.
6. A data visualization system, comprising:
the data acquisition module is used for acquiring detection data;
the data conversion module is used for converting the detection data into a filling area corresponding to the result grade of the detection data according to a reference range; and
and the data visualization module is used for performing color value filling on the filling area in a visualization graph to form the visualization graph of the detection data.
7. The data visualization system of claim 6, wherein the test data includes at least test items and test values.
8. The data visualization system according to claim 7, wherein the data conversion module is specifically configured to:
preprocessing the detection data to obtain an identification interval of the detection item;
dividing the reference range into at least one reference interval corresponding to a result grade;
determining result levels to which the detection values belong in all the reference intervals;
determining a fill area of the detection data based on the identification interval and the result level.
9. The data visualization system according to claim 8, wherein the data transformation module is specifically configured to:
and identifying the detection items based on a preset identification rule so as to determine the identification intervals of the detection items.
10. The data visualization system according to claim 8, wherein the fill area determination unit is specifically configured to:
determining a first position of the detection data within a visualization graph based on the identification interval;
determining a second location of the inspection data within a visualization graph based on the result rating;
determining a fill area of the inspection data within a visualization graph based on the first location and the second location.
11. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.
12. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, causes the processor to carry out the method of any one of claims 1 to 5.
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