CN107145713A - The data statistic analysis system of antidiastole - Google Patents
The data statistic analysis system of antidiastole Download PDFInfo
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- CN107145713A CN107145713A CN201710221968.0A CN201710221968A CN107145713A CN 107145713 A CN107145713 A CN 107145713A CN 201710221968 A CN201710221968 A CN 201710221968A CN 107145713 A CN107145713 A CN 107145713A
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- 238000004458 analytical method Methods 0.000 title claims abstract description 21
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 90
- 201000010099 disease Diseases 0.000 claims abstract description 89
- 238000003745 diagnosis Methods 0.000 claims abstract description 73
- 238000012423 maintenance Methods 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims abstract description 8
- 208000028659 discharge Diseases 0.000 claims description 11
- 238000013500 data storage Methods 0.000 claims description 10
- 238000007689 inspection Methods 0.000 claims description 10
- 238000010827 pathological analysis Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 238000011160 research Methods 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims description 3
- 230000017105 transposition Effects 0.000 claims description 3
- 238000011161 development Methods 0.000 abstract description 4
- 230000018109 developmental process Effects 0.000 abstract description 4
- 238000003759 clinical diagnosis Methods 0.000 abstract description 3
- 230000032683 aging Effects 0.000 abstract description 2
- 208000024891 symptom Diseases 0.000 description 3
- 208000037093 Menstruation Disturbances Diseases 0.000 description 2
- 206010027339 Menstruation irregular Diseases 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 208000025661 ovarian cyst Diseases 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 206010028980 Neoplasm Diseases 0.000 description 1
- 206010030113 Oedema Diseases 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000686 essence Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001225 therapeutic effect Effects 0.000 description 1
- 210000000115 thoracic cavity Anatomy 0.000 description 1
Abstract
The present invention discloses a kind of data statistic analysis system of antidiastole, including disease maintenance unit, case history counting unit, report generation unit and graph of a relation generation unit, pass through the analysis of accounts diagnosed in case history to items, searching is clinically difficult to the diagnosis of discriminating with the disease to be studied, and is ranked up；Or the case history number mutually differentiated between several diseases for select of statistics, the clinically correlation of antidiastole among the several diseases of discovery.By programming count and analyze, realize to the efficiently quickly data statistics of antidiastole in clinical diagnosis.And pass through the continuous updating to analyzing case history, it is easier to hold which disease under current technical status is easily obscured, with more ageing.And traditional antidiastole depends on the clinical experience of researcher, the development of Present clinical techniques possibly can not be closelyed follow.
Description
Technical field
The present invention relates to medical care statistics analysis technical field, a kind of data statistic analysis system of antidiastole is particularly related to
System.
Background technology
Antidiastole is among the Clinics and Practices of disease, according to the main suit of patient, to differentiate with other diseases, and exclude
The possible diagnosis of other diseases, so as to draw the process of final diagnosis result.Among the process of antidiastole, due to several diseases
Disease may have common Symptoms, and doctor needs the symptom and every result for checking inspection according to patient, excluded one by one
The possibility of various diseases.Only correctly exclude improperly disease possible, assign correct diagnosis, can just suit the remedy to the case, have
Effect makes correct therapeutic choice according to the disease process of patient.But do not have the differential diagnostic method of comparison system also now,
Fail quickly and accurately to draw a conclusion, be unfavorable for the development of medical treatment.
Therefore, it is necessary to a kind of data statistic analysis system of new antidiastole be designed, to solve above-mentioned technical problem.
The content of the invention
For problem present in background technology, it is an object of the invention to provide a kind of data statistic analysis of antidiastole
System, by carrying out data analysis to the passing case history of hospital, finds the association between various diseases, possible for various diseases
Antidiastole carries out statistical analysis, so as to be pinpointed the problems in clinical scientific research and teaching, study a question performance directive function.
The technical proposal of the invention is realized in this way：A kind of data statistic analysis system of antidiastole, including disease
Maintenance unit, case history counting unit, report generation unit and graph of a relation generation unit, wherein,
Disease maintenance unit：For safeguarding that disease ID is counted with the disease ID tables of disease title corresponding relation and diagnosis
Table, diagnosis statistical form includes four kinds, and respectively admission diagnosis-admission diagnosis checks statistical form, admission diagnosis-inspection diagnosis
Statistical form, admission diagnosis-pathological diagnosis statistical form and admission diagnosis-discharge diagnosis statistical form；
Case history counting unit：For importing hospital's history case history, recall the admission diagnosis of case history, check that diagnosis, case history are examined
Disconnected and discharge diagnosis；
Existing case history k parts, counting is proceeded by from first part：Firstly, it is necessary to every diagnostic result is converted into diagnosis ID,
What every part of case history had the reference record headed by 12 parameters of m1, m2, m3, n1, n2, n3, o1, o2, o3, p1, p2, p3, m be can
Can admission diagnosis ID, n headed by reference record be it is possible check diagnosis ID, the rest may be inferred, in m and p class parameters at least
It must not be sky to need a value, i.e. m1, p1, and other specification can be sky；According to diagnostic result, found accordingly in disease ID tables
Disease, so as to find corresponding ID, is stored among tetra- kinds of parameters of m, n, o, p；
For each part case history, whether be empty, if sky, then in admission diagnosis-inspection diagnosis statistical form if judging m2 values
Stringer ID is found for m1, row ID is that the corresponding data storage lattice counting of n1, n2, n3 Jia one；In admission diagnosis-pathological diagnosis
Stringer ID is found in statistical form for m1, row ID is that the corresponding data storage lattice counting of o1, o2, o3 Jia one；Admission diagnosis-
Discharge diagnosis statistical form finds stringer ID for m1, and row ID is that the corresponding data storage lattice counting of p1, p2, p3 Jia one；If no
For sky, then it is the number that m1, row are m2 to find stringer outside being counted more than in admission diagnosis-admission diagnosis statistical form again
Counted according to memory location and Jia one, then other each forms by row of m2 are counted as m1；M3, process are judged again
Such as m2, after k parts of case histories are counted and finished, counting is finished；
Report generation unit：For report generation, in the unit, for only choosing a kind of being calculated as disease：According to
Disease ID, finds the corresponding rows of the ID and stringer in four diagnosis statistical forms, four rows is aggregated into statistics summary table
Form；The corresponding stringers of the ID of each table are subjected to transposition and are changed into row, is added, obtains in the corresponding position of statistics summary table form
Last summary table；Each ID is ranked up according to accounting, retains the first two ten disease, at the same remove with self ID identical that
One disease, is presented final statistical report form；
Calculating for choosing n kind diseases：Due to have selected n kind diseases, then a shared n × (n- among this n kind disease
1) relation is planted, n × (n-1) is found in four diagnosis statistical forms and plants the corresponding individual unit lattice of theory relation, form is inserted
In, then the case summation that n × (n-1) plants actual relationship is counted, as select n kinds disease and be used as form during research object；
Graph of a relation generation unit：For production Methods view, in the unit, the meter for only choosing a certain disease
Calculate：Taken in the last summary table of generation and be ordered as first total cases for Smax, and calculate the standard value standard of antidiastole
Value is multiplied by maximum black value, obtains relatively black value, the different graph of a relation of generation lines blackness；
Calculating for choosing n kind diseases：Among the items of actual relationship are comprehensive, take be ordered as first total cases
For Smax, and the case load of each group actual relationship is calculated relative to SmaxStandard value, maximum black value is multiplied by with standard value, is obtained relatively
Black value；The graph of a relation between each group relation is drawn out according to relatively black value.
In the above-mentioned technical solutions, the black value of the maximum is 255.
In the above-mentioned technical solutions, the blackness of the lines=relatively black value.
The data statistic analysis system of antidiastole of the present invention, by, to the analysis of accounts of items diagnosis, being found in case history
The diagnosis of discriminating is clinically difficult to the disease to be studied, and is ranked up；Or several diseases for selecting of statistics it
Between the case history number that mutually differentiates, find among several diseases the clinically correlation of antidiastole.By programming count simultaneously
Analysis, is realized to the efficiently quickly data statistics of antidiastole in clinical diagnosis.
Brief description of the drawings
Fig. 1 counts flow chart for case history in the data statistic analysis system of antidiastole of the present invention；
Fig. 2 is a kind of algorithm graph of a relation of disease in the present invention；
Fig. 3 is the algorithm graph of a relation of a variety of diseases in the present invention；
Fig. 4 is the algorithm graph of a relation of instantiation disease.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.Based on this
Embodiment in invention, the every other reality that those of ordinary skill in the art are obtained under the premise of creative work is not made
Example is applied, the scope of protection of the invention is belonged to.
A kind of data statistic analysis system of antidiastole of the present invention, including disease maintenance unit, case history are counted
Unit, report generation unit and graph of a relation generation unit.
The following is the detailed description to each unit：
Disease maintenance unit：In disease maintenance unit, it is necessary first to safeguard disease ID tables.Included disease needs bag
Include various symptoms to look into because of, medical diagnosis on disease, example is as shown in the table:
| ID | Disease |
| 00001 | Thoracic tumour look into because |
| 00002 | Irregular menstruation look into because |
| 00003 | Oedema look into because |
| … | |
| N | Ovarian cyst |
| N+1 | Oophoroma |
| … |
Second need to safeguard is diagnosis statistical form, and diagnosis statistical form has four kinds, respectively admission diagnosis-be admitted to hospital and examine
Disconnected inspection statistical form, admission diagnosis-inspection diagnosis statistical form, admission diagnosis-pathological diagnosis statistical form and admission diagnosis-
Discharge diagnosis statistical form.Often several conditions are faced during due to admission diagnosis may need situation about differentiating, it is necessary to which one is admitted to hospital
Diagnosis-admission diagnosis checks statistical form, to record different admission diagnosis situations.
Admission diagnosis-admission diagnosis statistical representation for example under：
| 00001 | 00002 | … | N | |
| 00001 | ||||
| 00002 | ||||
| 00003 | ||||
| … |
First is classified as the ID corresponding to first possible admission diagnosis, the first possible admission diagnosis of behavior second
ID.The rest may be inferred, admission diagnosis-inspection diagnosis statistical form, admission diagnosis-pathological diagnosis statistical form and admission diagnosis-discharge
It is all consistent to diagnose statistical form structure.
Case history counting unit, is counted for case history, specific as follows：
By data-interface, hospital's history case history is imported into system.After the conversion of data-interface, case history is admitted to hospital
Diagnosis, inspection diagnosis, case history diagnosis and discharge diagnosis are recalled, among import system.
Existing case history k parts, counting is proceeded by from first part.Firstly, it is necessary to which every diagnostic result is converted into diagnosis ID.
What every part of case history had the reference record headed by 12 parameters of m1, m2, m3, n1, n2, n3, o1, o2, o3, p1, p2, p3, m be can
Can admission diagnosis ID, reference record headed by n is the possible ID for checking diagnosis, and the rest may be inferred, in m and p class parameters
It must not be sky at least to need a value, i.e. m1, p1, and other specification can be sky.According to diagnostic result, found in disease ID tables
Corresponding disease, so as to find corresponding ID, is stored among tetra- kinds of parameters of m, n, o, p.
Whether for each part case history, it is empty to judge m2 values.If sky, then in admission diagnosis-inspection diagnosis statistical form
Stringer ID is found for m1, row ID is that the corresponding data storage lattice counting of n1, n2, n3 Jia one；In admission diagnosis-pathological diagnosis
Stringer ID is found in statistical form for m1, row ID is that the corresponding data storage lattice counting of o1, o2, o3 Jia one；Admission diagnosis-
Discharge diagnosis statistical form finds stringer ID for m1, and row ID is that the corresponding data storage lattice counting of p1, p2, p3 Jia one.If no
For sky, then it is the number that m1, row are m2 to find stringer outside being counted more than in admission diagnosis-admission diagnosis statistical form again
Counted according to memory location and Jia one, then other each forms by row of m2 are counted as m1.M3, process are judged again
Such as m2.
After k parts of case histories are counted and finished, counting is finished, and flow is as shown in Figure 1.
Report generation, it is specific as follows for report generation：
User selects a certain disease or certain several disease to be checked, and form is generated by following calculate：For only
Choose the calculating of a certain disease：According to disease ID, the corresponding rows of the ID and stringer are found in four diagnosis statistical forms,
Four rows are aggregated into following statistics summary table form：
To the disease that ID is n：
| Classification | 00001 | 00002 | … | N | Summation |
| Admission diagnosis | W1 | W2 | WN | ||
| Check diagnosis | X1 | X2 | XN | ||
| Pathological diagnosis | Y1 | Y2 | YN | ||
| Discharge diagnosis | Z1 | Z2 | ZN | ||
| Summation | S1 | S2 | SN | H | |
| Accounting | T1 | T2 | TN |
Summation SN=WN+XN+YN+ZN；
Accounting TN=SN/H；
The corresponding stringers of the ID of each table are subjected to transposition and are changed into row, are added in the corresponding position of upper table, obtain last
Summary table.
Each ID is ranked up according to accounting, retains the first two ten disease, at the same remove with self ID identical that
Disease, is presented final statistical report form as follows：
Calculating for choosing n kind diseases：
Due to have selected n kind diseases, then shared n × (n-1) plants relation among this n kind disease.For example, existing three kinds
Disease is respectively A, B and C, then has A-B, B-A, A-C, C-A, C-B, six kinds of theory relation relations of B-C, A-B, A-C, tri- kinds of B-C
Actual relationship.The corresponding individual unit lattice of above-mentioned six kinds of theory relations are found in four diagnosis statistical forms, following form is inserted
In, additionally need to count the case summation of three kinds of actual relationships.
It is that selected n kinds disease is used as form during research object above.
Graph of a relation generation unit, it is specific as follows for production Methods view：
Calculating for only choosing a certain disease：
In the last summary table of generation, there are 20 kinds of diseases.Take and be ordered as first total cases for Smax, and before calculating
20 be the standard value of antidiastole.It is 255 to take maximum black value, and maximum black value is multiplied by with standard value, relatively black value is obtained.Following institute
Show：
| Sequence | 1 | 2 | … | 19 | 20 |
| Diagnosis | |||||
| Standard value | S1/Smax | S2/Smax | S19/Smax | S20/Smax | |
| Relatively black value | S1/SmaxX255 | S2/SmaxX255 | S19/SmaxX255 | S20/SmaxX255 |
Generation graph of a relation as shown in Figure 2, wherein, blackness=relatively black value of lines.
Calculating for choosing n kind diseases：
Among the items of actual relationship are comprehensive, take and be ordered as first total cases for Smax, and calculate the actual pass of each group
The case load of system is relative to SmaxStandard value.It is 255 to take maximum black value, and maximum black value is multiplied by with standard value, relatively black value is obtained,
It is as follows：
| Relation | Summation | Standard value | Relatively black value |
| Diagnosis | S1 | S1/Smax | S1/SmaxX255 |
| Standard value | S2 | S2/Smax | S2/SmaxX255 |
| Relatively black value | S3 | S3/Smax | S3/SmaxX255 |
Graph of a relation between each group relation is drawn out as shown in figure 3, form is completed with graph of a relation according to relatively black value
Afterwards, it is presented in system interface.
The following is further illustrating for combination example：
The three kinds of diseases to be studied now are selected, are that irregular menstruation is looked into because of, ovarian cyst and oophoroma.Imported into system
Case history, the counting that all case histories are diagnosed.Obtain after four diagnosis statistical forms, three kinds of diseases being studied are chosen, in disease
It is respectively 00002,00145,00146 to plant and search out respective ID in ID tables.
Judge to have 6 kinds of theory relations in system, respectively 00002-00145,00145-00002,00002-00146,
00146-00002、00145-00146、00146-00145.Above-mentioned 6 kinds of theory relations are found in four diagnosis statistical forms
This relation of data statistics lattice, such as 00002-00145 is, it is necessary to which it is 00002, row to find stringer in four diagnostics tables respectively
For 00145 data statistics lattice, following table is inserted, the rest may be inferred.
It is then above statistical report form, obtaining relatively black value is respectively：
| Relation | Summation | Standard value | Relatively black value |
| 00002-00145 | 500 | 1 | 255 |
| 00002-00146 | 200 | 0.4 | 102 |
| 00145-00146 | 100 | 0.2 | 51 |
It is as shown in Figure 4 so as to obtain graph of a relation.
The data statistic analysis system of antidiastole of the present invention, has the advantages that：
(1) imported and counted by the history case history produced to hospital, to items diagnosis analysis of accounts, find with
The disease to be studied clinically is difficult to the diagnosis differentiated, and is ranked up；Or between several diseases for selecting of statistics
The case history number mutually differentiated, finds among several diseases the clinically correlation of antidiastole.By programming count and point
Analysis, is realized to the efficiently quickly data statistics of antidiastole in clinical diagnosis.
(2) analysis of the tradition to antidiastole depends on the clinical experience of researcher, by its examining clinically
Control a certain disease of micro-judgment easily with some diseases to obscure, this process is easily influenceed by subjective factor.And the system is logical
The statistical analysis to case history is crossed, objectively the situation for clinically occurring mutually to obscure between ground disease is counted, more
It is considerable.
(3) due to the sustainable development of Clinics, the disease of diagnosis was once difficult in medical diagnosis to be become increasingly to hold
Easily diagnosis, and pass through the continuous updating to analyzing case history, it is easier to holding which disease under current technical status, easily generation is mixed
Confuse, with more ageing.And traditional antidiastole depends on the clinical experience of researcher, possibly Present clinical skill can not be closelyed follow
The development of art.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
God is with principle, and any modification, equivalent substitution and improvements made etc. should be included in the scope of the protection.
Claims (3)
1. a kind of data statistic analysis system of antidiastole, it is characterised in that：Count single including disease maintenance unit, case history
Member, report generation unit and graph of a relation generation unit, wherein,
Disease maintenance unit：Disease ID tables and diagnosis statistical form for safeguarding disease ID and disease title corresponding relation, are examined
Disconnected statistical form includes four kinds, and respectively admission diagnosis-admission diagnosis checks statistical form, admission diagnosis-inspection diagnosis statistics
Table, admission diagnosis-pathological diagnosis statistical form and admission diagnosis-discharge diagnosis statistical form；
Case history counting unit：For importing hospital's history case history, recall case history admission diagnosis, check diagnosis, case history diagnosis with
And discharge diagnosis；
Existing case history k parts, counting is proceeded by from first part：ID, every part are diagnosed firstly, it is necessary to which every diagnostic result is converted to
What case history had the reference record headed by 12 parameters of m1, m2, m3, n1, n2, n3, o1, o2, o3, p1, p2, p3, m is possible
Reference record headed by admission diagnosis ID, n be it is possible check diagnosis ID, the rest may be inferred, is at least needed in m and p class parameters
It must not be sky to have a value, i.e. m1, p1, and other specification can be sky；According to diagnostic result, corresponding disease is found in disease ID tables
Kind, so as to find corresponding ID, it is stored among tetra- kinds of parameters of m, n, o, p；
For each part case history, whether be empty, if sky, then found in admission diagnosis-inspection diagnosis statistical form if judging m2 values
Stringer ID is m1, and row ID is that the corresponding data storage lattice counting of n1, n2, n3 Jia one；In admission diagnosis-pathological diagnosis statistics
Stringer ID is found in table for m1, row ID is that the corresponding data storage lattice counting of o1, o2, o3 Jia one；In admission diagnosis-discharge
Diagnosis statistical form finds stringer ID for m1, and row ID is that the corresponding data storage lattice counting of p1, p2, p3 Jia one；If being not sky,
It is the data storage that m1, row are m2 to find stringer outside then being counted more than in admission diagnosis-admission diagnosis statistical form again
Lattice, which are counted, Jia one, then other each forms by row of m2 are counted as m1；M3, process such as m2 are judged again,
After k parts of case histories are counted and finished, counting is finished；
Report generation unit：For report generation, in the unit, for only choosing a kind of being calculated as disease：According to the disease
ID is planted, the corresponding rows of the ID and stringer are found in four diagnosis statistical forms, four rows are aggregated into statistics summary table form；
The corresponding stringers of the ID of each table are subjected to transposition and are changed into row, is added in the corresponding position of statistics summary table form, obtains last
Summary table；Each ID is ranked up according to accounting, retains the first two ten disease, while removing and that disease of self ID identical
Kind, final statistical report form is presented；
Calculating for choosing n kind diseases：Due to have selected n kind diseases, then shared n × (n-1) is planted among this n kind disease
Relation, n × (n-1) is found in four diagnosis statistical forms and plants the corresponding individual unit lattice of theory relation, is inserted in form, then
The case summation that n × (n-1) plants actual relationship is counted, n kinds disease is as selected and is used as form during research object；
Graph of a relation generation unit：For production Methods view, in the unit, the calculating for only choosing a certain disease：
Taken in the last summary table of generation and be ordered as first total cases for Smax, and calculate the standard value of antidiastole and multiplied with standard value
With the black value of maximum, relatively black value, the different graph of a relation of generation lines blackness are obtained；
Calculating for choosing n kind diseases：Among the items of actual relationship are comprehensive, take and be ordered as first total cases and be
Smax, and the case load of each group actual relationship is calculated relative to SmaxStandard value, maximum black value is multiplied by with standard value, obtains relatively black
Value；The graph of a relation between each group relation is drawn out according to relatively black value.
2. the data statistic analysis system of antidiastole according to claim 1, it is characterised in that：The black value of maximum is
255。
3. the data statistic analysis system of antidiastole according to claim 1, it is characterised in that：The blackness of the lines
=relatively black value.
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