A dual-modal composite tumor treatment system

Abstract

This invention provides a dual-modal composite tumor treatment system, belonging to the medical field, which solves the problem of inconvenient tumor examination due to the lack of routine tumor examinations. It includes a feature extraction module, a model construction module, a treatment determination module, and a real-time monitoring module. The feature extraction module extracts the physical characteristics of multiple tumor patients to obtain the frequency ranges of chest pain, cough, and chest tightness. The model construction module constructs a preliminary tumor identification model for each tumor patient. The real-time monitoring module monitors the physical characteristics of the examiner in real time to obtain the real-time frequency of chest pain, cough, and chest tightness. The treatment determination module makes a preliminary determination of the examiner's tumor condition. This invention achieves routine tumor examination based on a dual-modal approach.

Description

Technical Field

[0001] This invention relates to the field of medical technology, and in particular to a dual-modal composite tumor treatment system. Background Technology

[0002] A tumor is a new growth formed by the proliferation of local tissue cells under the influence of various tumorigenic factors. Because these new growths often present as space-occupying, mass-like protrusions, they are also called growths. Liver cancer and pancreatic cancer are both highly malignant tumors. Studies have found that tumor cells exhibit metabolic changes different from normal cells, and tumor cells themselves can adapt to changes in the metabolic environment through the switching between glycolysis and oxidative phosphorylation.

[0003] Currently, some community hospitals lack the necessary medical equipment to perform routine tumor examinations, resulting in patients not receiving timely checkups or needing to be examined at large medical facilities in the field. Routine examinations are inconvenient, and therefore, there is a lack of a dual-modal composite tumor treatment system to address these problems. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a dual-modal composite tumor treatment system.

[0005] The technical problem to be solved by this invention is:

[0006] How to achieve routine tumor screening based on a bimodal approach.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0008] A dual-modal composite tumor treatment system includes a data acquisition module, a feature extraction module, a model construction module, a treatment determination module, a real-time monitoring module, a user terminal, and a server. The data acquisition module is used to collect patient vital sign data from multiple tumor patients and send the patient vital sign data to the server. The server sends the patient vital sign data to the feature extraction module.

[0009] The feature extraction module is used to extract the physical characteristics of multiple cancer patients, obtain the frequency intervals of chest pain, cough, and chest tightness of the cancer patients, and feed them back to the server. The server sends the frequency intervals of chest pain, cough, and chest tightness of the cancer patients to the model building module. The model building module is used to build a preliminary tumor identification model for the cancer patients and feed the preliminary tumor identification model back to the server. The server sends the preliminary tumor identification model of the cancer patients to the treatment determination module.

[0010] The data acquisition module is used to collect real-time characteristic data of the inspectors and send the real-time vital signs data to the server. The server then sends the real-time characteristic data of the inspectors to the real-time monitoring module.

[0011] The real-time monitoring module is used to monitor the physical characteristics of the examinee in real time, and to get the real-time frequency of chest pain, real-time frequency of cough and real-time frequency of chest tightness of the examinee and feed it back to the server. The server sends the real-time frequency of chest pain, real-time frequency of cough and real-time frequency of chest tightness of the examinee to the treatment judgment module.

[0012] The treatment determination module is used to make a preliminary determination of the condition of the tumor patient by the examiner and generate abnormal or normal signs signals.

[0013] Furthermore, the patient's vital signs data included the frequency of chest pain, cough, and chest tightness in cancer patients.

[0014] Furthermore, the extraction process of the feature extraction module is as follows:

[0015] We obtained vital sign data from multiple cancer patients, including the frequency of chest pain, cough, and chest tightness.

[0016] By traversing the vital sign data of multiple cancer patients, the upper limit, lower limit, upper limit, lower limit, upper limit, and lower limit of chest pain frequency, cough frequency, chest tightness frequency, and chest tightness frequency of multiple cancer patients were obtained.

[0017] The upper limit and lower limit of chest pain frequency in multiple cancer patients constitute the chest pain frequency range for cancer patients.

[0018] Similarly, the upper and lower limits of cough frequency for multiple cancer patients constitute the cough frequency range for cancer patients, and the upper and lower limits of chest tightness frequency for multiple cancer patients constitute the chest tightness frequency range for cancer patients.

[0019] Furthermore, the construction process of the model building module is as follows:

[0020] Obtain the frequency ranges of chest pain, cough, and chest tightness in cancer patients;

[0021] The frequency ranges of chest pain, cough, and chest tightness in patients are integrated to form a preliminary tumor identification model for cancer patients.

[0022] Furthermore, the real-time feature data includes the physical characteristics of the examiner such as chest pain, cough, and chest tightness.

[0023] Furthermore, the real-time monitoring process of the real-time monitoring module is as follows:

[0024] Multiple real-time monitoring periods are set for inspectors, and the duration of each real-time monitoring period is the same.

[0025] Then, the physical characteristics of chest pain, cough, and chest tightness of the examinees were obtained during multiple real-time monitoring periods.

[0026] The number of times the examinee experienced chest pain, coughing, and chest tightness was recorded during multiple real-time monitoring periods.

[0027] The real-time frequency of chest pain, cough, and chest tightness of the examinees during multiple real-time monitoring periods is obtained by summing the data and taking the average.

[0028] Furthermore, the preliminary determination process of the treatment determination module is as follows:

[0029] Acquire the real-time frequency of chest pain, cough, and chest tightness of the examiners;

[0030] Then, a preliminary tumor identification model for cancer patients is obtained.

[0031] The real-time frequency of chest pain, cough, and chest tightness of the examiners were substituted into the preliminary tumor identification model.

[0032] If the real-time chest pain frequency matches the patient's chest pain frequency range, the real-time cough frequency matches the patient's cough frequency range, and the real-time chest tightness frequency matches the patient's chest tightness frequency range, then an abnormal sign signal is generated.

[0033] If the real-time chest pain frequency does not match the patient's chest pain frequency range, the real-time cough frequency does not match the patient's cough frequency range, or the real-time chest tightness frequency does not match the patient's chest tightness frequency range, then a normal vital sign signal is generated.

[0034] Furthermore, the treatment determination module feeds back abnormal or normal vital signs signals to the server, and the server sends the abnormal or normal vital signs signals to the corresponding user terminal.

[0035] The user terminal is used by medical staff to check the physical condition of the examinee after receiving abnormal or normal vital signs signals.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] 1. This invention first uses a feature extraction module to extract the physical characteristics of multiple cancer patients, obtains the patient's vital signs data, and obtains the frequency ranges of chest pain, cough, and chest tightness of the cancer patients based on the patient's vital signs data, which are then sent to the model building module. The model building module is then used to build a preliminary tumor identification model for the cancer patients, and the resulting preliminary tumor identification model is sent to the treatment determination module. This invention combines the physical characteristics of many currently diagnosed cancer patients to build a preliminary tumor identification model.

[0038] 2. This invention uses a real-time monitoring module to monitor the physical characteristics of the examinee in real time. Multiple real-time monitoring periods are set for the examinee. The number of times the examinee experiences chest pain, coughing, and chest tightness within these periods is summed and averaged to obtain the real-time frequency of chest pain, coughing, and chest tightness. These frequencies are then sent to a treatment assessment module, which makes a preliminary assessment of the examinee's tumor condition, generating either abnormal or normal signs. This invention combines a preliminary tumor identification module with the examinee's real-time physical condition, enabling routine tumor examinations on a dual-modal basis, thus improving the convenience of tumor examinations.

[0039] Advantages of additional aspects of the invention will be set forth in part in the detailed description of the invention below, and in part will be obvious from the description or may be learned by practice of the invention. Attached Figure Description

[0040] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0041] Figure 1 This is an overall system block diagram of the present invention;

[0042] Figure 2 This is a flowchart of the process of the present invention. Implementation

[0043] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0044] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0045] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other. Example 1

[0046] Please see Figure 1 The present invention provides a dual-modal composite tumor treatment system, including a data acquisition module, a feature extraction module, a model construction module, a treatment determination module, a real-time monitoring module, a user terminal, and a server;

[0047] In practice, the user terminal is used by medical staff to input personal information to register and log in to the system, and to send the personal information to the server for storage; the personal information includes the medical staff's name, real-name authenticated mobile phone number, etc.

[0048] In this embodiment, the data acquisition module is used to collect patient vital sign data from multiple cancer patients and send the patient vital sign data to the server. In actual use, the data acquisition module is a tumor monitoring device used by medical staff. The server sends the patient vital sign data to the feature extraction module.

[0049] It should be noted that the patient's vital signs data include the frequency of chest pain (number of chest pains per unit time), the frequency of cough (number of irritating dry coughs per unit time), and the frequency of chest tightness (number of chest tightnesses per unit time) in cancer patients. Only a few of the main manifestations of lung cancer patients are selected here. In actual implementation, more patient characteristics can be selected according to the actual situation. Among them, cancer patients can be lung cancer patients, pancreatic cancer patients, gastric cancer patients, colorectal cancer patients, etc. In this embodiment, the cancer patient is a lung cancer patient.

[0050] The feature extraction module is used to extract physical characteristics from multiple cancer patients. The extraction process is as follows:

[0051] Step S11: Obtain vital sign data from multiple cancer patients, including the frequency of chest pain, cough, and chest tightness.

[0052] Step S12: Iterate through the vital sign data of multiple cancer patients to obtain the upper limit of chest pain frequency, the lower limit of chest pain frequency, the upper limit of cough frequency, the lower limit of cough frequency, the upper limit of chest tightness frequency, and the lower limit of chest tightness frequency for multiple cancer patients.

[0053] Step S13: The upper limit and lower limit of chest pain frequency of multiple cancer patients constitute the chest pain frequency range of cancer patients.

[0054] Step S14, similarly, the upper limit and lower limit of the cough frequency of multiple cancer patients constitute the cough frequency range of cancer patients, and the upper limit and lower limit of the chest tightness frequency of multiple cancer patients constitute the chest tightness frequency range of cancer patients.

[0055] The feature extraction module feeds back the frequency intervals of chest pain, cough, and chest tightness of tumor patients to the server. The server then sends these same frequency intervals to the model building module. The model building module is used to construct a preliminary tumor identification model for tumor patients. The construction process is as follows:

[0056] Step S21: Obtain the frequency ranges of chest pain, cough, and chest tightness in the tumor patients obtained above.

[0057] Step S22: The frequency intervals of chest pain, cough, and chest tightness in patients are integrated to form a preliminary tumor identification model for cancer patients.

[0058] The model building module feeds back the preliminary tumor identification model of the cancer patient to the server, and the server sends the preliminary tumor identification model of the cancer patient to the treatment determination module;

[0059] In this embodiment, the data acquisition module is used to collect real-time characteristic data of the inspectors and send the real-time vital signs data to the server, and the server sends the real-time characteristic data of the inspectors to the real-time monitoring module.

[0060] It should be specifically noted that the real-time feature data refers to the physical characteristics of the examiners, such as chest pain, cough, and chest tightness.

[0061] The real-time monitoring module is used to monitor the physical characteristics of the inspectors in real time. The real-time monitoring process is as follows:

[0062] Step S31: Set multiple real-time monitoring periods for inspectors, with all multiple real-time monitoring periods having the same duration;

[0063] Step S32, then obtain the physical characteristics of the examinee such as chest pain, cough, and chest tightness during multiple real-time monitoring periods;

[0064] Step S33: Count the number of times the examinee experienced chest pain, coughing, and chest tightness during multiple real-time monitoring periods;

[0065] Step S34: The number of times chest pain, cough, and chest tightness of the examinee during multiple real-time monitoring periods are summed and averaged to obtain the real-time frequency of chest pain, real-time frequency of cough, and real-time frequency of chest tightness of the examinee.

[0066] For specific examples, the real-time monitoring period is labeled as u, u=1,2,...,z, where z is a positive integer. The number of times the examinee experiences chest pain, coughing, and chest tightness within multiple real-time monitoring periods are labeled as XTCu, KSCu, and XMCu, respectively.

[0067] Simultaneously, the number of real-time monitoring periods is counted and recorded as the period number SL;

[0068] Then use the formula The average number of chest pains for examinees during multiple real-time monitoring periods was calculated, which is the average number of chest pains for examinees, JXTC.

[0069] Similarly, using the formula and The average number of coughs and chest tightnesses of the examiners during multiple real-time monitoring periods were calculated, namely, the average number of coughs JKSC and the average number of chest tightnesses JXMC of the examiners.

[0070] The real-time monitoring module feeds back the real-time frequency of chest pain, cough, and chest tightness of the examinee to the server, and the server sends the real-time frequency of chest pain, cough, and chest tightness of the examinee to the treatment judgment module.

[0071] The treatment determination module is used to make a preliminary determination of the tumor patient's condition by the examiner. The preliminary determination process is as follows:

[0072] Obtain the real-time frequency of chest pain, real-time frequency of coughing, and real-time frequency of chest tightness of the examinees as described above;

[0073] Step S41, then obtain the preliminary tumor identification model of the cancer patient;

[0074] Step S42: Input the real-time frequency of chest pain, real-time frequency of cough, and real-time frequency of chest tightness of the examiner into the preliminary tumor identification model.

[0075] Step S43: If the real-time chest pain frequency matches the patient's chest pain frequency range, the real-time cough frequency matches the patient's cough frequency range, and the real-time chest tightness frequency matches the patient's chest tightness frequency range, then an abnormal sign signal is generated.

[0076] Step S44: If the real-time chest pain frequency does not match the patient's chest pain frequency range, the real-time cough frequency does not match the patient's cough frequency range, or the real-time chest tightness frequency does not match the patient's chest tightness frequency range, then a normal vital sign signal is generated.

[0077] The treatment determination module feeds back abnormal or normal vital signs signals to the server, and the server sends the abnormal or normal vital signs signals to the corresponding user terminal; the user terminal is used by medical staff to further examine the physical condition of the examinee after receiving the abnormal or normal vital signs signals.

[0078] The above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. For example, there are weighting coefficients and proportional coefficients. The values ​​set are to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. The values ​​of the weighting coefficients and proportional coefficients are only required to not affect the proportional relationship between the parameters and the quantified values. Example 2

[0079] Please see Figure 2 Based on another concept of the same invention, a working method for a dual-modal composite tumor treatment system is now proposed, the specific working method of which is as follows:

[0080] Step S101: The data acquisition module collects vital sign data from multiple cancer patients and sends the data to the server.

[0081] In step S102, the feature extraction module extracts the physical characteristics of multiple cancer patients, obtains the patient vital sign data of multiple cancer patients, and obtains the frequency of chest pain, cough, and chest tightness of multiple cancer patients. It iterates through the patient vital sign data of multiple cancer patients to obtain the upper limit, lower limit, upper limit, lower limit, upper limit, and lower limit of the frequency of chest pain, cough, chest tightness, and chest pain of multiple cancer patients. The upper limit and lower limit of the frequency of chest pain of multiple cancer patients constitute the frequency range of chest pain of cancer patients. Similarly, the upper limit and lower limit of the frequency of cough of multiple cancer patients constitute the frequency range of cough of cancer patients. The upper limit and lower limit of the frequency of chest tightness of multiple cancer patients constitute the frequency range of chest tightness of cancer patients. The feature extraction module feeds back the frequency range of chest pain, cough, and chest tightness of cancer patients to the server. The server sends the frequency range of chest pain, cough, and chest tightness of cancer patients to the model building module.

[0082] Step S103: The model building module constructs a preliminary tumor identification model for the cancer patient, obtains the frequency range of chest pain, cough, and chest tightness of the cancer patient, integrates the frequency range of chest pain, cough, and chest tightness to form a preliminary tumor identification model for the cancer patient, and feeds the preliminary tumor identification model of the cancer patient back to the server. The server sends the preliminary tumor identification model of the cancer patient to the treatment judgment module.

[0083] Step S104: The data acquisition module collects real-time characteristic data of the inspectors and sends the real-time vital signs data to the server. The server then sends the real-time characteristic data of the inspectors to the real-time monitoring module.

[0084] Step S105: The real-time monitoring module monitors the physical characteristics of the examinee in real time, sets multiple real-time monitoring periods for the examinee with the same duration, and then acquires the examinee's chest pain, cough, and chest tightness physical characteristics within the multiple real-time monitoring periods. The number of times chest pain, cough, and chest tightness occur within the multiple real-time monitoring periods is counted. The sum of the number of times chest pain, cough, and chest tightness occur within the multiple real-time monitoring periods is calculated and averaged to obtain the examinee's real-time chest pain frequency, real-time cough frequency, and real-time chest tightness frequency. The real-time monitoring module feeds back the examinee's real-time chest pain frequency, real-time cough frequency, and real-time chest tightness frequency to the server. The server sends the examinee's real-time chest pain frequency, real-time cough frequency, and real-time chest tightness frequency to the treatment judgment module.

[0085] Step S106: The treatment determination module makes a preliminary determination of the examinee's tumor patient condition, obtaining the examinee's real-time chest pain frequency, real-time cough frequency, and real-time chest tightness frequency. Then, it obtains the tumor preliminary identification model for the tumor patient and substitutes the examinee's real-time chest pain frequency, real-time cough frequency, and real-time chest tightness frequency into the tumor preliminary identification model. If the real-time chest pain frequency, real-time cough frequency, and real-time chest tightness frequency all match the patient's chest pain frequency range, an abnormal sign signal is generated. If the real-time chest pain frequency, real-time cough frequency, or real-time chest tightness frequency does not match the patient's chest pain frequency range, a normal sign signal is generated. The treatment determination module feeds back the abnormal sign signal or the normal sign signal to the server. The server sends the abnormal sign signal or the normal sign signal to the corresponding user terminal. After receiving the abnormal sign signal or the normal sign signal through the user terminal, the medical staff further examines the examinee's physical condition.

[0086] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0087] The above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and are not intended to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of protection of the claims.

Claims

1. A dual-modal composite tumor treatment system, characterized in that, include: The data acquisition module is used to collect vital sign data from multiple cancer patients and send the data to the server; the server sends the data to the feature extraction module; the patient vital sign data includes the frequency of chest pain, cough, and chest tightness in cancer patients. The feature extraction module obtains vital sign data from multiple cancer patients, including the frequency of chest pain, cough, and chest tightness. By traversing the vital sign data of multiple cancer patients, the upper limit, lower limit, upper limit, lower limit, upper limit, and lower limit of chest pain frequency, cough frequency, chest tightness frequency, and chest tightness frequency of multiple cancer patients were obtained. The upper and lower limits of chest pain frequency in multiple cancer patients constitute the chest pain frequency range for cancer patients. The server sends the frequency ranges of chest pain, cough, and chest tightness of cancer patients to the model building module; the model building module then feeds back the preliminary tumor identification model of the cancer patients to the server, which in turn sends the preliminary tumor identification model of the cancer patients to the treatment determination module. The data acquisition module is used to collect real-time characteristic data of the inspectors and send the real-time vital signs data to the server. The server then sends the real-time characteristic data of the inspectors to the real-time monitoring module. The real-time monitoring module is used to monitor the physical characteristics of the examinee in real time, and to get the real-time frequency of chest pain, real-time frequency of cough and real-time frequency of chest tightness of the examinee and feed it back to the server. The server sends the real-time frequency of chest pain, real-time frequency of cough and real-time frequency of chest tightness of the examinee to the treatment judgment module. The treatment assessment module is used to make a preliminary assessment of the condition of tumor patients examined by the personnel, and generate abnormal or normal signs signals.

2. The dual-modal composite tumor treatment system according to claim 1, characterized in that, The feature extraction module also includes the following extraction process: The upper and lower limits of cough frequency for multiple cancer patients constitute the cough frequency range for cancer patients, and the upper and lower limits of chest tightness frequency for multiple cancer patients constitute the chest tightness frequency range for cancer patients.

3. The dual-modal composite tumor treatment system according to claim 2, characterized in that, The construction process of the model building module is as follows: Obtain the frequency ranges of chest pain, cough, and chest tightness in cancer patients; The frequency ranges of chest pain, cough, and chest tightness in patients are integrated to form a preliminary tumor identification model for cancer patients.

4. The dual-modal composite tumor treatment system according to claim 1, characterized in that, Real-time feature data includes the physical characteristics of chest pain, cough, and chest tightness of the inspectors.

5. The dual-modal composite tumor treatment system according to claim 4, characterized in that, The real-time monitoring process of the real-time monitoring module is as follows: Multiple real-time monitoring periods are set for inspectors, and the duration of each real-time monitoring period is the same. Then, the physical characteristics of chest pain, cough, and chest tightness of the examinees were obtained during multiple real-time monitoring periods. The number of times the examinee experienced chest pain, coughing, and chest tightness was recorded during multiple real-time monitoring periods. The real-time frequency of chest pain, cough, and chest tightness of the examinees during multiple real-time monitoring periods is obtained by summing the data and taking the average.

6. The dual-modal composite tumor treatment system according to claim 5, characterized in that, The preliminary determination process of the treatment determination module is as follows: Acquire the real-time frequency of chest pain, cough, and chest tightness of the examiners; Then, a preliminary tumor identification model for cancer patients is obtained. The real-time frequency of chest pain, cough, and chest tightness of the examiners were substituted into the preliminary tumor identification model.

7. A dual-modal composite tumor treatment system according to claim 6, characterized in that, The preliminary determination process of the treatment determination module also includes: If the real-time chest pain frequency matches the patient's chest pain frequency range, the real-time cough frequency matches the patient's cough frequency range, and the real-time chest tightness frequency matches the patient's chest tightness frequency range, then an abnormal sign signal is generated. If the real-time chest pain frequency does not match the patient's chest pain frequency range, the real-time cough frequency does not match the patient's cough frequency range, or the real-time chest tightness frequency does not match the patient's chest tightness frequency range, then a normal vital sign signal is generated.

8. The dual-modal composite tumor treatment system according to claim 7, characterized in that, The treatment determination module feeds back abnormal or normal vital signs signals to the server, and the server sends the abnormal or normal vital signs signals to the corresponding user terminal. The user terminal is used by medical staff to check the physical condition of the examinee after receiving abnormal or normal vital signs signals.

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