CN111477333A - Data mining method and device - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000007418 data mining Methods 0.000 title claims abstract description 39
- 238000012549 training Methods 0.000 claims abstract description 48
- 208000032456 Hemorrhagic Shock Diseases 0.000 claims abstract description 28
- 206010049771 Shock haemorrhagic Diseases 0.000 claims abstract description 28
- 238000012800 visualization Methods 0.000 claims abstract description 8
- 238000004820 blood count Methods 0.000 claims description 16
- JVTAAEKCZFNVCJ-UHFFFAOYSA-N lactic acid Chemical compound CC(O)C(O)=O JVTAAEKCZFNVCJ-UHFFFAOYSA-N 0.000 claims description 16
- 210000004369 blood Anatomy 0.000 claims description 13
- 239000008280 blood Substances 0.000 claims description 13
- 238000009534 blood test Methods 0.000 claims description 13
- 238000010801 machine learning Methods 0.000 claims description 11
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 9
- 230000035487 diastolic blood pressure Effects 0.000 claims description 9
- 229910052760 oxygen Inorganic materials 0.000 claims description 9
- 239000001301 oxygen Substances 0.000 claims description 9
- 230000035488 systolic blood pressure Effects 0.000 claims description 9
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 claims description 8
- 210000003743 erythrocyte Anatomy 0.000 claims description 8
- 235000014655 lactic acid Nutrition 0.000 claims description 8
- 239000004310 lactic acid Substances 0.000 claims description 8
- 210000000265 leukocyte Anatomy 0.000 claims description 8
- 229910001414 potassium ion Inorganic materials 0.000 claims description 8
- 229910001415 sodium ion Inorganic materials 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 description 10
- NPYPAHLBTDXSSS-UHFFFAOYSA-N Potassium ion Chemical compound [K+] NPYPAHLBTDXSSS-UHFFFAOYSA-N 0.000 description 4
- FKNQFGJONOIPTF-UHFFFAOYSA-N Sodium cation Chemical compound [Na+] FKNQFGJONOIPTF-UHFFFAOYSA-N 0.000 description 4
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- 238000003745 diagnosis Methods 0.000 description 1
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- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
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Abstract
The invention provides a data mining method, which comprises the following steps: acquiring target related item information related to hemorrhagic shock from a database; establishing a training reference model based on the index range of each parameter in the related item information; determining each item of target related item information exceeding the upper limit of a normal value and exceeding the lower limit of the normal value in the target related item information by adopting the training reference model; classifying the information of each target related item exceeding the upper limit of the normal value and exceeding the lower limit of the normal value; carrying out data volume statistics on various target related item information obtained by classification; and the index range corresponding to the target related item information of which the data volume statistical result is greater than the preset value is displayed through a visualization tool, so that the reliability of selecting key indexes of hemorrhagic shock is ensured.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a data mining method and device.
Background
Data mining is the process of finding meaningful, valuable information from large, incomplete, noisy, fuzzy, random, and practical application data. At present, the application of data mining in the medical field is mainly focused on the application in the auxiliary diagnosis of diseases, drug development and hospital information systems and the application in genetics.
With the development of medical informatization, a great deal of application of systems such as L IS, EMR and the like, medical equipment and instruments tend to be more digital, and medical institutions accumulate a great deal of vital signs and test information about patients with hemorrhagic shock.
The existing method for selecting key indexes of hemorrhagic shock mainly comprises the steps that 1, medical workers artificially and subjectively store knowledge by learning relevant guidelines, consensus and other power-welfare documents; 2. learning is carried out in a helper belt (teachers in the hospital) mode; 3. the knowledge reserve is obtained through learning in higher medical institutions.
However, no matter through school learning or help-conveying belt (teacher in the hospital carries out teaching) or through learning of authoritative documents such as guidance consensus, in any way, too many subjective factors exist for storage and understanding of knowledge, problems occur in learning ability or memory ability, and busy daily medical work easily causes neglect of key abnormal indexes, so that medical potential hazards are caused; in addition, the establishment of the key abnormal indexes is more based on empirical medicine or the conclusion of manual analysis with small sample size at present, the analysis capability of manual data is limited, the acquisition of data size and the processing capability of data are both limited by subjective capability, and the problem of inaccurate establishment of the abnormal indexes exists.
Disclosure of Invention
In view of this, embodiments of the present invention provide a data mining method and apparatus, so as to solve the problem in the prior art that the selection of a key index of hemorrhagic shock is unreliable.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
a method of data mining, comprising:
acquiring target related item information related to hemorrhagic shock from a database;
establishing a training reference model based on the index range of each parameter in the target related item information;
determining each item of target related item information exceeding the upper limit of a normal value and exceeding the lower limit of the normal value in the target related item information by adopting the training reference model;
classifying the information of each target related item exceeding the upper limit of the normal value and exceeding the lower limit of the normal value;
carrying out data volume statistics on various target related item information obtained by classification;
and displaying the index range corresponding to the target related item information of which the data volume statistical result is greater than the preset value through a visualization tool.
Optionally, in the data mining method, the obtaining, from the database, target-related information related to hemorrhagic shock includes:
acquiring vital sign parameters, blood test parameters and general state parameters related to hemorrhagic shock stored in a database;
and capturing target related item information from the vital sign parameters, the blood test parameters and the general state parameters.
Optionally, in the data mining method, the target related item information includes, but is not limited to, one or more of systolic pressure, diastolic pressure, heart rate, blood oxygen saturation, lactic acid, central venous pressure, potassium ion, sodium ion, chloride ion, red blood cell count, white blood cell count, skin color, and state of consciousness.
Optionally, the data mining method further includes:
and taking the target related item information as a training parameter, and training the training reference model by adopting a machine learning algorithm so as to adjust the index range of each parameter in the training reference model.
Optionally, in the data mining method, the index range includes:
critical value gear, abnormally high value gear, and abnormally low value gear.
A data mining device, comprising:
the data acquisition unit is used for acquiring target related item information related to hemorrhagic shock from the database;
the model building unit is used for building a training reference model based on the index range of each parameter in the target related item information;
the data statistics unit is used for determining each item of target related item information exceeding the upper limit of a normal value and exceeding the lower limit of the normal value in the target related item information by adopting the training reference model; classifying the information of each target related item exceeding the upper limit of the normal value and exceeding the lower limit of the normal value; carrying out data volume statistics on various target related item information obtained by classification;
and the data display unit is used for displaying the index range corresponding to the target related item information of which the data volume statistical result is greater than the preset value through a visualization tool.
Optionally, in the data mining device, when the data acquisition unit acquires the target related item information related to hemorrhagic shock from the database, the data acquisition unit is specifically configured to:
acquiring vital sign parameters, blood test parameters and general state parameters related to hemorrhagic shock stored in a database;
and capturing target related item information from the vital sign parameters, the blood test parameters and the general state parameters.
Optionally, in the data mining device, the target related item information includes, but is not limited to, one or more of systolic pressure, diastolic pressure, heart rate, blood oxygen saturation, lactic acid, central venous pressure, potassium ion, sodium ion, chloride ion, red blood cell count, white blood cell count, skin color, and state of consciousness.
Optionally, the data mining apparatus further includes:
and taking the target related item information as a training parameter, and training the training reference model by adopting a machine learning algorithm so as to adjust the index range of each parameter in the training reference model.
Optionally, in the data mining apparatus, the index range includes:
critical value gear, abnormally high value gear, and abnormally low value gear.
Based on the above technical solution, in the above solution provided in the embodiment of the present invention, the index ranges of the parameters in the training reference model may also be trained through a machine learning algorithm, at this time, the target related item information is used as a training parameter, and the machine learning algorithm is adopted to train the training reference model, so as to adjust the index ranges of the parameters in the training reference model. Specifically, when the training reference model is trained, Waka can undertake a data mining task and a machine learning algorithm, the data mining modeling is performed by performing preprocessing, classification, regression, clustering and association algorithms on data, and data management is performed in a database and metadata management project mode.
The problem of inaccurate establishment of abnormal indexes is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart of a data mining method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a data mining method according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For the problem of unreliable selection of key indexes of hemorrhagic shock in the prior art, the application discloses a data mining method, and referring to fig. 1, the method may include:
step S101: acquiring target related item information related to hemorrhagic shock from a database;
hemorrhagic shock refers to shock caused by massive blood loss, in this step, the target related item information is collected characteristic parameters related to a patient suffering from hemorrhagic shock, and the characteristic parameters may include one or more of vital sign parameters, blood test parameters and general state parameters, wherein the vital sign parameters include blood pressure values (systolic pressure and diastolic pressure), blood oxygen saturation, body temperature, heart rate and respiration, the test indexes include various parameter indexes obtained by laboratory examinations performed on samples of venous blood, arterial blood and fingertip blood of the patient, and the general states include skin color, consciousness morphology and the like.
In the step, when the target related item information IS obtained, the target related item information can be directly obtained from a pre-constructed database, or can be obtained through obtaining the vital characteristic parameters, blood test parameters and general state parameters related to hemorrhagic shock stored in the database, and capturing the target related item information from the vital characteristic parameters, the blood test parameters and the general state parameters.
In this step, the target-related item information may include a combination of one or more of systolic blood pressure, diastolic blood pressure, heart rate, blood oxygen saturation, lactic acid, central venous pressure, potassium ion, sodium ion, chloride ion, red blood cell count, white blood cell count, skin color, state of consciousness, and the like;
step S102: establishing a training reference model based on the index range of each parameter in the target related item information;
the method comprises the steps that related item information of each project mark necessarily corresponds to a plurality of index ranges, the index ranges can include a critical value, an upper limit of a normal value, a lower limit of the normal value and the like, a training reference model can be established based on the index ranges of all parameters of the related item information of the target, all the parameters in the related item information of the target are counted and analyzed through the training reference model according to the index ranges, the index ranges can be obtained through clinical teaching materials, documents and the like, the normal value ranges of all the parameters are established through the clinical teaching materials, the documents and the like, the critical value, the upper limit of the normal value and the lower limit of the normal value are covered, and then the training reference model is determined.
Step S103: determining each item of target related item information exceeding the upper limit of a normal value and exceeding the lower limit of the normal value in the target related item information by adopting the training reference model;
in the step, a training reference model is adopted to classify each item of information in the target related item information, and each item of target related item information parameter exceeding the upper limit of a normal value and exceeding the lower limit of the normal value in each item of information is determined;
step S104: classifying the information of each target related item exceeding the upper limit of the normal value and exceeding the lower limit of the normal value;
in the step, parameters such as systolic pressure, diastolic pressure, heart rate, blood oxygen saturation, lactic acid, central venous pressure, potassium ions, sodium ions, chloride ions, red blood cell count, white blood cell count, skin color and consciousness state are divided into sets corresponding to the parameters one by one, and the parameters exceeding the upper limit of a normal value and exceeding the lower limit of the normal value in the systolic pressure, diastolic pressure, heart rate, blood oxygen saturation, lactic acid, central venous pressure, potassium ions, sodium ions, chloride ions, red blood cell count, white blood cell count, skin color and consciousness state are summarized into the corresponding sets;
step S105: carrying out data volume statistics on various target related item information obtained by classification;
in this step, the data amount in each set is counted, that is, the number of the information of each target related item exceeding the upper limit of the normal value and exceeding the lower limit of the normal value is calculated;
step S106: displaying the index range corresponding to the target related item information of which the data volume statistical result is greater than the preset value through a visualization tool;
if the number obtained by statistics is larger, the number is more reliable as a key index of hemorrhagic shock, in the step, the index range corresponding to the target related item information of which the data volume statistical result is larger than the preset value is displayed through a visualization tool, the index range of the related item parameter information is used as the key index of hemorrhagic shock, the reliability of the key index of hemorrhagic shock is improved, and of course, all the target related parameters of the statistical number result in other range intervals can be divided into common abnormal items (abnormal sample volume), low abnormal items (abnormal less sample volume) and abnormal-free items, and the related item parameter information corresponding to the items is displayed to a user through an interactive platform.
In the above scheme, the index ranges of the parameters in the training reference model may also be trained through a machine learning algorithm, and at this time, the target related item information is used as a training parameter to train the training reference model through the machine learning algorithm, so as to adjust the index ranges of the parameters in the training reference model. Specifically, when the training reference model is trained, Waka can undertake a data mining task and a machine learning algorithm, the data mining modeling is performed by performing preprocessing, classification, regression, clustering and association algorithms on data, and data management is performed in a database and metadata management project mode.
In the above scheme, the index range may include: the system comprises a critical value gear, an abnormal high value gear and an abnormal low value gear, wherein each gear corresponds to different data range intervals, and the data range intervals corresponding to different types of parameters are different.
Corresponding to the method, the present application also discloses a data mining device, and the specific working content of each unit in the device please refer to the content of the above method embodiment, and the data mining device provided by the embodiment of the present invention is described below, and the data mining device described below and the data mining method described above may be referred to correspondingly.
Referring to fig. 2, a data mining apparatus disclosed in an embodiment of the present application may include:
the data acquisition unit 100 is used for acquiring target related item information related to hemorrhagic shock from a database;
a model construction unit 200, corresponding to step S101 in the method, for establishing a training reference model based on the index ranges of the parameters in the target related item information;
a data statistics unit 300, corresponding to steps S102-S105 in the above method, for determining, by using the training reference model, each item of target related item information exceeding an upper limit of a normal value and exceeding a lower limit of the normal value; classifying the information of each target related item exceeding the upper limit of the normal value and exceeding the lower limit of the normal value; carrying out data volume statistics on various target related item information obtained by classification;
and a data display unit 400, corresponding to step S106 in the method, for displaying the index range corresponding to the target related item information of which the data volume statistic result is greater than the preset value through a visualization tool.
Corresponding to the method, the data acquisition unit is specifically configured to, when acquiring the target related item information related to hemorrhagic shock from the database:
acquiring vital sign parameters, blood test parameters and general state parameters related to hemorrhagic shock stored in a database;
and capturing target related item information from the vital sign parameters, the blood test parameters and the general state parameters.
Corresponding to the above method, the target related item information includes, but is not limited to, one or more of systolic blood pressure, diastolic blood pressure, heart rate, blood oxygen saturation, lactic acid, central venous pressure, potassium ion, sodium ion, chloride ion, red blood cell count, white blood cell count, skin color, and state of consciousness in combination.
Corresponding to the method, the method also comprises the following steps:
and taking the target related item information as a training parameter, and training the training reference model by adopting a machine learning algorithm so as to adjust the index range of each parameter in the training reference model.
Corresponding to the method, the index range comprises: critical value gear, abnormally high value gear, and abnormally low value gear.
For convenience of description, the above system is described with the functions divided into various modules, which are described separately. Of course, the functionality of the various modules may be implemented in the same one or more software and/or hardware implementations of the invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. 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.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of data mining, comprising:
acquiring target related item information related to hemorrhagic shock from a database;
establishing a training reference model based on the index range of each parameter in the target related item information;
determining each item of target related item information exceeding the upper limit of a normal value and exceeding the lower limit of the normal value in the target related item information by adopting the training reference model;
classifying the information of each target related item exceeding the upper limit of the normal value and exceeding the lower limit of the normal value;
carrying out data volume statistics on various target related item information obtained by classification;
and displaying the index range corresponding to the target related item information of which the data volume statistical result is greater than the preset value through a visualization tool.
2. The data mining method of claim 1, wherein the obtaining of the target-related item of information related to hemorrhagic shock from the database comprises:
acquiring vital sign parameters, blood test parameters and general state parameters related to hemorrhagic shock stored in a database;
and capturing target related item information from the vital sign parameters, the blood test parameters and the general state parameters.
3. The data mining method of claim 1, wherein the target related item information includes, but is not limited to, a combination of one or more of systolic blood pressure, diastolic blood pressure, heart rate, blood oxygen saturation, lactic acid, central venous pressure, potassium ions, sodium ions, chloride ions, red blood cell count, white blood cell count, skin color, and state of consciousness.
4. The data mining method of claim 1, further comprising:
and taking the target related item information as a training parameter, and training the training reference model by adopting a machine learning algorithm so as to adjust the index range of each parameter in the training reference model.
5. The data mining method of claim 1, wherein the index range comprises:
critical value gear, abnormally high value gear, and abnormally low value gear.
6. A data mining device, comprising:
the data acquisition unit is used for acquiring target related item information related to hemorrhagic shock from the database;
the model building unit is used for building a training reference model based on the index range of each parameter in the target related item information;
the data statistics unit is used for determining each item of target related item information exceeding the upper limit of a normal value and exceeding the lower limit of the normal value in the target related item information by adopting the training reference model; classifying the information of each target related item exceeding the upper limit of the normal value and exceeding the lower limit of the normal value; carrying out data volume statistics on various target related item information obtained by classification;
and the data display unit is used for displaying the index range corresponding to the target related item information of which the data volume statistical result is greater than the preset value through a visualization tool.
7. The data mining device of claim 6, wherein the data acquisition unit, when obtaining the target related item information related to hemorrhagic shock from the database, is specifically configured to:
acquiring vital sign parameters, blood test parameters and general state parameters related to hemorrhagic shock stored in a database;
and capturing target related item information from the vital sign parameters, the blood test parameters and the general state parameters.
8. The data mining device of claim 6, wherein the target related item information includes, but is not limited to, a combination of one or more of systolic blood pressure, diastolic blood pressure, heart rate, blood oxygen saturation, lactic acid, central venous pressure, potassium ions, sodium ions, chloride ions, red blood cell count, white blood cell count, skin color, and state of consciousness.
9. The data mining device of claim 6, further comprising:
and taking the target related item information as a training parameter, and training the training reference model by adopting a machine learning algorithm so as to adjust the index range of each parameter in the training reference model.
10. The data mining device of claim 6, wherein the metric range comprises:
critical value gear, abnormally high value gear, and abnormally low value gear.
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