Cancer insomnia nursing system and method based on acupuncture technology

Abstract

The invention relates to the technical field of supervision of acupuncture treatment of cancer insomnia, and discloses a cancer insomnia nursing system and method based on an acupuncture technology, and the method comprises the steps: obtaining historical sleep data of a cancer patient before acupuncture, and building an insomnia prediction model according to the historical sleep data. Acquiring sleep data and acupuncture treatment days of the cancer patient after acupuncture, and acquiring preset insomnia data of the cancer patient according to the acupuncture treatment days and the insomnia prediction model. And determining a treatment score according to a relationship between the sleep data and preset insomnia data. According to the relation between the treatment score and a preset first preset treatment score and a preset second preset treatment score, the acupuncture treatment effect is subjected to grade evaluation, and the grade of the evaluated acupuncture treatment effect is sent to a nursing station. According to the method, the historical sleep data, the acupuncture treatment days and the insomnia prediction model are combined, so that accurate evaluation and grade evaluation of the treatment effect of the cancer patient are realized, and the treatment effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of supervision of acupuncture treatment of cancer insomnia, and in particular to a cancer insomnia nursing system and method based on acupuncture technology. Background Art

[0002] Cancer patients often suffer from sleep disorders such as insomnia, which not only affects their physical recovery and treatment effects, but also seriously affects their mental health and quality of life. There are many causes of insomnia, including the disease itself, pain, chemotherapy, side effects and other factors. The insomnia problem of cancer patients is special. Traditional drug treatments are often accompanied by side effects, and the short-term effects are not lasting. Therefore, finding a safe and effective non-drug treatment method to alleviate cancer insomnia has become a hot issue in current clinical treatment.

[0003] As one of the traditional Chinese medicine therapies, acupuncture has a long history of application and has shown certain efficacy in improving sleep quality. In recent years, with the development and research of acupuncture technology, the effect of acupuncture in treating insomnia in cancer patients has gradually been clinically verified. Studies have shown that acupuncture can effectively improve insomnia symptoms by regulating the nervous system, improving the circulation of qi and blood in the body, and reducing anxiety and stress. However, traditional acupuncture therapy relies on the doctor's experience and the patient's subjective feelings, and the evaluation of treatment effects lacks quantitative standards. This makes it difficult to uniformly judge the effect of acupuncture in treating cancer insomnia, and also brings certain challenges to clinical care.

[0004] Therefore, how to quantify the effects of acupuncture treatment and evaluate and manage it based on scientific data becomes the key to improving the effectiveness of acupuncture treatment for cancer insomnia. Summary of the invention

[0005] In view of this, the present invention proposes a cancer insomnia care system and method based on acupuncture technology, aiming to solve the problem that acupuncture cancer insomnia therapy in current technology lacks quantitative evaluation standards, resulting in difficulty in uniformly judging the treatment effect, which brings challenges to clinical care.

[0006] The present invention proposes a cancer insomnia nursing method based on acupuncture technology, comprising:

[0007] Obtaining historical sleep data of cancer patients before acupuncture, and establishing an insomnia prediction model based on the historical sleep data;

[0008] Obtaining the sleep data of the cancer patient after acupuncture and the number of days of acupuncture treatment, and obtaining the preset insomnia data of the cancer patient according to the number of days of acupuncture treatment and the insomnia prediction model;

[0009] Determining a treatment score according to the relationship between the sleep data and the preset insomnia data;

[0010] According to the relationship between the treatment score and the preset first treatment score and the second treatment score, the acupuncture treatment effect is graded, and the grade of the acupuncture treatment effect after the assessment is sent to the nursing station, wherein:

[0011] When the treatment score is lower than or equal to the first preset treatment score, the acupuncture treatment effect is determined to be low level;

[0012] When the treatment score is higher than the first preset treatment score, and the treatment score is lower than or equal to the second preset treatment score, it is determined that the acupuncture treatment effect is of medium grade;

[0013] When the treatment score is higher than the second preset treatment score, it is determined that the acupuncture treatment effect is of a high level;

[0014] Among them, the first preset treatment score is lower than the second preset treatment score, and the acupuncture treatment effect levels are arranged in sequence as high level, medium level, and low level.

[0015] Furthermore, when establishing an insomnia prediction model based on the historical sleep data, it includes:

[0016] Obtain each historical sleep duration, each historical sleep onset duration, and each deep sleep duration in the historical sleep data, and establish a sleep relationship equation according to the historical sleep duration, the historical sleep onset duration, and the deep sleep duration in the sleep;

[0017] Acquire a time axis between the sleep relationships, and establish a linear relationship between the sleep relationships according to the time axis;

[0018] The distance metric between the sleep relationship equations is obtained, and the insomnia prediction model is established according to the linear relationship between the sleep relationship equations and the distance metric between the sleep relationship equations.

[0019] Furthermore, when establishing the insomnia prediction model according to the linear relationship between the sleep relationships and the distance measurement between the sleep relationship equations, it includes:

[0020] Establishing a distance matrix according to the distance metric, and iteratively merging the sleep relationship equations according to the distance matrix;

[0021] Acquire the sleep relationship equations after iterative merging, and determine the sleep relationship equations after iterative merging as sleep types;

[0022] Acquire the sleep duration feature, the falling asleep feature and the deep sleep duration feature in the sleep type, and classify each of the sleep relationship equations based on the sleep duration feature, the falling asleep feature and the deep sleep duration feature;

[0023] Eliminate the normal sleep type and the corresponding sleep relationship equations in the sleep type, and obtain the time axis between the sleep relationship equations in the abnormal sleep type;

[0024] A linear relationship between the sleep relationship equations in the abnormal sleep type is established according to the time axis, and based on the linear relationship, the insomnia prediction model is established, and a preset insomnia interval within a preset time period of the cancer patient, as well as a preset sleep duration, a preset falling asleep time, and a preset deep sleep time of each preset insomnia in the preset insomnia interval are obtained.

[0025] Further, when obtaining preset insomnia data of the cancer patient according to the acupuncture treatment days and the insomnia prediction model, it includes:

[0026] Obtaining the insomnia intervals during the acupuncture treatment days, and determining whether to conduct a treatment score assessment on the cancer patient based on the relationship between the insomnia intervals and the preset insomnia intervals;

[0027] When the insomnia interval is lower than the preset insomnia interval, the treatment score of the cancer patient is not evaluated, and the acupuncture treatment effect of the cancer patient is determined to be high;

[0028] When the insomnia interval is equal to or higher than the preset insomnia interval, the treatment score of the cancer patient is evaluated according to the sleep duration, falling asleep time and deep sleep time of each insomnia period of the cancer patient.

[0029] Furthermore, when evaluating the treatment score of the cancer patient according to the sleep duration, falling asleep time and deep sleep time of each insomnia of the cancer patient, it includes:

[0030] Obtaining the sleep duration of each insomnia period within the insomnia interval and the preset sleep duration of each insomnia period within the preset insomnia interval;

[0031] According to the relationship between each of the sleep durations and each of the preset sleep durations, the treatment score of the cancer patient after acupuncture treatment is evaluated based on Formula I, wherein Formula I is as follows:

[0032]

[0033] Wherein, S is the treatment score, n is the total number of insomnia episodes, (t, i) is the sleep duration during the i-th insomnia episode, and (T, i) is the preset sleep duration during the i-th insomnia episode.

[0034] Furthermore, when determining the treatment score S of the cancer patient after acupuncture treatment, it includes:

[0035] Obtain the duration of falling asleep at each insomnia time within the insomnia interval and the preset duration of falling asleep at each insomnia time within the preset insomnia interval;

[0036] According to the relationship between each of the sleeping time and each of the preset sleeping time, the sleeping score of the cancer patient after acupuncture treatment is evaluated based on Formula II, wherein Formula II is as follows:

[0037]

[0038] Wherein, Q is the sleeping score, (m, i) is the sleeping time during the i-th insomnia, and (M, i) is the preset sleeping time during the i-th insomnia;

[0039] According to the relationship between the sleep score Q and the pre-configured preset sleep score, determine whether to adjust the treatment score S:

[0040] When the sleeping score Q is greater than or equal to the preset sleeping score, it is determined that the treatment score S is not to be adjusted;

[0041] When the sleeping score Q is less than the preset sleeping score, an adjustment coefficient is determined according to the relationship between the sleeping score Q and the preset sleeping score, and the treatment score S is adjusted according to the adjustment coefficient.

[0042] Furthermore, when determining the adjustment coefficient according to the relationship between the sleeping score Q and the preset sleeping score, it includes:

[0043] Obtaining a sleep score difference between the sleep score Q and the preset sleep score, and determining the adjustment coefficient according to a relationship between the sleep score difference and a pre-configured first preset sleep score difference and a second preset sleep score difference;

[0044] When the sleeping score difference is less than or equal to the first preset sleeping score difference, the adjustment coefficient is determined to be L3;

[0045] When the sleeping score difference is greater than the first preset sleeping score difference, and the sleeping score difference is less than or equal to the second preset sleeping score difference, determining the adjustment coefficient to be L2;

[0046] When the sleeping score difference is greater than the second preset sleeping score difference, the adjustment coefficient is determined to be L1;

[0047] The first preset sleeping score difference is smaller than the second preset sleeping score difference, and L1<L2<L3<1.

[0048] Further, when the adjustment coefficient is determined to be Li, i=1, 2, 3, including:

[0049] Obtaining the deep sleep duration of each insomnia period within the insomnia interval and the preset deep sleep duration of each insomnia period within the preset insomnia interval;

[0050] According to the relationship between each of the deep sleep durations and each of the preset deep sleep durations, the sleep quality score of the cancer patient after acupuncture treatment is evaluated based on Formula III, wherein Formula III is as follows:

[0051]

[0052] Wherein, W is the sleep quality score, (h, i) is the duration of falling asleep during the i-th insomnia, and (H, i) is the preset duration of falling asleep during the i-th insomnia;

[0053] According to the relationship between the sleep quality score W and the pre-configured preset sleep quality score, determine whether to modify the adjustment coefficient Li:

[0054] When the sleep quality score W is greater than or equal to the preset sleep quality score, it is determined that the adjustment coefficient Li is not adjusted;

[0055] When the sleep quality score W is less than the preset sleep quality score, a correction coefficient is determined according to the relationship between the sleep quality score W and the preset sleep quality score, and the adjustment coefficient Li is corrected according to the correction coefficient.

[0056] Furthermore, when determining the correction coefficient according to the relationship between the sleep quality score W and the preset sleep quality score, it includes:

[0057] Obtaining a quality score difference between the sleep quality score W and the preset sleep quality score, and determining the correction coefficient according to a relationship between the quality score difference and a pre-configured first preset quality score difference and a second preset quality score difference;

[0058] When the quality score difference is less than or equal to the first preset quality score difference, determining the adjustment coefficient to be K3;

[0059] When the quality score difference is greater than the first preset quality score difference, and the quality score difference is less than or equal to the second preset quality score difference, determining the correction coefficient to be K2;

[0060] When the quality score difference is greater than the second preset quality score difference, determining the correction coefficient to be K1;

[0061] The first preset quality score difference is smaller than the second preset quality score difference, and K1<K2<K3<1.

[0062] Compared with the prior art, the beneficial effect of the present invention is that a personalized insomnia prediction model is established by analyzing the historical sleep data of cancer patients before acupuncture. The model can predict the insomnia condition of the patient and provide a scientific basis for the formulation of the treatment plan. During the treatment process, the patient's sleep data will be compared with the preset insomnia data to obtain a treatment score, which can objectively reflect the effect of acupuncture treatment and the patient's sleep improvement. In addition, the grading method based on the treatment score can provide a clear judgment standard for the effect of acupuncture treatment. By comparing with the preset first and second treatment scores, the system can divide the treatment effect into high, medium and low levels. This grading evaluation method can help nursing staff to understand the patient's treatment progress in a timely manner, thereby providing more accurate and personalized nursing plans for patients of different levels. Especially in cancer treatment, the patient's condition may change rapidly, so the treatment plan can be dynamically adjusted and personalized nursing can be given, which significantly improves the pertinence and effect of the treatment. The implementation of this method can not only improve the effect evaluation standard of acupuncture treatment, but also optimize the nursing workflow. By sending the grade information of the treatment effect to the nursing station, the nursing staff can obtain the patient's treatment feedback in real time, so as to adjust the nursing strategy in time.

[0063] On the other hand, the present application also provides a cancer insomnia care system based on acupuncture technology, comprising:

[0064] A model building module is configured to obtain historical sleep data of cancer patients before acupuncture, and to build an insomnia prediction model based on the historical sleep data;

[0065] a central control module, electrically connected to the model building module, wherein the central control module is configured to obtain the sleep data of the cancer patient after acupuncture and the number of days of acupuncture treatment, and obtain preset insomnia data of the cancer patient according to the number of days of acupuncture treatment and the insomnia prediction model;

[0066] An evaluation module, electrically connected to the central control module, and configured to determine a treatment score according to a relationship between the sleep data and the preset insomnia data;

[0067] A communication module is electrically connected to the evaluation module, and is configured to grade the acupuncture treatment effect according to the relationship between the treatment score and a preset treatment score, and send the grade of the acupuncture treatment effect after the assessment to the nursing station.

[0068] It can be understood that the cancer insomnia care system and method based on acupuncture technology in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0070] Figure 1 A flowchart of a method for nursing cancer insomnia based on acupuncture technology provided by an embodiment of the present invention;

[0071] Figure 2 This is a functional block diagram of a cancer insomnia care system based on acupuncture technology provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0072] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0073] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a method for nursing cancer insomnia based on acupuncture technology, comprising:

[0074] Step S100: Obtain the historical sleep data of the cancer patient before acupuncture, and establish an insomnia prediction model based on the historical sleep data.

[0075] Specifically, when establishing an insomnia prediction model based on historical sleep data, it includes: obtaining each historical sleep duration, each historical sleep duration, and each deep sleep duration in the historical sleep data, and establishing a sleep relationship based on the historical sleep duration, the historical sleep duration, and the deep sleep duration in the sleep. Obtain the time axis between each sleep relationship, and establish a linear relationship between each sleep relationship based on the time axis. Obtain the distance measurement between each sleep relationship, and establish an insomnia prediction model based on the linear relationship between each sleep relationship and the distance measurement between each sleep relationship.

[0076] Specifically, when establishing an insomnia prediction model based on the linear relationship between each sleep relationship and the distance measurement between each sleep relationship formula, it includes: establishing a distance matrix based on the distance measurement, and iteratively merging each sleep relationship formula based on the distance matrix. Obtain each sleep relationship formula after iterative merging, and determine each sleep relationship formula after iterative merging as a sleep type. Obtain the sleep duration characteristics, sleep characteristics and deep sleep duration characteristics in the sleep type, and classify each sleep relationship formula based on the sleep duration characteristics, sleep characteristics and deep sleep duration characteristics. Eliminate the normal sleep type and the corresponding sleep relationship formulas in the sleep type, and obtain the time axis between each sleep relationship formula in the abnormal sleep type. Establish a linear relationship between each sleep relationship formula in the abnormal sleep type based on the time axis, establish an insomnia prediction model based on the linear relationship, and obtain the preset insomnia interval within the preset time period of the cancer patient, as well as the preset sleep duration, preset sleep duration and preset deep sleep duration of each preset insomnia in the preset insomnia interval.

[0077] It can be seen that by collecting historical sleep data of cancer patients, especially key indicators such as sleep duration, sleep duration and deep sleep duration, sleep patterns are analyzed and sleep relationships are constructed. These data reflect the overall sleep structure of the patient, and through the combination of these sleep relationships, the correlation between sleep quality and time can be captured, providing a data basis for the subsequent prediction model. Secondly, in order to further improve the accuracy of the prediction, the time sequence and mutual influence between different sleep relationships are analyzed through the concept of time axis. By modeling the linear relationship between each sleep relationship, the changing law of different sleep characteristics can be accurately grasped and the interaction between them can be quantified. At the same time, by establishing a distance metric matrix, each sleep relationship is iteratively merged to focus on sleep types that can reflect the characteristics of abnormal sleep (such as insomnia). Through this process, the system can eliminate normal sleep patterns and focus on those types that show sleep disorders, thereby ensuring the pertinence and accuracy of the insomnia prediction model. However, when dealing with abnormal sleep types, the prediction of insomnia is further refined by establishing linear relationships between these sleep relationships. By modeling and analyzing various sleep characteristics during the insomnia period (such as sleep duration, time to fall asleep, and time to deep sleep), it is possible to provide patients with personalized insomnia data predictions within a preset time period. This process not only helps to identify the patient's insomnia condition, but also helps clinicians evaluate the treatment effect and adjust treatment strategies to better improve the sleep quality of cancer patients. Finally, based on this insomnia prediction model, scientific preset insomnia data can be provided to cancer patients, and dynamic assessment of patients' sleep problems can be achieved in a data-driven manner. With this method, caregivers can understand the patient's sleep status in real time and take targeted care measures. Through the combination of multi-dimensional sleep characteristics and models, this technical principle not only improves the accuracy of insomnia prediction, but also provides cancer patients with more refined and personalized treatment plans.

[0078] It is understandable that by establishing a refined insomnia prediction model, it is possible to comprehensively evaluate and predict the patient's sleep quality based on the historical sleep data of cancer patients. By extracting and analyzing key sleep characteristics such as sleep duration, sleep time, and deep sleep time, combined with the modeling of timeline and linear relationships, the system can accurately identify normal and abnormal sleep types, and effectively distinguish the different characteristics of insomnia. Through this method, personalized insomnia prediction plans can be tailored for cancer patients, providing more accurate sleep management and treatment recommendations. In addition, accurate predictions after excluding normal sleep types can help improve the targeted treatment of insomnia and provide a scientific basis for subsequent acupuncture treatment, thereby improving the treatment effect and improving the overall sleep quality of patients.

[0079] Step S200, obtaining the sleep data of the cancer patient after acupuncture and the number of days of acupuncture treatment, and obtaining the preset insomnia data of the cancer patient according to the number of days of acupuncture treatment and the insomnia prediction model.

[0080] Specifically, when obtaining the preset insomnia data of cancer patients based on the acupuncture treatment days and the insomnia prediction model, it includes: obtaining the insomnia intervals in the acupuncture treatment days, and determining whether to evaluate the treatment score of the cancer patient based on the relationship between the insomnia intervals and the preset insomnia intervals. When the insomnia interval is lower than the preset insomnia interval, the treatment score of the cancer patient is not evaluated, and the acupuncture treatment effect of the cancer patient is determined to be high. When the insomnia interval is equal to or higher than the preset insomnia interval, the treatment score of the cancer patient is evaluated based on the sleep duration, sleep duration and deep sleep duration of each insomnia of the cancer patient.

[0081] It is understandable that a dynamic assessment of the insomnia interval of cancer patients is performed to determine whether to conduct a treatment score assessment. When the patient's insomnia interval is lower than the preset insomnia interval, it indicates that the treatment effect is more significant, so there is no need for a score assessment, and the treatment effect is directly judged to be high; when the insomnia interval is equal to or higher than the preset insomnia interval, it indicates that the patient's sleep problem has not been effectively improved, and further treatment score assessment is required. At this time, the treatment score will be quantified based on the patient's sleep characteristics such as sleep duration, sleep duration, and deep sleep duration, so as to scientifically evaluate the treatment effect. This process, by combining the relationship between insomnia intervals and sleep characteristics, helps to provide more accurate feedback for clinical treatment, and make timely treatment adjustments based on the evaluation results of the treatment effect.

[0082] Step S300: Determine a treatment score according to the relationship between the sleep data and the preset insomnia data.

[0083] Specifically, the evaluation of the treatment score of cancer patients based on the sleep duration, sleep onset time and deep sleep time of each insomnia period includes: obtaining the sleep duration of each insomnia period within the insomnia interval and the preset sleep duration of each insomnia period within the preset insomnia interval. According to the relationship between each sleep duration and each preset sleep duration, the treatment score of cancer patients after acupuncture treatment is evaluated based on Formula I, where Formula I is as follows: Wherein, S is the treatment score, n is the total number of insomnia episodes, (t, i) is the sleep duration during the ith insomnia episode, and (T, i) is the preset sleep duration during the ith insomnia episode.

[0084] Specifically, when determining the treatment score S of cancer patients after acupuncture treatment, it includes: obtaining the sleep duration of each insomnia in the insomnia interval and the preset sleep duration of each insomnia in the preset insomnia interval. According to the relationship between each sleep duration and each preset sleep duration, the sleep score of cancer patients after acupuncture treatment is evaluated based on formula II, where formula II is as follows: Wherein, Q is the sleep score, (m, i) is the sleep duration during the i-th insomnia, and (M, i) is the preset sleep duration during the i-th insomnia. According to the relationship between the sleep score Q and the pre-configured preset sleep score, determine whether to adjust the treatment score S: when the sleep score Q is greater than or equal to the preset sleep score, it is determined not to adjust the treatment score S. When the sleep score Q is less than the preset sleep score, the adjustment coefficient is determined according to the relationship between the sleep score Q and the preset sleep score, and the treatment score S is adjusted according to the adjustment coefficient.

[0085] Specifically, when determining the adjustment coefficient based on the relationship between the sleeping score Q and the preset sleeping score, it includes: obtaining the sleeping score difference between the sleeping score Q and the preset sleeping score, and determining the adjustment coefficient based on the relationship between the sleeping score difference and the pre-configured first preset sleeping score difference and the second preset sleeping score difference. When the sleeping score difference is less than or equal to the first preset sleeping score difference, the adjustment coefficient is determined to be L3. When the sleeping score difference is greater than the first preset sleeping score difference, and the sleeping score difference is less than or equal to the second preset sleeping score difference, the adjustment coefficient is determined to be L2. When the sleeping score difference is greater than the second preset sleeping score difference, the adjustment coefficient is determined to be L1. Among them, the first preset sleeping score difference is less than the second preset sleeping score difference, and L1<L2<L3<1.

[0086] Specifically, when the adjustment coefficient is determined to be Li, i = 1, 2, 3, including: obtaining the deep sleep duration of each insomnia period within the insomnia interval and the preset deep sleep duration of each insomnia period within the preset insomnia interval. According to the relationship between each deep sleep duration and each preset deep sleep duration, the sleep quality score of the cancer patient after acupuncture treatment is evaluated based on formula III, where formula III is as follows: Wherein, W is the sleep quality score, (h, i) is the duration of falling asleep during the i-th insomnia, and (H, i) is the preset duration of falling asleep during the i-th insomnia. According to the relationship between the sleep quality score W and the pre-configured preset sleep quality score, determine whether to correct the adjustment coefficient Li: when the sleep quality score W is greater than or equal to the preset sleep quality score, it is determined that the adjustment coefficient Li is not adjusted. When the sleep quality score W is less than the preset sleep quality score, the correction coefficient is determined according to the relationship between the sleep quality score W and the preset sleep quality score, and the adjustment coefficient Li is corrected according to the correction coefficient.

[0087] Specifically, when determining the correction coefficient based on the relationship between the sleep quality score W and the preset sleep quality score, it includes: obtaining the quality score difference between the sleep quality score W and the preset sleep quality score, and determining the correction coefficient based on the relationship between the quality score difference and the pre-configured first preset quality score difference and the second preset quality score difference. When the quality score difference is less than or equal to the first preset quality score difference, the adjustment coefficient is determined to be K3. When the quality score difference is greater than the first preset quality score difference, and the quality score difference is less than or equal to the second preset quality score difference, the correction coefficient is determined to be K2. When the quality score difference is greater than the second preset quality score difference, the correction coefficient is determined to be K1. Among them, the first preset quality score difference is less than the second preset quality score difference, and K1<K2<K3<1.

[0088] It can be seen that the evaluation of the treatment score is based on various sleep data of cancer patients during the insomnia interval, including important indicators such as sleep duration, sleep duration and deep sleep duration. The relationship between each sleep feature and its preset data is quantified to form a specific formula for scoring, thereby effectively evaluating the degree of improvement of acupuncture treatment on insomnia symptoms in cancer patients. In order to enhance the accuracy of the evaluation, this technology further refines the judgment of the treatment effect by combining the sleep score, sleep quality score and treatment score. The sleep score analyzes the relationship between the patient's sleep duration and the preset sleep duration to evaluate the improvement of the sleep process. If the sleep score is lower than the preset standard, the system will calculate the difference between the sleep score and the preset sleep score, and set the adjustment coefficient based on the difference to adjust the treatment score. This process ensures that the patient's treatment effect is more accurately reflected by dynamically adjusting the treatment score, especially the changes in the key indicator of sleep duration. Further, the sleep quality score is evaluated by analyzing the deep sleep duration. As one of the core indicators for measuring sleep quality, the degree of improvement of deep sleep has a direct impact on the patient's overall sleep quality. By comparing the actual deep sleep duration of the patient with the preset deep sleep duration, the sleep quality score is assessed, and the quality score is compared with the preset quality score to determine whether the adjustment coefficient needs to be corrected. If the quality score is lower than the preset standard, the correction coefficient is determined according to the preset quality score difference range, and the adjustment coefficient is further corrected to ensure the accurate adjustment of the treatment score. This correction mechanism incorporates sleep quality factors into the scoring system to fully reflect the patient's treatment progress. Finally, by combining sleep data in multiple dimensions such as sleep duration, sleep duration, deep sleep duration, and dynamic adjustment of preset standards, a multi-level and multi-angle treatment scoring system was established. This comprehensive evaluation method not only improves the quantitative standard of treatment effect, but also provides a more scientific and objective basis for treatment evaluation in clinic. Through the detailed analysis and graded evaluation of different sleep characteristics, the treatment effect of cancer patients can be monitored in real time, and accurate feedback can be provided to doctors at different stages of treatment, so as to optimize personalized treatment plans and further improve the efficacy of acupuncture treatment for cancer insomnia.

[0089] It is understandable that by comparing the patient's sleep duration, sleep duration and deep sleep duration during the insomnia interval with the preset data, the sleep improvement before and after treatment can be quantified, and the treatment score, sleep score and sleep quality score can be evaluated by formula based on these data. Its advantage is that it can objectively reflect the patient's sleep changes at each stage of treatment, so as to accurately evaluate the treatment effect. Secondly, by setting multiple preset scoring criteria and adjustment coefficients, it can be dynamically adjusted according to the patient's actual sleep performance to ensure that the scoring system is adapted to individual differences. This not only helps doctors evaluate the treatment effect more accurately, but also adjusts the treatment plan in time to improve the personalization and pertinence of the treatment. Finally, the correction mechanism of the sleep quality score further improves this evaluation process. When key indicators such as the patient's deep sleep duration do not meet expectations, the treatment effect can be dynamically adjusted through the correction coefficient to optimize the treatment effect.

[0090] Step S400: According to the relationship between the treatment score and the preset first and second treatment scores, the acupuncture treatment effect is graded, and the grade of the assessed acupuncture treatment effect is sent to the nursing station.

[0091] Specifically, when the acupuncture treatment effect is graded according to the relationship between the treatment score and the preset first preset treatment score and the second preset treatment score, the following is included: when the treatment score is lower than or equal to the first preset treatment score, the acupuncture treatment effect is determined to be a low grade. When the treatment score is higher than the first preset treatment score, and the treatment score is lower than or equal to the second preset treatment score, the acupuncture treatment effect is determined to be a medium grade. When the treatment score is higher than the second preset treatment score, the acupuncture treatment effect is determined to be a high grade. Among them, the first preset treatment score is lower than the second preset treatment score, and the acupuncture treatment effect grades are arranged in sequence as high grade, medium grade, and low grade.

[0092] It is understandable that by establishing the relationship between the treatment score and the preset score, the acupuncture treatment effect of cancer patients can be accurately graded. This evaluation mechanism provides a scientific and systematic standardized method. Secondly, by comparing the treatment score with the two-level preset treatment score, the treatment effect of the patient is classified as low, medium or high. The main advantage of this method is that it can clearly and intuitively reflect the treatment effect of the patient, thereby providing a reliable basis for clinicians to help them judge whether the patient needs to adjust the treatment plan or further optimize the treatment strategy. Finally, by clearly distinguishing between the treatment score and the preset level, the evaluation process is more quantitative, avoiding the deviation that may be caused by subjective judgment, and ensuring the accurate evaluation of the treatment effect. This hierarchical evaluation not only improves the operability of the treatment results, but also facilitates doctors to take targeted measures according to different levels of treatment effects, further improving the personalization and refinement of treatment.

[0093] In the above embodiment, a personalized insomnia prediction model is established by analyzing the historical sleep data of cancer patients before acupuncture. The model can predict the insomnia condition of the patient and provide a scientific basis for the formulation of the treatment plan. During the treatment process, the patient's sleep data will be compared with the preset insomnia data to obtain a treatment score, which can objectively reflect the effect of acupuncture treatment and the patient's sleep improvement. In addition, the grading method based on the treatment score can provide a clear judgment standard for the effect of acupuncture treatment. By comparing with the preset first and second treatment scores, the system can divide the treatment effect into high, medium and low levels. This grading evaluation method can help nursing staff to understand the patient's treatment progress in a timely manner, thereby providing more accurate and personalized nursing plans for patients of different levels. Especially in cancer treatment, the patient's condition may change rapidly, so the treatment plan can be dynamically adjusted and personalized care can be given, which significantly improves the pertinence and effect of the treatment. The implementation of this method can not only improve the effect evaluation standard of acupuncture treatment, but also optimize the nursing workflow. By sending the grade information of the treatment effect to the nursing station, nursing staff can obtain the patient's treatment feedback in real time, so as to adjust the nursing strategy in time.

[0094] In another preferred embodiment based on the above embodiment, Figure 2 As shown, this embodiment provides a cancer insomnia nursing system based on acupuncture technology, including: a model building module, a central control module, an evaluation module and a communication module.

[0095] Specifically, the model building module is configured to obtain the historical sleep data of cancer patients before acupuncture, and to establish an insomnia prediction model based on the historical sleep data. The central control module is electrically connected to the model building module, and the central control module is configured to obtain the sleep data of cancer patients after acupuncture and the number of days of acupuncture treatment, and to obtain the preset insomnia data of cancer patients based on the number of days of acupuncture treatment and the insomnia prediction model. The evaluation module is electrically connected to the central control module, and the evaluation module is configured to determine the treatment score based on the relationship between the sleep data and the preset insomnia data. The communication module is electrically connected to the evaluation module, and the communication module is configured to grade the acupuncture treatment effect based on the relationship between the treatment score and the preset treatment score, and send the grade of the assessed acupuncture treatment effect to the nursing station.

[0096] It can be understood that the cancer insomnia care system and method based on acupuncture technology in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0097] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0098] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0101] Finally, 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for nursing cancer insomnia based on acupuncture technology, characterized in that: include: Obtaining historical sleep data of cancer patients before acupuncture, and establishing an insomnia prediction model based on the historical sleep data; Obtaining the sleep data of the cancer patient after acupuncture and the number of days of acupuncture treatment, and obtaining the preset insomnia data of the cancer patient according to the number of days of acupuncture treatment and the insomnia prediction model; Determining a treatment score according to the relationship between the sleep data and the preset insomnia data; According to the relationship between the treatment score and the preset first treatment score and the second treatment score, the acupuncture treatment effect is graded, and the grade of the acupuncture treatment effect after the assessment is sent to the nursing station, wherein: When the treatment score is lower than or equal to the first preset treatment score, the acupuncture treatment effect is determined to be low level; When the treatment score is higher than the first preset treatment score, and the treatment score is lower than or equal to the second preset treatment score, it is determined that the acupuncture treatment effect is of medium grade; When the treatment score is higher than the second preset treatment score, it is determined that the acupuncture treatment effect is of a high level; Among them, the first preset treatment score is lower than the second preset treatment score, and the acupuncture treatment effect levels are arranged in sequence as high level, medium level, and low level.

2. The cancer insomnia nursing method based on acupuncture technology as claimed in claim 1, characterized in that: When establishing an insomnia prediction model based on the historical sleep data, it includes: Obtain each historical sleep duration, each historical sleep onset duration, and each deep sleep duration in the historical sleep data, and establish a sleep relationship equation according to the historical sleep duration, the historical sleep onset duration, and the deep sleep duration in the sleep; Acquire a time axis between the sleep relationships, and establish a linear relationship between the sleep relationships according to the time axis; The distance metric between the sleep relationship equations is obtained, and the insomnia prediction model is established according to the linear relationship between the sleep relationship equations and the distance metric between the sleep relationship equations.

3. The cancer insomnia nursing method based on acupuncture technology as claimed in claim 2, characterized in that: When establishing the insomnia prediction model according to the linear relationship between the sleep relationships and the distance measurement between the sleep relationship equations, it includes: Establishing a distance matrix according to the distance metric, and iteratively merging the sleep relationship equations according to the distance matrix; Acquire the sleep relationship equations after iterative merging, and determine the sleep relationship equations after iterative merging as sleep types; Acquire the sleep duration feature, the falling asleep feature and the deep sleep duration feature in the sleep type, and classify each of the sleep relationship equations based on the sleep duration feature, the falling asleep feature and the deep sleep duration feature; Eliminate the normal sleep type and the corresponding sleep relationship equations in the sleep type, and obtain the time axis between the sleep relationship equations in the abnormal sleep type; A linear relationship between the sleep relationship equations in the abnormal sleep type is established according to the time axis, and based on the linear relationship, the insomnia prediction model is established, and a preset insomnia interval within a preset time period of the cancer patient, as well as a preset sleep duration, a preset falling asleep time, and a preset deep sleep time of each preset insomnia in the preset insomnia interval are obtained.

4. The cancer insomnia nursing method based on acupuncture technology as claimed in claim 3, characterized in that: Acquiring preset insomnia data of the cancer patient according to the acupuncture treatment days and the insomnia prediction model includes: Obtaining the insomnia intervals during the acupuncture treatment days, and determining whether to conduct a treatment score assessment on the cancer patient based on the relationship between the insomnia intervals and the preset insomnia intervals; When the insomnia interval is lower than the preset insomnia interval, the treatment score of the cancer patient is not evaluated, and the acupuncture treatment effect of the cancer patient is determined to be high; When the insomnia interval is equal to or higher than the preset insomnia interval, the treatment score of the cancer patient is evaluated according to the sleep duration, falling asleep time and deep sleep time of each insomnia period of the cancer patient.

5. The cancer insomnia nursing method based on acupuncture technology as claimed in claim 4, characterized in that: When evaluating the treatment score of the cancer patient according to the sleep duration, sleep onset time and deep sleep time of each insomnia of the cancer patient, it includes: Obtaining the sleep duration of each insomnia period within the insomnia interval and the preset sleep duration of each insomnia period within the preset insomnia interval; According to the relationship between each of the sleep durations and each of the preset sleep durations, the treatment score of the cancer patient after acupuncture treatment is evaluated based on Formula I, wherein Formula I is as follows: Wherein, S is the treatment score, n is the total number of insomnia episodes, (t, i) is the sleep duration during the i-th insomnia episode, and (T, i) is the preset sleep duration during the i-th insomnia episode.

6. The cancer insomnia nursing method based on acupuncture technology as claimed in claim 5, characterized in that: When determining the treatment score S of the cancer patient after acupuncture treatment, it includes: Obtain the duration of falling asleep at each insomnia time within the insomnia interval and the preset duration of falling asleep at each insomnia time within the preset insomnia interval; According to the relationship between each of the sleeping time and each of the preset sleeping time, the sleeping score of the cancer patient after acupuncture treatment is evaluated based on Formula II, wherein Formula II is as follows: Wherein, Q is the sleeping score, (m, i) is the sleeping time during the i-th insomnia, and (M, i) is the preset sleeping time during the i-th insomnia; According to the relationship between the sleep onset score Q and a pre-configured preset sleep onset score, determine whether to adjust the treatment score S: When the sleeping score Q is greater than or equal to the preset sleeping score, it is determined that the treatment score S is not to be adjusted; When the sleeping score Q is less than the preset sleeping score, an adjustment coefficient is determined according to the relationship between the sleeping score Q and the preset sleeping score, and the treatment score S is adjusted according to the adjustment coefficient.

7. The cancer insomnia nursing method based on acupuncture technology as claimed in claim 6, characterized in that: When determining the adjustment coefficient according to the relationship between the sleeping score Q and the preset sleeping score, it includes: Obtaining a sleep score difference between the sleep score Q and the preset sleep score, and determining the adjustment coefficient according to a relationship between the sleep score difference and a pre-configured first preset sleep score difference and a second preset sleep score difference; When the sleeping score difference is less than or equal to the first preset sleeping score difference, the adjustment coefficient is determined to be L3; When the sleeping score difference is greater than the first preset sleeping score difference, and the sleeping score difference is less than or equal to the second preset sleeping score difference, determining the adjustment coefficient to be L2; When the sleeping score difference is greater than the second preset sleeping score difference, the adjustment coefficient is determined to be L1; The first preset sleeping score difference is smaller than the second preset sleeping score difference, and L1<L2<L3<1.

8. The cancer insomnia nursing method based on acupuncture technology as claimed in claim 7, characterized in that: When the adjustment coefficient is determined to be Li, i=1, 2, 3, including: Obtaining the deep sleep duration of each insomnia period within the insomnia interval and the preset deep sleep duration of each insomnia period within the preset insomnia interval; According to the relationship between each of the deep sleep durations and each of the preset deep sleep durations, the sleep quality score of the cancer patient after acupuncture treatment is evaluated based on Formula III, wherein Formula III is as follows: Wherein, W is the sleep quality score, (h, i) is the duration of falling asleep during the i-th insomnia, and (H, i) is the preset duration of falling asleep during the i-th insomnia; According to the relationship between the sleep quality score W and the pre-configured preset sleep quality score, determine whether to modify the adjustment coefficient Li: When the sleep quality score W is greater than or equal to the preset sleep quality score, it is determined that the adjustment coefficient Li is not adjusted; When the sleep quality score W is less than the preset sleep quality score, a correction coefficient is determined according to the relationship between the sleep quality score W and the preset sleep quality score, and the adjustment coefficient Li is corrected according to the correction coefficient.

9. The cancer insomnia nursing method based on acupuncture technology as claimed in claim 8, characterized in that: When determining the correction coefficient according to the relationship between the sleep quality score W and the preset sleep quality score, it includes: Obtaining a quality score difference between the sleep quality score W and the preset sleep quality score, and determining the correction coefficient according to a relationship between the quality score difference and a pre-configured first preset quality score difference and a second preset quality score difference; When the quality score difference is less than or equal to the first preset quality score difference, determining the adjustment coefficient to be K3; When the quality score difference is greater than the first preset quality score difference, and the quality score difference is less than or equal to the second preset quality score difference, determining the correction coefficient to be K2; When the quality score difference is greater than the second preset quality score difference, determining the correction coefficient to be K1; The first preset quality score difference is smaller than the second preset quality score difference, and K1<K2<K3<1.

10. A cancer insomnia nursing system based on acupuncture technology, which adopts the cancer insomnia nursing method based on acupuncture technology as described in any one of claims 1 to 9, characterized in that: include: A model building module is configured to obtain historical sleep data of cancer patients before acupuncture, and to build an insomnia prediction model based on the historical sleep data; a central control module, electrically connected to the model building module, wherein the central control module is configured to obtain the sleep data of the cancer patient after acupuncture and the number of days of acupuncture treatment, and obtain preset insomnia data of the cancer patient according to the number of days of acupuncture treatment and the insomnia prediction model; An evaluation module, electrically connected to the central control module, and configured to determine a treatment score according to a relationship between the sleep data and the preset insomnia data; A communication module is electrically connected to the evaluation module, and is configured to grade the acupuncture treatment effect according to the relationship between the treatment score and a preset treatment score, and send the grade of the acupuncture treatment effect after the assessment to the nursing station.

Images