Data processing method and system for digestive tract tumors

Abstract

The invention discloses a data processing method and system for digestive tract tumors, and relates to the technical field of medical information processing, key physiological parameters and tumor features of a patient are collected from a plurality of data sources to form a tumor identification data set P of the patient, and the tumor identification data set P is obtained through standardized preprocessing and feature vector integration. And the consistency and the computability of the data are ensured. A disease progression prediction model is constructed by using a machine learning method, and the feature vector X of the patient is used as input, so that the disease progression fluctuation probability index Progress of the patient can be efficiently predicted. Furthermore, in combination with a correction coefficient algorithm and a set correction fluctuation threshold Cth, the recovery period content extracted from the initial medical record of the patient can be dynamically corrected, the corrected recovery period Tad after correction is obtained, and through feedback and iterative optimization, a calculation formula of the corrected lifetime is continuously verified and updated, so that the recovery period of the patient can be corrected. Finally, the effects of improving prediction precision and adapting to individual differences of patients are achieved.

Description

Technical Field

[0001] The present invention relates to the field of medical information processing technology, and in particular to a data processing method and system for digestive tract tumors. Background Art

[0002] In the field of modern medicine, cancer diagnosis and treatment is a huge research and application field. Among them, digestive tract tumors, including gastric cancer, esophageal cancer and colorectal cancer, as a common and fatal type of cancer, have become a major challenge to public health worldwide. With the advancement of medical technology, the early diagnosis and treatment of digestive tract tumors have gradually made certain progress.

[0003] Although doctors currently generally use traditional methods based on clinical experience in the recovery period assessment of gastrointestinal tumors, such as TNM staging, determination of tumor biomarkers and imaging results, there are still many limitations. First, traditional recovery period prediction methods often rely on limited clinical indicators, and it is difficult to fully consider individual differences among patients, such as differences in age groups, genetic backgrounds and treatment responses, which may directly affect the rate of disease progression and treatment effectiveness. Therefore, when faced with complex cases, traditional methods often cannot give accurate prognostic assessments, leading to errors in treatment decisions. Furthermore, existing recovery period assessment methods are often relatively static, cannot be updated and adjusted in real time, and cannot make dynamic predictions based on changes in the patient's treatment process. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a data processing method and system for digestive tract tumors, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data processing method for digestive tract tumors, comprising the following steps:

[0006] S1, collecting and obtaining from the patient's electronic health record source, imaging examination record source, blood test record source and genomic test record source to form the patient's tumor identification data set P;

[0007] S2, performing data preprocessing on the patient tumor identification data set P, including standardization preprocessing, and then integrating the processed patient tumor identification data set P to obtain a feature vector X;

[0008] S3. A disease progression prediction model is constructed by using a machine learning method, and the feature vector X is used as an input feature of the disease progression prediction model. Through training, a disease progression fluctuation probability index Progress predicted for the patient is obtained;

[0009] S4. Substituting the obtained disease progression fluctuation probability index Progress into the correction coefficient algorithm formula for calculation, obtaining the correction coefficient Cm, and comparing it with the preset correction operation fluctuation threshold Cthe, obtaining the correction trigger result, and correcting the recovery period content extracted from the patient's initial medical record according to the correction trigger result, to obtain the corrected correction recovery period Tad;

[0010] S5. Provide feedback and iterative optimization based on the modified recovery period Tad, including verifying and updating the modified recovery period Tad.

[0011] Preferably, said S1 includes S11 and S12;

[0012] S11, by collecting individual difference related parameters from clinical data sources, including extracting patient individual difference related parameters from electronic health record sources, imaging examination record sources, blood test record sources and genomic test record sources, including age A, gender S, weight W, height H, blood pressure BP1, gene mutation GM and tumor size TS, to form a query collection data group CC = {A, S, W, H, BP, GM, TS};

[0013] Among them, age A, gender S, weight W and height H are obtained by entering the patient's unique information from the electronic health record source; tumor size TS is obtained by entering the patient's unique information from the imaging examination record source; blood pressure BP is obtained by entering the patient's unique information in the blood test record source to query the blood pressure measurement record; whether the gene is mutated GM is obtained by entering the patient's unique information in the genomic detection record source to obtain the gene sequence analysis results and mutation screening results.

[0014] Preferably, S12, integrating and processing the query collection data set CC={A, S, W, H, BP, GM, TS} to construct the patient tumor identification data set P;

[0015] Specifically, binary coding processing and categorical variable coding processing are performed on the sex S and whether the gene is mutated GM in the query collection data group CC to obtain the categorical variable sex Se and binary gene mutation GMe;

[0016] Among them, the categorical variable sex Se includes classifying male as 1 and female as 2 to distinguish sex; the binary gene mutation GMe includes setting 0 as no gene mutation and 1 as gene mutation;

[0017] Substitute the categorical variable sex Se and the binary gene mutation GMe into the query collection data set CC = {A, S, W, H, BP, GM, TS} for replacement integration to obtain the integrated patient tumor identification data set P;

[0018] The patient tumor identification data set P = {A, Se, W, H, BP, GMe, TS}.

[0019] Preferably, said S2 includes S21;

[0020] S21, performing data preprocessing on the age A, weight W, height H, blood pressure BP and tumor size TS in the patient tumor identification data set P, including standardization preprocessing, obtaining the preprocessed processed age Ac, processed weight Wc, processed height Hc, processed blood pressure BPc and processed tumor size TSc, and synchronously substituting them into the patient tumor identification data set P for replacement, to obtain a feature vector X = {Ac, Se, Wc, Hc, BPc, GMe, TSc};

[0021] Among them, the standardization preprocessing eliminates the dimensional differences of the data by using feature scaling technology.

[0022] Preferably, said S3 includes S31 and S32;

[0023] S31. A disease progression prediction model is constructed by using a machine learning method, and the feature vector X is used as an input feature of the disease progression prediction model for training to obtain a weighted linear combination z, and the weighted linear combination z is simultaneously transformed using a Sigmoid function to obtain a predicted disease progression fluctuation probability index Progress of the patient;

[0024] The disease progression fluctuation probability index Progress is obtained by the following calculation formula:

[0025] z=(a1*Ac+a2*Se+a3*Wc+a4*Hc+a5*BPc+a6*GMe+a7*TSc+β);

[0026]

[0027] Wherein, z represents a weighted linear combination, e represents a Sigmoid function, which is specifically used to map the disease progression fluctuation probability index Progress to the range of 0 to 1, a1, a2, a3, a4, a5, a6 and a7 represent the weight coefficients of treatment age Ac, categorical variable gender Se, treatment weight Wc, treatment height Hc, treatment blood pressure BPc, binary gene mutation GMe and treatment tumor size TSc, β represents a bias term, and a1+a2+a3+a4+a5+a6+a7=1, and the specific value is set by the user.

[0028] Preferably, S32, using a supervised learning algorithm to find the optimal weight coefficient and bias term β for the disease progression prediction model, including using a cross entropy loss function to measure the difference between the prediction and the actual value, obtaining the total loss of the cross entropy loss function, and then using a gradient descent algorithm to gradually adjust the weight coefficient and bias term β to minimize the total loss, so as to iteratively optimize the constructed disease progression prediction model.

[0029] Preferably, said S4 includes S41 and S42;

[0030] S41, substituting the obtained disease progression fluctuation probability index Progress into the correction coefficient algorithm formula for calculation to obtain the correction coefficient Cm;

[0031] The correction coefficient Cm is obtained by the following calculation formula:

[0032]

[0033] Where B represents the exponential power, γ represents the influence coefficient of age A on the correction coefficient Cm, c1, c2 and c3 represent the influence coefficients on the correction coefficient Cm, c4 represents the nonlinear influence coefficient of weight W on the correction coefficient Cm, exp represents the exponential function, SZ represents the body mass index, and is specifically expressed by Obtained from the calculation formula, W represents weight and H represents height.

[0034] Preferably, S42, the correction coefficient Cm is compared with the preset correction operation fluctuation threshold Cthe to obtain the correction trigger result, and the recovery period content extracted from the patient's initial medical record is corrected according to the correction trigger result to obtain the initial recovery period Tinit, and the corrected recovery period Tad is obtained by combining the correction coefficient Cm;

[0035] The correction trigger result is obtained by the following comparison method:

[0036] When the correction coefficient Cm is less than the correction operation fluctuation threshold Cthe, the correction trigger result is obtained as a non-trigger result;

[0037] When the correction coefficient Cm≥correction operation fluctuation threshold Cthe, the correction trigger result is obtained as the trigger result;

[0038] The modified recovery period Tad is obtained by the calculation formula Tad=Tinit*(1+Cm).

[0039] Preferably, the S5 includes S51;

[0040] S51, providing feedback and iterative optimization according to the modified recovery period Tad, including verifying and updating the modified recovery period Tad, wherein the verification includes comparing the modified recovery period Tad with the diagnostic recovery period data provided by the patient's attending physician, checking the accuracy of the modified recovery period Tad, and recording the recovery period difference △Tad. When the recovery period difference △Tad is less than 5%, the modified recovery period Tad is not optimized and updated. When the recovery period difference △Tad is greater than or equal to 5%, the modified recovery period Tad is optimized and updated.

[0041] Among them, the optimization update includes using the recovery period difference △Tad to proportionally adjust the influence coefficient of the correction coefficient Cm and the weight coefficient of the disease progression prediction model.

[0042] The data processing system for digestive tract tumors includes a data acquisition module, a data preprocessing module, a modeling and analysis module, a correction and evaluation module, and an iterative optimization module;

[0043] The data collection module collects data from the patient's electronic health record source, imaging examination record source, blood test record source and genomic test record source to form a patient tumor identification data set P;

[0044] The data preprocessing module performs data preprocessing on the patient tumor identification data group P, including standardization preprocessing, and then integrates the processed patient tumor identification data group P to obtain a feature vector X;

[0045] The modeling and analysis module constructs a disease progression prediction model by using a machine learning method, and uses the feature vector X as an input feature of the disease progression prediction model, and obtains a disease progression fluctuation probability index Progress predicted for the patient through training;

[0046] The correction evaluation module substitutes the acquired disease progression fluctuation probability index Progress into the correction coefficient algorithm formula for calculation, obtains the correction coefficient Cm, and compares it with the preset correction operation fluctuation threshold Cthe, obtains the correction trigger result, and corrects the recovery period content extracted from the patient's initial medical record according to the correction trigger result, and obtains the corrected recovery period Tad after correction;

[0047] The iterative optimization module performs feedback and iterative optimization according to the modified recovery period Tad, including verifying and updating the modified recovery period Tad.

[0048] The present invention provides a data processing method and system for digestive tract tumors, which has the following beneficial effects:

[0049] (1) Through steps S1 to S5, accurate prediction and dynamic adjustment of the recovery progress of the patient's disease are achieved, and the patient's key physiological parameters and tumor characteristics are collected from multiple data sources to form the patient's tumor identification data set P, and the consistency and computability of the data are ensured through standardized preprocessing and feature vector integration. The disease progression prediction model constructed by machine learning method takes the patient's feature vector X as input and can efficiently predict the patient's disease progression fluctuation probability index Progress. Furthermore, combined with the correction coefficient algorithm and the set correction fluctuation threshold Cthe, the recovery period content extracted from the patient's initial medical record can be dynamically corrected to obtain the corrected correction recovery period Tad, and through feedback and iterative optimization, the calculation formula of the corrected survival period is continuously verified and updated, ultimately achieving the effect of improving prediction accuracy and adapting to individual differences in patients. It can effectively make up for the single prediction deficiency problem existing in the traditional method during the processing process and avoid the prediction error caused by ignoring individual differences in patients.

[0050] (2) By standardizing and preprocessing the parameters in the patient tumor identification data set P, the dimensional differences between different parameters can be eliminated. The feature vector X is constructed so that the individual features extracted from the patient data can be input into the disease progression prediction model in a consistent format. Combining the weighted linear combination z with the Sigmoid function, the patient's disease progression fluctuation probability index Progress can be accurately predicted. Through the training of the supervised learning algorithm and the optimization of the cross-entropy loss function, the prediction performance of the model has been continuously improved through iteration, which not only improves the prediction accuracy of the model, but also reduces the deviation caused by data inconsistency or errors, enhances the generalization ability of the model, and provides more reliable data support for clinical personalized treatment and decision-making.

[0051] (3) By comparing with the preset correction operation fluctuation threshold Cthe, the correction mechanism can be automatically triggered. When the correction coefficient Cm exceeds the threshold, the system will start the recovery period correction and then calculate the corrected recovery period Tad. This process makes the patient's treatment cycle prediction more personalized and accurate. The combination of the corrected recovery period Tad and the initial recovery period Tinit ensures the flexibility and adjustment space of the clinical treatment plan. In the further feedback and iterative optimization process, the system verifies and optimizes the corrected recovery period by comparing with the diagnostic recovery period data provided by the attending physician, thereby ensuring the accuracy and reliability of the prediction model. Through this dynamic optimization, the accuracy of the prediction can be significantly improved, errors can be reduced, and the scientific nature of clinical decision-making can be improved, providing patients with more personalized and accurate treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a schematic diagram of the steps of the data processing method for digestive tract tumors of the present invention;

[0053] Figure 2 The figure is a schematic diagram of a data processing system for digestive tract tumors according to the present invention. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] The present invention provides a data processing method for digestive tract tumors, please refer to Figure 1 , including the following steps:

[0057] S1, collecting and obtaining from the patient's electronic health record source, imaging examination record source, blood test record source and genomic test record source to form the patient's tumor identification data set P;

[0058] S2, performing data preprocessing on the patient tumor identification data set P, including standardization preprocessing, and then integrating the processed patient tumor identification data set P to obtain a feature vector X;

[0059] S3. A disease progression prediction model is constructed by using a machine learning method, and the feature vector X is used as an input feature of the disease progression prediction model. Through training, a disease progression fluctuation probability index Progress predicted for the patient is obtained;

[0060] S4. Substituting the obtained disease progression fluctuation probability index Progress into the correction coefficient algorithm formula for calculation, obtaining the correction coefficient Cm, and comparing it with the preset correction operation fluctuation threshold Cthe, obtaining the correction trigger result, and correcting the recovery period content extracted from the patient's initial medical record according to the correction trigger result, to obtain the corrected correction recovery period Tad;

[0061] S5. Provide feedback and iterative optimization based on the modified recovery period Tad, including verifying and updating the modified recovery period Tad.

[0062] In this embodiment, through steps S1 to S5, accurate prediction and dynamic adjustment of the recovery progress of the patient's disease are achieved, and the patient's key physiological parameters and tumor characteristics are collected from multiple data sources to form the patient's tumor identification data group P, and the consistency and computability of the data are ensured through standardized preprocessing and feature vector integration. The disease progression prediction model constructed by machine learning method takes the patient's feature vector X as input, and can efficiently predict the patient's disease progression fluctuation probability index Progress. Further, combined with the correction coefficient algorithm and the set correction fluctuation threshold Cthe, the recovery period content extracted from the patient's initial medical record can be dynamically corrected to obtain the corrected correction recovery period Tad, and through feedback and iterative optimization, the calculation formula of the corrected survival period is continuously verified and updated, and finally the effect of improving the prediction accuracy and adapting to the individual differences of patients is achieved. It can effectively make up for the single prediction problem existing in the traditional method during the processing process, and avoid the prediction error caused by ignoring the individual differences of patients.

[0063] Example 2

[0064] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11 and S12;

[0065] S11, by collecting individual difference related parameters from clinical data sources, including extracting patient individual difference related parameters from electronic health record sources, imaging examination record sources, blood test record sources and genomic test record sources, including age A, gender S, weight W, height H, blood pressure BP1, gene mutation GM and tumor size TS, to form a query collection data group CC = {A, S, W, H, BP, GM, TS};

[0066] Among them, age A, gender S, weight W and height H are obtained by entering the patient's unique information from the electronic health record source; tumor size TS is obtained by entering the patient's unique information from the imaging examination record source; blood pressure BP is obtained by entering the patient's unique information in the blood test record source to query the blood pressure measurement record; whether the gene is mutated GM is obtained by entering the patient's unique information in the genomic detection record source to obtain the gene sequence analysis results and mutation screening results.

[0067] S12, integrating and processing the query collection data set CC={A, S, W, H, BP, GM, TS} to construct the patient tumor identification data set P;

[0068] Specifically, binary coding processing and categorical variable coding processing are performed on the sex S and whether the gene is mutated GM in the query collection data group CC to obtain the categorical variable sex Se and binary gene mutation GMe;

[0069] Among them, the categorical variable sex Se includes classifying male as 1 and female as 2 to distinguish sex; the binary gene mutation GMe includes setting 0 as no gene mutation and 1 as gene mutation;

[0070] Substitute the categorical variable sex Se and the binary gene mutation GMe into the query collection data set CC = {A, S, W, H, BP, GM, TS} for replacement integration to obtain the integrated patient tumor identification data set P;

[0071] The patient tumor identification data set P = {A, Se, W, H, BP, GMe, TS}.

[0072] In this embodiment, by collecting individual difference related parameters from multiple clinical data sources, it is possible to comprehensively obtain and integrate the multi-dimensional health information of the patient, including age A, gender S, weight W, height H, blood pressure BP1, whether the gene is mutated GM and tumor size TS, to form a complete query collection data group C. By processing and encoding these data, especially the binary encoding processing of gender S and gene mutation GM, the original data is converted into standardized categorical variables and binary variables, which effectively improves the calculation applicability and consistency of the data. In this process, the processing method of specific parameters such as gender and gene mutation makes the integration of data more in line with the needs of subsequent analysis and model building. Through these processes, the patient's tumor identification data group P is formed, which provides a more refined and adaptable data input basis for subsequent personalized diagnosis and disease prediction. The advantage of this method is that it fully considers individual differences, not only improves the utilization efficiency of data, but also lays a solid foundation for the accurate prediction of the model, ensuring that the individualized treatment plan can be adjusted according to the specific characteristics of the patient, thereby avoiding the deviation caused by improper data processing and enhancing the accuracy and operability of the prediction.

[0073] Example 3

[0074] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: the S2 includes S21;

[0075] S21, performing data preprocessing on the age A, weight W, height H, blood pressure BP and tumor size TS in the patient tumor identification data set P, including standardization preprocessing, obtaining the preprocessed processed age Ac, processed weight Wc, processed height Hc, processed blood pressure BPc and processed tumor size TSc, and synchronously substituting them into the patient tumor identification data set P for replacement, to obtain a feature vector X = {Ac, Se, Wc, Hc, BPc, GMe, TSc};

[0076] Among them, the standardization preprocessing eliminates the dimensional differences of the data by using feature scaling technology.

[0077] The S3 includes S31 and S32;

[0078] S31. A disease progression prediction model is constructed by using a machine learning method, and the feature vector X is used as an input feature of the disease progression prediction model for training to obtain a weighted linear combination z, and the weighted linear combination z is simultaneously transformed using a Sigmoid function to obtain a predicted disease progression fluctuation probability index Progress of the patient;

[0079] The disease progression fluctuation probability index Progress is obtained by the following calculation formula:

[0080] z=(a1*Ac+a2*Se+a3*Wc+a4*Hc+a5*BPc+a6*GMe+a7*TSc+β);

[0081]

[0082] Wherein, z represents a weighted linear combination, e represents a Sigmoid function, which is specifically used to map the disease progression fluctuation probability index Progress to the range of 0 to 1, a1, a2, a3, a4, a5, a6 and a7 represent the weight coefficients of treatment age Ac, categorical variable gender Se, treatment weight Wc, treatment height Hc, treatment blood pressure BPc, binary gene mutation GMe and treatment tumor size TSc, β represents a bias term, and a1+a2+a3+a4+a5+a6+a7=1, and the specific value is set by the user.

[0083] S32. Use a supervised learning algorithm to find the optimal weight coefficient and bias term β for the disease progression prediction model, including using a cross entropy loss function to measure the difference between the prediction and the actual value, obtaining the total loss L(ai, β) of the cross entropy loss function, and then using a gradient descent algorithm to gradually adjust the weight coefficient and bias term β to minimize the total loss L(ai, β) to iteratively optimize the constructed disease progression prediction model. The smaller the total loss L(ai, β), the better the model prediction result;

[0084] The total loss L(ai, β) is obtained by the following calculation formula:

[0085]

[0086] Wherein, m represents the number of samples in the training set, specifically how many patient data are used to train the disease progression prediction model, yj represents the true label of the jth patient, the value of yj is 0 or 1, when the jth patient has disease progression, yj=1, otherwise yj=0, which is specifically obtained by extracting the training data set, P(Progressj) represents the disease progression fluctuation probability index of the jth patient output by the disease progression prediction model, ranging from 0 to 1, log represents the logarithmic function, which is used to convert the probability value into the amount of information in the loss function. By taking the logarithm of the predicted probability, an incorrect prediction will produce a larger loss, for example, the predicted probability is close to 0 and the true label is 1.

[0087] In this embodiment, by standardizing and preprocessing the key parameters in the patient tumor identification data group P, such as the processed age Ac, weight Wc, height Hc, blood pressure BPc and tumor size TSc, the dimensional differences between different parameters can be eliminated, and a unified scale of data input is provided for subsequent model training. The construction of the feature vector X enables the individual features extracted from the patient data to be input into the disease progression prediction model in a consistent format, thereby enhancing the adaptability and computing power of the model to various types of data. When using a machine learning method to construct a disease progression prediction model, the feature vector X is used as input, combined with the weighted linear combination z and the Sigmoid function, to accurately predict the patient's disease progression fluctuation probability index Progress, and the output of the model is mapped to a range of 0 to 1, ensuring the interpretability of the prediction results and the practicality of clinical applications. Through the training of the supervised learning algorithm and the optimization of the cross entropy loss function, the prediction performance of the model has been continuously iteratively improved, so that the weight coefficient and the bias term β are optimized and adjusted, and the disease progression prediction model finally constructed can achieve more accurate disease progression prediction. Compared with traditional methods, this process not only improves the prediction accuracy of the model, but also reduces the deviation caused by data inconsistency or errors, enhances the generalization ability of the model, and provides more reliable data support for clinical personalized treatment and decision-making.

[0088] Example 4

[0089] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: the S4 includes S41 and S42;

[0090] S41, substituting the obtained disease progression fluctuation probability index Progress into the correction coefficient algorithm formula for calculation to obtain the correction coefficient Cm;

[0091] The correction coefficient Cm is obtained by the following calculation formula:

[0092]

[0093] Wherein, B represents the exponential power, which is specifically used to adjust the nonlinear effect of disease progression on the correction coefficient Cm, γ represents the effect coefficient of age A on the correction coefficient Cm, c1, c2 and c3 represent the effect coefficients on the correction coefficient Cm, specifically c1 represents the effect coefficient of disease progression fluctuation probability index Progress on the correction coefficient Cm, c2 represents the effect coefficient of age A on the correction coefficient Cm, c3 represents the effect coefficient of weight W on the correction coefficient Cm, c4 represents the nonlinear effect coefficient of weight W on the correction coefficient Cm, exp represents the exponential function, SZ represents the body mass index, specifically through Obtained from the calculation formula, W represents weight and H represents height.

[0094] S42, comparing the correction coefficient Cm with the preset correction operation fluctuation threshold Cthe to obtain a correction trigger result, and correcting the recovery period content extracted from the patient's initial medical record according to the correction trigger result to obtain the initial recovery period Tinit, and obtaining the corrected correction recovery period Tad by combining the correction coefficient Cm, wherein the initial recovery period Tinit is obtained by extracting the treatment cycle from the initial medical record evaluated by the patient's attending physician;

[0095] The correction trigger result is obtained by the following comparison method:

[0096] When the correction coefficient Cm is less than the correction operation fluctuation threshold Cthe, the correction trigger result is obtained as a non-trigger result;

[0097] When the correction coefficient Cm≥correction operation fluctuation threshold Cthe, the correction trigger result is obtained as the trigger result;

[0098] The modified recovery period Tad is obtained by the calculation formula Tad=Tinit*(1+Cm).

[0099] The S5 includes S51;

[0100] S51, providing feedback and iterative optimization according to the modified recovery period Tad, including verifying and updating the modified recovery period Tad, wherein the verification includes comparing the modified recovery period Tad with the diagnostic recovery period data provided by the patient's attending physician, checking the accuracy of the modified recovery period Tad, and recording the recovery period difference △Tad. When the recovery period difference △Tad is less than 5%, the modified recovery period Tad is not optimized and updated. When the recovery period difference △Tad is greater than or equal to 5%, the modified recovery period Tad is optimized and updated.

[0101] Among them, the optimization update includes using the recovery period difference △Tad to proportionally adjust the influence coefficient of the correction coefficient Cm and the weight coefficient of the disease progression prediction model.

[0102] In this embodiment, by introducing the correction coefficient Cm and combining the disease progression fluctuation probability index Progress, the patient's age A and weight W factors, the patient's recovery period prediction model can be accurately adjusted. The calculation of the correction coefficient Cm not only takes into account the nonlinear effect of disease progression, but also integrates individual differences such as age, weight and body mass index, so that it can more flexibly adapt to the physiological characteristics of different patients and optimize the prediction results of the recovery period. By comparing with the preset correction operation fluctuation threshold Cthe, the correction mechanism can be automatically triggered. When the correction coefficient Cm exceeds the threshold, the system will start the recovery period correction and then calculate the corrected recovery period Tad. This process makes the patient's treatment cycle prediction more personalized and accurate. The combination of the corrected recovery period Tad and the initial recovery period Tinit ensures the flexibility and adjustment space of the clinical treatment plan. In the further feedback and iterative optimization process, the system verifies and optimizes the corrected recovery period by comparing with the diagnostic recovery period data provided by the attending physician, ensuring the accuracy and reliability of the prediction model. Through this dynamic optimization, the accuracy of the prediction can be significantly improved, the error can be reduced, and the scientific nature of clinical decision-making can be improved, providing patients with more personalized and accurate treatment plans.

[0103] Example 5

[0104] For data processing systems for digestive tract tumors, please refer to Figure 2 ,Specifically: including data acquisition module, data preprocessing module, modeling and analysis module, correction and evaluation module and iterative optimization module;

[0105] The data collection module collects data from the patient's electronic health record source, imaging examination record source, blood test record source and genomic test record source to form a patient tumor identification data set P;

[0106] The data preprocessing module performs data preprocessing on the patient tumor identification data group P, including standardization preprocessing, and then integrates the processed patient tumor identification data group P to obtain a feature vector X;

[0107] The modeling and analysis module constructs a disease progression prediction model by using a machine learning method, and uses the feature vector X as an input feature of the disease progression prediction model, and obtains a disease progression fluctuation probability index Progress predicted for the patient through training;

[0108] The correction evaluation module substitutes the acquired disease progression fluctuation probability index Progress into the correction coefficient algorithm formula for calculation, obtains the correction coefficient Cm, and compares it with the preset correction operation fluctuation threshold Cthe, obtains the correction trigger result, and corrects the recovery period content extracted from the patient's initial medical record according to the correction trigger result, and obtains the corrected recovery period Tad after correction;

[0109] The iterative optimization module performs feedback and iterative optimization according to the modified recovery period Tad, including verifying and updating the modified recovery period Tad.

[0110] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data processing method for digestive tract tumors, characterized by: The following steps are involved: S1, collecting and obtaining from the patient's electronic health record source, imaging examination record source, blood test record source and genomic test record source to form the patient's tumor identification data set P; S2, performing data preprocessing on the patient tumor identification data set P, including standardization preprocessing, and then integrating the processed patient tumor identification data set P to obtain a feature vector X; S3. A disease progression prediction model is constructed by using a machine learning method, and the feature vector X is used as an input feature of the disease progression prediction model. Through training, a disease progression fluctuation probability index Progress predicted for the patient is obtained; S4. Substituting the obtained disease progression fluctuation probability index Progress into the correction coefficient algorithm formula for calculation, obtaining the correction coefficient Cm, and comparing it with the preset correction operation fluctuation threshold Cthe, obtaining the correction trigger result, and correcting the recovery period content extracted from the patient's initial medical record according to the correction trigger result, to obtain the corrected correction recovery period Tad; S5. Provide feedback and iterative optimization based on the modified recovery period Tad, including verifying and updating the modified recovery period Tad.

2. The data processing method for digestive tract tumors according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, by collecting individual difference related parameters from clinical data sources, including extracting patient individual difference related parameters from electronic health record sources, imaging examination record sources, blood test record sources and genomic test record sources, including age A, gender S, weight W, height H, blood pressure BP1, gene mutation GM and tumor size TS, to form a query collection data group CC = {A, S, W, H, BP, GM, TS}; Among them, age A, gender S, weight W and height H are obtained by entering the patient's unique information from the electronic health record source; tumor size TS is obtained by entering the patient's unique information from the imaging examination record source; blood pressure BP is obtained by entering the patient's unique information in the blood test record source to query the blood pressure measurement record; whether the gene is mutated GM is obtained by entering the patient's unique information in the genomic detection record source to obtain the gene sequence analysis results and mutation screening results.

3. The data processing method for digestive tract tumors according to claim 2, characterized in that: S12, integrating and processing the query collection data set CC={A, S, W, H, BP, GM, TS} to construct the patient tumor identification data set P; Specifically, binary coding processing and categorical variable coding processing are performed on the sex S and whether the gene is mutated GM in the query collection data group CC to obtain the categorical variable sex Se and binary gene mutation GMe; Among them, the categorical variable sex Se includes classifying male as 1 and female as 2 to distinguish sex; the binary gene mutation GMe includes setting 0 as no gene mutation and 1 as gene mutation; Substitute the categorical variable sex Se and the binary gene mutation GMe into the query collection data set CC = {A, S, W, H, BP, GM, TS} for replacement integration to obtain the integrated patient tumor identification data set P; The patient tumor identification data set P = {A, Se, W, H, BP, GMe, TS}.

4. The data processing method for digestive tract tumors according to claim 3, characterized in that: Said S2 includes S21; S21, performing data preprocessing on the age A, weight W, height H, blood pressure BP and tumor size TS in the patient tumor identification data set P, including standardization preprocessing, obtaining the preprocessed processed age Ac, processed weight Wc, processed height Hc, processed blood pressure BPc and processed tumor size TSc, and synchronously substituting them into the patient tumor identification data set P for replacement, to obtain a feature vector X = {Ac, Se, Wc, Hc, BPc, GMe, TSc}; Among them, the standardization preprocessing eliminates the dimensional differences of the data by using feature scaling technology.

5. The data processing method for digestive tract tumors according to claim 4, characterized in that: The S3 includes S31 and S32; S31. A disease progression prediction model is constructed by using a machine learning method, and the feature vector X is used as an input feature of the disease progression prediction model for training to obtain a weighted linear combination z, and the weighted linear combination z is simultaneously transformed using a Sigmoid function to obtain a predicted disease progression fluctuation probability index Progress of the patient; The disease progression fluctuation probability index Progress is obtained by the following calculation formula: z=(a1*Ac+a2*Se+a3*Wc+a4*Hc+a5*BPc+a6*GMe+a7*TSc+β); Wherein, z represents a weighted linear combination, e represents a Sigmoid function, which is specifically used to map the disease progression fluctuation probability index Progress to the range of 0 to 1, a1, a2, a3, a4, a5, a6 and a7 represent the weight coefficients of treatment age Ac, categorical variable gender Se, treatment weight Wc, treatment height Hc, treatment blood pressure BPc, binary gene mutation GMe and treatment tumor size TSc, β represents a bias term, and a1+a2+a3+a4+a5+a6+a7=1, and the specific value is set by the user.

6. The data processing method for digestive tract tumors according to claim 5, characterized in that: S32. Use a supervised learning algorithm to find the optimal weight coefficient and bias term β for the disease progression prediction model, including using the cross entropy loss function to measure the difference between the prediction and the actual value, obtaining the total loss of the cross entropy loss function, and then using the gradient descent algorithm to gradually adjust the weight coefficient and bias term β to minimize the total loss, so as to iteratively optimize the constructed disease progression prediction model.

7. The data processing method for digestive tract tumors according to claim 2, characterized in that: The S4 includes S41 and S42; S41, substituting the obtained disease progression fluctuation probability index Progress into the correction coefficient algorithm formula for calculation to obtain the correction coefficient Cm; The correction coefficient Cm is obtained by the following calculation formula: Where B represents the exponential power, γ represents the influence coefficient of age A on the correction coefficient Cm, c1, c2 and c3 represent the influence coefficients on the correction coefficient Cm, c4 represents the nonlinear influence coefficient of weight W on the correction coefficient Cm, exp represents the exponential function, SZ represents the body mass index, and is specifically expressed by Obtained from the calculation formula, W represents weight and H represents height.

8. The data processing method for digestive tract tumors according to claim 7, characterized in that: S42, comparing the correction coefficient Cm with the preset correction operation fluctuation threshold Cthe to obtain a correction trigger result, and correcting the recovery period content extracted from the patient's initial medical record according to the correction trigger result to obtain the initial recovery period Tinit, and obtaining the corrected recovery period Tad by combining the correction coefficient Cm; The correction trigger result is obtained by the following comparison method: When the correction coefficient Cm is less than the correction operation fluctuation threshold Cthe, the correction trigger result is obtained as a non-trigger result; When the correction coefficient Cm≥correction operation fluctuation threshold Cthe, the correction trigger result is obtained as the trigger result; The modified recovery period Tad is obtained by the calculation formula Tad=Tinit*(1+Cm).

9. The data processing method for digestive tract tumors according to claim 1, characterized in that: The S5 includes S51; S51, providing feedback and iterative optimization according to the modified recovery period Tad, including verifying and updating the modified recovery period Tad, wherein the verification includes comparing the modified recovery period Tad with the diagnostic recovery period data provided by the patient's attending physician, checking the accuracy of the modified recovery period Tad, and recording the recovery period difference △Tad. When the recovery period difference △Tad is less than 5%, the modified recovery period Tad is not optimized and updated. When the recovery period difference △Tad is greater than or equal to 5%, the modified recovery period Tad is optimized and updated. Among them, the optimization update includes using the recovery period difference △Tad to proportionally adjust the influence coefficient of the correction coefficient Cm and the weight coefficient of the disease progression prediction model.

10. A data processing system for digestive tract tumors, applied to the data processing method for digestive tract tumors according to any one of claims 1 to 9, characterized in that: It includes data acquisition module, data preprocessing module, modeling and analysis module, correction and evaluation module and iterative optimization module; The data collection module collects data from the patient's electronic health record source, imaging examination record source, blood test record source and genomic test record source to form a patient tumor identification data set P; The data preprocessing module performs data preprocessing on the patient tumor identification data group P, including standardization preprocessing, and then integrates the processed patient tumor identification data group P to obtain a feature vector X; The modeling and analysis module constructs a disease progression prediction model by using a machine learning method, and uses the feature vector X as an input feature of the disease progression prediction model, and obtains a disease progression fluctuation probability index Progress predicted for the patient through training; The correction evaluation module substitutes the acquired disease progression fluctuation probability index Progress into the correction coefficient algorithm formula for calculation, obtains the correction coefficient Cm, and compares it with the preset correction operation fluctuation threshold Cthe, obtains the correction trigger result, and corrects the recovery period content extracted from the patient's initial medical record according to the correction trigger result, and obtains the corrected recovery period Tad after correction; The iterative optimization module performs feedback and iterative optimization according to the modified recovery period Tad, including verifying and updating the modified recovery period Tad.

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