Intelligent processing system and method for sporadic reimbursement

Abstract

The invention discloses a sporadic reimbursement intelligent processing system and method, and belongs to the technical field of data processing, and the system comprises a bill information obtaining unit which is used for obtaining text information on a medical bill corresponding to a preset patient, and the bill information obtaining unit comprises an image scanning module, a recognition and editing module, a marking module, an auditing module and an information storage module; the image scanning module is used for scanning corresponding medical bills and acquiring scanned images and / or pictures; the recognition and editing module is used for performing text information recognition on the obtained scanning image and / or picture, performing text information editing according to a recognition result, and collecting feature data in the edited text information; according to the invention, the feature data of the text information on the bill and the text information covered by the seal image on the bill are obtained by scanning the medical bill, and the loss function of the text information overlapped with the seal image is calculated through the gradient boosting tree algorithm, so that the accurate auditing of the medical bill is realized, and the reimbursement period is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent processing system and method for sporadic reimbursement. Background Art

[0002] During the medical insurance reimbursement process, if the insured person is unable to settle the medical expenses directly due to special reasons, he or she needs to pay the medical expenses first and then apply for reimbursement from the medical insurance agency in accordance with regulations. This is a case of sporadic reimbursement. This usually includes the expenses of going to designated medical institutions in other places but not swiping the card to settle the expenses, the expenses of going to non-designated medical institutions due to sudden illness, and other medical expenses that meet the regulations.

[0003] Traditional medical insurance sporadic reimbursement adopts the method of offline review and online entry, that is, it requires manual marking by staff with medical experience and the use of computers to calculate the reimbursement amount. This is equivalent to manually entering all the information of the case during the entry stage of the medical insurance system. Not only is the workload large, but also the differences between the hospital project name and the medical insurance catalog name will lead to long reimbursement cycles and inaccurate audits, which will lead to low satisfaction of the insured. Among them, when the staff manually enters the text and data of medical bills, vouchers and / or other proofs of reimbursement, the stamps on the medical bills, vouchers and / or other proofs of reimbursement often affect the accuracy of text and data recognition and entry, which not only leads to low efficiency in text and data entry, but also leads to the wrong issuance of medical reimbursement amounts, thereby affecting the supervision of medical reimbursement funds. Therefore, how to improve the efficiency and audit accuracy of medical insurance sporadic reimbursement has become an urgent problem that medical institutions need to solve. Summary of the invention

[0004] In view of the above problems existing in the technical field of existing data processing, the present invention is proposed.

[0005] Therefore, one of the objects of the present invention is to provide an intelligent processing system and method for sporadic reimbursement, which obtains the feature data of the text information on the bill and the text information covered by the seal image on the bill by scanning the medical bill, and calculates the loss function of the text information overlapping with the seal image through the gradient boosting tree algorithm, and can calculate the relevant recognition difficulty according to the clarity of the text information at the overlapping position, and can also adjust the pixel parameters of the contour fault according to the deep learning of the convolutional neural network to obtain the complete text information contour, and then completely obtain the relevant data, thereby realizing the accurate review of medical bills and shortening the reimbursement cycle.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In one aspect, the present invention provides an intelligent processing system for sporadic reimbursements, comprising:

[0008] A bill information acquisition unit, used to acquire text information on a medical bill corresponding to a preset patient, the bill information acquisition unit comprising an image scanning module, a recognition and editing module, a marking module, a review module and an information storage module;

[0009] The image scanning module is used to scan the corresponding medical bill and obtain the scanned image and / or picture;

[0010] The recognition and editing module is used to recognize text information on the acquired scanned image and / or picture, edit the text information according to the recognition result, and collect feature data in the edited text information;

[0011] The marking module is used to mark the corresponding medical bill according to the acquired scanned image and / or picture, so as to identify the scanned medical bill;

[0012] The audit module responds to the feature data collected by the identification and editing module, and is used to check the collected feature data with the bill data of the hospital inpatient department data management terminal, and the bill data includes outpatient medical records, inpatient medical records, examination sheets, test sheets and prescription sheets;

[0013] The information storage module is used to store the images and / or pictures acquired by the image scanning module, and upload the stored images and / or pictures to the corresponding medical reimbursement management platform;

[0014] A seal image acquisition module, used to acquire the seal image on the corresponding medical bill, and obtain its shape and outline according to the acquired seal image, and at the same time acquire text information covered by the seal image;

[0015] The text information fusion processing unit is used to segment the text information and the seal image based on the collected text information covered by the seal image, wherein the segmentation of the seal image is the main segmentation, and the text information under the seal image is the secondary segmentation, and the number of segmented text information is counted in the secondary segmentation, and the overlapping position of each text information and the seal image is collected, and the text information corresponding to each overlapping position is marked as a loss function through a gradient boosting tree algorithm. And through the regularization term The difficulty of recognizing the text information is calculated based on the clarity of the text information corresponding to the overlapping position, as shown below:

[0016] Wherein, O represents the color saturation of the seal image overlapping with each of the text information;

[0017] In the formula, is the loss function used to measure the true value yi and the predicted value u i The gap between the predicted value u i is the predicted value given for the color saturation, the true value y i The real value y is obtained for the color saturation of the seal image overlapping with each of the text information, and the real value y is obtained. i and the predicted value u i The squared error loss between , is as follows:

[0018]

[0019] is a regularization term used to control the complexity of the boosted tree to prevent overfitting. The complexity is given according to the color saturation. The given formula is as follows:

[0020]

[0021] In the formula, r represents the number of leaf nodes of the boosted tree, w is the weight of each leaf node, and λ represents the hyperparameter;

[0022] Among them, the number of leaf nodes corresponds to the number of text information statistically segmented in the sub-segmentation. The more text information there is, the greater the corresponding leaf node weight is; the hyperparameter is given according to the adjacent text information interval data. When the text information interval data is small, the system determines that the recognition difficulty of the corresponding text information is large, otherwise, no determination is made; the text information fusion processing unit includes a calculation module, an exposure module and an identification and warning module.

[0023] As a preferred solution of the present invention, in the identification and editing module, the characteristic data includes total amount data, medical insurance fund payment data, other payment data, personal account payment data, personal cash payment data, personal out-of-pocket payment data and personal self-funded data.

[0024] As a preferred solution of the present invention, the calculation module includes a horizontal calculation module, a vertical calculation module and an oblique calculation module, wherein the horizontal calculation module is used to calculate the length of the left and right sides of the seal image according to the outline of the seal image; the vertical calculation module is used to calculate the length of the upper and lower sides of the seal image according to the outline of the seal image; the oblique calculation module is used to calculate the oblique length of the seal image other than the upper and lower sides and the left and right sides according to the outline of the seal image; and the number of covered text information is obtained from the three length data of the left and right sides length, the upper and lower sides length and the oblique length, and the exposure point is marked for any covered text information in the seal image.

[0025] As a preferred solution of the present invention, the exposure module responds to the exposure point marked by any covered text information in the seal image by the calculation module, and is used to perform exposure processing on the exposure point, and the exposure processing includes feedback control of the exposure point according to the automatic exposure algorithm, and presets the target brightness tolerance threshold for medical bill scanning, and the feedback control of the exposure point includes:

[0026] Perform brightness statistics on the currently received seal image;

[0027] Evaluate the current brightness to determine whether the current brightness reaches the target brightness allowable threshold;

[0028] If the target brightness tolerance threshold is not reached, the exposure parameters are reset and the medical document is scanned until the current brightness reaches the target brightness tolerance threshold.

[0029] As a preferred solution of the present invention, when adjusting the exposure parameter, the parameter itself is marked as the input value, and the obtained seal image brightness is marked as the initial threshold value, until the adjusted input value obtains the edge contour of the text information, the input value is marked as the reference value, and the input value and the initial threshold value are used as the two ends of the equation to obtain the change rule of the initial threshold value to the input value adjustment, as follows:

[0030]

[0031] Where F represents the adjusted exposure parameter, t Indicates the duration of exposure parameter adjustment, L s represents the initial threshold, P represents the initial threshold that changes with the exposure parameter, and k represents the factor for adjusting both sides of the equation, wherein the factor includes the ambient light when the seal image is exposed.

[0032] As a preferred solution of the present invention, wherein: the identification warning module is based on the reference value, and a warning threshold is preset with the reference value. When the exposure parameters of the exposure point are adjusted in the future time period, if the adjusted exposure parameters exceed the warning threshold and the edge contour of the text information cannot be obtained, the system issues a warning and stops exposing the exposure point. Otherwise, no warning is issued.

[0033] As a preferred solution of the present invention, wherein: based on the length of the left and right sides of the seal image in the horizontal calculation module and the length of the upper and lower sides of the seal image in the vertical calculation module, the text information on the left and right sides and / or the upper and lower sides of the text information within the length is collected, and the collection method includes giving a text information, when no other text information is collected on the left and right sides and / or the upper and lower sides of the text information, the system determines that the text information interval data is large, otherwise, no determination is made.

[0034] As a preferred solution of the present invention, when there is a contour fault in the edge contour of the acquired text information, the contour fault is marked, and an edge detection algorithm is used to obtain contour data of the corresponding fault. The edge detection algorithm includes using a Roberts operator for detection and acquisition, and constructing a mixed Gaussian model to obtain the contour shape of the corresponding fault. The acquisition method includes:

[0035] Initializing matrix parameters in the Gaussian mixture model, the matrix parameters including pixel parameters;

[0036] Collecting the change of the contour shape according to the change of the matrix parameters;

[0037] When the contour shape is obtained, the pixel parameters at the time of obtaining are recorded, and data is updated;

[0038] The contour shape is matched with the contour fault of the corresponding text information. If they match and overlap, the system determines that the contour data is successfully acquired. Otherwise, no determination is made and an early warning is issued.

[0039] As a preferred solution of the present invention, when the contour data is successfully obtained, the pixel parameters are deeply learned through a convolutional neural network in the mixed Gaussian model to obtain the adjustment changes of the pixel parameters, and the contour shapes corresponding to the adjustment changes are marked, and a fault contour database is constructed. When the same fault contour is encountered in a future time period, the contour shape is obtained according to the deeply learned pixel parameters.

[0040] As a preferred solution of the present invention, wherein: the contour length is obtained from the acquired contour data, the number of contour corners is collected in the contour length, and the corner length data is calculated at each contour corner, the corner length data is divided into first data and second data, wherein the first data is the starting data to the median data in the corner length data, and the second data is the median data to the ending data, the change pattern of the pixel parameters in the two sets of data is analyzed, and a data set is generated.

[0041] As a preferred solution of the present invention, in the data set, 3 data closest to the median data in the two groups of data are respectively intercepted, and the 6 data are divided into several evaluation indicators, and the weight of each evaluation indicator in the total data in the data set is calculated. When the weight is ≥50%, the contour data is obtained in the future time period according to the change law, otherwise, it is adjusted.

[0042] On the other hand, the present invention also provides a method for applying to a sporadic reimbursement intelligent processing system, comprising the following steps:

[0043] Scanning the medical bills of a preset patient and obtaining scanned images and / or pictures, performing text information recognition on the obtained scanned images and / or pictures, editing the text information according to the recognition results, and collecting feature data in the edited text information;

[0044] Marking the acquired scanned images and / or pictures to identify the medical bills that have been scanned;

[0045] The collected characteristic data is compared with the bill data of the hospital inpatient department data management terminal, wherein the bill data includes outpatient medical records, inpatient medical records, examination sheets, test sheets and prescription sheets;

[0046] Storing the images and / or pictures acquired by the image scanning module, and uploading the stored images and / or pictures to a corresponding medical reimbursement management platform;

[0047] Collecting the seal image on the corresponding medical bill, and acquiring its shape and outline according to the collected seal image, and collecting text information covered by the seal image;

[0048] Based on the collected text information covered by the seal image, the text information and the seal image are segmented, and the segmentation method includes:

[0049] The segmentation of the seal image is primary segmentation, and the segmentation of the text information under the seal image is secondary segmentation;

[0050] In the sub-segmentation, the number of segmented text information is counted, the overlapping position of each text information and the seal image is collected, and the text information corresponding to each overlapping position is marked as a loss function L by a gradient boosting tree algorithm. ▽ , and through the regularization term Ω ▽ The recognition difficulty of the text information is calculated based on the clarity of the text information corresponding to the overlapping position.

[0051] The present invention obtains characteristic data of text information on the bill and text information covered by the seal image on the bill by scanning the medical bill, and by performing primary and secondary segmentation on the seal image and the covered text information, and calculating the loss function of the text information overlapping with the seal image through the gradient boosting tree algorithm, the relevant recognition difficulty can be calculated according to the clarity of the text information at the overlapping position, and the text information with high recognition difficulty is marked to expose the marked points to obtain the edge contour of the text information. At the same time, when a contour fault occurs in the edge contour, the pixel parameters of the contour fault can be adjusted according to the deep learning of the convolutional neural network to obtain a complete text information contour, and then the relevant data is completely obtained to ensure the correct sending of the reimbursement amount, which not only reduces the workload of the staff, but also can accurately review the medical bills and shorten the reimbursement cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0053] Figure 1 A schematic diagram of the modular structure of the intelligent processing system for sporadic reimbursements according to an embodiment of the present invention;

[0054] Figure 2 A schematic diagram of the modular structure of a bill information acquisition unit according to an embodiment of the present invention;

[0055] Figure 3 A schematic diagram of a method flow of an embodiment of the present invention;

[0056] Numbers in the figure: 110-invoice information acquisition unit; 1101-image scanning module; 1102-recognition and editing module; 1103-marking module; 1104-audit module; 1105-information storage module; 120-seal image acquisition module; 130-text information fusion processing unit; 1301-calculation module; 1302-exposure module; 1303-recognition and warning module. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0058] In the past, when staff manually entered the text and data of medical bills, vouchers and / or other proofs of reimbursement, the stamps on the medical bills, vouchers and / or other proofs of reimbursement often affected the accuracy of text and data recognition and entry, which not only led to low efficiency in text and data entry, but also led to incorrect issuance of medical reimbursement amounts, thus affecting the supervision of medical reimbursement funds. Therefore, how to improve the efficiency and accuracy of medical insurance sporadic reimbursement has become an urgent problem that medical institutions need to solve.

[0059] Based on this, the present invention proposes an intelligent processing system and method for sporadic reimbursements. The present application obtains the feature data of the text information on the bill and the text information covered by the seal image on the bill by scanning the medical bill, and calculates the loss function of the text information overlapping with the seal image through the gradient boosting tree algorithm. The relevant recognition difficulty can be calculated according to the clarity of the text information at the overlapping position, and the pixel parameters of the contour fault can be adjusted according to the deep learning of the convolutional neural network to obtain the complete text information contour, and then the relevant data can be completely obtained, thereby realizing the accurate review of medical bills and shortening the reimbursement cycle.

[0060] The present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0061] Reference Figures 1 to 3 , is an embodiment of the present invention, which provides an intelligent processing system for sporadic reimbursement, including:

[0062] The bill information acquisition unit 110 is used to acquire text information on the medical bill corresponding to the preset patient. The bill information acquisition unit 110 includes an image scanning module 1101, a recognition and editing module 1102, a marking module 1103, a review module 1104 and an information storage module 1105;

[0063] The image scanning module 1101 is used to scan the corresponding medical bills and obtain scanned images and / or pictures;

[0064] The image scanning module 1101 includes a scanning exposure machine;

[0065] The recognition and editing module 1102 is used to recognize text information on the acquired scanned image and / or picture, edit the text information according to the recognition result, and collect feature data in the edited text information;

[0066] It should be emphasized in this embodiment that the characteristic data includes total amount data, medical insurance pooling fund payment data, other payment data, personal account payment data, personal cash payment data, personal out-of-pocket payment data, and personal out-of-pocket payment data;

[0067] The marking module 1103 is used to mark the corresponding medical bills according to the acquired scanned images and / or pictures, so as to identify the scanned medical bills;

[0068] The review module 1104 responds to the feature data collected by the recognition and editing module 1102, and is used to check the collected feature data with the bill data of the hospital inpatient department data management terminal, and the bill data includes outpatient medical records, inpatient medical records, examination orders, test orders and prescription orders;

[0069] The information storage module 1105 is used to store the images and / or pictures acquired by the image scanning module 1101, and upload the stored images and / or pictures to the corresponding medical reimbursement management platform;

[0070] The seal image acquisition module 120 is used to acquire the seal image on the corresponding medical bill, and obtain the shape and outline of the seal image according to the acquired seal image, and at the same time acquire the text information covered by the seal image;

[0071] The text information fusion processing unit 130 is used to segment the text information and the seal image based on the collected text information covered by the seal image, wherein the segmentation of the seal image is the main segmentation, and the text information under the seal image is the secondary segmentation, and the number of segmented text information is counted in the secondary segmentation, and the overlapping position of each text information and the seal image is collected, and the text information corresponding to each overlapping position is marked as a loss function through the gradient boosting tree algorithm And through the regularization term The difficulty of recognizing the text information is calculated based on the clarity of the text information corresponding to the overlapping position, as shown below:

[0072] Wherein, O represents the color saturation of the seal image overlapping with each text information;

[0073] In the formula, is the loss function used to measure the true value y i and the predicted value u i The gap between the predicted value u i is the predicted value given by the color saturation, the true value y i The true value y is obtained for the color saturation of the seal image overlapping with each text information, and the true value y is obtained i and the predicted value u i The squared error loss between , is as follows:

[0074]

[0075] It is a regularization term used to control the complexity of the boosted tree to prevent overfitting. The complexity is given according to the color saturation. The given formula is as follows:

[0076]

[0077] In the formula, r represents the number of leaf nodes of the boosted tree, w is the weight of each leaf node, and λ represents the hyperparameter;

[0078] The number of leaf nodes corresponds to the number of text information counted in the sub-segmentation. The more text information there is, the greater the corresponding leaf node weight is. The hyperparameter is given according to the adjacent text information interval data. When the text information interval data is small, the system determines that the recognition difficulty of the corresponding text information is large, otherwise, no determination is made. The text information fusion processing unit 130 includes a calculation module 1301, an exposure module 1302 and an identification warning module 1303.

[0079] Specifically, in this embodiment, the calculation module 1301 includes a horizontal calculation module, a vertical calculation module and an oblique calculation module, wherein the horizontal calculation module is used to calculate the length of the left and right sides of the seal image according to the outline of the seal image; the vertical calculation module is used to calculate the length of the upper and lower sides of the seal image according to the outline of the seal image; the oblique calculation module is used to calculate the oblique length of the seal image other than the upper and lower sides and the left and right sides according to the outline of the seal image; and the number of covered text information is obtained from the three length data of the left and right sides length, the upper and lower sides length and the oblique length, and the exposure point mark is performed on any covered text information in the seal image;

[0080] The exposure module 1302 responds to the exposure point marked by any covered text information in the seal image by the calculation module 1301, and is used to perform exposure processing on the exposure point. The exposure processing includes feedback control of the exposure point according to the automatic exposure algorithm, and presets the target brightness tolerance threshold for the medical bill scanning, and feedback control of the exposure point includes:

[0081] Perform brightness statistics on the currently received seal image;

[0082] Evaluate the current brightness to determine whether the current brightness reaches the target brightness tolerance threshold;

[0083] If the target brightness tolerance threshold is not reached, the exposure parameters are reset and the medical bills are scanned until the current brightness reaches the target brightness tolerance threshold;

[0084] When adjusting the exposure parameter, the parameter itself is marked as the input value, and the obtained seal image brightness is marked as the initial threshold value. Until the adjusted input value obtains the edge contour of the text information, the input value is marked as the reference value. At the same time, the input value and the initial threshold value are used as the two ends of the equation to obtain the change rule of the initial threshold value to the input value adjustment, as follows:

[0085]

[0086] Where F represents the adjusted exposure parameter, t represents the duration of exposure parameter adjustment, and L s represents the initial threshold, P represents the initial threshold that changes with the exposure parameters, k represents the factors on both sides of the adjustment equation, and the factors include the ambient light when exposing the seal image;

[0087] Past experience in scanning paper objects shows that partial exposure of the acquired image can ensure that every detail in the scanned image is clearly visible, making the color of the image more vivid and realistic, and thus facilitating the acquisition of the edge contour of the text information covered by the stamp image;

[0088] The recognition warning module 1303 is based on the reference value and presets the warning threshold value with the reference value. When the exposure parameter of the exposure point is adjusted in the future period, if the adjusted exposure parameter exceeds the warning threshold value and the edge contour of the text information cannot be obtained, the system issues a warning and stops exposing the exposure point. Otherwise, no warning is issued.

[0089] On the basis of the above, the present embodiment further collects text information on the left and right sides and / or the upper and lower sides of the text information within the length based on the length of the left and right sides of the seal image in the horizontal calculation module and the length of the upper and lower sides of the seal image in the vertical calculation module. The collection method includes a given text information, when no other text information is collected on the left and right sides and / or the upper and lower sides of the text information, the system determines that the text information interval data is large, otherwise, no determination is made;

[0090] It should be noted that in this embodiment, when there is a contour fault in the edge contour of the acquired text information, the contour fault is marked, and an edge detection algorithm is used to obtain contour data of the corresponding fault. The edge detection algorithm includes using a Roberts operator for detection and acquisition, and constructing a mixed Gaussian model to obtain the contour shape of the corresponding fault. The acquisition method includes:

[0091] Initializing matrix parameters in a Gaussian mixture model, the matrix parameters including pixel parameters;

[0092] Collect changes in contour shape according to changes in matrix parameters;

[0093] When the contour shape is obtained, the pixel parameters at the time of acquisition are recorded and the data is updated;

[0094] The contour shape is matched with the contour fault of the corresponding text information. If they match and overlap, the system determines that the contour data is successfully acquired. Otherwise, no judgment is made and an early warning is issued.

[0095] Further, based on the above, when the contour data is successfully obtained, the present embodiment performs deep learning on the pixel parameters through the convolutional neural network in the mixed Gaussian model, obtains the adjustment changes of the pixel parameters, marks the contour shape corresponding to the adjustment changes, and constructs a fault contour database. When the same fault contour is encountered in the future period, the contour shape is obtained according to the deep-learned pixel parameters;

[0096] The present embodiment needs to determine that the contour length is obtained from the acquired contour data, the number of contour corners is collected from the contour length, and the corner length data is calculated at each contour corner, the corner length data is divided into first data and second data, wherein the first data is from the start data to the median data in the corner length data, and the second data is from the median data to the end data, the variation rules of the pixel parameters in the two sets of data are analyzed, and a data set is generated;

[0097] Furthermore, in the data set, 3 data closest to the median data in the two groups of data are respectively intercepted, and the 6 data are divided into several evaluation indicators, and the weight of each evaluation indicator in the total data in the data set is calculated. When the weight is ≥50%, the contour data is obtained in the future time period according to the change law, otherwise, it is adjusted.

[0098] Based on the above, the present application obtains the feature data of the text information on the bill and the text information covered by the seal image on the bill by scanning the medical bill, and calculates the loss function of the text information overlapping with the seal image through the gradient boosting tree algorithm. The relevant recognition difficulty can be calculated according to the clarity of the text information at the overlapping position, and the pixel parameters of the contour fault can be adjusted according to the deep learning of the convolutional neural network to obtain the complete text information contour, and then the relevant data can be fully obtained, thereby realizing the accurate review of medical bills and shortening the reimbursement cycle.

[0099] In combination with the above-mentioned intelligent processing system for sporadic reimbursement, this embodiment also proposes a working method of the system, as follows:

[0100] Scanning the medical bills of a preset patient and obtaining scanned images and / or pictures, performing text information recognition on the obtained scanned images and / or pictures, editing the text information according to the recognition results, and collecting feature data in the edited text information;

[0101] Marking the acquired scanned images and / or pictures to identify the scanned medical bills;

[0102] The collected characteristic data is compared with the bill data of the hospital inpatient department data management terminal, which includes outpatient medical records, inpatient medical records, examination sheets, test sheets and prescription sheets;

[0103] Storing images and / or pictures acquired by the image scanning module, and uploading the stored images and / or pictures to the corresponding medical reimbursement management platform;

[0104] Collect the seal image on the corresponding medical bill, and obtain its shape and outline according to the collected seal image, and collect the text information covered by the seal image;

[0105] Based on the collected text information covered by the seal image, the text information and the seal image are segmented, and the segmentation method includes:

[0106] The segmentation of the seal image is the primary segmentation, and the text information under the seal image is the secondary segmentation;

[0107] In this segmentation, the number of segmented text information is counted, the overlapping positions of each text information and the seal image are collected, and the text information corresponding to each overlapping position is marked as the loss function L through the gradient boosting tree algorithm. ▽ , and through the regularization term Ω ▽ The recognition difficulty of the text information is calculated based on the clarity of the text information corresponding to the overlapping position.

[0108] In summary, the present invention obtains the characteristic data of the text information on the bill and the text information covered by the seal image on the bill by scanning the medical bill, and by performing primary and secondary segmentation on the seal image and the covered text information, and calculating the loss function of the text information overlapping with the seal image through the gradient boosting tree algorithm, the relevant recognition difficulty can be calculated according to the clarity of the text information at the overlapping position, and the text information with high recognition difficulty can be marked to expose the marked points to obtain the edge contour of the text information. At the same time, when a contour fault occurs in the edge contour, the pixel parameters of the contour fault can be adjusted according to the deep learning of the convolutional neural network to obtain a complete text information contour, and then the relevant data is completely obtained to ensure the correct sending of the reimbursement amount, which not only reduces the workload of the staff, but also can accurately review the medical bills and shorten the reimbursement cycle.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent processing system for sporadic reimbursement, characterized in that: include: A bill information acquisition unit, used to acquire text information on a medical bill corresponding to a preset patient, the bill information acquisition unit comprising an image scanning module, a recognition and editing module, a marking module, a review module and an information storage module; The image scanning module is used to scan the corresponding medical bill and obtain the scanned image and / or picture; The recognition and editing module is used to recognize text information on the acquired scanned image and / or picture, edit the text information according to the recognition result, and collect feature data in the edited text information; The marking module is used to mark the corresponding medical bill according to the acquired scanned image and / or picture, so as to identify the scanned medical bill; The audit module responds to the feature data collected by the identification and editing module, and is used to check the collected feature data with the bill data of the hospital inpatient department data management terminal, and the bill data includes outpatient medical records, inpatient medical records, examination sheets, test sheets and prescription sheets; The information storage module is used to store the images and / or pictures acquired by the image scanning module, and upload the stored images and / or pictures to the corresponding medical reimbursement management platform; A seal image acquisition module, used to acquire the seal image on the corresponding medical bill, and obtain its shape and outline according to the acquired seal image, and at the same time acquire text information covered by the seal image; The text information fusion processing unit is used to segment the text information and the seal image based on the collected text information covered by the seal image, wherein the segmentation of the seal image is the main segmentation, and the text information under the seal image is the secondary segmentation, and the number of segmented text information is counted in the secondary segmentation, and the overlapping position of each text information and the seal image is collected, and the text information corresponding to each overlapping position is marked as a loss function through a gradient boosting tree algorithm. And through the regularization term Ω ▽ The difficulty of recognizing the text information is calculated based on the clarity of the text information corresponding to the overlapping position, as shown below: Wherein, O represents the color saturation of the seal image overlapping with each of the text information; In the formula, is the loss function used to measure the true value y i and the predicted value u i The gap between the predicted value u i is the predicted value given for the color saturation, the true value y i The real value y is obtained for the color saturation of the seal image overlapping with each of the text information, and the real value y is obtained. i and the predicted value u i The squared error loss between , is as follows: is a regularization term used to control the complexity of the boosted tree to prevent overfitting. The complexity is given according to the color saturation. The given formula is as follows: In the formula, r represents the number of leaf nodes of the boosted tree, w is the weight of each leaf node, and λ represents the hyperparameter; Among them, the number of leaf nodes corresponds to the number of text information statistically segmented in the sub-segmentation. The more text information there is, the greater the corresponding leaf node weight is; the hyperparameter is given according to the adjacent text information interval data. When the text information interval data is small, the system determines that the recognition difficulty of the corresponding text information is large, otherwise, no determination is made; the text information fusion processing unit includes a calculation module, an exposure module and an identification and warning module.

2. The intelligent processing system for sporadic reimbursement as claimed in claim 1, characterized in that: In the identification and editing module, the characteristic data includes total amount data, medical insurance fund payment data, other payment data, personal account payment data, personal cash payment data, personal out-of-pocket payment data and personal self-funded data.

3. The intelligent processing system for sporadic reimbursement as claimed in claim 1, characterized in that: The calculation module includes a horizontal calculation module, a vertical calculation module and an oblique calculation module, wherein the horizontal calculation module is used to calculate the length of the left and right sides of the seal image according to the outline of the seal image; the vertical calculation module is used to calculate the length of the upper and lower sides of the seal image according to the outline of the seal image; the oblique calculation module is used to calculate the oblique length of the seal image other than the upper and lower sides and the left and right sides according to the outline of the seal image; and the number of covered text information is obtained from the three length data of the left and right sides length, the upper and lower sides length and the oblique length, and an exposure point mark is performed on any covered text information in the seal image.

4. The intelligent processing system for sporadic reimbursement as claimed in claim 3, characterized in that: The exposure module responds to the exposure point marked by the calculation module for any covered text information in the seal image, and is used to perform exposure processing on the exposure point. The exposure processing includes feedback control of the exposure point according to the automatic exposure algorithm, and presets the target brightness tolerance threshold for the medical bill scanning, and the feedback control of the exposure point includes: Perform brightness statistics on the currently received seal image; Evaluate the current brightness to determine whether the current brightness reaches the target brightness allowable threshold; If the target brightness tolerance threshold is not reached, the exposure parameters are reset and the medical document is scanned until the current brightness reaches the target brightness tolerance threshold.

5. The intelligent processing system for sporadic reimbursement as claimed in claim 4, characterized in that: When adjusting the exposure parameter, the parameter itself is marked as the input value, and the obtained seal image brightness is marked as the initial threshold value, until the adjusted input value obtains the edge contour of the text information, the input value is marked as the reference value, and the input value and the initial threshold value are used as the two ends of the equation to obtain the change rule of the initial threshold value to the input value adjustment, as follows: Where F represents the adjusted exposure parameter, t Indicates the duration of exposure parameter adjustment, L s represents the initial threshold, P represents the initial threshold that changes with the exposure parameter, and k represents the factor for adjusting both sides of the equation, wherein the factor includes the ambient light when the seal image is exposed.

6. The intelligent processing system for sporadic reimbursement as claimed in claim 5, characterized in that: The identification and warning module is based on the reference value and presets a warning threshold with the reference value. When the exposure parameter of the exposure point is adjusted in the future period, if the adjusted exposure parameter exceeds the warning threshold and the edge contour of the text information cannot be obtained, the system issues a warning and stops exposing the exposure point. Otherwise, no warning is issued.

7. The intelligent processing system for sporadic reimbursement as claimed in claim 3, characterized in that: Based on the length of the left and right sides of the seal image in the horizontal calculation module and the length of the upper and lower sides of the seal image in the vertical calculation module, the text information on the left and right sides and / or the upper and lower sides of the text information within the length is collected. The collection method includes giving a text information. When no other text information is collected on the left and right sides and / or the upper and lower sides of the text information, the system determines that the text information interval data is large, otherwise, no determination is made.

8. The intelligent processing system for sporadic reimbursement as claimed in claim 5, characterized in that: When there is a contour fault in the edge contour of the acquired text information, the contour fault is marked, and an edge detection algorithm is used to obtain contour data of the corresponding fault. The edge detection algorithm includes using a Roberts operator for detection and acquisition, and constructing a mixed Gaussian model to obtain the contour shape of the corresponding fault. The acquisition method includes: Initializing matrix parameters in the Gaussian mixture model, the matrix parameters including pixel parameters; Collecting the change of the contour shape according to the change of the matrix parameters; When the contour shape is obtained, the pixel parameters at the time of obtaining are recorded, and data is updated; The contour shape is matched with the contour fault of the corresponding text information. If they match and overlap, the system determines that the contour data is successfully acquired. Otherwise, no determination is made and an early warning is issued.

9. The intelligent processing system for sporadic reimbursement as claimed in claim 8, characterized in that: When the contour data is successfully acquired, the pixel parameters are deeply learned through a convolutional neural network in the mixed Gaussian model to obtain the adjusted changes of the pixel parameters, and the contour shapes corresponding to the adjusted changes are marked, and a fault contour database is constructed. When the same fault contour is encountered in a future period, the contour shape is acquired according to the deeply learned pixel parameters.

10. The intelligent processing system for sporadic reimbursement as claimed in claim 8, characterized in that: The contour length is obtained from the acquired contour data, the number of contour corners is collected in the contour length, and the corner length data is calculated at each contour corner, and the corner length data is divided into first data and second data, wherein the first data is the starting data to the median data in the corner length data, and the second data is the median data to the ending data, the changing rules of the pixel parameters in the two sets of data are analyzed, and a data set is generated.

11. The intelligent processing system for sporadic reimbursement as claimed in claim 10, characterized in that: In the data set, three data closest to the median data in the two groups of data are respectively intercepted, and the six data are divided into several evaluation indicators, and the weight of each evaluation indicator in the total data in the data set is calculated. When the weight is ≥50%, the profile data is obtained in the future time period according to the change law, otherwise, it is adjusted.

12. A method applied to a sporadic reimbursement intelligent processing system as claimed in claim 1, characterized in that: The following steps are involved: Scanning the medical bills of a preset patient and obtaining scanned images and / or pictures, performing text information recognition on the obtained scanned images and / or pictures, editing the text information according to the recognition results, and collecting feature data in the edited text information; Marking the acquired scanned images and / or pictures to identify the medical bills that have been scanned; The collected characteristic data is compared with the bill data of the hospital inpatient department data management terminal, wherein the bill data includes outpatient medical records, inpatient medical records, examination sheets, test sheets and prescription sheets; Storing the images and / or pictures acquired by the image scanning module, and uploading the stored images and / or pictures to a corresponding medical reimbursement management platform; Collecting the seal image on the corresponding medical bill, and acquiring its shape and outline according to the collected seal image, and collecting text information covered by the seal image; Based on the collected text information covered by the seal image, the text information and the seal image are segmented, and the segmentation method includes: The segmentation of the seal image is primary segmentation, and the segmentation of the text information under the seal image is secondary segmentation; In the sub-segmentation, the number of segmented text information is counted, the overlapping position of each text information and the seal image is collected, and the text information corresponding to each overlapping position is marked as a loss function L by a gradient boosting tree algorithm. ▽ , and through the regularization term Ω ▽ The recognition difficulty of the text information is calculated based on the clarity of the text information corresponding to the overlapping position.

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