Tumbling risk assessment method for tumor patient during chemotherapy and related equipment

Abstract

The invention discloses a tumble risk assessment method for a tumor patient during chemotherapy and related equipment. The method comprises the following steps: acquiring basic assessment information of a tumor patient, and performing fall risk assessment based on the tumor patient information; under the condition that at least one of the physiological state change of the tumor patient, the use of a chemotherapeutic drug or the chemotherapeutic drug adjustment information is monitored, automatically triggering an updating process for the falling risk assessment of the tumor patient; and generating falling risk early warnings of different risk levels based on a falling risk assessment result, so as to recommend corresponding falling prevention measures according to the levels of the falling risk early warnings. The problems that an existing evaluation method is not accurate enough in evaluation result and poor in early warning effect during tumble risk evaluation during chemotherapy of a hospitalized tumor patient can be solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart medical care, and more specifically, the present invention relates to a method for assessing the risk of falls of cancer patients during chemotherapy and related equipment. Background Art

[0002] Falling is a common and serious problem among cancer patients undergoing chemotherapy. According to statistics, at least 50% of patients have experienced falls during chemotherapy. The malignant consumption of the tumor itself and the side effects of chemotherapy drugs often lead to impaired balance, decreased cognitive ability, and fluctuations in psychological state in patients. These multiple factors interact with each other, significantly increasing the risk of falls for patients in the inpatient environment. Once a fall occurs, it may not only cause a series of secondary injuries, such as fractures, soft tissue injuries, etc., but also affect their quality of life and even death. Therefore, how to efficiently and accurately identify the risk of falls in hospitalized cancer patients during chemotherapy, and implement timely and effective early warning and personalized intervention measures for high-risk groups based on this, is a problem that needs to be solved at present.

[0003] Although there are fall risk assessment and warning methods and systems in the prior art, there is no dedicated systematic solution for the special physiological and medication characteristics of cancer patients during chemotherapy. The special physiological changes brought about by chemotherapy to cancer patients are not considered in a targeted manner, so there is a large error between the accuracy of the assessment results and the actual fall risk of chemotherapy patients. At the same time, the assessment model constructed based on daily life scenarios is difficult to accurately reflect the fall risk characteristics of cancer patients during chemotherapy. Therefore, when facing the fall risk assessment of hospitalized cancer patients during chemotherapy, the existing assessment methods are not accurate enough and the warning effect is not good. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] In order to solve the problem that the existing assessment methods are not accurate enough and the early warning effect is poor when assessing the fall risk of hospitalized cancer patients during chemotherapy, in the first aspect, the present invention proposes a method for assessing the fall risk of cancer patients during chemotherapy, the method comprising:

[0006] Obtain basic assessment information of cancer patients to conduct fall risk assessment based on the cancer patients' information;

[0007] Automatically triggering an update process for the fall risk assessment of the cancer patient when at least one of a change in the cancer patient's physiological state, the use of chemotherapy drugs, or chemotherapy drug adjustment information is monitored;

[0008] Based on the fall risk assessment results, fall risk warnings of different risk levels are generated to recommend corresponding fall prevention measures according to the level of the fall risk warning.

[0009] Optionally, the fall risk assessment based on tumor patient information includes:

[0010] Query the factors affecting human body functions of the chemotherapy drugs currently used by the tumor patient;

[0011] Analyze the specific influencing factors related to the risk of falls among the influencing factors;

[0012] The fall risk of cancer patients was assessed based on the specific influencing factors and the Morse Fall Assessment Scale.

[0013] Optionally, when chemotherapy drug adjustment information of a tumor patient is monitored, automatically triggering an update process for the fall risk assessment of the tumor patient, including:

[0014] When the chemotherapy drug adjustment information indicates that the influence factors of the adjusted chemotherapy drugs on human body functions are different from those before the adjustment, an update process of the fall risk assessment for the tumor patient is automatically triggered.

[0015] Optionally, the physiological state change includes a gait change, and the method further includes:

[0016] Monitor the acceleration and gyroscope information of wearable smart terminals worn by tumor patients;

[0017] Analyzing gait changes of the tumor patient based on the acceleration information and the gyroscope information;

[0018] When the gait change indicates that the matching degree between the gait of the tumor patient and the normal gait is decreasing, an update process of the fall risk assessment for the tumor patient is automatically triggered.

[0019] Optionally, the physiological state change includes a gait change, and the method further includes:

[0020] Monitor the gait image data of tumor patients based on video monitoring equipment;

[0021] Analyzing gait changes of the tumor patient based on the gait image data;

[0022] When the gait change indicates that the matching degree between the gait of the tumor patient and the normal gait is decreasing, an update process of the fall risk assessment for the tumor patient is automatically triggered.

[0023] Optionally, also include:

[0024] Real-time monitoring of the action status of cancer patients associated with high risk levels;

[0025] When the action status of the tumor patient associated with the high risk level changes, sending a reminder message to the nursing end user corresponding to the tumor patient; and / or,

[0026] When the action state of the tumor patient associated with the high risk level changes and there is no nursing end user within the preset area of ​​the tumor patient, an alarm message is sent to the nursing end user corresponding to the tumor patient.

[0027] Optionally, also include:

[0028] When a tumor patient falls, analyzing the falling mode and landing position of the tumor patient based on the acceleration information and the gyroscope information or the image data;

[0029] Predicting the injury information of the tumor patient based on the falling mode and landing location;

[0030] The injury information is sent to the medical user corresponding to the tumor patient.

[0031] In a second aspect, the present invention further provides a device for assessing the risk of falling of a tumor patient during chemotherapy, comprising:

[0032] An acquisition unit, used for acquiring basic assessment information of the tumor patient, so as to perform a fall risk assessment based on the tumor patient information;

[0033] An updating unit, configured to automatically trigger an update process of a fall risk assessment for a tumor patient upon monitoring at least one of a change in the patient's physiological state, use of chemotherapy drugs, or adjustment information of chemotherapy drugs;

[0034] The early warning unit is used to generate fall risk warnings of different risk levels based on the fall risk assessment results, so as to recommend corresponding fall prevention measures according to the level of the fall risk warning.

[0035] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the method for assessing the risk of falls in cancer patients during chemotherapy as described in any one of the first aspects above when executing the computer program stored in the memory.

[0036] In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for assessing the risk of falls in cancer patients during chemotherapy according to any one of the above items in the first aspect is implemented.

[0037] In summary, the method for assessing the risk of falling during chemotherapy for tumor patients proposed in this application obtains the basic assessment information of tumor patients to perform fall risk assessment based on the information of tumor patients; when at least one of the changes in the physiological state of tumor patients, the use of chemotherapy drugs or chemotherapy drug adjustment information is monitored, the update process of the fall risk assessment for the tumor patients is automatically triggered; based on the fall risk assessment results, fall risk warnings of different risk levels are generated to recommend corresponding fall prevention measures according to the level of the fall risk warning. This method can adjust the fall risk assessment results in real time as the patient's physiological state changes by dynamically updating the assessment process, ensuring that the assessment is more accurate and avoiding the lag of static assessment in traditional methods. The system automatically obtains patient data, assesses risks and generates intervention recommendations, which greatly reduces the burden on medical staff and enables them to devote more energy to the management of high-risk patients. Through real-time warnings and personalized intervention measures, the system can effectively reduce the risk of falling during chemotherapy for tumor patients, thereby reducing secondary injuries related to falls and improving patient safety.

[0038] The method for assessing the risk of falls in tumor patients during chemotherapy of the present invention, and other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by technicians in this field through research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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 specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0040] Figure 1 A schematic flow chart of a method for assessing the risk of falls in tumor patients during chemotherapy provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the structure of a device for assessing the risk of falling of a tumor patient during chemotherapy provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the structure of an electronic device for assessing the risk of falls of cancer patients during chemotherapy provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0044] In order to solve the problem that the existing assessment methods are not accurate enough and the early warning effect is poor when assessing the risk of falls in hospitalized cancer patients during chemotherapy, please refer to Figure 1 , is a flow chart of a method for assessing the risk of falls in cancer patients during chemotherapy provided in an embodiment of the present application, which may specifically include: steps S110 to S130.

[0045] S110, obtaining basic assessment information of the cancer patient to perform a fall risk assessment based on the cancer patient information.

[0046] For example, when cancer patients undergo chemotherapy, their physical condition is usually unstable. Affected by the tumor itself and the side effects of chemotherapy drugs, they are prone to problems such as decreased balance, fatigue, and anemia. These factors increase the risk of falling. By obtaining basic physiological information, it is possible to provide necessary data support for fall risk assessment.

[0047] Exemplarily, basic physiological parameters of patients, such as weight, BMI, blood pressure, heart rate, etc., are collected before chemotherapy. Medical history of tumor patients, especially tumor type, chemotherapy regimen, use of chemotherapy drugs, etc., is collected. Recent symptom records (such as nausea, vomiting, dizziness, etc.) and living conditions (such as sleep quality, fatigue) of patients are obtained. The assessment tool can be an electronic form or data acquisition is automatically completed by the nursing information system. Specifically, the basic information of the patient includes routine physiological information and special information. After the patient is admitted to the hospital, the Morse fall risk assessment form is used to complete the assessment of the risk of falls of tumor patients. In the Morse fall assessment scale, each item is evaluated as "no" with 0 points, "yes" with "10 to 30"; the lowest score of the scale is 0 points, and the highest score is 125 points. The higher the score, the greater the risk of falls. 0 to 24 points indicate low risk; 25 to 44 points indicate medium risk; 45 to 125 points indicate high risk.

[0048] After the patient undergoes chemotherapy, the following information should be obtained to assess the risk of falls in cancer patients during chemotherapy: ① In the cachexia stage: weight loss > 5% or BMI < 18.5 kg / m within 6 months 2 , weight loss > 2% within 6 months; ② tumor-related anemia: Hb < 90g / L; ③ cancer pain numerical score (0-10 points) > 3 points; ④ sleep disorder: difficulty falling asleep / dreaming / easy awakening; ⑤ cancer-related fatigue numerical score (0-10 points) > 3 points. And ① nighttime medication: 22:00-06:00; ② use of diuretics / steroids / opioids / benzodiazepines / antipsychotics; ③ auxiliary examinations: Na + ≤135mmol / L / K + ≤3.5mmol / L / Cl - ≤96mmol / L / Ca 2+ ≤2.25mmol / L. ④ Nervous system: dizziness / chemotherapy-induced peripheral neuropathy / delirium; ⑤ Digestive system: nausea / vomiting / constipation / diarrhea; ⑥ Urinary system: frequent urination / urgency / nocturia; ⑦ Circulatory system: hypotension / postural hypotension. The Fall Risk Assessment Form for Inpatients with Oncology can be used to assess the risk of falls during chemotherapy. The assessment content is not limited to the above items. In the Fall Risk Assessment Form for Inpatients with Oncology, each item is scored as "yes" with 1 point and "no" with 0 points; the scoring criteria for sleep disorders and chemotherapy-related symptoms and signs are: 1 point for any symptom that meets the criteria, and no cumulative scoring for a single item; the lowest score on the scale is 0 points and the highest score is 51 points. The higher the score, the greater the risk of falls; 20-25 points are low risk; 26-33 points are medium risk; ≥34 points are high risk.

[0049] S120, automatically triggering an update process for the fall risk assessment of the cancer patient when at least one of a change in the cancer patient's physiological state, the use of chemotherapy drugs, or chemotherapy drug adjustment information is monitored.

[0050] It is understandable that the physiological state of cancer patients will change dynamically with the progress of chemotherapy. For example, the side effects of chemotherapy drugs may cause electrolyte imbalance or postural hypotension, which will affect the patient's risk of falling. By monitoring these changes, the system can automatically trigger the assessment update process when the risk increases, maintaining the real-time nature of risk assessment.

[0051] For example, the system monitors the patient's physiological indicators in real time, including blood pressure, heart rate, body temperature, electrolyte levels, etc. When the patient's physiological state changes significantly, or the dosage or type of chemotherapy drugs is adjusted, the system automatically updates the assessment model, automatically recalculates the fall risk score, and generates a new warning based on the new situation.

[0052] S130, generating fall risk warnings of different risk levels based on the fall risk assessment result, so as to recommend corresponding fall prevention measures according to the level of the fall risk warning.

[0053] It is understandable that according to the real-time assessment of the fall risk results, the system divides the patient's fall risk into different levels (such as low, medium, and high), and takes corresponding preventive measures according to different levels. This process ensures the pertinence and timeliness of nursing intervention.

[0054] For example, the system generates a fall risk warning based on the assessment results, which is divided into low risk, medium risk and high risk. For low-risk patients, the system may recommend basic care measures, such as regularly checking the patient's status and ensuring that the patient has assistive tools when moving. For medium- and high-risk patients, the system may recommend increased nursing monitoring, or even arrange 24-hour monitoring, and provide physical support, such as installing handrails, using wheelchairs, etc., to avoid falls.

[0055] In summary, the fall risk assessment method for tumor patients during chemotherapy provided in the embodiment of the present application obtains the basic assessment information of tumor patients to perform fall risk assessment based on the tumor patient information; when at least one of the physiological state changes of tumor patients, the use of chemotherapy drugs or chemotherapy drug adjustment information is monitored, the update process of the fall risk assessment for the tumor patient is automatically triggered; based on the fall risk assessment results, fall risk warnings of different risk levels are generated to recommend corresponding fall prevention measures according to the level of the fall risk warning. By dynamically updating the assessment process, the method can adjust the fall risk assessment results in real time as the patient's physiological state changes, ensure that the assessment is more accurate, and avoid the lag of static assessment in traditional methods. The system automatically obtains patient data, assesses risks and generates intervention suggestions, which greatly reduces the burden on medical staff and enables them to devote more energy to the management of high-risk patients. Through real-time warnings and personalized intervention measures, the system can effectively reduce the risk of falls in tumor patients during chemotherapy, thereby reducing secondary injuries related to falls and improving patient safety.

[0056] According to some embodiments, the step of performing fall risk assessment based on tumor patient information includes:

[0057] Query the factors affecting human body functions of the chemotherapy drugs currently used by the tumor patient;

[0058] Analyze the specific influencing factors related to the risk of falls among the influencing factors;

[0059] The fall risk of cancer patients was assessed based on the specific influencing factors and the Morse Fall Assessment Scale.

[0060] Understandably, the Morse Fall Assessment Scale is an assessment tool commonly used in hospitals that assesses a patient's risk of falling through a series of standardized questions. Combined with specific drug influencing factors, a more personalized assessment can be customized for cancer patients to further improve the accuracy of the assessment. The core items of the Morse Fall Assessment Scale: whether there is a history of falls; whether walking aids are used; whether there is gait instability or movement disorders; cognitive status (whether the patient is unconscious or cognitively impaired).

[0061] Exemplarily, in combination with specific influencing factors (such as drug-induced nerve damage, fatigue, etc.) and the Morse Fall Assessment Scale, the system automatically generates an assessment questionnaire and inputs the patient information into the fall risk assessment model. The Morse scale assigns weights to each item, and combined with the weights of drug influencing factors, the final fall risk score is obtained by weighted total score. According to the scoring results, the system divides the patient's fall risk into low risk, medium risk or high risk. For example, a patient's recent chemotherapy drugs caused obvious balance disorders and cognitive problems, and the patient had a history of falls. The system evaluated these risks according to the standards of the Morse scale, and finally concluded that the patient's fall risk score was 55, which was high risk. Therefore, the nursing staff increased the monitoring of the patient and took corresponding protective measures.

[0062] Understandably, by querying the chemotherapy drugs currently used by the patient, the system can quickly identify the possible side effects of these drugs. This can timely and accurately find factors closely related to the risk of falling, ensuring a more accurate assessment. This function greatly reduces the time that caregivers spend on manually querying drug side effects, and provides instant drug impact analysis, facilitating personalized fall risk assessment. By screening out specific factors directly related to the risk of falling, the system can assess the risk more specifically. This analysis makes the assessment process more personalized and avoids the interference of irrelevant factors on the results. The system can automatically screen factors that are highly related to the risk of falling, so that caregivers can quickly understand the source of risk without complex analysis, improving the efficiency of assessment. Combining the Morse Fall Assessment Scale with specific drug influencing factors, the system can more accurately assess the patient's risk of falling and automatically generate warnings of different risk levels. Through this comprehensive assessment method, the system can provide more accurate fall risk predictions and help caregivers take appropriate preventive measures in a timely manner. Especially for high-risk patients, the system will issue an alarm to ensure that caregivers can respond quickly.

[0063] In some examples, when chemotherapy drug adjustment information of a tumor patient is monitored, automatically triggering an update process for a fall risk assessment for the tumor patient includes:

[0064] When the chemotherapy drug adjustment information indicates that the influence factors of the adjusted chemotherapy drugs on human body functions are different from those before the adjustment, an update process of the fall risk assessment for the tumor patient is automatically triggered.

[0065] It is understandable that different chemotherapy drugs have different effects on the human body. Some drugs may cause damage to the nervous system, blood pressure fluctuations, digestive disorders, etc., while others may cause more severe fatigue or muscle weakness. When the patient's chemotherapy regimen is adjusted, the factors affecting the human body may change significantly, and the key factors affecting the risk of falls also change accordingly. The system needs to monitor these changes in an automated way and update the fall risk assessment results in a timely manner. For example, a patient changes from using doxorubicin (mainly affecting the heart and blood system) to using paclitaxel (which may cause nervous system disorders). At this time, the impact of the drug adjustment is obviously different, and the potential risk to the nervous system is significantly increased. Therefore, the system should automatically trigger the update process of the fall risk assessment.

[0066] It is understood that the system automatically monitors the adjustment information of chemotherapy drugs through integration with the hospital information system or patient management system. The drug adjustment information can enter the system through doctor's prescription changes, nursing staff's records or patient self-reporting.

[0067] For example, the system can be connected to the hospital information management system through an API interface to automatically obtain the patient's chemotherapy drug records. When the drug is adjusted, the system will identify the drug change and query the factors affecting the human body after the adjustment. The system will compare the drug influencing factors before and after the adjustment and identify the differences.

[0068] Understandably, when it is detected that the factors affecting the human body after the drug adjustment are significantly different from those before the adjustment, the system will automatically trigger the risk assessment update process. The different side effects that new drugs may bring (such as effects on the nervous system or cardiovascular system) may significantly affect the patient's risk of falling. By automatically triggering assessment updates, the system can dynamically adjust risk warnings for patients.

[0069] For example, the system identifies the differences before and after medication adjustment, paying special attention to factors that are closely related to the risk of falls, such as damage to the nervous system, risk of hypotension, cognitive impairment, etc. Once the difference in impact is detected, the system automatically starts the update process of the fall risk assessment, recalculates the fall risk score, and adjusts the risk level based on the latest results. If the updated assessment shows a significant increase in risk, the system will issue a new warning to the caregiver and recommend corresponding preventive measures.

[0070] Therefore, the system can detect changes in the patient's chemotherapy regimen in real time by automatically monitoring the patient's medication change information. This step realizes real-time detection of medication adjustments and ensures timely response of the evaluation process. Nurses no longer need to manually check and enter medication adjustment information. The system automatically completes this process, saving human resources and ensuring that no medication changes are missed. The system can automatically compare the factors affecting the human body before and after the adjustment, especially the factors related to fall risk. Through accurate difference identification, the system can more accurately determine whether an updated assessment is needed. After the medication is adjusted, the system can accurately determine which new side effects may affect the risk of falling, avoiding unnecessary assessment updates and improving the efficiency and pertinence of the assessment. When the influencing factors caused by medication adjustment change, the system can automatically start the fall risk assessment update process to ensure that the assessment results reflect the patient's latest status in real time. This function ensures that the system can timely assess the new fall risk level after the patient's chemotherapy regimen is adjusted, avoiding the potential fall risk from being ignored, and providing timely risk warnings for nursing staff. By real-time monitoring of medication adjustments and automatically updating risk assessments, the system can keep dynamic tracking of patients' fall risks and ensure that risk assessments are timely and effective. The impact of chemotherapy drug adjustments on the risk of falls varies for each patient. Through differential analysis and automatic assessment updates, the system can provide personalized fall risk assessment and intervention measures for each patient. The system automatically triggers risk assessment updates, reducing the burden of manual operations on nursing staff. At the same time, through timely warnings, the system effectively prevents the occurrence of falls and improves patient safety.

[0071] In some examples, the change in physiological state includes a change in gait, and the method further includes:

[0072] Monitor the acceleration and gyroscope information of wearable smart terminals worn by tumor patients;

[0073] Analyzing gait changes of the tumor patient based on the acceleration information and the gyroscope information;

[0074] When the gait change indicates that the matching degree between the gait of the tumor patient and the normal gait is decreasing, an update process of the fall risk assessment for the tumor patient is automatically triggered.

[0075] It is understandable that gait is one of the important manifestations of human activity. Under normal circumstances, the gait of the human body has a relatively stable pattern in acceleration, balance and stride. If the gait changes due to the side effects of chemotherapy or other physiological problems, such as shortened stride, slower step frequency, body tilt, etc., this may be a signal that the patient's risk of falling is increased. Smart wearable devices can monitor the patient's gait changes in real time through sensors and provide key data for risk assessment. Common manifestations of gait changes: Shortened stride: may be caused by fatigue, muscle weakness or joint pain. Unstable gait: Neuropathy or cognitive impairment caused by chemotherapy drugs may cause unstable gait. Increased sway amplitude: may be related to loss of balance or postural hypotension. Smart wearable devices, such as bracelets or smart shoes, usually have built-in accelerometers and gyroscopes. These sensors can capture the movement status of tumor patients in real time. By analyzing the acceleration and rotational angular velocity of the device in three axes (X, Y, Z), the patient's gait information can be obtained and gait abnormalities can be identified.

[0076] Exemplarily, the smart wearable device is worn on the patient's feet or waist to collect acceleration information and gyroscope data in real time. Acceleration information is used to capture the acceleration changes of the steps, and gyroscope information is used to detect the patient's body rotation and tilt angle. The system transmits these sensor data to the central server for real-time gait monitoring and analysis. For example, a patient wears a smart bracelet with a built-in accelerometer and gyroscope to monitor the patient's movement status during walking in real time. When the patient walks, the bracelet captures the shortening of the stride and the increase in the amplitude of the body shaking, and uploads the data to the system, which identifies gait abnormalities.

[0077] It is understood that by analyzing the acceleration information and gyroscope information, the system can identify the degree of match between the patient's gait and the normal gait. If the match decreases, it means that the patient's gait is abnormal, which may indicate an increased risk of falling.

[0078] Exemplarily, the system compares the patient's current gait data with a conventional gait pattern, which can be pre-defined by a machine learning algorithm or an average gait pattern based on normal walking data. The system calculates the degree of match and evaluates the significance of the gait change. If the gait change exceeds a certain threshold, the system will consider the gait abnormal. If the gait change indicates a decrease in the degree of match, such as the gait becomes unstable or the body tilt increases, the system will determine that the patient's risk of falling may increase. For example, a patient's normal gait pattern is a stride of about 0.8 meters per step and a step frequency of about 60 steps per minute, but recently due to the side effects of chemotherapy, the system has monitored that the patient's stride has been shortened to 0.6 meters, the step frequency has decreased, and there are obvious signs of body instability. The system detects a decrease in the degree of match and believes that there is a risk of falling.

[0079] It is understandable that when the gait analysis indicates that the patient's gait is significantly less compatible with the normal gait, the system automatically triggers the update process of the fall risk assessment. The new assessment results can combine gait changes, current physiological status and the effects of chemotherapy drugs to timely reflect the patient's latest fall risk.

[0080] Exemplarily, when the gait change reaches the trigger threshold, the system automatically starts the fall risk assessment process. The system combines the patient's gait data with the original risk assessment data (such as the score of the Morse Fall Assessment Scale) to recalculate the patient's fall risk score. The updated assessment results will generate a new risk warning, and if the patient's fall risk increases significantly, the system will issue an alarm. For example, the system detects that the gait of a cancer patient is abnormal and the match degree decreases significantly. The system automatically triggers the fall risk assessment process, and combined with the patient's current physical condition (such as fatigue and neuropathy caused by chemotherapy), reassesses his fall risk, derives a high-risk score, and issues an alarm to the caregiver.

[0081] It is understandable that through the accelerometer and gyroscope data of smart wearable devices, the system can capture the patient's movement status in real time, especially the changes in gait. This automated monitoring improves the accuracy and efficiency of data collection. Patients do not need to actively report gait changes. The system can automatically and in real time obtain gait information, greatly reducing the workload of nursing staff. By comparing the matching degree of the patient's current gait with the normal gait, the system can identify abnormal changes in gait and provide accurate indications of fall risk. The system can accurately identify subtle changes in gait, providing higher accuracy and timeliness for the patient's fall risk assessment. When gait changes indicate an increased risk, the system automatically triggers an assessment update to ensure that the patient's fall risk assessment remains real-time and dynamic. Combining gait analysis with other assessment parameters, the system can adjust risk warnings more accurately. Based on the latest risk assessment results, nursing staff can take timely intervention measures to prevent the occurrence of fall accidents, especially for patients with unstable gait. Through the monitoring of smart wearable devices, the system can capture gait changes in real time and automatically update the fall risk assessment based on gait changes. This process ensures the timeliness and accuracy of the assessment. By combining the patient's actual gait data, the system can provide each patient with a personalized fall risk assessment, ensuring that the assessment results reflect the patient's current physiological state and the impact of chemotherapy. The system automatically monitors and analyzes gait changes, eliminating the need for manual operation by nursing staff, reducing the workload. At the same time, timely risk warnings and updated assessments can effectively reduce the occurrence of falls and improve patient safety.

[0082] In some examples, the change in physiological state includes a change in gait, and the method further includes:

[0083] Monitor the gait image data of tumor patients based on video monitoring equipment;

[0084] Analyzing gait changes of the tumor patient based on the gait image data;

[0085] When the gait change indicates that the matching degree between the gait of the tumor patient and the normal gait is decreasing, an update process of the fall risk assessment for the tumor patient is automatically triggered.

[0086] It is understandable that gait is a basic movement characteristic of the human body and can reflect the patient's physical health status. By capturing the patient's gait image data while walking through video surveillance equipment and combining it with image processing algorithms, the system can identify information such as stride length, step frequency, and body inclination. If there are abnormal changes in gait, such as shortened stride, instability, changes in step frequency, etc., it may be a sign of increased risk of falling. Video surveillance equipment collects this data in a non-contact manner and analyzes it, which can provide real-time and dynamic information for risk assessment. Common manifestations of gait changes: reduced stride length and irregular steps; increased body swaying when walking, or obvious posture instability; slowed walking speed or suddenly accelerated walking speed, reflecting fatigue or loss of balance.

[0087] It is understandable that video surveillance equipment monitors the walking status of cancer patients in real time through cameras and captures their gait image data. Camera equipment is usually installed in ward corridors or activity areas to continuously record the patient's movement behavior as the original data source for gait analysis.

[0088] Exemplarily, high-definition cameras are installed in wards or public areas to cover the areas where patients frequently move. The system captures image data of patients walking through video surveillance equipment, and stores or transmits these data in real time to the central processing system. Video surveillance equipment can be combined with deep learning technology (such as convolutional neural networks, CNN) to automatically identify key points in gait, such as joint positions, leg movements, trunk angles, etc., to provide basic data for gait analysis. For example, when a patient was walking in a hospital corridor, the video surveillance equipment recorded the patient's pace changes. The system captured through image data that the patient's stride became unstable and the body swayed more when walking.

[0089] It is understood that the image data processing system identifies whether the patient's gait is different from the normal gait by analyzing the gait image data captured by the video monitoring device. Through image processing technology, the system can automatically detect the trend of gait changes and calculate the matching degree of gait with the normal gait.

[0090] Exemplarily, the system uses video data processing software to analyze gait image data and extract key parameters such as joint motion trajectory, stride, cadence, and trunk motion angle. Use machine learning or deep learning models to match the patient's gait data with a conventional gait model to evaluate the degree of match. If the degree of match decreases, the system will mark it as an abnormal gait. The key technologies for gait image analysis include human posture estimation, motion trajectory tracking, and gait feature extraction. These technologies can identify changes in stride length, frequency, and body center of gravity. For example, the video surveillance equipment detects that the patient's steps are unstable when walking. The system uses image processing technology to identify that the patient's stride is shortened by 10%, and the body sway amplitude is increased compared to usual, and the matching degree drops to 70%. The system considers the patient's gait to be abnormal.

[0091] In some examples, this also includes:

[0092] Real-time monitoring of the action status of cancer patients associated with high risk levels;

[0093] When the action status of the tumor patient associated with the high risk level changes, sending a reminder message to the nursing end user corresponding to the tumor patient; and / or,

[0094] When the action state of the tumor patient associated with the high risk level changes and there is no nursing end user within the preset area of ​​the tumor patient, an alarm message is sent to the nursing end user corresponding to the tumor patient.

[0095] It is understandable that high-risk cancer patients usually have a greater risk of falling. The system monitors the movement status of these patients in real time through video surveillance, smart wearable devices or other sensors. If the patient's movement status is abnormal, such as standing up suddenly, falling or unstable movement, the system will immediately capture these changes. Through continuous monitoring, the system can quickly respond to potential falls or other safety-threatening events.

[0096] Exemplarily, when a patient is assessed as having a high risk of falling, the system will start real-time monitoring mode. Video surveillance equipment, smart wearable devices or other sensors are used to monitor the patient's walking, standing and other action states. The system uses algorithms to analyze the patient's movement patterns and identify any abnormal changes or behaviors, such as standing up suddenly, falling, etc. The data can be transmitted to the central system in real time through sensors or video surveillance equipment to track the patient's action status at any time. For example, a high-risk cancer patient was moving alone in the ward, and the system detected through a smart bracelet that the patient suddenly stood up and walked unsteadily. The system recognizes this abnormal action state and prepares to send a reminder to the nursing end user.

[0097] Understandably, when a high-risk patient’s mobility status changes, caregivers need to be alerted in time so that appropriate interventions can be taken. The system can ensure that caregivers are kept informed of the patient’s dynamics by sending reminder messages to their mobile devices or computer terminals.

[0098] Exemplarily, if the system detects a significant change in the patient's action state, such as changing from lying to standing or moving, the system will immediately generate a reminder message. The reminder message can be sent to the caregiver's terminal device via an application, text message, email, or other information system. Based on the reminder information, the caregiver can immediately check the patient's condition and take intervention measures as needed, such as arranging special care or using assistive devices. For example, if the system detects that a patient suddenly stands up and walks unsteadily, it will immediately send a reminder message to the responsible caregiver's mobile terminal, prompting the caregiver to check the patient's condition to prevent falls.

[0099] It is understandable that in some cases, there may be no caregivers in the area where the patient is located, such as at night or when the caregiver temporarily leaves the ward. At this time, if the system detects that the action status of the high-risk patient has changed and there is no caregiver in the preset area, the system will send a more urgent alarm message to notify the caregiver to rush to the scene to handle it quickly. The system determines whether the current caregiver is near the patient or in a designated area through preset area monitoring (such as through location sensing devices). If the patient's action status changes and there is no caregiver in the area, the system will immediately send an alarm message, not just a reminder, to ensure that the caregiver can arrive as soon as possible. The alarm message may be sent in a more eye-catching way, such as a phone notification, an emergency push or a ringtone prompt, to ensure that the caregiver responds quickly. For example, at night, a high-risk patient tries to get up and walk, and the system detects that there is no caregiver in the area, and immediately sends an emergency alarm message to the caregiver's mobile terminal, accompanied by a loud alarm sound, prompting the caregiver to rush to the ward for inspection.

[0100] It is understandable that through video surveillance equipment, smart wearable devices or sensors, the system can monitor the action status of high-risk patients in real time and seamlessly. This monitoring process ensures that the care team can quickly respond to changes in action that may lead to falls or injuries. Patients do not need to actively seek help, the system can automatically monitor and detect abnormal actions, improving the timeliness and efficiency of care response. When the system detects a significant change in the patient's action status, it will promptly remind the caregiver, which will help to intervene in time before the patient has an accident. This process allows caregivers to keep abreast of the dynamics of high-risk patients and avoid potential dangers. Through reminder messages, caregivers can take immediate action to prevent accidents such as patient falls, improve the quality of care and patient safety. When there are no caregivers in the preset area, the system sends an emergency alarm to ensure that it attracts the attention of the care team in the shortest time. This automated alarm mechanism effectively fills the blind spots of care and ensures that patients can receive timely attention even when no one is watching. The immediacy and urgency of the alarm message enable caregivers to take quick action to reduce the risk of patient injury, especially at night or when caregivers are temporarily absent. Through continuous monitoring of high-risk patients, the system can promptly detect and report changes in the patient's action status. Whether it is a reminder message or an alarm message, the system can quickly notify the nursing staff to intervene, improving the efficiency of risk prevention and control. Through real-time preset area monitoring and alarm mechanisms, the system ensures that the safety of patients can be guaranteed even if the nursing staff is temporarily absent, avoiding the occurrence of nursing blind spots. When the system detects changes in the action status of high-risk patients, timely reminders and alarms greatly reduce the probability of falls, especially when there is no one to watch over them. The system can actively issue an alarm to ensure that the safety of patients is guaranteed in real time.

[0101] In some examples, this also includes:

[0102] When a tumor patient falls, analyzing the falling mode and landing position of the tumor patient based on the acceleration information and the gyroscope information or the image data;

[0103] Predicting the injury information of the tumor patient based on the falling mode and landing location;

[0104] The injury information is sent to the medical user corresponding to the tumor patient.

[0105] It is understandable that the system can detect the occurrence of patient falls in real time through the accelerometer and gyroscope of smart wearable devices, or through the image data of video monitoring equipment. Once a fall is detected, the system will analyze the way of falling (such as falling forward, falling sideways, falling backward) and the part of the patient's body that landed on the ground (such as the head, back, arms, etc.). This information is an important basis for judging the injury.

[0106] For example, when a smart wearable device (such as a bracelet, smart shoes, etc.) detects a sharp change in the patient's acceleration and angular velocity of rotation (such as a high-amplitude acceleration drop or rapid rotation), the system will determine that the patient has fallen. Similarly, the video surveillance device captures the patient's fall process through image data, and identifies the direction of the fall and the location of the landing through posture analysis and motion detection technology. The system analyzes the way of falling, determines whether the patient falls forward, sideways or backward, and determines which part of the body first touches the ground, such as the head, hips or arms. For example, a cancer patient falls in the corridor, and the acceleration sensor of the smart wearable device detects a sharp change in vertical acceleration, and the gyroscope information shows that the patient falls sideways. By analyzing the data, the system determines that the patient landed on the left side of the body, which may involve an arm or shoulder injury.

[0107] It is understandable that the way of falling and the place of landing are closely related to the patient's injuries. By analyzing this information, the system can predict the patient's possible injuries, such as fractures, head trauma, soft tissue injuries, etc. Combined with the special physical conditions of tumor patients (such as osteoporosis, muscle weakness, etc.), the system can make more accurate injury predictions.

[0108] Exemplarily, based on the way the person fell and where they landed, the system uses a built-in injury prediction model, combined with the patient's medical history (such as osteoporosis, anemia, etc.), to predict the possible type and severity of the injury. The system can predict possible fracture sites (such as hip, wrist, head) or soft tissue injuries based on the force and angle of the fall and the part that contacts the ground. The prediction model can use a machine learning algorithm to provide a personalized injury assessment based on historical data and the patient's specific physical condition. For example, the system detects that the patient fell on the left side and the arm landed first. Combined with the patient's medical history (osteoporosis), the system predicts that the patient may have a wrist fracture or shoulder dislocation, and assesses the injury as a moderate risk.

[0109] Understandably, injury information is crucial for caregivers and doctors, helping them make quick decisions on treatment. After a fall, the system automatically generates an injury report and sends it to the medical user responsible for the patient. Medical staff can quickly take appropriate measures based on the injury information, such as arranging further examinations or emergency treatment.

[0110] Exemplarily, the system generates a detailed report including the mode of fall, landing site, and injury prediction. The injury information is transmitted to the relevant nursing staff or doctors through the nursing information system (NIS) or the electronic health record system (EHR). After receiving the information, the nursing staff can immediately take action based on the content of the report, such as arranging for the patient to undergo an X-ray examination or sending the patient to the emergency department for treatment. For example, after a patient falls, the system determines that the risk of left wrist fracture is high, and immediately generates an injury report and sends the report to the responsible nursing staff through the hospital's nursing information system. After receiving the report, the nursing staff immediately arranges for the patient to undergo a wrist X-ray examination.

[0111] It is understandable that through acceleration and gyroscope information, or through image data, the system can accurately identify the occurrence of a fall, the way the fall occurred, and the part where the patient landed. This information provides important basic data for subsequent injury prediction. Nursing staff do not need to rely on the patient's subjective description. The system can automatically and objectively provide information on the way the fall occurred and the part where the patient landed, helping to more accurately judge the patient's injury. The system can make reasonable injury predictions by analyzing the way the fall occurred, the force, and the part where the fall occurred, combined with the patient's previous medical history. This technology helps medical staff to predict in advance the possible injury site and the severity of the injury of the patient, so as to carry out subsequent treatment more accurately. Through automated injury prediction, medical staff can respond to patients' needs faster and more effectively, reduce possible diagnostic delays, and improve the efficiency of emergency care. The system automatically generates an injury report and transmits it to nursing staff or doctors, shortening the time for information transmission. Medical staff can make medical decisions in advance based on injury information to improve nursing efficiency. Nursing staff can quickly obtain detailed information about the patient's fall and injury assessment, and take timely intervention measures to avoid worsening of the injury or delay in treatment. Through accelerometers, gyroscopes or video surveillance systems, the system can detect falls in real time and accurately, quickly analyze the way the person fell and where they landed, and ensure that medical staff can quickly obtain relevant information. By combining the way the person fell, where they landed, and the patient's health information, the system can predict injuries in advance and provide personalized risk assessments. This feature enables caregivers and doctors to handle falls more effectively and avoid worsening injuries. Automatically generated injury reports provide medical staff with detailed decision-making basis, reduce the risk of misdiagnosis or delayed treatment after a fall, and improve patient safety and care quality.

[0112] See also Figure 2 , an embodiment of the device for assessing the risk of falling of a tumor patient during chemotherapy in the embodiment of the present application may include:

[0113] An acquisition unit 21 is used to acquire basic assessment information of the tumor patient to perform a fall risk assessment based on the tumor patient information;

[0114] An updating unit 22, configured to automatically trigger an update process of a fall risk assessment for a tumor patient when at least one of a change in the patient's physiological state, use of chemotherapy drugs, or adjustment information of chemotherapy drugs is monitored;

[0115] The warning unit 23 is used to generate fall risk warnings of different risk levels based on the fall risk assessment results, so as to recommend corresponding fall prevention measures according to the levels of the fall risk warnings.

[0116] In summary, the fall risk assessment device for tumor patients during chemotherapy provided by the embodiment of the present application obtains the basic assessment information of tumor patients to perform fall risk assessment based on the tumor patient information; when at least one of the physiological state changes of tumor patients, the use of chemotherapy drugs or chemotherapy drug adjustment information is monitored, the update process of the fall risk assessment for the tumor patients is automatically triggered; based on the fall risk assessment results, fall risk warnings of different risk levels are generated to recommend corresponding fall prevention measures according to the level of the fall risk warning. By dynamically updating the assessment process, the method can adjust the fall risk assessment results in real time as the patient's physiological state changes, ensure that the assessment is more accurate, and avoid the lag of static assessment in traditional methods. The system automatically obtains patient data, assesses risks and generates intervention suggestions, which greatly reduces the burden on medical staff and enables them to devote more energy to the management of high-risk patients. Through real-time warnings and personalized intervention measures, the system can effectively reduce the risk of falls in tumor patients during chemotherapy, thereby reducing secondary injuries related to falls and improving patient safety.

[0117] like Figure 3 As shown, the embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any one of the above-mentioned methods for assessing the risk of falls of tumor patients during chemotherapy are implemented:

[0118] Obtain basic assessment information of cancer patients to conduct fall risk assessment based on the cancer patients' information;

[0119] Automatically triggering an update process for the fall risk assessment of the cancer patient when at least one of a change in the cancer patient's physiological state, the use of chemotherapy drugs, or chemotherapy drug adjustment information is monitored;

[0120] Based on the fall risk assessment results, fall risk warnings of different risk levels are generated to recommend corresponding fall prevention measures according to the level of the fall risk warning.

[0121] Since the electronic device introduced in this embodiment is a device used to implement a fall risk assessment device for cancer patients during chemotherapy in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0122] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiment:

[0123] Obtain basic assessment information of cancer patients to conduct fall risk assessment based on the cancer patients' information;

[0124] Automatically triggering an update process for the fall risk assessment of the cancer patient when at least one of a change in the cancer patient's physiological state, the use of chemotherapy drugs, or chemotherapy drug adjustment information is monitored;

[0125] Based on the fall risk assessment results, fall risk warnings of different risk levels are generated to recommend corresponding fall prevention measures according to the level of the fall risk warning.

[0126] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0127] 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 in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the 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 the 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 computer, 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.

[0129] 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 comprising 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.

[0130] 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.

[0131] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process of assessing the risk of falls of cancer patients during chemotherapy in the corresponding embodiment.

[0132] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0138] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for assessing the risk of falling in cancer patients during chemotherapy, characterized in that: include: Obtain basic assessment information of cancer patients to conduct fall risk assessment based on the cancer patients' information; Automatically triggering an update process for the fall risk assessment of the cancer patient when at least one of a change in the cancer patient's physiological state, the use of chemotherapy drugs, or chemotherapy drug adjustment information is monitored; Based on the fall risk assessment results, fall risk warnings of different risk levels are generated to recommend corresponding fall prevention measures according to the level of the fall risk warning.

2. The method according to claim 1, characterized in that The fall risk assessment based on tumor patient information includes: Query the factors affecting human body functions of the chemotherapy drugs currently used by the tumor patient; Analyze the specific influencing factors related to the risk of falls among the influencing factors; The fall risk of cancer patients before chemotherapy was assessed based on the Morse Fall Assessment Scale; the fall risk of cancer patients during chemotherapy was assessed based on the specific influencing factors.

3. The method according to claim 2, characterized in that The process of automatically triggering an update process of a fall risk assessment for a tumor patient when chemotherapy drug adjustment information of the tumor patient is monitored includes: When the chemotherapy drug adjustment information indicates that the influence factors of the adjusted chemotherapy drugs on human body functions are different from those before the adjustment, an update process of the fall risk assessment for the tumor patient is automatically triggered.

4. The method according to claim 1, characterized in that The physiological state change includes a gait change, and the method further includes: Monitor the acceleration and gyroscope information of wearable smart terminals worn by tumor patients; Analyzing gait changes of the tumor patient based on the acceleration information and the gyroscope information; When the gait change indicates that the matching degree between the gait of the tumor patient and the normal gait is decreasing, an update process of the fall risk assessment for the tumor patient is automatically triggered.

5. The method according to claim 1, characterized in that The physiological state change includes a gait change, and the method further includes: Monitor the gait image data of tumor patients based on video monitoring equipment; Analyzing gait changes of the tumor patient based on the gait image data; When the gait change indicates that the matching degree between the gait of the tumor patient and the normal gait is decreasing, an update process of the fall risk assessment for the tumor patient is automatically triggered.

6. The method according to any one of claims 1 to 5, characterized in that Also includes: Real-time monitoring of the action status of cancer patients associated with high risk levels; When the action status of the tumor patient associated with the high risk level changes, sending a reminder message to the nursing end user corresponding to the tumor patient; and / or, When the action state of the tumor patient associated with the high risk level changes and there is no nursing end user within the preset area of ​​the tumor patient, an alarm message is sent to the nursing end user corresponding to the tumor patient.

7. The method according to claim 4 or 5, characterized in that Also includes: When a tumor patient falls, analyzing the falling mode and landing position of the tumor patient based on the acceleration information and the gyroscope information or the image data; Predicting the injury information of the tumor patient based on the falling mode and landing location; The injury information is sent to the medical user corresponding to the tumor patient.

8. A device for assessing the risk of falling of a tumor patient during chemotherapy, characterized in that: include: An acquisition unit, used for acquiring basic assessment information of the tumor patient, so as to perform a fall risk assessment based on the tumor patient information; An updating unit, configured to automatically trigger an update process of a fall risk assessment for a tumor patient upon monitoring at least one of a change in the patient's physiological state, use of chemotherapy drugs, or adjustment information of chemotherapy drugs; The early warning unit is used to generate fall risk warnings of different risk levels based on the fall risk assessment results, so as to recommend corresponding fall prevention measures according to the level of the fall risk warning.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the method for assessing the risk of falls in tumor patients during chemotherapy as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for assessing the risk of falling in a tumor patient during chemotherapy according to any one of claims 1 to 7 is implemented.

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