Information-based planning of senile pruritus disease management system

Abstract

The application discloses an information planning-based senile pruritus condition management system and belongs to the field of condition management. The application acquires the morphological characteristics of the patient's hand nails, evaluates the sharpness of the nails based on the morphological characteristics of the nails, acquires the patient's night sleep behavior and pruritus behavior, evaluates the patient's night pruritus degree, acquires the patient's skin surface condition, evaluates the skin surface damage degree based on the skin surface condition, evaluates the patient's skin damage risk based on the nail sharpness degree, the night pruritus degree and the skin surface damage degree, matches the nursing strategy based on the patient's skin damage risk, and improves the nursing efficiency and intervention timeliness.

Description

Technical Field

[0001] This application falls under the field of disease management, specifically a disease management system for geriatric pruritus based on information technology planning. Background Technology

[0002] Pruritus senile is a common chronic skin condition characterized by persistent or recurrent itching, particularly worsening at night, severely impacting sleep quality and comfort in the elderly. Prolonged scratching can lead to skin damage, lichenification, pigmentation, and secondary infections, increasing treatment difficulty and nursing burden. With the increasing global population aging trend, the incidence of pruritus senile is on the rise, placing higher demands on public health and geriatric care. Traditional management of pruritus senile relies primarily on patient-reported symptoms and regular checkups by healthcare professionals. However, due to variations in patient subjective experiences and the difficulty in accurately recording nighttime scratching behavior, the severity of itching and the risk of skin damage are challenging to quantify and dynamically assess.

[0003] Current technologies monitor activity data or sleep patterns in the elderly using wearable devices, but these are mostly limited to sleep quality assessments, lacking the identification and quantitative analysis of scratching behavior itself, and thus unable to accurately determine the risk of skin damage caused by itching. Furthermore, traditional nursing interventions often employ a one-size-fits-all approach, lacking tiered management based on individual behavior, skin condition, and risk level, making it difficult to achieve precise and dynamic personalized care. Moreover, existing systems lack objective and quantifiable evaluation indicators for key factors such as nail sharpness and the degree of skin damage, failing to form a scientific, data-driven basis for intervention decisions, resulting in poor intervention timeliness, low nursing efficiency, and a high incidence of complications.

[0004] This application obtains nail sharpness and skin surface damage characteristics through high-definition image analysis, identifies nighttime scratching behavior through wrist motion sensors and deep learning models, forms a complete scratching event time series, calculates a skin damage risk index based on nail sharpness, nighttime itching intensity, and skin damage severity, and combines it with set thresholds to achieve graded nursing strategy matching, improve the timeliness of intervention and nursing efficiency, and help reduce the risk of skin damage and the incidence of complications. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application proposes a disease management system for senile pruritus based on information planning.

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] The information-based management system for geriatric pruritus is characterized by comprising the following modules: a nail sharpness assessment module, a nighttime pruritus assessment module, a skin surface damage assessment module, a skin damage risk assessment module, and a care matching module. Specifically, the nail sharpness assessment module acquires the morphological characteristics of the patient's fingernails and assesses nail sharpness based on these characteristics; the nighttime pruritus assessment module acquires the patient's nighttime sleep behavior and pruritus behavior and assesses the severity of nighttime pruritus; the skin surface damage assessment module acquires the patient's skin surface condition and assesses the degree of skin surface damage; the skin damage risk assessment module assesses the patient's skin damage risk based on nail sharpness, nighttime pruritus, and skin surface damage; and the care matching module matches care strategies based on the patient's skin damage risk.

[0008] Preferably, the step of acquiring the morphological characteristics of the patient's fingernails and assessing the sharpness of the nails based on these morphological characteristics includes the following specific steps:

[0009] S11. Acquire images of the front, side and free edge of the nail under standard lighting conditions using a high-definition camera. Calculate the free edge length of the nail by converting the image pixels to the actual length. The free edge length represents the length of the nail extending beyond the fingertip. Calculate the nail edge curvature by fitting a curve to the nail edge contour. The greater the curvature, the sharper the edge. Obtain the nail edge irregularity by statistically analyzing the undulation amplitude of the nail edge.

[0010] S12. Substitute the free edge length, edge curvature, and edge irregularity of the nail into the nail sharpness assessment formula to evaluate the sharpness of the nail. The sharpness assessment formula for the i-th nail is as follows:

[0011]

[0012] ,in, This refers to the length of the free edge of the nail. This is a reference length for the free edge of the nail. The curvature of the nail edge. For irregularities at the edge of the nail, , and These are the weighting coefficients;

[0013] S13. The sharpness of the nails of each finger of the patient is weighted and fused according to the weight of use in scratching behavior to obtain the overall nail sharpness of the patient, which is used to characterize the overall risk level of skin damage caused by nail factors during itching.

[0014] Preferably, the process of acquiring the patient's nighttime sleep behavior and itch behavior, and assessing the severity of the patient's nighttime itch, includes the following specific steps:

[0015] S21. Acquire patient sleep state data through wearable sleep monitoring devices. When a change from sleep to wakefulness or a significant decrease in sleep continuity is detected, record the corresponding time points to form a time series of sleep state interruption events.

[0016] S22. Using the motion sensing device on the patient's wrist, collect the acceleration and angular velocity motion signals of the hand. Based on the pre-established scratching action feature model, identify repetitive and periodic hand movements, extract the time points of scratching behavior, and form a scratching event time series.

[0017] S23. Within a preset nighttime time window, count the number of events in the scratching event time series, and obtain the nighttime scratching behavior intensity by dividing the total number of nighttime scratching behavior events by the nighttime monitoring duration, which is used to characterize the frequency of the patient's nighttime scratching behavior.

[0018] S24. Within the set time allowance window, calculate the proportion of scratching events occurring before and after the sleep interruption event to the total number of scratching events at night, and obtain the time correlation parameter. The higher the time correlation, the stronger the synchronicity between scratching behavior and sleep disturbance.

[0019] S25. The intensity of nighttime scratching behavior is obtained by normalizing the time correlation parameter and then weighting it.

[0020] Preferably, the process of acquiring the patient's skin surface condition and assessing the degree of skin surface damage based on the skin surface condition includes the following specific steps:

[0021] S31. Obtain an image of the patient's skin surface, segment the scratch area in the skin image using image texture features and linear structure recognition algorithms, and calculate the scratch area.

[0022] S32. By analyzing the changes in skin surface texture roughness, color and local thickness, identify the lichenification area and calculate the lichenification area.

[0023] S33. By analyzing the color difference between the lesion area and the surrounding normal skin area, color change parameters are obtained;

[0024] S34. Calculate the degree of skin damage based on the weighted average of scratch area, lichenification area, and color change parameters.

[0025] Preferably, the assessment of the patient's skin damage risk based on nail sharpness, nighttime itching intensity, and degree of skin surface damage includes the following specific steps:

[0026] The risk of scratching leading to skin damage is calculated by substituting the patient's overall nail sharpness, nighttime itching intensity, and skin lesion severity into the skin damage risk index assessment formula. The skin damage risk index assessment formula is as follows:

[0027]

[0028] ,in, For the overall sharpness of the nails, The degree of itching at night, The degree of skin damage is used to indicate the current baseline state of the skin lesions. , and These are weighting coefficients used to balance the influence of each parameter on the risk outcome.

[0029] Preferably, the patient-based skin injury risk-matching care strategy includes the following specific steps:

[0030] The skin damage risk index is compared with the skin damage threshold to assess the patient's skin condition. When the skin damage risk index is less than the first skin damage threshold, the patient is at low risk of itching and the current intervention continues. When the skin damage risk index is greater than or equal to the first skin damage threshold but less than the second skin damage threshold, the patient is at medium risk of itching and an anti-inflammatory ointment is applied topically, and the frequency of care is increased. When the skin damage risk index is greater than or equal to the second skin damage threshold, the patient is at high risk of itching and emergency medical treatment is provided, supplemented by behavioral intervention, and the skin condition is monitored in real time.

[0031] This application also provides a method for managing senile pruritus based on information-based planning, which is implemented based on the aforementioned information-based management system for senile pruritus, and specifically includes:

[0032] The information-based management system for geriatric pruritus includes the following specific steps:

[0033] Obtain the morphological characteristics of the patient's fingernails and assess the sharpness of the nails based on these characteristics;

[0034] Acquire patients' nocturnal sleep behavior and pruritus behavior, and assess the severity of nocturnal pruritus.

[0035] Obtain information about the patient's skin surface and assess the degree of skin surface damage based on this information.

[0036] The risk of skin damage to patients is assessed based on the sharpness of the nails, the degree of nighttime itching, and the extent of skin surface damage.

[0037] Nursing strategies are matched based on the patient's skin damage risk.

[0038] An electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0039] The processor executes the above-mentioned information-based planning-based geriatric pruritus management method by calling the computer program stored in the memory.

[0040] A computer-readable storage medium is characterized by storing instructions that, when executed on a computer, cause the computer to perform the aforementioned information-based planning-based method for managing senile pruritus.

[0041] Compared with the prior art, the beneficial effects of this application are:

[0042] This application obtains the morphological characteristics of the patient's fingernails, assesses the sharpness of the nails based on the morphological characteristics, obtains the patient's nocturnal sleep behavior and itching behavior, assesses the degree of nocturnal itching, obtains the patient's skin surface condition, assesses the degree of skin surface damage based on the skin surface condition, assesses the patient's skin damage risk based on the sharpness of the nails, the degree of nocturnal itching, and the degree of skin surface damage, and matches nursing strategies based on the patient's skin damage risk to improve nursing efficiency and intervention timeliness. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the structure of an embodiment of the system in this application;

[0044] Figure 2 This is a flowchart for the nail sharpness assessment process in this application;

[0045] Figure 3 This is a flowchart of the skin damage risk index assessment process for this application;

[0046] Figure 4 This is a schematic diagram of the overall process of an embodiment of the method of this application; Detailed Implementation

[0047] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0048] Example 1

[0049] Please see Figure 1 One embodiment provided in this application is as follows: Figure 1As shown, the information-based management system for geriatric pruritus includes the following specific modules: a nail sharpness assessment module, a nighttime pruritus assessment module, a skin surface damage assessment module, a skin damage risk assessment module, and a care matching module. Specifically, the nail sharpness assessment module acquires the morphological characteristics of the patient's fingernails and assesses nail sharpness based on these characteristics; the nighttime pruritus assessment module acquires the patient's nighttime sleep behavior and pruritus behavior and assesses the severity of nighttime pruritus; the skin surface damage assessment module acquires the patient's skin surface condition and assesses the degree of skin surface damage; the skin damage risk assessment module assesses the patient's skin damage risk based on nail sharpness, nighttime pruritus, and skin surface damage; and the care matching module matches care strategies based on the patient's skin damage risk.

[0050] In this embodiment, it is important to specifically explain that obtaining the morphological characteristics of the patient's fingernails and assessing the sharpness of the nails based on these characteristics includes the following specific steps:

[0051] S11. Acquire images of the front, side, and free edge of the nail under standard lighting conditions using a high-definition camera. Calculate the free edge length of the nail by calibrating the image pixels with the actual length. The free edge length represents the length of the nail extending beyond the fingertip. Calculate the nail edge curvature by fitting a curve to the nail edge contour, indicating the degree of curvature of the nail edge. The greater the curvature, the sharper the edge. Obtain the nail edge irregularity by statistically analyzing the undulation amplitude of the nail edge. The nail edge irregularity indicates the neatness of the nail edge. The higher the irregularity, the greater the potential risk of damage. Calculate the actual length through image calibration to ensure the accuracy of nail feature measurement.

[0052] S12. Substitute the free edge length, edge curvature, and edge irregularity of the nail into the nail sharpness assessment formula to evaluate the sharpness of the nail. The sharpness assessment formula for the i-th nail is as follows:

[0053]

[0054] ,in, This refers to the length of the free edge of the nail. This is a reference length for the free edge of the nail. The curvature of the nail edge. For irregularities at the edge of the nail, , and These are the weighting coefficients;

[0055] S13. The sharpness of the nails on each finger of the patient is weighted and fused according to the weight of use in scratching behavior to obtain the patient's overall nail sharpness. This is used to characterize the overall risk level of skin damage caused by nail factors during itching. The sharpness of the nails directly affects the degree of mechanical damage to the skin barrier caused by scratching behavior. The overall sharpness is obtained by fusing the data of each finger according to the weight of use in scratching behavior, reflecting the contribution of different nails of the hand to skin damage and quantifying the overall risk.

[0056] In this embodiment, it should be specifically explained that obtaining the patient's nighttime sleep behavior and itching behavior, and assessing the patient's nighttime itching severity, includes the following specific steps:

[0057] S21. Acquire patient sleep state data through wearable sleep monitoring devices. When a change from sleep to wakefulness or a significant decrease in sleep continuity is detected, record the corresponding time points to form a time series of sleep state interruption events.

[0058] S22. Using a motion sensing device on the patient's wrist, the device collects hand acceleration and angular velocity motion signals. Based on a pre-established scratching motion feature model, it identifies repetitive and periodic hand movements, extracts the time points of scratching behavior, and forms a scratching event time series. The sensor continuously collects wrist acceleration data (x, y, z axis directions) and angular velocity data to describe the linear and rotational motion characteristics of the hand. The collected data undergoes preliminary filtering to remove high-frequency noise and environmental interference. Simultaneously, a calibration algorithm corrects sensor drift and offset. The preprocessed signal is then input into a machine-based... In the scratching action feature model established by learning or deep learning, the model learns typical patterns of scratching actions through historical training data, including repetitiveness, periodicity, amplitude and velocity features. It can distinguish scratching behavior from other daily hand movements, such as turning over, rubbing eyes or slight arm movements. During the recognition process, the time series data is analyzed by sliding window. Motion signal features (such as mean acceleration, variance, spectral energy, etc.) are calculated in each window, and the rotation pattern of angular velocity is combined to determine whether it is a scratching action. After the scratching action is identified, the system records the specific time point of the action and generates a scratching event time series.

[0059] For example, in this embodiment, the construction of the scratching action feature model includes: the model input layer includes the original sensor signal and features processed by a sliding window, such as the mean acceleration, variance, and spectral energy, used to describe the hand movement pattern; the first hidden layer is a fully connected layer with 128 neurons, using ReLU activation function to introduce non-linear features, and a regularization dropout rate of 0.3 is set to prevent overfitting; the second hidden layer is a fully connected layer with 64 neurons, using ReLU activation function and regularization of 0.3; the third hidden layer is a fully connected layer... The first layer has 32 neurons, ReLU activation function, and regularization of 0.2. The output layer is a fully connected layer with 1 neuron and Sigmoid activation function, used to output the probability of scratching behavior occurring within the given time window. During model training, binary cross-entropy is used as the loss function, Adam optimizer is selected, learning rate is set to 0.001, training batch size is 32, and number of iterations is 200. 20% of the training data is used as a validation set to monitor overfitting. Accuracy, precision, recall, and F1 score are used to evaluate model performance. During training, historical scratching behavior data is divided into training, validation, and test sets (e.g., 70% / 15% / 15%). The model is fitted on the training set, and the loss and performance metrics are monitored on the validation set to ensure that the training and validation loss curves decrease synchronously and eventually converge stably. After training, a final evaluation is performed on the test set to obtain the model's generalization ability. The trained model can be saved and applied to real-time scratching recognition. When patients are active at night, newly collected wrist signals are input into the model, and scratching probabilities are output. After thresholding, a scratching event time series is formed, providing a scientific basis for assessing the intensity of nighttime pruritus and calculating the risk of skin damage. At the same time, the model can be personalized and optimized according to the hand movement habits of different patients. Through sliding window and continuous action verification mechanism, continuous scratching actions are fully captured, reducing misjudgments and missed detections, realizing automated, continuous, and non-invasive monitoring, and improving the accuracy of nighttime management and nursing intervention efficiency for geriatric pruritus.

[0060] S23. Within a preset nighttime time window, count the number of events in the scratching event time series, and obtain the nighttime scratching behavior intensity by dividing the total number of nighttime scratching behavior events by the nighttime monitoring duration, which is used to characterize the frequency of the patient's nighttime scratching behavior.

[0061] S24. Within the set time allowance window, calculate the proportion of scratching events occurring before and after the sleep interruption event to the total number of scratching events at night, and obtain the time correlation parameter. The higher the time correlation, the stronger the synchronicity between scratching behavior and sleep disturbance.

[0062] S25. After normalizing the parameters of nighttime scratching intensity and temporal correlation, the weighted average is used to obtain the degree of nighttime itching. Geriatric pruritus is characterized by nighttime exacerbation. Patients are prone to unconscious scratching during sleep, which leads to continuous aggravation of skin damage and affects sleep quality. This step establishes the temporal correlation between itching onset time, nighttime scratching frequency, and sleep interruption by recording the time of itching onset, scratching behavior, and sleep impact, so as to achieve an objective and dynamic assessment of the degree of itching.

[0063] In this embodiment, it is necessary to specifically explain that obtaining the patient's skin surface condition and assessing the degree of skin surface damage based on the skin surface condition includes the following specific steps:

[0064] S31. Acquire images of the patient's skin surface. Using image texture features and linear structure recognition algorithms, segment the scratch area in the skin image. When identifying scratches in the target skin area, acquire standardized skin images collected under uniform lighting conditions and a fixed shooting distance. Perform brightness and color normalization on the images to reduce the impact of environmental factors at different acquisition times on the image results. Analyze the pixel grayscale distribution and local brightness changes in the skin image. Identify structural features on the skin surface that are elongated, directional, and continuously distributed. Scratches are caused by fingernails on the skin surface and are characterized by linear or near-linear regions with a length significantly greater than their width. By judging the consistency of the gray-level gradient change direction in a local area, candidate areas with significant linear characteristics are selected. The candidate areas are then compared and analyzed with the surrounding normal skin areas. If the candidate areas have stable and obvious differences from the surrounding skin in terms of color depth, brightness change, or texture continuity, the area is determined to meet the typical characteristics of scratch formation and is identified as a scratch area. For the identified scratch areas, the system marks and segments the contour range, and converts the pixel area of ​​the scratch area into the actual area value through the relationship between pixel count and image space calibration, to obtain the total scratch area parameter in the target skin area and calculate the total scratch area.

[0065] S32. By analyzing the changes in skin surface texture roughness, color, and local thickness, lichenification areas are identified. When identifying lichenification in the target skin area, local texture analysis is performed on the skin image to evaluate the density and arrangement of skin surface texture within a unit area. Skin areas with lichenification are characterized by deepened texture, disordered arrangement, and reduced texture spacing. By analyzing the variation amplitude of the above texture features, abnormal areas that are significantly different from the normal skin texture are initially screened out. The abnormal areas are compared with adjacent undamaged skin areas to further verify the continuous differences in texture roughness and structural complexity. The color features of the abnormal areas are analyzed simultaneously. If the area shows a deeper color or a hue change related to chronic inflammation compared to the surrounding skin, the credibility of its identification as a lichenification area is further enhanced. The confirmed lichenification areas are segmented by boundary and the actual area they occupy within the target skin area is calculated to form lichenification area parameters.

[0066] S33. By analyzing the color difference between the lesion area and the surrounding normal skin area, color change parameters are obtained. The greater the degree of color change, the higher the degree of skin inflammation or chronic damage. When analyzing the color depth changes of the lesion, the range of the lesion area within the target skin area is first determined based on the identified scratch area and lichenification area. The surrounding normal skin area without obvious damage is selected as the control area. The image color information of the lesion area and the control area are extracted to obtain their brightness value and color distribution characteristic parameters, reflecting the color state of the skin in different areas. The color parameters are statistically processed using the regional averaging method to obtain the color description value that can represent the overall regional characteristics. At the same time, the color difference between the lesion area and the control area is calculated to quantify the degree of color shift of the lesion area relative to normal skin. After normalization of the color difference, the lesion color change parameters are formed. The lesion color change parameters reflect the degree of inflammatory response or pigmentation caused by repeated scratching or long-term stimulation of the skin.

[0067] S34. The degree of skin damage is calculated by weighting the scratch area, lichenification area and color change parameters. The integrity of the skin barrier and the degree of skin damage are objectively assessed by periodically collecting and analyzing the skin physiological parameters and skin lesion images of the target skin area.

[0068] In this embodiment, it should be specifically explained that assessing the patient's skin damage risk based on nail sharpness, nighttime itching intensity, and skin surface lesion severity includes the following specific steps:

[0069] The risk of scratching leading to skin damage is calculated by substituting the patient's overall nail sharpness, nighttime itching intensity, and skin lesion severity into the skin damage risk index assessment formula. The skin damage risk index assessment formula is as follows:

[0070]

[0071] ,in, For the overall sharpness of the nails, The degree of itching at night, The degree of skin damage is used to indicate the current baseline state of the skin lesions. , and The weighting coefficient is used to balance the influence of each parameter on the risk outcome. The comprehensive evaluation of the skin damage risk index reflects the true threat of overall scratching behavior to the skin.

[0072] In this embodiment, it should be specifically explained that the patient skin damage risk-matching nursing strategy includes the following specific steps:

[0073] The skin damage risk index is compared with the skin damage threshold to assess the patient's skin condition. When the skin damage risk index is less than the first skin damage threshold, the patient is at low risk of itching and the current intervention continues. When the skin damage risk index is greater than or equal to the first skin damage threshold and less than the second skin damage threshold, the patient is at medium risk of itching and an anti-inflammatory ointment is applied topically, and the frequency of care is increased. When the skin damage risk index is greater than or equal to the second skin damage threshold, the patient is at high risk of itching and emergency medical treatment is provided, supplemented by behavioral intervention, and the skin condition is monitored in real time to improve the efficiency of care and the timeliness of intervention.

[0074] It should be noted that the skin damage threshold in this embodiment is obtained by acquiring a large amount of long-term clinical data from elderly patients with pruritus, including the skin damage index, frequency of scratching behavior, intensity of nighttime pruritus, nail sharpness, and the actual skin damage of the patients (such as scratch area, lichenification area, and changes in inflammation or color). Simultaneously, the efficacy and disease progression of different intervention strategies are recorded. The collected data are statistically analyzed, and the relationship between the skin damage index and clinical risk level is identified through ROC curve analysis, percentile distribution, cluster analysis, and other methods. Preliminary cutoff intervals for low-risk, medium-risk, and high-risk patients are determined. These are then combined with the experience of clinical experts and nursing guidelines. The initial ranges are validated and fine-tuned to ensure that the thresholds not only scientifically reflect the risk of skin damage but also guide actual nursing procedures, such as adjusting medication use, nursing frequency, or taking emergency interventions. The thresholds are set using a tiered management model: below the first threshold is low risk, allowing for routine interventions; between the first and second thresholds is medium risk, requiring local medication intervention and increased nursing frequency; above the second threshold is high risk, requiring emergency medical treatment, behavioral interventions, and real-time monitoring. To adapt to individual differences among patients and long-term disease progression, the thresholds can be optimized through a dynamic update strategy, iteratively adjusted based on new data, to achieve long-term, accurate, scientific, and personalized skin damage management.

[0075] It should be noted that the weights in this embodiment are determined as follows: By collecting a large amount of clinical data from elderly patients with pruritus, including nail morphology, nocturnal scratching behavior, degree of skin damage and its actual impact on skin damage, a multi-dimensional database is constructed. Statistical analysis methods, such as multiple regression analysis or principal component analysis, are used to quantify the correlation between different indicators and skin damage outcomes, obtaining the contribution of each indicator to the risk of skin damage. At the same time, the statistical results are appropriately adjusted based on clinical expert experience and nursing practice, so that the weights conform to both data regularity and clinical operability. Finally, the weight coefficients are standardized to a unified quantitative range to achieve a scientific and reasonable risk assessment.

[0076] The advantages of this embodiment compared to the prior art are:

[0077] This application obtains the morphological characteristics of the patient's fingernails, assesses the sharpness of the nails based on the morphological characteristics, obtains the patient's nocturnal sleep behavior and itching behavior, assesses the degree of nocturnal itching, obtains the patient's skin surface condition, assesses the degree of skin surface damage based on the skin surface condition, assesses the patient's skin damage risk based on the sharpness of the nails, the degree of nocturnal itching, and the degree of skin surface damage, and matches nursing strategies based on the patient's skin damage risk to improve nursing efficiency and intervention timeliness.

[0078] Example 2

[0079] like Figure 4 As shown, this embodiment provides a method for managing senile pruritus based on information-based planning. It is implemented based on the aforementioned information-based management system for senile pruritus, and specifically includes the following steps:

[0080] Obtain the morphological characteristics of the patient's fingernails and assess the sharpness of the nails based on these characteristics;

[0081] Acquire patients' nocturnal sleep behavior and pruritus behavior, and assess the severity of nocturnal pruritus.

[0082] Obtain information about the patient's skin surface and assess the degree of skin surface damage based on this information.

[0083] The risk of skin damage to patients is assessed based on the sharpness of the nails, the degree of nighttime itching, and the extent of skin surface damage.

[0084] Nursing strategies are matched based on the patient's skin damage risk.

[0085] Example 3

[0086] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0087] The processor executes the aforementioned information-based pruritus management system by calling computer programs stored in memory.

[0088] This electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the information-based planning-based geriatric pruritus management system provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Further details are omitted here.

[0089] Example 4

[0090] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0091] When a computer program runs on a computer device, it causes the computer device to execute the above-mentioned ( ) information-based planning-based geriatric pruritus disease management system.

[0092] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0093] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. A geriatric pruritus management system based on information-based planning, characterized in that, It includes the following specific modules: a nail sharpness assessment module, a nighttime itching severity assessment module, a skin surface damage severity assessment module, a skin damage risk assessment module, and a care matching module. Specifically, the nail sharpness assessment module acquires the morphological characteristics of the patient's fingernails and assesses nail sharpness based on these characteristics; the nighttime itching severity assessment module acquires the patient's nighttime sleep behavior and itching behavior and assesses the severity of nighttime itching; the skin surface damage severity assessment module acquires the patient's skin surface condition and assesses the degree of skin surface damage based on this condition; the skin damage risk assessment module assesses the patient's skin damage risk based on nail sharpness, nighttime itching severity, and skin surface damage severity; and the care matching module matches care strategies based on the patient's skin damage risk.

2. The geriatric pruritus management system based on information planning as described in claim 1, characterized in that, The process of obtaining the morphological characteristics of the patient's fingernails and assessing the sharpness of the nails based on these characteristics includes the following specific steps: S11. Acquire images of the front, side and free edge of the nail under standard lighting conditions using a high-definition camera. Calculate the free edge length of the nail by converting the image pixels to the actual length. Calculate the nail edge curvature by fitting a curve to the nail edge contour. Calculate the nail edge irregularity by statistically analyzing the undulation amplitude of the nail edge. S12. Substitute the free edge length, edge curvature, and edge irregularity of the nail into the nail sharpness assessment formula to evaluate the sharpness of the nail. The sharpness assessment formula for the i-th nail is as follows: ， in, This refers to the length of the free edge of the nail. This is a reference length for the free edge of the nail. The curvature of the nail edge. For irregularities at the edge of the nail, , and These are the weighting coefficients; S13. The sharpness of the nails of each finger of the patient is weighted and fused according to the weight of use in the scratching behavior to obtain the overall sharpness of the patient's nails.

3. The geriatric pruritus management system based on information planning as described in claim 2, characterized in that, The process of obtaining patients' nighttime sleep behavior and itch behavior, and assessing the severity of nighttime itch, includes the following specific steps: S21. Acquire patient sleep state data through wearable sleep monitoring devices. When a change from sleep to wakefulness or a significant decrease in sleep continuity is detected, record the corresponding time points to form a time series of sleep state interruption events. S22. Using the motion sensing device on the patient's wrist, collect the acceleration and angular velocity motion signals of the hand. Based on the pre-established scratching action feature model, identify repetitive and periodic hand movements, extract the time points of scratching behavior, and form a scratching event time series. S23. Within the preset nighttime time window, count the number of events in the scratching event time series, and obtain the nighttime scratching behavior intensity by dividing the total number of nighttime scratching behavior events by the nighttime monitoring duration. S24. Within the set time allowance window, calculate the proportion of scratching events occurring before and after the sleep interruption event to the total number of scratching events at night, and obtain the time correlation parameter. S25. The intensity of nighttime scratching behavior is obtained by normalizing the time correlation parameter and then weighting it.

4. The geriatric pruritus management system based on information planning as described in claim 3, characterized in that, The process of obtaining the patient's skin surface condition and assessing the degree of skin surface damage based on this condition includes the following specific steps: S31. Obtain an image of the patient's skin surface, segment the scratch area in the skin image using image texture features and linear structure recognition algorithms, and calculate the scratch area. S32. By analyzing the changes in skin surface texture roughness, color and local thickness, identify the lichenification area and calculate the lichenification area. S33. By analyzing the color difference between the lesion area and the surrounding normal skin area, color change parameters are obtained; S34. Calculate the degree of skin damage based on the weighted average of scratch area, lichenification area, and color change parameters.

5. The geriatric pruritus management system based on information planning as described in claim 4, characterized in that, The assessment of a patient's skin damage risk based on nail sharpness, nighttime itching intensity, and degree of skin surface damage includes the following specific steps: The risk of scratching leading to skin damage is calculated by substituting the patient's overall nail sharpness, nighttime itching intensity, and skin lesion severity into the skin damage risk index assessment formula. The skin damage risk index assessment formula is as follows: ， in, For the overall sharpness of the nails, The degree of itching at night, The degree of skin damage, , and These are the weighting coefficients.

6. The geriatric pruritus management system based on information planning as described in claim 5, characterized in that, The patient-based skin injury risk matching nursing strategy includes the following specific steps: The skin damage risk index is compared with the skin damage threshold to assess the patient's skin condition. When the skin damage risk index is less than the first skin damage threshold, the patient is at low risk of itching and the current intervention continues. When the skin damage risk index is greater than or equal to the first skin damage threshold but less than the second skin damage threshold, the patient is at medium risk of itching and an anti-inflammatory ointment is applied topically, and the frequency of care is increased. When the skin damage risk index is greater than or equal to the second skin damage threshold, the patient is at high risk of itching and emergency medical treatment is provided, supplemented by behavioral intervention, and the skin condition is monitored in real time.

7. A method for managing senile pruritus based on information-based planning, which is implemented based on the aforementioned information-based management system for senile pruritus, characterized in that: The specific steps include the following: The information-based management system for geriatric pruritus includes the following specific steps: Obtain the morphological characteristics of the patient's fingernails and assess the sharpness of the nails based on these characteristics; Acquire patients' nocturnal sleep behavior and pruritus behavior, and assess the severity of nocturnal pruritus. Obtain information about the patient's skin surface and assess the degree of skin surface damage based on this information. The risk of skin damage to patients is assessed based on the sharpness of the nails, the degree of nighttime itching, and the extent of skin surface damage. Nursing strategies are matched based on the patient's risk of skin damage.

8. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor is characterized in that it executes the geriatric pruritus management method based on information planning as described in any one of claims 7 by calling a computer program stored in the memory.

9. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the information-based planning-based management method for senile pruritus as described in any one of claims 7.

Images