CN111652737A - Insurance underwriting method and device based on text semantic processing - Google Patents
Insurance underwriting method and device based on text semantic processing Download PDFInfo
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- 201000010099 disease Diseases 0.000 claims abstract description 109
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- 238000001914 filtration Methods 0.000 claims description 6
- 238000003058 natural language processing Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 2
- 230000017105 transposition Effects 0.000 description 4
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
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- JRIFOENGVFOJPK-UHFFFAOYSA-N 2,2-difluoro-N-(2-hydroxypropyl)-3-(3-nitro-1,2,4-triazol-1-yl)propanamide Chemical compound CC(O)CNC(=O)C(F)(F)CN1C=NC([N+]([O-])=O)=N1 JRIFOENGVFOJPK-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention provides an insurance underwriting method and device based on text semantic processing. Receiving a disease name input by a user; matching all synonymous diseases of the input disease names; matching all the sub-diseases of the input disease name and corresponding synonyms; matching all father diseases with the disease names and corresponding synonyms; aiming at the sub-diseases, matching all the sub-diseases and corresponding synonyms of the sub-diseases according to the inclusion relation in the related table knowledge base; aiming at the father disease, matching all father diseases and corresponding synonyms of the father disease according to the inclusion relation in the related table knowledge base; matching the dangerous species names which cannot be guaranteed according to the disease names of all synonymous diseases and a dangerous species non-guaranteed disease knowledge base; matching out the name of the suspected dangerous case which can be thrown according to a dangerous case non-insurable disease knowledge base; matching the name of the non-insurable dangerous species according to a knowledge base of the non-insurable diseases of the dangerous species; finally, non-insurable and suspected dangerous species are determined.
Description
Technical Field
The invention relates to the field of insurance, in particular to an insurance underwriting method and device based on text semantic processing.
Background
Most insurance products require health notification from the user. Generally speaking, health advice lists a variety of diseases or symptoms at great length, and requires that the user be assured that they are not suffering from the listed disease or symptom. But insurance products in the market are quite rich, the requirements of each product for health notification are different, and once a user suffers from some past symptoms, the user can hardly judge which products meet the requirements for health notification and which products do not meet the requirements for health notification. Therefore, the method brings trouble to insurance users greatly and also influences the operation efficiency of the insurance market.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an insurance underwriting method and device based on text semantic processing, which can automatically match insurance products which can be applied, cannot be applied and are suspected to be applied according to matching rules based on the input of a client.
According to the invention, the insurance underwriting method based on text semantic processing is provided, and comprises the following steps:
the first step is as follows: establishing a knowledge base based on insurance underwriting correlation;
the second step is as follows: receiving a disease name input by a user;
the third step: matching all synonymous diseases of the input disease names according to the synonym relation in the related table knowledge base;
the fourth step: matching all the sub-diseases of the input disease names and corresponding synonyms according to the inclusion relation in the related table knowledge base;
the fifth step: matching all father diseases and corresponding synonyms of the disease names according to the inclusion relation in the related table knowledge base;
a sixth step: aiming at the sub-diseases, matching all the sub-diseases and corresponding synonyms of the sub-diseases according to the inclusion relation in the related table knowledge base;
a seventh step of: aiming at the father disease, matching all father diseases and corresponding synonyms of the father disease according to the inclusion relation in the related table knowledge base;
an eighth step: matching the names of the dangerous seeds which cannot be guaranteed according to the disease names of all the synonymous diseases obtained in the third step and a dangerous seed knowledge base of the dangerous seeds which cannot be guaranteed;
a ninth step: matching the suspected name of the dangerous case that can be put according to all the names of the diseases obtained in the fourth step and the sixth step and a dangerous case non-insurable disease knowledge base;
a tenth step: matching the names of the dangerous seeds which cannot be insurable according to all the disease names obtained in the fifth step and the seventh step and according to a dangerous seed insurable disease knowledge base;
an eleventh step: filtering repeated dangerous species in the results of the eighth step, the ninth step and the tenth step according to a filtering rule, and finally determining non-insurable dangerous species and suspected dangerous species;
a twelfth step: and determining the insurable seeds from all the remaining dangerous seeds in the dangerous seed bank according to the finally determined insurable seeds and the suspected insurable seeds.
Preferably, the insurance underwriting correlation-based knowledge base comprises:
a library of conditions in which disease names, disease interpretations and underwriting conclusions are stored in an associated manner;
a risk seed non-insurable disease knowledge base in which risk seed identification codes and non-insurable disease names are stored in an associated manner;
a correlation table knowledge base in which synonym relationships of disease names are stored in an associated manner and inclusion relationships between the disease names are stored;
a health advice rich text knowledge base in which a dangerous seed identification code and a health advice rich text format are stored in an associated manner;
a health advice highlight table in which disease names and health advice text contents are stored in an associated manner.
Preferably, in the second step, a text similarity measure is performed on the disease name inputted by the user using a natural language processing model Jaro-Winkler score, and a text having Jaro-Winkler score greater than a predetermined threshold (e.g., 0.6) is determined as a similar text.
Preferably, the insurance underwriting method based on text semantic processing further comprises the following steps: and outputting the disease explanation and the conclusion of the serious illness, the medical insurance and the life insurance check based on the disease name input by the user.
Preferably, the insurance underwriting method based on text semantic processing further comprises the following steps:
extracting the disease names obtained in the third step to the seventh step, and matching the health notification text contents which may exist by combining the health notification highlight table;
matching health notification rich texts of each dangerous species by combining a health notification rich text knowledge base aiming at the dangerous species obtained in the eighth step, the ninth step and the tenth step;
and matching the health notification text content with the health notification rich text to obtain a health notification text.
Preferably, the non-insurable relevant text content is highlighted in the health advice for each risk category.
According to the invention, the invention also provides an insurance underwriting device based on text semantic processing, which is used for realizing the insurance underwriting method based on the text semantic processing.
The invention provides an insurance underwriting method and device based on text semantic processing, which can automatically match insurance products which can be applied, cannot be applied and are suspected to be applied according to matching rules based on input of a client.
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A more complete understanding of the present invention, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:
fig. 1 schematically shows a flowchart of an insurance underwriting method based on text semantic processing according to a preferred embodiment of the present invention.
Fig. 2 schematically shows an insurance matching example of the text semantic processing-based insurance underwriting method according to the preferred embodiment of the present invention.
It is to be noted, however, that the appended drawings illustrate rather than limit the invention. It is noted that the drawings representing structures may not be drawn to scale. Also, in the drawings, the same or similar elements are denoted by the same or similar reference numerals.
Detailed Description
In order that the present disclosure may be more clearly and readily understood, reference will now be made in detail to the present disclosure as illustrated in the accompanying drawings.
Fig. 1 schematically shows a flowchart of an insurance underwriting method based on text semantic processing according to a preferred embodiment of the present invention.
As shown in fig. 1, the insurance underwriting method based on text semantic processing according to the preferred embodiment of the present invention includes:
the first step is as follows: establishing a knowledge base based on insurance underwriting correlation;
the insurance underwriting correlation-based knowledge base comprises:
a library of conditions in which disease names, disease interpretations and underwriting conclusions are stored in an associated manner;
a risk seed non-insurable disease knowledge base in which risk seed identification codes and non-insurable disease names are stored in an associated manner;
a correlation table knowledge base in which synonym relationships of disease names are stored in an associated manner and inclusion relationships between the disease names are stored;
a health advice rich text knowledge base in which a dangerous seed identification code and a health advice rich text format are stored in an associated manner;
a health advice highlight table in which disease names and health advice text contents are stored in an associated manner.
The second step is as follows: receiving a disease name input by a user;
more specifically, in the second step, text similarity measurement is performed on the disease name input by the user using a natural language processing model Jaro-Winkler score, and a text having Jaro-Winkler score greater than a predetermined threshold (e.g., 0.6) is determined as a similar text.
The third step: matching all synonymous diseases T1, T2, … and TN of the input disease names according to the synonym relation in the related table knowledge base;
the fourth step: matching all sub-diseases Z1, Z2, … and ZN of the input disease names and corresponding synonyms Z1T1, Z1T2, …, Z1TN, Z2T1, Z2T2, …, Z2TN, …, ZNT1, ZNT2, … and ZNTN according to the inclusion relation in the relevant table knowledge base;
the fifth step: matching all parent diseases F1, F2, … and FN of the disease names and corresponding synonyms F1T1, F1T2, …, F1TN, F2T1, F2T2, …, F2TN, …, FNT1, FNT2, … and FNTN according to the inclusion relationship in the knowledge base of the correlation table, and the method is shown in FIG. 2;
a sixth step: aiming at the sub-diseases Z1, Z2, … and ZN, taking Z1 as an example, all the sub-diseases Z1Z1, Z1Z2, … and Z1ZN of Z1 and corresponding synonyms Z1Z1T1, Z1Z1T2, …, Z1Z1TN, Z1Z2T1, Z1Z2T2, …, Z1Z2TN, Z1ZNT1, Z1ZNT2, … and Z1TN are matched according to the inclusion relation in a knowledge base of a relevant table, and are shown in FIG. 2;
a seventh step of: for the parent diseases F1, F2, … and FN, taking F1 as an example, according to the inclusion relationship in the knowledge base of the correlation table, matching out all the parent diseases F1F1, F1F2, … and F1FN of F1 and corresponding synonyms F1T1, F1T2, …, F1TN, F1F2T1, F1F2T2, …, F1F2TN, F1F1, F1FNT2, … and F1FNTN, as shown in fig. 2;
an eighth step: matching the names of the dangerous seeds which cannot be guaranteed according to the disease names of all the synonymous diseases obtained in the third step and a dangerous seed knowledge base of the dangerous seeds which cannot be guaranteed;
a ninth step: matching the suspected name of the dangerous case that can be put according to all the names of the diseases obtained in the fourth step and the sixth step and a dangerous case non-insurable disease knowledge base;
a tenth step: matching the names of the dangerous seeds which cannot be insurable according to all the disease names obtained in the fifth step and the seventh step and according to a dangerous seed insurable disease knowledge base;
an eleventh step: filtering repeated dangerous species in the results of the eighth step, the ninth step and the tenth step according to a filtering rule, and finally determining non-insurable dangerous species and suspected dangerous species;
a twelfth step: and determining the insurable seeds from all the remaining dangerous seeds in the dangerous seed bank according to the finally determined insurable seeds and the suspected insurable seeds.
Therefore, based on the disease name input by the user, insurance product names which can be applied, cannot be applied and are suspected to be applied are matched according to the knowledge base related to the underwriting.
In addition, for the output of the underwriting conclusion, the insurance underwriting method based on text semantic processing according to the preferred embodiment of the invention may further include: and outputting the disease explanation and the conclusion of the serious illness, the medical insurance and the life insurance check based on the disease name input by the user.
In addition, the insurance underwriting method based on text semantic processing according to the preferred embodiment of the present invention may further include:
extracting the disease names obtained in the third step to the seventh step, and matching the health notification text contents which may exist by combining the health notification highlight table;
matching health notification rich texts of each dangerous species by combining a health notification rich text knowledge base aiming at the dangerous species obtained in the eighth step, the ninth step and the tenth step;
and matching the health notification text content with the health notification rich text to obtain a health notification text. Preferably, the non-insurable relevant text content is highlighted in the health advice for each risk category.
Therefore, the invention provides an insurance underwriting method and device based on text semantic processing, which can automatically match insurance products which can be applied, cannot be applied and are suspected to be applied according to matching rules based on the input of a client.
< text similarity coefficient technique in Natural language processing >
Jaro Distance: a measure of the similarity of character strings is also an edit distance, and the higher the Jaro distance, the higher the similarity of text, the similarity is defined as follows:
wherein, M represents the matching number (ensuring the same sequence), | s | represents the length of the character string, t represents the transposition number, and the transposition number represents: two characters from S1 and S2, respectively, if not more than
The two strings are considered to match; the characters that match with each other determine the number of transpositions T, which is simply half of the number of matching characters in different orders, i.e. the number of transpositions T, for example, characters of MARTHA and MARTHA are matched, but in the matched characters, T and H need to be transposed to change MARTHA into MARTHA, then T and H are matching characters in different orders, and T is 2/2 is 1.
Jaro-Winklerdistance is a variation of JaroDistance. Jaro-Winkler gives a higher score to the same string for the initial part, where a prefix p is defined, and two strings, and if the prefix part has a part of length iota that is the same, Jaro-Winkler Distance is:
dw＝dj+(ιp(1-dj))；
wherein d isjIs the Jaro Distance of two strings; iota is the same length of the prefix, but specifies a maximum of 4; p is a constant for adjusting the fraction, provided that d cannot exceed 0.25, otherwise d may occurwFor greater than 1, Winkler defines this constant as 0.1.
It should be noted that the terms "first", "second", "third", and the like in the description are used for distinguishing various components, elements, steps, and the like in the description, and are not used for indicating a logical relationship or a sequential relationship between the various components, elements, steps, and the like, unless otherwise specified.
It is to be understood that while the present invention has been described in conjunction with the preferred embodiments thereof, it is not intended to limit the invention to those embodiments. It will be apparent to those skilled in the art from this disclosure that many changes and modifications can be made, or equivalents modified, in the embodiments of the invention without departing from the scope of the invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.
Claims (7)
1. An insurance underwriting method based on text semantic processing is characterized by comprising the following steps:
the first step is as follows: establishing a knowledge base based on insurance underwriting correlation;
the second step is as follows: receiving a disease name input by a user;
the third step: matching all synonymous diseases of the input disease names according to the synonym relation in the related table knowledge base;
the fourth step: matching all the sub-diseases of the input disease names and corresponding synonyms according to the inclusion relation in the related table knowledge base;
the fifth step: matching all father diseases and corresponding synonyms of the disease names according to the inclusion relation in the related table knowledge base;
a sixth step: aiming at the sub-diseases, matching all the sub-diseases and corresponding synonyms of the sub-diseases according to the inclusion relation in the related table knowledge base;
a seventh step of: aiming at the father disease, matching all father diseases and corresponding synonyms of the father disease according to the inclusion relation in the related table knowledge base;
an eighth step: matching the names of the dangerous seeds which cannot be guaranteed according to the disease names of all the synonymous diseases obtained in the third step and a dangerous seed knowledge base of the dangerous seeds which cannot be guaranteed;
a ninth step: matching the suspected name of the dangerous case that can be put according to all the names of the diseases obtained in the fourth step and the sixth step and a dangerous case non-insurable disease knowledge base;
a tenth step: matching the names of the dangerous seeds which cannot be insurable according to all the disease names obtained in the fifth step and the seventh step and according to a dangerous seed insurable disease knowledge base;
an eleventh step: filtering repeated dangerous species in the results of the eighth step, the ninth step and the tenth step according to a filtering rule, and finally determining non-insurable dangerous species and suspected dangerous species;
a twelfth step: and determining the insurable seeds from all the remaining dangerous seeds in the dangerous seed bank according to the finally determined insurable seeds and the suspected insurable seeds.
2. The text semantic processing-based insurance underwriting method according to claim 1, wherein the knowledge base based on insurance underwriting correlation comprises:
a library of conditions in which disease names, disease interpretations and underwriting conclusions are stored in an associated manner;
a risk seed non-insurable disease knowledge base in which risk seed identification codes and non-insurable disease names are stored in an associated manner;
a correlation table knowledge base in which synonym relationships of disease names are stored in an associated manner and inclusion relationships between the disease names are stored;
a health advice rich text knowledge base in which a dangerous seed identification code and a health advice rich text format are stored in an associated manner;
a health advice highlight table in which disease names and health advice text contents are stored in an associated manner.
3. The insurance underwriting method based on text semantic processing according to claim 1 or 2, characterized in that in the second step, text similarity measurement is performed on the disease name inputted by the user by using a natural language processing model Jaro-Winkler score, and the text with Jaro-Winkler score larger than a predetermined threshold (e.g. 0.6) is determined as the similar text.
4. The insurance underwriting method based on text semantic processing according to claim 1 or 2, characterized by further comprising: and outputting the disease explanation and the conclusion of the serious illness, the medical insurance and the life insurance check based on the disease name input by the user.
5. The insurance underwriting method based on text semantic processing according to claim 1 or 2, characterized by further comprising:
extracting the disease names obtained in the third step to the seventh step, and matching the health notification text contents which may exist by combining the health notification highlight table;
matching health notification rich texts of each dangerous species by combining a health notification rich text knowledge base aiming at the dangerous species obtained in the eighth step, the ninth step and the tenth step;
and matching the health notification text content with the health notification rich text to obtain a health notification text.
6. The text semantic processing-based insurance underwriting method according to claim 1 or 2, characterized in that in the health notification of each risk category, text contents related to non-insurable are highlighted.
7. An insurance underwriting device based on text semantic processing, which is characterized by being used for realizing the insurance underwriting method based on text semantic processing according to one of claims 1 to 6.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100031134A1 (en) * | 2009-08-17 | 2010-02-04 | Netform Exchange, Inc. | System for electronically collecting and distributing health insurance information |
| CN102332142A (en) * | 2011-07-22 | 2012-01-25 | 大连亿创天地科技发展有限公司 | On-line insurance recommendation method and system |
| CN105809549A (en) * | 2016-02-23 | 2016-07-27 | 平安科技（深圳）有限公司 | Medical affair intelligent insurance checking method and system |
| US20170069035A1 (en) * | 2009-03-06 | 2017-03-09 | Marilyn C. Quinlan | Systems and methods for matching consumer requests with insurance underwriter appetites |
| JPWO2017013712A1 (en) * | 2015-07-17 | 2018-03-08 | 株式会社日立製作所 | Insurance information providing system and insurance information providing method |
| CN108922633A (en) * | 2018-06-22 | 2018-11-30 | 北京海德康健信息科技有限公司 | A kind of disease name standard convention method and canonical system |
| CN109064343A (en) * | 2018-08-13 | 2018-12-21 | 中国平安人寿保险股份有限公司 | Risk model method for building up, risk matching process, device, equipment and medium |
| CN109285076A (en) * | 2018-02-07 | 2019-01-29 | 中国平安人寿保险股份有限公司 | Intelligent core protects processing method, server and storage medium |
| CN109829830A (en) * | 2018-12-22 | 2019-05-31 | 中国平安人寿保险股份有限公司 | A danger marketing method, device, electronic equipment and computer readable storage medium |
| CN109994215A (en) * | 2019-04-25 | 2019-07-09 | 清华大学 | Disease automatic coding system, method, equipment and storage medium |
| CN110070449A (en) * | 2019-03-15 | 2019-07-30 | 平安科技（深圳）有限公司 | Project kind matching process, device, computer equipment and storage medium |
| CN110111207A (en) * | 2019-04-12 | 2019-08-09 | 中国平安人寿保险股份有限公司 | Core protects method and relevant device |
| CN110993103A (en) * | 2019-11-28 | 2020-04-10 | 阳光人寿保险股份有限公司 | Method for establishing disease risk prediction model and method for recommending disease insurance product |
-
2020
- 2020-04-17 CN CN202010306058.4A patent/CN111652737B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170069035A1 (en) * | 2009-03-06 | 2017-03-09 | Marilyn C. Quinlan | Systems and methods for matching consumer requests with insurance underwriter appetites |
| US20100031134A1 (en) * | 2009-08-17 | 2010-02-04 | Netform Exchange, Inc. | System for electronically collecting and distributing health insurance information |
| CN102332142A (en) * | 2011-07-22 | 2012-01-25 | 大连亿创天地科技发展有限公司 | On-line insurance recommendation method and system |
| JPWO2017013712A1 (en) * | 2015-07-17 | 2018-03-08 | 株式会社日立製作所 | Insurance information providing system and insurance information providing method |
| CN105809549A (en) * | 2016-02-23 | 2016-07-27 | 平安科技（深圳）有限公司 | Medical affair intelligent insurance checking method and system |
| CN109285076A (en) * | 2018-02-07 | 2019-01-29 | 中国平安人寿保险股份有限公司 | Intelligent core protects processing method, server and storage medium |
| CN108922633A (en) * | 2018-06-22 | 2018-11-30 | 北京海德康健信息科技有限公司 | A kind of disease name standard convention method and canonical system |
| CN109064343A (en) * | 2018-08-13 | 2018-12-21 | 中国平安人寿保险股份有限公司 | Risk model method for building up, risk matching process, device, equipment and medium |
| CN109829830A (en) * | 2018-12-22 | 2019-05-31 | 中国平安人寿保险股份有限公司 | A danger marketing method, device, electronic equipment and computer readable storage medium |
| CN110070449A (en) * | 2019-03-15 | 2019-07-30 | 平安科技（深圳）有限公司 | Project kind matching process, device, computer equipment and storage medium |
| CN110111207A (en) * | 2019-04-12 | 2019-08-09 | 中国平安人寿保险股份有限公司 | Core protects method and relevant device |
| CN109994215A (en) * | 2019-04-25 | 2019-07-09 | 清华大学 | Disease automatic coding system, method, equipment and storage medium |
| CN110993103A (en) * | 2019-11-28 | 2020-04-10 | 阳光人寿保险股份有限公司 | Method for establishing disease risk prediction model and method for recommending disease insurance product |
Non-Patent Citations (2)
| Title |
|---|
| 宋占军: "配置保险要"避坑"", 《金融博览(财富)》 * |
| 宋占军: "配置保险要"避坑"", 《金融博览(财富)》, no. 7, 31 July 2019 (2019-07-31), pages 56 - 59 * |
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