CN109545383A - Actual clinical path mutation detection method and device, storage medium, electronic equipment - Google Patents

Actual clinical path mutation detection method and device, storage medium, electronic equipment Download PDF

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Publication number
CN109545383A
CN109545383A CN201811339164.1A CN201811339164A CN109545383A CN 109545383 A CN109545383 A CN 109545383A CN 201811339164 A CN201811339164 A CN 201811339164A CN 109545383 A CN109545383 A CN 109545383A
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China
Prior art keywords
data element
feature words
real data
data unit
clinical path
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李林峰
张春宇
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Beijing Yiyi Medical Cloud Technology Co Ltd
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Beijing Yiyi Medical Cloud Technology Co Ltd
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

This disclosure relates to field of computer technology more particularly to a kind of actual clinical path mutation detection method and device, storage medium, electronic equipment.This method comprises: obtaining actual clinical path of the target patient based on target disease；Actual clinical path is divided into multiple real data units according to a dividing unit；Calculate separately the matching value of each real data unit with corresponding standard data element in the standard clinical path of target disease；The matching value in actual clinical path and standard clinical path is calculated with the matching value of corresponding standard data element according to each real data unit；According to matching value and combine whether preset matching value detection actual clinical path morphs.The disclosure improves the efficiency of actual clinical path variation detection, and then solve the problems, such as that the hysteresis quality of actual clinical variation detection is strong, the problem of human cost also greatly reduces, avoids error detection simultaneously improves the accuracy of actual clinical path variation detection.

Description

Actual clinical path mutation detection method and device, storage medium, electronic equipment
Technical field
This disclosure relates to field of computer technology more particularly to a kind of actual clinical path mutation detection method and device, Storage medium, electronic equipment.
Background technique
Standard clinical path, which refers to, establishes a set of standardized therapeutic mode and treatment procedure for a certain disease, and being one has The aggregative model of clinical treatment is closed, promotes the side for the treatment of tissue and disease control so that evidence-based medical and guide are guidance Method finally plays canonical medical behavior, reduces variation, reduces cost, improve the effect of quality.
By the way that standard clinical path and actual clinical path are compared to whether detection actual clinical path becomes Change, and after detecting that actual clinical path is morphed, by being analyzed the actual clinical path morphed with right Standard clinical path optimizes, judges whether medical worker observes standard clinical path etc., therefore whether detects actual clinical It morphs and is played an important role in the work of medical worker.Currently, generalling use artificial mode for actual clinical road Diameter is compared with standard clinical path, to detect whether actual clinical path changes.
Clearly as by the way of artificial detection, so that the variation detection efficiency in actual clinical path is low, human cost Height, hysteresis quality are strong；Further, since by the way of artificial detection, so that the variation in actual clinical path is detected by human factor The influence of (for example, detection experience, careful degree etc.), in fact it could happen that the problem of error detection.
It should be noted that information is only used for reinforcing the reason to the background of the disclosure disclosed in above-mentioned background technology part Solution, therefore may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Summary of the invention
The disclosure is designed to provide a kind of actual clinical path mutation detection method and device, storage medium, electronics Equipment, and then overcome at least to a certain extent due to by the way of artificial detection, so that the variation in actual clinical path is examined It is strong to survey low efficiency, human cost height, hysteresis quality, while the problem of error detection may also occur.
According to one aspect of the disclosure, a kind of actual clinical path mutation detection method is provided, comprising:
Obtain actual clinical path of the target patient based on target disease；
The actual clinical path is divided into multiple real data units according to a dividing unit；
Calculate separately each real data unit and corresponding criterion numeral in the standard clinical path of the target disease According to the matching value of unit；
It is calculated and described is actually faced according to the matching value of each real data unit and the corresponding standard data element The matching value in bed path and the standard clinical path；
Detect whether the actual clinical path morphs according to the matching value and in conjunction with a preset matching value.
It is described to calculate separately each real data unit and the target in a kind of exemplary embodiment of the disclosure The matching value of corresponding standard data element includes: in the standard clinical path of disease
Obtain the Feature Words in each real data unit；
Respectively according to Feature Words and the feature in the corresponding standard data element in each real data unit The number of matches of word calculates the matching value of each the real data unit and the corresponding standard data element.
In a kind of exemplary embodiment of the disclosure, each real data unit and the mesh are calculated separately described It marks in the standard clinical path of disease before the matching value of corresponding standard data element further include:
Obtain the standard clinical path of the target disease, and according to the dividing unit by the standard clinical road Diameter is divided into multiple standard data elements；And
Obtain the Feature Words in each standard data element.
In a kind of exemplary embodiment of the disclosure, the Feature Words in the standard data element include optional feature word With essential Feature Words；
It is described respectively according in each real data unit Feature Words in the corresponding standard data element The number of matches of Feature Words calculates each real data unit with the matching value of the corresponding standard data element
According to Feature Words and the essential feature in the corresponding standard data element in each real data unit The number of matches of word calculates the first matching value of each the real data unit and the corresponding standard data element；
According to Feature Words and the optional feature in the corresponding standard data element in each real data unit The number of matches of word calculates the second matching value of each the real data unit and the corresponding standard data element；
According to each real data unit and first matching value of the corresponding standard data element and described Second matching value calculates the matching value of each the real data unit and the corresponding standard data element.
In a kind of exemplary embodiment of the disclosure, the Feature Words in the real data unit include drug characteristic Word, verification characteristics word and inspection Feature Words；Optional feature word in the standard data element include optional drug characteristic word, Optional inspection Feature Words, optional verification characteristics word；Essential Feature Words in the standard data element include essential drug characteristic Word, essential inspection Feature Words, essential verification characteristics word.
In a kind of exemplary embodiment of the disclosure, the Feature Words according in each real data unit with The number of matches of essential Feature Words in the corresponding standard data element calculate each real data unit with it is corresponding Before first matching value of the standard data element further include:
By each drug characteristic word and each essential medicine in corresponding standard data element in each real data unit Object Feature Words are matched one by one, to obtain the Feature Words in each real data unit and the corresponding normal data list First number of matches of the essential Feature Words in member；
By each inspection Feature Words and each essential inspection in corresponding standard data element in each real data unit It looks into Feature Words to be matched one by one, to obtain the Feature Words in each real data unit and the corresponding normal data list Second number of matches of the essential Feature Words in member；
By each verification characteristics word and each essential inspection in corresponding standard data element in each real data unit It tests Feature Words to be matched one by one, to obtain the Feature Words in each real data unit and the corresponding normal data list The third number of matches of essential Feature Words in member；
According to Feature Words and the essential feature in the corresponding standard data element in each real data unit First number of matches, second number of matches and the third number of matches of word calculate each real data list Feature Words in member and the number of matches of the essential Feature Words in the corresponding standard data element.
In a kind of exemplary embodiment of the disclosure, the Feature Words according in each real data unit with The number of matches of optional feature word in the corresponding standard data element calculate each real data unit with it is corresponding Before second matching value of the standard data element further include:
By each drug characteristic word and each optional medicine in corresponding standard data element in each real data unit Object Feature Words are matched one by one, to obtain the Feature Words in each real data unit and the corresponding normal data list First number of matches of the optional feature word in member；
It can Selected Inspection with each in corresponding standard data element by each inspection Feature Words in each real data unit It looks into Feature Words to be matched one by one, to obtain the Feature Words in each real data unit and the corresponding normal data list Second number of matches of the optional feature word in member；
It can Selected Inspection with each in corresponding standard data element by each verification characteristics word in each real data unit It tests Feature Words to be matched one by one, to obtain the Feature Words in each real data unit and the corresponding normal data list The third number of matches of optional feature word in member；
According to Feature Words and the optional feature in the corresponding standard data element in each real data unit First number of matches, second number of matches and the third number of matches of word calculate each real data list Feature Words in member and the number of matches of the optional feature word in the corresponding standard data element.
According to one aspect of the disclosure, a kind of actual clinical path variation detection device is provided, comprising:
Module is obtained, for obtaining actual clinical path of the target patient based on target disease；
Dividing unit, for the actual clinical path to be divided into multiple real data units according to a dividing unit；
First computing unit, for calculating separately the standard clinical road of each the real data unit and the target disease The matching value of corresponding standard data element in diameter；
Second computing unit, for the matching according to each the real data unit and the corresponding standard data element Value calculates the matching value in the actual clinical path and the standard clinical path；
Detection unit, for whether detecting the actual clinical path according to the matching value and in conjunction with a preset matching value It morphs.
According to one aspect of the disclosure, a kind of computer readable storage medium is provided, computer program is stored thereon with, The computer program realizes actual clinical path mutation detection method described in above-mentioned any one when being executed by processor.
According to one aspect of the disclosure, a kind of electronic equipment is provided, comprising:
Processor；And
Memory, for storing the executable instruction of the processor；
Wherein, the processor be configured to execute via the executable instruction is executed it is any one of above-mentioned described in Actual clinical path mutation detection method.
A kind of actual clinical path mutation detection method and device that there is provided in the present example embodiment, storage medium, Electronic equipment, this method is by calculating separately each real data unit in actual clinical path and the target disease The matching value of corresponding standard data element in standard clinical path, and according to each real data unit with it is corresponding described The matching value of standard data element calculates the matching value in the actual clinical path and the standard clinical path, and according to institute It states matching value and detects whether the actual clinical path morphs in conjunction with a preset matching value.On the one hand, real by calculating The matching value of border clinical path and standard clinical path, and combine a preset matching value to detect actual clinical path according to matching value Whether morph, compare and the prior art, since not by the way of artificial, this improves the variations of actual clinical path The efficiency of detection, and then solve the problems, such as that the hysteresis quality of actual clinical path variation detection is strong, while also greatly reducing Human cost；On the other hand, it due to avoiding the influence of human factor, can be to avoid error detection the problem of, improves and actually faces The accuracy of bed path variation detection.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not The disclosure can be limited.
Detailed description of the invention
It is described in detail its exemplary embodiment by referring to accompanying drawing, the above and other feature and advantage of the disclosure will become It obtains more obvious.It should be evident that the accompanying drawings in the following description is only some embodiments of the present disclosure, it is common for this field For technical staff, without creative efforts, it is also possible to obtain other drawings based on these drawings.Attached In figure:
Fig. 1 is a kind of flow chart of actual clinical path mutation detection method of the disclosure；
Fig. 2 is each real data unit of calculating for providing and the corresponding mark in one exemplary embodiment of the disclosure The flow chart of the matching value of quasi- data cell；
Fig. 3 be Feature Words in each real data unit of calculating provided in one exemplary embodiment of the disclosure with it is right The flow chart of the number of matches for the essential Feature Words in the standard data element answered；
Fig. 4 be Feature Words in each real data unit of calculating provided in one exemplary embodiment of the disclosure with it is right The flow chart of the number of matches for the optional feature word in the standard data element answered；
Fig. 5 is a kind of block diagram of actual clinical path variation detection device of the disclosure；
Fig. 6 is the module diagram that the disclosure shows the electronic equipment in an exemplary embodiment.
Fig. 7 is that the disclosure shows the program product schematic diagram in an exemplary embodiment.
Specific embodiment
Example embodiment is described more fully with reference to the drawings.However, example embodiment can be real in a variety of forms It applies, and is not understood as limited to embodiment set forth herein；On the contrary, thesing embodiments are provided so that the disclosure will be comprehensively and complete It is whole, and the design of example embodiment is comprehensively communicated to those skilled in the art.Identical appended drawing reference indicates in figure Same or similar part, thus repetition thereof will be omitted.
In addition, described feature, structure or characteristic can be incorporated in one or more implementations in any suitable manner In example.In the following description, many details are provided to provide and fully understand to embodiment of the disclosure.However, It will be appreciated by persons skilled in the art that can be with technical solution of the disclosure without one in the specific detail or more It is more, or can be using other methods, constituent element, material, device, step etc..In other cases, it is not shown in detail or describes Known features, method, apparatus, realization, material or operation are to avoid fuzzy all aspects of this disclosure.
Block diagram shown in the drawings is only functional entity, not necessarily must be corresponding with physically separate entity. I.e., it is possible to realize these functional entitys using software form, or these are realized in the module of one or more softwares hardening A part of functional entity or functional entity, or realized in heterogeneous networks and/or processor device and/or microcontroller device These functional entitys.
A kind of actual clinical path mutation detection method is disclosed in the present exemplary embodiment first, shown referring to Fig.1, institute Stating actual clinical path mutation detection method may comprise steps of:
Step S110, actual clinical path of the target patient based on target disease is obtained；
Step S120, the actual clinical path is divided into multiple real data units according to a dividing unit；
Step S130, it is corresponding with the standard clinical path of the target disease to calculate separately each real data unit Standard data element matching value；
Step S140, institute is calculated with the matching value of the corresponding standard data element according to each real data unit State the matching value in actual clinical path and the standard clinical path；
Step S150, detect whether the actual clinical path occurs according to the matching value and in conjunction with a preset matching value Variation.
Actual clinical path mutation detection method according to the present exemplary embodiment, on the one hand, actually faced by calculating The matching value in bed path and standard clinical path, and whether preset matching value detection actual clinical path is combined according to matching value It morphs, compares and the prior art, since not by the way of artificial, this improves the variations of actual clinical path to detect Efficiency, and then solve the problems, such as that the hysteresis quality of actual clinical path variation detection is strong, while manpower also greatly reduce Cost；On the other hand, due to avoiding the influence of human factor, can be to avoid error detection the problem of, actual clinical road is improved The accuracy of diameter variation detection.
Next, with reference to Fig. 1, the actual clinical path mutation detection method in the present exemplary embodiment is made furtherly It is bright.
In step s 110, actual clinical path of the target patient based on target disease is obtained.
In the present example embodiment, can according to the identification information (for example, identity card, admission number etc.) of target patient with And the title of target disease obtains doctor's advice of the target patient in therapeutic purpose lysis in the medical data base of hospital Data, and order data is ranked up according to each order data recording temporal sequencing, by the doctor's advice after sequence Data are determined as actual clinical path.
In the step s 120, the actual clinical path is divided into multiple real data units according to a dividing unit.
In the present example embodiment, the dividing unit can be by developer's self-setting, for example, the division is single It is one day that position, which can be, or two days, can also be three days etc., the present exemplary embodiment is not particularly limited this.Example Such as, when dividing unit is one, clinical path can be drawn according to the record time of the order data in actual clinical path Be divided into the real data unit of day grade, even on record time from the May of the order data in actual clinical path 1 to May 5 actual clinical path can be then divided into 5 real data units, wherein 5 real data units are successively are as follows: 1st real data unit (i.e. first day real data unit), the 2nd real data unit (i.e. second day actual number According to unit), the 3rd real data unit (i.e. the real data unit in third day), the 4th real data unit (i.e. the 4th day Real data unit) and the 5th real data unit (i.e. the 5th day real data unit).
Before step S130, the method can also include: to obtain the standard clinical path of the target disease, and press The standard clinical path is divided into multiple standard data elements according to the dividing unit；And obtain each standard Feature Words in data cell.
In the present example embodiment, the standard clinical path of various diseases can be stored in medical data base, often The standard clinical path of a disease can be formulated by expert.The standard clinical path of each disease includes treating corresponding disease The therapeutic scheme of disease, and include daily treatment data in therapeutic scheme.It, can when obtaining the standard clinical path of target disease The title of target disease to be input in medical data base, to obtain the standard clinical path of the target disease.
The dividing unit of criteria for classifying clinical path is identical as the dividing unit in above-mentioned division actual clinical path.For example, It, can be according to the treatment of each treatment data in standard clinical path when the dividing unit of criteria for classifying clinical path is one Standard clinical path is divided into the standard data element of multiple days grades by the time.For example, if the treatment time in standard clinical path It is 5 days, then according to the treatment time of each treatment data in standard clinical path, standard clinical path is divided into 5 standards Data cell, 5 standard data elements are respectively the 1st standard data element (i.e. first day standard data element), the 2nd A standard data element (i.e. second day standard data element), the 3rd standard data element (the i.e. normal data list in third day Member), the 4th standard data element (i.e. the 4th day standard data element), the 5th standard data element (i.e. the 5th day standard Data cell).
It should be noted that since actual clinical path and standard clinical path are there may be difference, although all pressing It is divided according to the same dividing unit, but the quantity and standard clinical of the real data unit after the division of actual clinical path The quantity of standard data element after the division of path may be different.For example, when actual clinical path shows that the treatment of the patient is total A length of 3 days, and the treatment total duration in standard clinical path is 5 days, when dividing unit is one, actual clinical path is divided For 3 real data units, and standard clinical path is divided into 5 standard data elements.For another example actual clinical path is aobvious The treatment total duration for showing the patient is 7 days, and standard clinical path shows that the treatment total duration of the patient is 5 days, single dividing When position is one, actual clinical path is divided into 7 real data units, and standard clinical path is divided into 5 standards Data cell.
After obtaining each standard data element, semantic analysis is carried out to each standard data element, to obtain each normal data Feature Words in unit, the Feature Words in each standard data element may include drug characteristic word, verification characteristics word and Check Feature Words etc., in the present exemplary embodiment to this not particular determination.It should be noted that drug characteristic word can refer to drug Title, verification characteristics word can refer to inspection project title, check Feature Words can be with the title of digital examination project.
In step s 130, in the standard clinical path for calculating separately each real data unit and the target disease The matching value of corresponding standard data element.
In the present example embodiment, standard data element identical with the stripe sequence of real data unit is the reality The corresponding standard data element of data cell.For example, being one day in dividing unit, and actual clinical path is divided into five realities Border data cell, when standard clinical path is each divided into five standard data elements, first real data unit (i.e. first It practical counting unit) corresponding standard data element be first standard data element (i.e. first day standard counting unit), The corresponding standard data element of second real data unit (i.e. second day practical counting unit) is second normal data list First (i.e. second day standard counting unit), the corresponding criterion numeral of third real data unit (i.e. the practical counting unit in third day) According to unit be third standard data element (i.e. the standard counting unit in third day), the 4th real data unit (i.e. the 4th day Practical counting unit) corresponding standard data element is the 4th standard data element (i.e. the 4th day standard counting unit), the The corresponding standard data element of five real data units (i.e. the 5th day practical counting unit) is the 5th standard data element (i.e. the 5th day standard counting unit).
It is described to calculate separately each real data unit and corresponding mark in the standard clinical path of the target disease The matching value of quasi- data cell may include: the Feature Words obtained in each real data unit；Respectively according to each reality Feature Words in the data cell of border calculate each reality with the number of matches of the Feature Words in the corresponding standard data element The matching value of border data cell and the corresponding standard data element.
Specifically, semantic analysis can be carried out to each real data unit respectively, to obtain in each real data unit Feature Words, the Feature Words in each real data unit may include drug characteristic word, verification characteristics word and check Feature Words etc., In the present exemplary embodiment to this not particular determination.It should be noted that drug characteristic word can refer to the title of drug, check special Sign word can refer to the title of inspection project with the title of digital examination project, verification characteristics word.
After getting the Feature Words in each real data unit, can by each real data unit Feature Words with it is right The Feature Words in standard data element answered are matched one by one, with obtain the Feature Words in each real data unit with it is corresponding The number of matches of Feature Words in standard data element.Getting the Feature Words in each real data unit and corresponding standard After the number of matches of Feature Words in data cell, each real data unit and corresponding mark can be calculated according to following formula The matching value of quasi- data cell.The formula are as follows:
Wherein, goal (i) is the matching value of i-th real data unit and corresponding standard data element, aiIt is i-th Feature Words in real data unit and the number of matches of the Feature Words in corresponding standard data element, biFor with i-th it is real The quantity of Feature Words in the corresponding standard data element of border data cell, 1≤i≤n, n are integer, the value specification of n are as follows: When the quantity of real data unit is greater than the quantity of standard data element, the numerical value of n is the quantity of real data unit, in reality When the quantity of data cell is less than the quantity of standard data element, the numerical value of n is the quantity of standard data element, in real data When the quantity of unit and the identical quantity of standard data element, n takes the quantity of standard data element or the number of real data unit Amount.
It should be noted that under the premise of the quantity of real data unit is less than the quantity of standard data element, it is big in i When the quantity of real data unit, 0 is set by the value of goal (i).For example, the quantity in real data unit is 5, standard When the quantity of data cell is 7, then the value of goal (6) and goal (7) are 0.It is greater than standard in the quantity of real data unit Under the premise of the quantity of data cell, when i is greater than the quantity of labeled data unit, 0 is set by the value of goal (i).For example, When the quantity of real data unit is 8, and the quantity of standard data element is 4, then goal (5), goal (6), goal (7), The value of goal (8) is 0.
In order to improve the matching of the Feature Words in each real data unit with the Feature Words in corresponding standard data element Efficiency, and then the efficiency for calculating each real data unit and the matching value of corresponding standard data element is improved, it can be by each reality The Feature Words in Feature Words and each standard data element in the data cell of border are classified, and will be in each real data unit Feature Words are according to type matched with the Feature Words in corresponding standard data element.
For example, if the keyword in keyword and standard data element in real data unit includes drug characteristic Word, verification characteristics word and inspection Feature Words.Obtaining the Feature Words in a practical counting unit and corresponding standard data element In Feature Words number of matches when, first by each drug characteristic word and the corresponding normal data list in the real data unit Each drug characteristic word in member is matched one by one, to obtain the drug characteristic word in the real data unit and corresponding standard The number of matches of drug characteristic word in data cell；Then, by the real data unit each verification characteristics word with it is corresponding Standard data element in each verification characteristics word matched one by one, with obtain the verification characteristics word in real data unit with The number of matches of verification characteristics word in corresponding standard data element；Subsequently, by each inspection in the real data unit Feature Words are matched one by one with each inspection Feature Words in corresponding standard book list member, to obtain the inspection in real data unit Look into the number of matches of Feature Words with the inspection Feature Words in corresponding standard data element；Finally, by the real data unit Drug characteristic word and the inspection in the number of matches of the drug characteristic word in corresponding standard data element, real data unit Feature Words and the inspection in the number of matches of the verification characteristics word in corresponding standard data element and real data unit are special Sign word is determined as in the real data unit with the sum of the number of matches of the inspection Feature Words in corresponding standard data element The number of matches of Feature Words and the Feature Words in corresponding standard data element.
In conclusion the number of matches of each Feature Words is greatly reduced due to being matched according to the classification of Feature Words, because This, improves Feature Words in each real data unit and the matching efficiency of the Feature Words in corresponding standard data element, into And improve the efficiency for calculating each real data unit and the matching value of corresponding standard data element.
Further, the Feature Words in the standard data element may include optional feature word and essential Feature Words.It needs It is noted that optional feature word quantity can be 0, or 1, can also be 2 or 3 etc., this example Property embodiment this is not particularly limited, the quantity of the essential Feature Words can for 0 or 1, can also be 2 A or 3 etc., the present exemplary embodiment is not particularly limited this.It is described respectively according to each referring to shown in Fig. 2 based on this Feature Words in the real data unit calculate each with the number of matches of the Feature Words in the corresponding standard data element The real data unit may comprise steps of S210~S230 with the matching value of the corresponding standard data element, In:
In step S210, according to Feature Words and the corresponding standard data element in each real data unit In the number of matches of essential Feature Words calculate each real data unit and the first of the corresponding standard data element Matching value.
It in the present example embodiment, can be by each Feature Words and the corresponding normal data list in a real data unit Each essential Feature Words in member are matched one by one, to obtain the Feature Words in the real data unit and corresponding normal data Essential Feature Words number of matches in unit.Need to illustrate when, can successively obtain each real data list by the above process Feature Words in member and the number of matches of the essential Feature Words in corresponding standard data element.Getting each real data list Feature Words in member are calculated with after the number of matches of the essential Feature Words in corresponding standard data element according to following formula First matching value of each real data unit and corresponding standard data element.The formula includes:
Wherein, goal1 (i) is the first matching value of i-th real data unit and corresponding standard data element, a1iFor Feature Words in i-th of real data unit and the number of matches of the essential Feature Words in corresponding standard data element, b1iFor The quantity of essential Feature Words in standard data element corresponding with i-th of real data unit, 1≤i≤n, n are integer, n's Value specification are as follows: when the quantity of real data unit is greater than the quantity of standard data element, the numerical value of n is real data unit Quantity, when the quantity of real data unit is less than the quantity of standard data element, the numerical value of n is the number of standard data element Amount, in the quantity of real data unit and the identical quantity of standard data element, n takes the quantity or reality of standard data element The quantity of data cell.
In step S220, according to Feature Words and the corresponding standard data element in each real data unit In the number of matches of optional feature word calculate each real data unit and the second of the corresponding standard data element Matching value.
It in the present example embodiment, can be by each Feature Words and the corresponding normal data list in a real data unit Each optional feature word in member is matched one by one, to obtain the Feature Words in the real data unit and corresponding normal data Optional feature word number of matches in unit.Need to illustrate when, can successively obtain each real data list by the above process Feature Words in member and the number of matches of the optional feature word in corresponding standard data element.Getting each real data list Feature Words in member are calculated with after the number of matches of the optional feature word in corresponding standard data element according to following formula Second matching value of each real data unit and corresponding standard data element.The formula includes:
Wherein, goal2 (i) is the second matching value of i-th real data unit and corresponding standard data element, a2iFor Feature Words in i-th of real data unit and the number of matches of the optional feature word in corresponding standard data element, b2iFor The quantity of optional feature word in standard data element corresponding with i-th of real data unit, 1≤i≤n, n are integer, n's Value specification are as follows: when the quantity of real data unit is greater than the quantity of standard data element, the numerical value of n is real data unit Quantity, when the quantity of real data unit is less than the quantity of standard data element, the numerical value of n is the number of standard data element Amount, in the quantity of real data unit and the identical quantity of standard data element, n takes the quantity or reality of standard data element The quantity of data cell.
In step S230, according to each real data unit and described the first of the corresponding standard data element Matching value and second matching value calculate the matching value of each the real data unit and the corresponding standard data element.
It in the present example embodiment, can be to the first matching of each real data unit and corresponding standard data element Value and the second matching value are summed to calculate the matching value of each real data unit and corresponding standard data element.Specifically , calculation formula is as follows:
Goal (i)=goal1 (i)+goal2 (i)
Wherein, goal (i) is the matching value of i-th real data unit and corresponding standard data element, goal1 (i) For the first matching value of i-th of real data unit and corresponding standard data element, goal2 (i) is i-th of real data list First the second matching value with corresponding standard data element, 1≤i≤n, n are integer, the value specification of n are as follows: in real data list When the quantity of member is greater than the quantity of standard data element, the numerical value of n is the quantity of real data unit, in real data unit When quantity is less than the quantity of standard data element, the numerical value of n is the quantity of standard data element, in the quantity of real data unit When identical with the quantity of standard data element, n takes the quantity of standard data element or the quantity of real data unit.
From the foregoing, it will be observed that by the way that the Feature Words in standard data element are divided into essential Feature Words and optional feature word, and Based on the essential Feature Words and each real data unit of optional feature word calculating and corresponding criterion numeral in each standard data element According to the first matching value and the second matching value of unit, and according to each real data unit and the first of corresponding standard data element Matching value and the second matching value calculate the matching value of each real data unit and corresponding standard data element, improve matching value Computational accuracy, and then increase detection actual clinical path variation accuracy rate.
In order to improve the Feature Words in each real data unit in corresponding standard data element essential Feature Words and The matching efficiency of optional feature word, so improve each real data unit and the first matching value of corresponding standard data element and The computational efficiency of second matching value, can be by the essential spy in the Feature Words and each standard data element in each real data unit Sign word and optional feature word classify, and by each real data unit Feature Words in corresponding standard data element Essential Feature Words and optional feature word are according to type matched.
For example, the Feature Words in the real data unit may include drug characteristic word, verification characteristics word and inspection Feature Words；Optional feature word in the standard data element may include optional drug characteristic word, optional inspection Feature Words, can Feature Words are tested in Selected Inspection；Essential Feature Words in the standard data element may include essential drug characteristic word, essential inspection spy Levy word, essential verification characteristics word.
Based on the type of features described above word, as shown in figure 3, calculate Feature Words in each real data unit with it is corresponding The standard data element in the processes of number of matches of essential Feature Words may comprise steps of S310~S340, In:
In step s310, by each drug characteristic word and the corresponding standard data element in each real data unit In each essential drug characteristic word matched one by one, to obtain the Feature Words in each real data unit and corresponding institute State the first number of matches of the essential Feature Words in standard data element.In the present example embodiment, each real data The quantity of drug characteristic word in unit may be the same or different, and the present exemplary embodiment is not particularly limited this.Institute The quantity for stating the essential drug characteristic word in each standard data element may be the same or different, the present exemplary embodiment pair This is not particularly limited.Obtaining the Feature Words in a real data unit and the essential feature in corresponding standard data element When the first number of matches of word, by the real data unit each drug characteristic word with it is each in corresponding standard data element Essential drug characteristic word is matched one by one, and statistical match quantity, and the number of matches finally counted is determined as the reality Feature Words in the data cell of border and the first number of matches of the essential Feature Words in corresponding standard data element.It needs to illustrate , Feature Words in other real data units and corresponding normal data list can be obtained by process same as described above First number of matches of the essential Feature Words in member.
In step s 320, by each inspection Feature Words and the corresponding standard data element in each real data unit In each essential inspection Feature Words matched one by one, to obtain the Feature Words in each real data unit and corresponding institute State the second number of matches of the essential Feature Words in standard data element.In the present example embodiment, each real data The quantity of inspection Feature Words in unit may be the same or different, and the present exemplary embodiment is not particularly limited this.Institute The quantity for stating the essential inspection Feature Words in each standard data element may be the same or different, the present exemplary embodiment pair This is not particularly limited.Obtaining the Feature Words in a real data unit and the essential feature in corresponding standard data element When the second number of matches of word, by the real data unit each inspection Feature Words with it is each in corresponding standard data element Essential inspection Feature Words are matched one by one, and statistical match quantity, and the number of matches finally counted is determined as the reality Feature Words in the data cell of border and the second number of matches of the essential Feature Words in corresponding standard data element.It needs to illustrate , Feature Words in other real data units and corresponding normal data list can be obtained by process same as described above Second number of matches of the essential Feature Words in member.
In step S330, by each verification characteristics word and the corresponding standard data element in each real data unit In each essential verification characteristics word matched one by one, to obtain the Feature Words in each real data unit and corresponding institute State the third number of matches of the essential Feature Words in standard data element.In the present example embodiment, each real data The quantity of verification characteristics word in unit may be the same or different, and the present exemplary embodiment is not particularly limited this.Institute The quantity for stating the essential verification characteristics word in each standard data element may be the same or different, the present exemplary embodiment pair This is not particularly limited.Obtaining the Feature Words in a real data unit and the essential feature in corresponding standard data element When the third number of matches of word, by the real data unit each verification characteristics word with it is each in corresponding standard data element Essential verification characteristics word is matched one by one, and statistical match quantity, and the number of matches finally counted is determined as the reality Feature Words in the data cell of border and the third number of matches of the essential Feature Words in corresponding standard data element.It needs to illustrate , Feature Words in other real data units and corresponding normal data list can be obtained by process same as described above The third number of matches of essential Feature Words in member.
In step S340, according to Feature Words and the corresponding standard data element in each real data unit In first number of matches of essential Feature Words, second number of matches and the third number of matches calculate each institute State Feature Words in real data unit and the number of matches of the essential Feature Words in the corresponding standard data element.At this In exemplary embodiment, respectively to Feature Words and the essential feature in corresponding standard data element in each real data unit The first number of matches, the second number of matches and the summation of third number of matches of word, can be obtained each real data unit In Feature Words and the number of matches of the essential Feature Words in corresponding standard data element.
Based on the type of features described above word, as shown in figure 4, calculate Feature Words in each real data unit with it is corresponding The standard data element in the process of number of matches of optional feature word may comprise steps of S410~S440, In:
In step S410, by each drug characteristic word and the corresponding standard data element in each real data unit In each optional drug characteristic word matched one by one, to obtain the Feature Words in each real data unit and corresponding institute State the first number of matches of the optional feature word in standard data element.In the present example embodiment, each real data The quantity of drug characteristic word in unit may be the same or different, and the present exemplary embodiment is not particularly limited this.Institute The quantity for stating the optional drug characteristic word in each standard data element may be the same or different, the present exemplary embodiment pair This is not particularly limited.Obtaining the Feature Words in a real data unit and the optional feature in corresponding standard data element When the first number of matches of word, by the real data unit each drug characteristic word with it is each in corresponding standard data element Optional drug characteristic word is matched one by one, and statistical match quantity, and the number of matches finally counted is determined as the reality Feature Words in the data cell of border and the first number of matches of the optional feature word in corresponding standard data element.It needs to illustrate , Feature Words in other real data units and corresponding normal data list can be obtained by process same as described above First number of matches of the optional feature word in member.
In the step s 420, by each inspection Feature Words and the corresponding standard data element in each real data unit In each optional inspection Feature Words matched one by one, to obtain the Feature Words in each real data unit and corresponding institute State the second number of matches of the optional feature word in standard data element.In the present example embodiment, each real data The quantity of inspection Feature Words in unit may be the same or different, and the present exemplary embodiment is not particularly limited this.Institute The quantity for stating the optional inspection Feature Words in each standard data element may be the same or different, the present exemplary embodiment pair This is not particularly limited.Obtaining the Feature Words in a real data unit and the optional feature in corresponding standard data element When the second number of matches of word, by the real data unit each inspection Feature Words with it is each in corresponding standard data element Optional inspection Feature Words are matched one by one, and statistical match quantity, and the number of matches finally counted is determined as the reality Feature Words in the data cell of border and the second number of matches of the optional feature word in corresponding standard data element.It needs to illustrate , Feature Words in other real data units and corresponding normal data list can be obtained by process same as described above Second number of matches of the optional feature word in member.
In step S430, by each verification characteristics word and the corresponding standard data element in each real data unit In each optional verification characteristics word matched one by one, to obtain the Feature Words in each real data unit and corresponding institute State the third number of matches of the optional feature word in standard data element.In the present example embodiment, each real data The quantity of verification characteristics word in unit may be the same or different, and the present exemplary embodiment is not particularly limited this.Institute The quantity for stating the optional verification characteristics word in each standard data element may be the same or different, the present exemplary embodiment pair This is not particularly limited.Obtaining the Feature Words in a real data unit and the optional feature in corresponding standard data element When the third number of matches of word, by the real data unit each verification characteristics word with it is each in corresponding standard data element Optional verification characteristics word is matched one by one, and statistical match quantity, and the number of matches finally counted is determined as the reality Feature Words in the data cell of border and the third number of matches of the optional feature word in corresponding standard data element.It needs to illustrate , Feature Words in other real data units and corresponding normal data list can be obtained by process same as described above The third number of matches of optional feature word in member.
In step S440, according to Feature Words and the corresponding standard data element in each real data unit In first number of matches of optional feature word, second number of matches and the third number of matches calculate each institute State Feature Words in real data unit and the number of matches of the optional feature word in the corresponding standard data element.At this In exemplary embodiment, respectively to Feature Words and the optional feature in corresponding standard data element in each real data unit The first number of matches, the second number of matches and the summation of third number of matches of word, can be obtained each real data unit In Feature Words and the number of matches of the optional feature word in corresponding standard data element.
In conclusion the number of matches of each Feature Words is greatly reduced due to being matched according to the classification of Feature Words, because This, improves Feature Words in each real data unit and the essential Feature Words and optional feature word in corresponding standard data element Matching efficiency, and then improve each real data unit and the first matching value of corresponding standard data element and the second matching The computational efficiency of value.
In step S140, according to the matching value meter of each the real data unit and the corresponding standard data element Calculate the matching value in the actual clinical path and the standard clinical path.
It in the present example embodiment, can be according to the matching value of each real data unit and corresponding standard data element And combine the matching value in following formula calculating actual clinical path and standard clinical path.The calculation formula is as follows:
Wherein, goal is the matching value in actual clinical path and standard clinical path, and goal (i) is i-th of real data The matching value of unit and corresponding standard data element, 1≤i≤n, n are integer, the value specification of n are as follows: in real data unit Quantity when being greater than the quantity of standard data element, the numerical value of n is the quantity of real data unit, in the number of real data unit When amount is less than the quantity of standard data element, the numerical value of n is the quantity of standard data element, real data unit quantity and When the quantity of standard data element is identical, n takes the quantity of standard data element or the quantity of real data unit.
In step S150, whether the actual clinical path is detected according to the matching value and in conjunction with a preset matching value It morphs.
In the present example embodiment, by the matching value and a preset matching value in actual clinical path and standard clinical path It is compared, when the matching value in actual clinical path and standard clinical path is less than preset matching value, illustrates actual clinical road Diameter morphs.After detecting that actual clinical path is morphed, variation prompting message can be generated, and to medical worker The variation prompting message is shown, so that medical worker arranges the work of next step according to the variation prompting message.The variation mentions Awake information can be the prompting message of written form, can also be the prompting message etc. of graphic form, the present exemplary embodiment pair This is not particularly limited.
In conclusion by the matching value for calculating actual clinical path and standard clinical path, and combined according to matching value Whether one preset matching value detection actual clinical path morphs, and compares and the prior art, due to not by the way of artificial, This improves the efficiency of actual clinical path variation detection, and then the hysteresis quality for solving actual clinical variation detection is strong asks Topic, while human cost also greatly reduces；Further, since avoiding the influence of human factor, asking for error detection is avoided Topic improves the accuracy of actual clinical path variation detection.
It should be noted that although describing each step of method in the disclosure in the accompanying drawings with particular order, This does not require that or implies must execute these steps in this particular order, or have to carry out step shown in whole Just it is able to achieve desired result.Additional or alternative, it is convenient to omit multiple steps are merged into a step and held by certain steps Row, and/or a step is decomposed into execution of multiple steps etc..
In an exemplary embodiment of the disclosure, a kind of actual clinical path variation detection device is additionally provided, such as Fig. 5 institute Show, the actual clinical path variation detection device 500 may include: to obtain module 501, the calculating list of dividing unit 502, first First 503, second computing unit 504, detection unit 505, in which:
Module 501 is obtained, can be used for obtaining actual clinical path of the target patient based on target disease；
Dividing unit 502 can be used for that the actual clinical path is divided into multiple actual numbers according to a dividing unit According to unit；
First computing unit 503 can be used for calculating separately the mark of each the real data unit and the target disease The matching value of corresponding standard data element in quasi- clinical path；
Second computing unit 504 can be used for according to each real data unit and the corresponding normal data list The matching value of member calculates the matching value in the actual clinical path and the standard clinical path；
Detection unit 505 can be used for detecting the actual clinical according to the matching value and in conjunction with a preset matching value Whether morph in path.
The detail of each actual clinical path variation detection device module is on corresponding actual clinical road among the above It is described in detail in diameter mutation detection method, therefore details are not described herein again.
It should be noted that although being referred to several modules or unit of the equipment for execution in the above detailed description, But it is this divide it is not enforceable.In fact, according to embodiment of the present disclosure, two or more above-described modules Either the feature and function of unit can embody in a module or unit.Conversely, an above-described module or The feature and function of person's unit can be to be embodied by multiple modules or unit with further division.
In an exemplary embodiment of the disclosure, a kind of electronic equipment that can be realized the above method is additionally provided.
Person of ordinary skill in the field it is understood that various aspects of the invention can be implemented as system, method or Program product.Therefore, various aspects of the invention can be embodied in the following forms, it may be assumed that complete hardware embodiment, complete The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.) or hardware and software, can unite here Referred to as circuit, " module " or " system ".
The electronic equipment 600 of this embodiment according to the present invention is described referring to Fig. 6.The electronics that Fig. 6 is shown Equipment 600 is only an example, should not function to the embodiment of the present invention and use scope bring any restrictions.
As shown in fig. 7, electronic equipment 600 is showed in the form of universal computing device.The component of electronic equipment 600 can wrap It includes but is not limited to: at least one above-mentioned processing unit 610, at least one above-mentioned storage unit 620, the different system components of connection The bus 630 of (including storage unit 620 and processing unit 610), display unit 640.
Wherein, the storage unit is stored with program code, and said program code can be held by the processing unit 610 Row, so that various according to the present invention described in the execution of the processing unit 610 above-mentioned " illustrative methods " part of this specification The step of illustrative embodiments.For example, the processing unit 610 can execute step S110 as shown in Figure 2, obtain mesh Mark actual clinical path of the patient based on target disease；Step S120, the actual clinical path is drawn according to a dividing unit It is divided into multiple real data units；Step S130, the standard of each the real data unit and the target disease is calculated separately The matching value of corresponding standard data element in clinical path；Step S140, according to each real data unit with it is corresponding The matching value of the standard data element calculates the matching value in the actual clinical path and the standard clinical path；Step S150, detect whether the actual clinical path morphs according to the matching value and in conjunction with a preset matching value.
Storage unit 620 may include the readable medium of volatile memory cell form, such as Random Access Storage Unit (RAM) 6201 and/or cache memory unit 6202, it can further include read-only memory unit (ROM) 6203.
Storage unit 620 can also include program/utility with one group of (at least one) program module 6205 6204, such program module 6205 includes but is not limited to: operating system, one or more application program, other program moulds It may include the realization of network environment in block and program data, each of these examples or certain combination.
Bus 630 can be to indicate one of a few class bus structures or a variety of, including storage unit bus or storage Cell controller, peripheral bus, graphics acceleration port, processing unit use any bus structures in a variety of bus structures Local bus.
Electronic equipment 600 can also be with one or more external equipments 670 (such as keyboard, sensing equipment, bluetooth equipment Deng) communication, can also be enabled a user to one or more equipment interact with the electronic equipment 600 communicate, and/or with make Any equipment (such as the router, modulation /demodulation that the electronic equipment 600 can be communicated with one or more of the other calculating equipment Device etc.) communication.This communication can be carried out by input/output (I/O) interface 650.Also, electronic equipment 600 can be with By network adapter 660 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network, Such as internet) communication.As shown, network adapter 660 is communicated by bus 630 with other modules of electronic equipment 600. It should be understood that although not shown in the drawings, other hardware and/or software module can not used in conjunction with electronic equipment 600, including but not Be limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and Data backup storage system etc..
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the disclosure The technical solution of embodiment can be embodied in the form of software products, which can store non-volatile at one Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are so that a calculating Equipment (can be personal computer, server, terminal installation or network equipment etc.) is executed according to disclosure embodiment Method.
In an exemplary embodiment of the disclosure, a kind of computer readable storage medium is additionally provided, energy is stored thereon with Enough realize the program product of this specification above method.In some possible embodiments, various aspects of the invention may be used also In the form of being embodied as a kind of program product comprising program code, when described program product is run on the terminal device, institute Program code is stated for executing the terminal device described in above-mentioned " illustrative methods " part of this specification according to this hair The step of bright various illustrative embodiments.
Refering to what is shown in Fig. 7, describing the program product for realizing the above method of embodiment according to the present invention 700, can using portable compact disc read only memory (CD-ROM) and including program code, and can in terminal device, Such as it is run on PC.However, program product of the invention is without being limited thereto, in this document, readable storage medium storing program for executing can be with To be any include or the tangible medium of storage program, the program can be commanded execution system, device or device use or It is in connection.
Described program product can be using any combination of one or more readable mediums.Readable medium can be readable letter Number medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or System, device or the device of semiconductor, or any above combination.The more specific example of readable storage medium storing program for executing is (non exhaustive List) include: electrical connection with one or more conducting wires, portable disc, hard disk, random access memory (RAM), read-only Memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory (CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal, In carry readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetic signal, Optical signal or above-mentioned any appropriate combination.Readable signal medium can also be any readable Jie other than readable storage medium storing program for executing Matter, the readable medium can send, propagate or transmit for by instruction execution system, device or device use or and its The program of combined use.
The program code for including on readable medium can transmit with any suitable medium, including but not limited to wirelessly, have Line, optical cable, RF etc. or above-mentioned any appropriate combination.
The program for executing operation of the present invention can be write with any combination of one or more programming languages Code, described program design language include object oriented program language-Java, C++ etc., further include conventional Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user It calculates and executes in equipment, partly executes on a user device, being executed as an independent software package, partially in user's calculating Upper side point is executed on a remote computing or is executed in remote computing device or server completely.It is being related to far Journey calculates in the situation of equipment, and remote computing device can pass through the network of any kind, including local area network (LAN) or wide area network (WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP To be connected by internet).
In addition, above-mentioned attached drawing is only the schematic theory of processing included by method according to an exemplary embodiment of the present invention It is bright, rather than limit purpose.It can be readily appreciated that the time that above-mentioned processing shown in the drawings did not indicated or limited these processing is suitable Sequence.In addition, be also easy to understand, these processing, which can be, for example either synchronously or asynchronously to be executed in multiple modules.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to its of the disclosure His embodiment.This application is intended to cover any variations, uses, or adaptations of the disclosure, these modifications, purposes or Adaptive change follow the general principles of this disclosure and including the undocumented common knowledge in the art of the disclosure or Conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the disclosure are by claim It points out.
It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and And various modifications and changes may be made without departing from the scope thereof.The scope of the present disclosure is only limited by the attached claims.

Claims (10)

1. a kind of actual clinical path mutation detection method characterized by comprising
Obtain actual clinical path of the target patient based on target disease；
The actual clinical path is divided into multiple real data units according to a dividing unit；
Calculate separately each real data unit and corresponding normal data list in the standard clinical path of the target disease The matching value of member；
The actual clinical road is calculated with the matching value of the corresponding standard data element according to each real data unit The matching value of diameter and the standard clinical path；
Detect whether the actual clinical path morphs according to the matching value and in conjunction with a preset matching value.
2. actual clinical path according to claim 1 mutation detection method, which is characterized in that described to calculate separately each institute Real data unit, which is stated, with the matching value of corresponding standard data element in the standard clinical path of the target disease includes:
Obtain the Feature Words in each real data unit；
Respectively according to Feature Words and the Feature Words in the corresponding standard data element in each real data unit Number of matches calculates the matching value of each the real data unit and the corresponding standard data element.
3. actual clinical path according to claim 2 mutation detection method, which is characterized in that calculated separately respectively described The real data unit with before the matching value of corresponding standard data element in the standard clinical path of the target disease Further include:
The standard clinical path of the target disease is obtained, and draws the standard clinical path according to the dividing unit It is divided into multiple standard data elements；And
Obtain the Feature Words in each standard data element.
4. actual clinical path according to claim 2 mutation detection method, which is characterized in that the standard data element In Feature Words include optional feature word and essential Feature Words；
It is described respectively according to Feature Words and the feature in the corresponding standard data element in each real data unit The number of matches of word calculates each real data unit with the matching value of the corresponding standard data element
According to Feature Words and the essential Feature Words in the corresponding standard data element in each real data unit Number of matches calculates the first matching value of each the real data unit and the corresponding standard data element；
According to Feature Words and the optional feature word in the corresponding standard data element in each real data unit Number of matches calculates the second matching value of each the real data unit and the corresponding standard data element；
According to each real data unit and first matching value of the corresponding standard data element and described second Matching value calculates the matching value of each the real data unit and the corresponding standard data element.
5. actual clinical path according to claim 4 mutation detection method, which is characterized in that the real data unit In Feature Words include drug characteristic word, verification characteristics word and check Feature Words；Optional spy in the standard data element Levying word includes optional drug characteristic word, optional inspection Feature Words, optional verification characteristics word；It is essential in the standard data element Feature Words include essential drug characteristic word, essential inspection Feature Words, essential verification characteristics word.
6. actual clinical path according to claim 5 mutation detection method, which is characterized in that described according to each described Feature Words in real data unit calculate each with the number of matches of the essential Feature Words in the corresponding standard data element The real data unit with before the first matching value of the corresponding standard data element further include:
Each drug characteristic word in each real data unit and each essential drug in corresponding standard data element is special Sign word matched one by one, with obtain the Feature Words in each real data unit in the corresponding standard data element Essential Feature Words the first number of matches；
Each inspection Feature Words in each real data unit and each essential inspection in corresponding standard data element are special Sign word matched one by one, with obtain the Feature Words in each real data unit in the corresponding standard data element Essential Feature Words the second number of matches；
Each verification characteristics word in each real data unit and each essential inspection in corresponding standard data element is special Sign word matched one by one, with obtain the Feature Words in each real data unit in the corresponding standard data element Essential Feature Words third number of matches；
According to Feature Words and the essential Feature Words in the corresponding standard data element in each real data unit First number of matches, second number of matches and the third number of matches calculate in each real data unit Feature Words and the number of matches of the essential Feature Words in the corresponding standard data element.
7. actual clinical path according to claim 5 mutation detection method, which is characterized in that described according to each described Feature Words in real data unit calculate each with the number of matches of the optional feature word in the corresponding standard data element The real data unit with before the second matching value of the corresponding standard data element further include:
Each drug characteristic word in each real data unit and each optional drug in corresponding standard data element is special Sign word matched one by one, with obtain the Feature Words in each real data unit in the corresponding standard data element Optional feature word the first number of matches；
Each inspection Feature Words in each real data unit and each optional inspection in corresponding standard data element are special Sign word matched one by one, with obtain the Feature Words in each real data unit in the corresponding standard data element Optional feature word the second number of matches；
Each verification characteristics word in each real data unit and each optional inspection in corresponding standard data element is special Sign word matched one by one, with obtain the Feature Words in each real data unit in the corresponding standard data element Optional feature word third number of matches；
According to Feature Words and the optional feature word in the corresponding standard data element in each real data unit First number of matches, second number of matches and the third number of matches calculate in each real data unit Feature Words and the number of matches of the optional feature word in the corresponding standard data element.
The detection device 8. a kind of actual clinical path makes a variation characterized by comprising
Module is obtained, for obtaining actual clinical path of the target patient based on target disease；
Dividing unit, for the actual clinical path to be divided into multiple real data units according to a dividing unit；
First computing unit, in the standard clinical path for calculating separately each real data unit and the target disease The matching value of corresponding standard data element；
Second computing unit, by according to each real data unit based on the matching value of the corresponding standard data element Calculate the matching value in the actual clinical path and the standard clinical path；
Detection unit, for detecting whether the actual clinical path occurs according to the matching value and in conjunction with a preset matching value Variation.
9. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program quilt Actual clinical path mutation detection method described in any one of claim 1~7 is realized when processor executes.
10. a kind of electronic equipment characterized by comprising
Processor；And
Memory, for storing the executable instruction of the processor；
Wherein, the processor is configured to come any one of perform claim requirement 1~7 institute via the execution executable instruction The actual clinical path mutation detection method stated.
CN201811339164.1A 2018-11-12 2018-11-12 Actual clinical path mutation detection method and device, storage medium, electronic equipment Pending CN109545383A (en)

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