Automatic nursing grading method for intelligent nursing

Abstract

The invention discloses an automatic nursing grading method for intelligent nursing, and relates to the technical field of nursing grading, and the method comprises the steps: collecting basic information and disease information data of a patient, and carrying out the preprocessing of the collected data; according to the nursing demand of the chronic disease patient, extracting an evaluation index related to a nursing state to obtain a nursing state data sequence; and based on natural language processing, analyzing the medical record and the examination report of the patient. According to the automatic nursing grading method for intelligent nursing, the nursing resources can be intelligently allocated according to the nursing demand evaluation index and the risk level of the patient, the system preferentially allocates more nursing resources for the patient with the high risk level, it is ensured that the patient is treated and nursed timely and effectively, and the system does not need to be managed by the system for the patient with the low risk level. By means of the method, distribution of nursing resources can be properly reduced, more resources can be put into other patients needing more resources, the utilization efficiency of the nursing resources is improved, and waste and shortage of the resources are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of nursing grading, and in particular to an automatic nursing grading method for intelligent nursing. Background Art

[0002] Nowadays, chronic diseases have become one of the major health problems worldwide. Patients usually require long-term care and management. In the case of limited medical resources, how to reasonably allocate and utilize resources has become an important issue. Intelligent nursing technology can be used in an automated and intelligent way to optimize the allocation of nursing resources and improve the utilization efficiency of medical resources. The development of intelligent nursing technology can not only meet the society's demand for medical care services and reduce the labor intensity of nursing staff, but also improve the level of nursing services to a certain extent.

[0003] In the existing technology, since the nursing needs of patients with chronic diseases are more complex and long-term than those of patients with acute diseases, and the chronic disease nursing process is often accompanied by complications, it will cause certain interference to the evaluation results and thus affect the implementation of nursing measures. Therefore, there is an urgent need for an automatic nursing grading method for intelligent nursing. Summary of the invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: an automatic nursing grading method for intelligent nursing, comprising the following steps:

[0005] Step 1: Collect the patient's basic information and symptom information data, and pre-process the collected data. Symptom information includes chronic disease type, severity of disease, ability to take care of oneself, and psychological state;

[0006] Step 2: According to the nursing needs of patients with chronic diseases, the evaluation indicators related to the nursing status are extracted to obtain the nursing status data sequence;

[0007] Step 3: Based on natural language processing, analyze the patient's medical records and examination reports to identify the patient's complications and assess their impact on the patient's care status based on the type and severity of the complications;

[0008] Step 4: Based on the nursing status data sequence and complication assessment results, calculate the nursing demand evaluation index and analyze the urgency of patient care;

[0009] Step 5: Determine the risk level of patient care based on the nursing needs evaluation index, and output nursing recommendations based on the risk level of patient care.

[0010] Preferably, in step 1, the process of acquiring the patient's symptom information data includes:

[0011] Obtain patient basic information and disease information data based on electronic health records, patient questionnaires, and medical monitoring equipment. Disease information includes chronic disease type, disease severity, ability to take care of oneself, psychological state, and past medical history. For patient basic information, collect the patient's name, gender, age, contact information, and the patient's medical insurance information or social security number;

[0012] Based on the patient's clinical manifestations, examination results and doctor's diagnosis, the type of chronic disease the patient suffers from is identified, and the severity of the disease is determined. The patient's ability to take care of himself in daily life, including dressing, washing, eating, and walking, is then assessed. Based on the assessment results, it is determined whether the patient needs additional nursing support. The patient's psychological state is assessed through psychological assessment questionnaires, including the anxiety self-rating scale and the depression self-rating scale;

[0013] Verify the collected data, check the completeness and accuracy of the data, and pre-process the data. Pre-processing includes data cleaning and data standardization operations. Check the collected data, remove duplicate, erroneous or invalid information, and supplement or mark missing data to ensure data integrity. Convert the data into a unified format and unit for subsequent analysis and processing, and classify and encode different types of data for better understanding and use.

[0014] The pre-processed patient's basic information and symptom information data are integrated together to form a complete medical record, which is then archived and stored for subsequent review and use.

[0015] Preferably, in step 2, the process of acquiring the nursing status data sequence includes:

[0016] Analyze the pre-processed disease information data, and determine the evaluation indicators related to the nursing status in combination with the nursing needs of patients with chronic diseases, which are vital signs, nutritional status, disease status and emotional state;

[0017] Based on the determined evaluation indicators, extract data related to the evaluation indicators from the medical records, including obtaining vital signs data from the electronic health record, obtaining nutritional status data from the nutritional assessment report, obtaining disease status data from the doctor's diagnosis and examination results, and obtaining emotional state data from the psychological assessment questionnaire;

[0018] The scoring criteria and standard thresholds of different evaluation indicators are preset in combination with the nursing history data. The extracted data of each evaluation indicator are analyzed using the preset scoring criteria and standard thresholds to determine the score range of each evaluation indicator and integrate them to obtain the nursing status data sequence.

[0019] Preferably, the specific scoring criteria and standard thresholds of the nursing status assessment indicators are:

[0020] Vital signs assessment indicators include body temperature, pulse, respiration and blood pressure. Among them, body temperature between 36.0-37.2℃ is normal, between 37.3-38.0℃ is low fever, between 38.1-39.0℃ is moderate fever, and ≥39.1℃ is high fever. Pulse between 60-100 beats / minute is normal, less than 60 beats / minute is bradycardia, and greater than 100 beats / minute is tachycardia. Respiration between 12-20 beats / minute is normal, less than 12 beats / minute is bradypnea, and more than 20 beats / minute is tachypnea. Blood pressure less than 140 / 90 mmHg is normal, 140-159 / 90-99 mmHg is first-level hypertension, 160-179 / 100-109 mmHg is second-level hypertension, and ≥180 / 110 mmHg is third-level hypertension.

[0021] Nutritional status assessment indicators include weight, body mass index (BMI), and protein intake. A weight that is within ±10% of the standard weight is normal, a weight that is 11%-20% different from the standard weight is mild malnutrition / overweight, a weight that is 21%-30% different from the standard weight is moderate malnutrition / obesity, and a weight that is ≥31% different from the standard weight is severe malnutrition / severe obesity. A body mass index between 18.5-23.9kg / m² is normal, less than 18.5kg / m² is thin, 24.0-27.9 is overweight, and ≥28.0 is obese. Protein intake: Normal if it is within ±10% of the recommended intake calculated based on age, gender and weight, less than -10% of the recommended intake is insufficient protein intake, and more than +10% of the recommended intake is excessive protein intake.

[0022] The disease status assessment indicators include the severity of the patient's condition and the treatment effect. The severity score is between 0-25 for mild, 26-50 for moderate, 51-75 for severe, and 76-100 for extremely severe. The treatment effect score is between 90-100 for significantly effective, 75-89 for effective, 50-74 for fair, and less than 50 for ineffective.

[0023] For the emotional state assessment indicators, including the patient's anxiety and depression scores, the anxiety level score between 0-49 is no anxiety or mild anxiety, between 50-59 is moderate anxiety, and ≥60 is severe anxiety. The depression level score between 0-49 is no depression or mild depression, between 50-59 is moderate depression, and ≥60 is severe depression.

[0024] Preferably, in step 3, the process of identifying and evaluating complications includes:

[0025] Extract text data from patients' medical records and examination reports, clean and segment the text, remove irrelevant characters, punctuation marks, and stop words in the text to improve the accuracy of subsequent processing, and segment long medical records and examination reports into smaller text units for subsequent analysis and processing;

[0026] Organize and build a knowledge base containing common complications and their related symptoms and diagnostic criteria. Use NLP technology to extract symptoms and diagnostic information related to complications from patients' medical records and examination reports, and match them with the complication knowledge base to identify complications in patients.

[0027] Combined with the patient's symptom description, examination results and the standards in the complication knowledge base, the severity of the patient's complications is quantitatively evaluated. According to the type and severity of the complications, the evaluation indicators that affect the patient's daily life and treatment plan are determined. Based on the evaluation indicators and the patient's specific situation, the complication interference score is calculated to analyze the degree of interference of complications on the patient's overall nursing status;

[0028] Combined with the complication interference score and the patient's specific situation, the patient's current care needs are analyzed to determine whether more frequent monitoring or adjustment of the treatment plan is needed.

[0029] Preferably, the calculation formula for the complication interference score is:

[0030] ;

[0031] in, is the complication interference score, is the total number of complication assessment indicators for patients, It is The weight of each complication assessment indicator is determined according to the severity and type of the complication. It is The scores of the complication assessment indicators are quantified according to the severity of the complication assessment indicators, reflecting the severity of the individual complication assessment indicators. The scores are scored according to the severity of the patient's condition, with mild being 1 point, moderate being 2 points, severe being 3 points, and extremely severe being 4 points.

[0032] Preferably, in step 4, the calculation process of the nursing demand evaluation index includes:

[0033] Summarize the patient's nursing status data series, including assessment indicators of vital signs, nutritional status, disease status, and emotional state, and extract complication assessment results, including complication type, severity, and impact on the patient's life;

[0034] According to clinical experience and guidelines, weights are assigned to different nursing status assessment indicators and complication severity, and a unified scoring standard is recommended to convert the data of each assessment indicator into a quantitative score;

[0035] According to the scoring criteria and weights, calculate the patient's nursing needs evaluation index, analyze the nursing needs evaluation index, and determine the patient's nursing urgency;

[0036] According to the nursing needs evaluation index, patients are divided into different risk levels, namely low risk level, medium risk level and high risk level, and a risk evaluation threshold associated with the nursing needs evaluation index is matched for each risk level;

[0037] According to the patient's nursing needs evaluation index and urgency, an individualized nursing plan is formulated for each risk level. Nursing resources are allocated in priority according to the patient's nursing urgency. By collecting and analyzing comprehensive data on the patient's vital signs, nutritional status, disease status, emotional state, and complications, the nursing needs evaluation index can be calculated to determine the patient's nursing risk level. Compared with traditional manual assessments, it greatly reduces the interference of human factors and improves the accuracy and objectivity of the assessment. At the same time, it can update patient data and adjust nursing plans in a timely manner to ensure that patients receive timely and effective care. This not only improves nursing efficiency, but also ensures the rational allocation of nursing resources, so that high-risk patients can receive priority attention and treatment, reducing the risk of nursing errors.

[0038] Preferably, the calculation formula of the nursing demand evaluation index is:

[0039] ;

[0040] in, is the nursing needs assessment index. is the total number of nursing status assessment indicators, It is The weight value of the nursing status evaluation index, It is The score of each nursing status evaluation indicator reflects the quantitative score of a single nursing status evaluation indicator. is the maximum possible score of the nursing status assessment indicator, which is used to standardize the score. is the complication interference score, is the maximum value of the complication interference score, which is used to standardize the interference score. The value range is between 0 and 1. The closer the value is to 1, the higher the patient's care needs and the higher the urgency. The closer the value is to 0, the lower the patient's care needs and the lower the urgency.

[0041] Preferably, the multiple risk levels correspond to the multiple risk assessment thresholds, and the corresponding relationship between each risk level and the risk assessment threshold is as follows:

[0042] Low risk level Patients in this range are considered to be low risk and have relatively low care needs, requiring only basic nursing services and regular health monitoring;

[0043] Medium risk level , requiring more attention and care resources, including regular medical examinations, specific treatment measures, and more frequent health assessments;

[0044] High risk level , requiring urgent and intensive care measures, including close monitoring, intensive treatment, and collaborative care from a multidisciplinary team;

[0045] in, Nursing needs assessment index, is the upper threshold of the low risk level and the lower threshold of the medium risk level, is the upper threshold of the medium risk level and the lower threshold of the high risk level, , .

[0046] Preferably, in step 5, the process of outputting nursing advice includes:

[0047] Combine the relevant data of the patient's nursing status data series and the complication-related data to calculate the nursing needs evaluation index to determine the risk level of the patient's current nursing;

[0048] Output corresponding nursing suggestions based on the patient's current nursing risk level. For low-risk levels, regularly monitor vital signs and nutritional status, provide basic health education and dietary guidance, and encourage patients to maintain a positive attitude and good living habits; for medium-risk levels, increase the frequency of monitoring vital signs and nutritional status, formulate targeted nursing measures according to the disease condition, provide psychological support and emotional management suggestions, and pay close attention to the development of complications; for high-risk levels, closely monitor vital signs and nutritional status, detect abnormal conditions in a timely manner, implement strict disease management and complication prevention measures, provide comprehensive psychological support and emotional comfort, prioritize the allocation of nursing resources, and ensure that patients receive timely and effective care;

[0049] Prioritize nursing resources based on the patient's care urgency, regularly monitor the patient's care status and changes in complications, and update the nursing needs evaluation index based on the latest data to adjust the nursing plan to adapt to the patient's changes.

[0050] The present invention provides an automatic nursing grading method for intelligent nursing, which has the following beneficial effects:

[0051] 1. The automatic nursing grading method of this intelligent nursing can calculate the nursing needs evaluation index by collecting and analyzing comprehensive data on the patient's vital signs, nutritional status, disease status, emotional state and complications, thereby determining the patient's nursing risk level. Compared with traditional manual evaluation, it greatly reduces the interference of human factors and improves the accuracy and objectivity of the evaluation. At the same time, it can update patient data and adjust nursing plans in a timely manner to ensure that patients receive timely and effective care. It not only improves nursing efficiency, but also ensures the rational allocation of nursing resources, so that high-risk patients can receive priority attention and treatment, reducing the risk of nursing errors.

[0052] 2. The automatic nursing grading method of this intelligent nursing can intelligently allocate nursing resources according to the patient's nursing needs evaluation index and risk level. For patients with high risk levels, the system will prioritize the allocation of more nursing resources to ensure that patients receive timely and effective treatment and care. For patients with low risk levels, the allocation of nursing resources will be appropriately reduced, and more resources will be invested in other patients who are in greater need. This not only improves the utilization efficiency of nursing resources, but also avoids waste and shortage of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A method flow chart of an automatic nursing grading method for intelligent nursing of the present invention;

[0054] Figure 2 is a flow chart for the identification and assessment of complications of the present invention;

[0055] Figure 3 The figure is a calculation flow chart of the nursing demand evaluation index of the present invention. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0057] The first embodiment, as Figure 1 , Figure 2 As shown, the present invention provides a technical solution: an automatic nursing grading method for intelligent nursing, comprising the following steps:

[0058] Step 1: Collect the patient's basic information and symptom information data, and pre-process the collected data. Symptom information includes chronic disease type, disease severity, self-care ability, and psychological state. Obtain the patient's basic information and symptom information data based on electronic health records, patient questionnaires, and medical monitoring equipment. Symptom information includes chronic disease type, disease severity, self-care ability, psychological state, and past medical history. For the patient's basic information, collect the patient's name, gender, age, contact information, and medical insurance information or social insurance number. According to the patient's clinical manifestations, examination results, and doctor's diagnosis, clarify the type of chronic disease the patient suffers from, determine the severity of the disease, and then evaluate the patient's self-care ability in daily life, including dressing, washing, eating, and walking. According to the evaluation results, Determine whether the patient needs additional nursing support, evaluate the patient's psychological state through psychological assessment questionnaires, including the anxiety self-rating scale and the depression self-rating scale, verify the collected data, check the completeness and accuracy of the data, and pre-process the data. Pre-processing includes data cleaning and data standardization procedures, check the collected data, remove duplicate, erroneous or invalid information, and supplement or mark missing data to ensure data integrity, convert data into a unified format and unit for subsequent analysis and processing, classify and encode different types of data for better understanding and use, integrate the basic information and symptom information of the pre-processed patient data to form a complete medical record, and archive and store the medical record for subsequent review and use;

[0059] Step 2: According to the nursing needs of patients with chronic diseases, evaluation indicators related to nursing status are extracted to obtain a nursing status data sequence. The pre-processed disease information data is analyzed. In combination with the nursing needs of patients with chronic diseases, evaluation indicators related to nursing status are determined, which are vital signs, nutritional status, disease status and emotional status. Based on the determined evaluation indicators, data related to the evaluation indicators are extracted from medical records, including vital signs data from electronic health records, nutritional status data from nutritional assessment reports, disease status data from doctors' diagnoses and examination results, and emotional status data from psychological assessment questionnaires. Scoring standards and standard thresholds for different evaluation indicators are preset in combination with nursing history data. The extracted data of each evaluation indicator are analyzed using the preset scoring standards and standard thresholds to determine the score range of each evaluation indicator, and the nursing status data sequence is obtained by integration.

[0060] Furthermore, the specific scoring criteria and standard thresholds for the nursing status assessment indicators are as follows:

[0061] Vital signs assessment indicators include body temperature, pulse, respiration and blood pressure. Among them, body temperature between 36.0-37.2℃ is normal, between 37.3-38.0℃ is low fever, between 38.1-39.0℃ is moderate fever, and ≥39.1℃ is high fever. Pulse between 60-100 beats / minute is normal, less than 60 beats / minute is bradycardia, and greater than 100 beats / minute is tachycardia. Respiration between 12-20 beats / minute is normal, less than 12 beats / minute is bradypnea, and more than 20 beats / minute is tachypnea. Blood pressure less than 140 / 90 mmHg is normal, 140-159 / 90-99 mmHg is first-level hypertension, 160-179 / 100-109 mmHg is second-level hypertension, and ≥180 / 110 mmHg is third-level hypertension.

[0062] Nutritional status assessment indicators include weight, body mass index (BMI), and protein intake. A weight that is within ±10% of the standard weight is normal, a weight that is 11%-20% different from the standard weight is mild malnutrition / overweight, a weight that is 21%-30% different from the standard weight is moderate malnutrition / obesity, and a weight that is ≥31% different from the standard weight is severe malnutrition / severe obesity. A body mass index between 18.5-23.9kg / m² is normal, less than 18.5kg / m² is thin, 24.0-27.9 is overweight, and ≥28.0 is obese. Protein intake: Normal if it is within ±10% of the recommended intake calculated based on age, gender and weight, less than -10% of the recommended intake is insufficient protein intake, and more than +10% of the recommended intake is excessive protein intake.

[0063] The disease status assessment indicators include the severity of the patient's condition and the treatment effect. The severity score is between 0-25 for mild, 26-50 for moderate, 51-75 for severe, and 76-100 for extremely severe. The treatment effect score is between 90-100 for significantly effective, 75-89 for effective, 50-74 for fair, and less than 50 for ineffective.

[0064] For the emotional state assessment indicators, including the patient's anxiety and depression scores, the anxiety score between 0-49 is no anxiety or mild anxiety, between 50-59 is moderate anxiety, and ≥60 is severe anxiety. The depression score between 0-49 is no depression or mild depression, between 50-59 is moderate depression, and ≥60 is severe depression;

[0065] Step 3: Based on natural language processing, analyze the patient's medical records and examination reports, identify the complications of the patient, and evaluate their impact on the patient's nursing status based on the type and severity of the complications. Extract the text data of the patient's medical records and examination reports, and clean and segment the text to remove irrelevant characters, punctuation marks, and stop words in the text to improve the accuracy of subsequent processing. Segment long medical records and examination reports into smaller text units to facilitate subsequent analysis and processing. Organize and build a knowledge base containing common complications and their related symptoms and diagnostic criteria. Use NLP technology to extract relevant information from the patient's medical records and examination reports. Obtain symptoms and diagnostic information related to complications and match them with the complication knowledge base to identify complications in patients. Combined with the patient's symptom description, examination results and the standards in the complication knowledge base, quantitatively evaluate the severity of the patient's complications. According to the type and severity of complications, determine the evaluation indicators that affect the patient's daily life and treatment plan. Based on the evaluation indicators and the patient's specific situation, calculate the complication interference score, analyze the degree of interference of complications on the patient's overall nursing status, combine the complication interference score and the patient's specific situation, analyze the patient's current nursing needs, and analyze whether more frequent monitoring is needed and whether the treatment plan needs to be adjusted;

[0066] Furthermore, the calculation formula for the complication interference score is:

[0067] ;

[0068] in, is the complication interference score, is the total number of complication assessment indicators for patients, It is The weight of each complication assessment indicator is determined according to the severity and type of the complication. It is The scores of the complication assessment indicators are quantitatively scored according to the severity of the complication assessment indicators, reflecting the severity of the individual complication assessment indicators. The scores are scored according to the severity of the patient's condition, with mild being 1 point, moderate being 2 points, severe being 3 points, and extremely severe being 4 points. As the number of complications increases and the severity increases, The larger the value, the more serious the patient's complications are, and the greater the impact on the patient's daily life and treatment plan;

[0069] Step 4: Based on the nursing status data sequence and complication assessment results, calculate the nursing demand evaluation index and analyze the urgency of patient care;

[0070] Step 5: Determine the risk level of patient care based on the nursing needs evaluation index, and output nursing recommendations based on the risk level of patient care.

[0071] The second embodiment is based on the first embodiment. Figure 3 As shown, in step 4, the calculation process of the nursing needs evaluation index includes:

[0072] Summarize the patient's nursing status data series, including assessment indicators of vital signs, nutritional status, disease status and emotional state, and extract complication assessment results, including complication type, severity and impact on patient life. According to clinical experience and guidelines, assign weights to different nursing status assessment indicators and complication severity, and recommend a unified scoring standard. Convert the data of each assessment indicator into a quantitative score. According to the scoring standard and weight, calculate the patient's nursing need evaluation index, analyze the nursing need evaluation index, determine the patient's nursing urgency, divide the patient into different risk levels according to the nursing need evaluation index, namely low risk level, medium risk level and high risk level, and match each risk level with the risk assessment threshold associated with the nursing need evaluation index. Value, according to the patient's nursing needs evaluation index and urgency, formulate individualized nursing plans for each risk level, and prioritize the allocation of nursing resources according to the patient's nursing urgency. By collecting and analyzing the patient's vital signs, nutritional status, disease status, emotional state and comprehensive data on complications, the nursing needs evaluation index can be calculated to determine the patient's nursing risk level. Compared with traditional manual evaluation, it greatly reduces the interference of human factors and improves the accuracy and objectivity of the evaluation. At the same time, it can update patient data in a timely manner, adjust nursing plans in a timely manner, and ensure that patients receive timely and effective care. It not only improves nursing efficiency, but also ensures the rational allocation of nursing resources, so that high-risk patients can receive priority attention and treatment, and reduce the risk of nursing errors;

[0073] Furthermore, the calculation formula of the nursing needs evaluation index is:

[0074] ;

[0075] in, is the nursing needs assessment index. is the total number of nursing status assessment indicators, It is The weight values ​​of the nursing status assessment indicators are determined based on clinical experience and guidelines, reflecting the impact of different nursing status assessment indicators and complication severity on patient nursing needs. It is The score of each nursing status evaluation indicator reflects the quantitative score of a single nursing status evaluation indicator. is the maximum possible score of the nursing status assessment indicator, which is used to standardize the score. is the complication interference score, is the maximum value of the complication interference score, which is used to standardize the interference score. The value range is between 0 and 1. The closer the value is to 1, the higher the patient's care needs and the higher the urgency. The closer the value is to 0, the lower the patient's care needs and the lower the urgency;

[0076] Furthermore, multiple risk levels correspond to multiple risk assessment thresholds, and the corresponding relationship between each risk level and the risk assessment threshold is as follows:

[0077] Low risk level Patients in this range are considered to be low risk and have relatively low care needs, requiring only basic nursing services and regular health monitoring;

[0078] Medium risk level , requiring more attention and care resources, including regular medical examinations, specific treatment measures, and more frequent health assessments;

[0079] High risk level , requiring urgent and intensive care measures, including close monitoring, intensive treatment, and collaborative care from a multidisciplinary team;

[0080] in, Nursing needs assessment index, is the upper threshold of the low risk level and the lower threshold of the medium risk level, is the upper threshold of the medium risk level and the lower threshold of the high risk level, , ;

[0081] In step 5, the output process of nursing recommendations includes:

[0082] Combined with the relevant data of the patient's nursing status data series and complication-related data, the nursing needs evaluation index is calculated to determine the risk level of the patient's current nursing. According to the patient's current nursing risk level, the corresponding nursing suggestions are output. For low-risk levels, vital signs and nutritional status are monitored regularly, basic health education and dietary guidance are provided, and patients are encouraged to maintain a positive attitude and good living habits; for medium-risk levels, the monitoring frequency of vital signs and nutritional status is strengthened, targeted nursing measures are formulated according to the disease status, psychological support and emotional management suggestions are provided, and the development of complications is closely monitored; for high-risk levels, vital signs and nutritional status are closely monitored, abnormal conditions are detected in a timely manner, strict disease management and complication prevention measures are implemented, and Provide comprehensive psychological support and emotional comfort, prioritize the allocation of nursing resources to ensure that patients receive timely and effective care, prioritize the allocation of nursing resources according to the urgency of the patient's care, regularly monitor the patient's care status and changes in complications, and update the nursing needs evaluation index based on the latest data to adjust the nursing plan to adapt to the patient's changes. It can intelligently allocate nursing resources according to the patient's nursing needs evaluation index and risk level. For patients with high risk levels, the system will prioritize the allocation of more nursing resources to ensure that patients receive timely and effective treatment and care. For patients with low risk levels, the allocation of nursing resources will be appropriately reduced, and more resources will be invested in other patients who are in greater need. This not only improves the utilization efficiency of nursing resources, but also avoids waste and shortage of resources.

[0083] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. An automatic nursing grading method for intelligent nursing, characterized in that: The following steps are involved: Step 1: Collect the patient's basic information and disease information data, and pre-process the collected data; Step 2: According to the nursing needs of patients with chronic diseases, the evaluation indicators related to the nursing status are extracted to obtain the nursing status data sequence; Step 3: Based on natural language processing, analyze the patient's medical records and examination reports to identify the patient's complications and assess their impact on the patient's care status based on the type and severity of the complications; Step 4: Based on the nursing status data sequence and complication assessment results, calculate the nursing demand evaluation index and analyze the urgency of patient care; Step 5: Determine the risk level of patient care based on the nursing needs evaluation index, and output nursing recommendations based on the risk level of patient care.

2. The automatic nursing grading method of intelligent nursing according to claim 1 is characterized in that: In step 1, the process of obtaining patient symptom information data includes: Obtaining basic patient information and disease information data based on electronic health records, patient questionnaires, and medical monitoring equipment, including chronic disease type, disease severity, ability to take care of oneself, psychological state, and past medical history; Based on the patient's clinical manifestations, examination results and doctor's diagnosis, the type of chronic disease the patient suffers from is identified, and the severity of the disease is determined. The patient's ability to take care of himself in daily life, including dressing, washing, eating, and walking, is then assessed. Based on the assessment results, it is determined whether the patient needs additional nursing support. The patient's psychological state is assessed through psychological assessment questionnaires, including the anxiety self-rating scale and the depression self-rating scale; Verify the collected data and pre-process the data, where pre-processing includes data cleaning and data standardization procedures; The pre-processed patient's basic information and symptom information data are integrated together to form a complete medical record, which is then archived and stored.

3. The automatic nursing grading method of intelligent nursing according to claim 2 is characterized by: In the step 2, the process of obtaining the nursing status data sequence includes: Analyze the pre-processed disease information data, and determine the evaluation indicators related to the nursing status in combination with the nursing needs of patients with chronic diseases, which are vital signs, nutritional status, disease status and emotional state; Based on the determined evaluation indicators, extract data related to the evaluation indicators from the medical records, including obtaining vital signs data from the electronic health record, obtaining nutritional status data from the nutritional assessment report, obtaining disease status data from the doctor's diagnosis and examination results, and obtaining emotional state data from the psychological assessment questionnaire; The scoring criteria and standard thresholds of different evaluation indicators are preset in combination with the nursing history data. The extracted data of each evaluation indicator are analyzed using the preset scoring criteria and standard thresholds to determine the score range of each evaluation indicator and integrate them to obtain the nursing status data sequence.

4. The automatic nursing grading method of intelligent nursing according to claim 3 is characterized in that: The specific scoring criteria and standard thresholds of the nursing status assessment indicators are: Vital signs assessment indicators include body temperature, pulse, respiration and blood pressure. Among them, body temperature between 36.0-37.2℃ is normal, between 37.3-38.0℃ is low fever, between 38.1-39.0℃ is moderate fever, and ≥39.1℃ is high fever. Pulse between 60-100 beats / minute is normal, less than 60 beats / minute is bradycardia, and greater than 100 beats / minute is tachycardia. Respiration between 12-20 beats / minute is normal, less than 12 beats / minute is bradypnea, and more than 20 beats / minute is tachypnea. Blood pressure less than 140 / 90 mmHg is normal, 140-159 / 90-99 mmHg is first-level hypertension, 160-179 / 100-109 mmHg is second-level hypertension, and ≥180 / 110 mmHg is third-level hypertension. Nutritional status assessment indicators include weight, body mass index, and protein intake. A weight that is within ±10% of the standard weight is normal, a weight that is 11%-20% different from the standard weight is mild malnutrition / overweight, a weight that is 21%-30% different from the standard weight is moderate malnutrition / obesity, and a weight that is ≥31% different from the standard weight is severe malnutrition / severe obesity. A body mass index between 18.5-23.9kg / m² is normal, less than 18.5kg / m² is thin, 24.0-27.9 is overweight, and ≥28.0 is obese. Protein intake: Normal if it is within ±10% of the recommended intake calculated based on age, gender, and weight, less than -10% of the recommended intake is insufficient protein intake, and more than +10% of the recommended intake is excessive protein intake. The disease status assessment indicators include the severity of the patient's condition and the treatment effect. The severity score is between 0-25 for mild, 26-50 for moderate, 51-75 for severe, and 76-100 for extremely severe. The treatment effect score is between 90-100 for significantly effective, 75-89 for effective, 50-74 for fair, and less than 50 for ineffective. For the emotional state assessment indicators, including the patient's anxiety and depression scores, the anxiety level score between 0-49 is no anxiety or mild anxiety, between 50-59 is moderate anxiety, and ≥60 is severe anxiety. The depression level score between 0-49 is no depression or mild depression, between 50-59 is moderate depression, and ≥60 is severe depression.

5. The automatic nursing grading method of intelligent nursing according to claim 4 is characterized in that: In step 3, the process of identifying and evaluating complications includes: Extract text data from patients’ medical records and examination reports, clean and segment the text, and remove irrelevant characters, punctuation marks, and stop words from the text; Organize and build a knowledge base containing common complications and their related symptoms and diagnostic criteria. Use NLP technology to extract symptoms and diagnostic information related to complications from patients' medical records and examination reports, and match them with the complication knowledge base to identify complications in patients. Combined with the patient's symptom description, examination results and the standards in the complication knowledge base, the severity of the patient's complications is quantitatively evaluated. According to the type and severity of the complications, the evaluation indicators that affect the patient's daily life and treatment plan are determined. Based on the evaluation indicators and the patient's specific situation, the complication interference score is calculated to analyze the degree of interference of complications on the patient's overall nursing status; Combined with the complication interference score and the patient's specific situation, the patient's current care needs are analyzed to determine whether more frequent monitoring or adjustment of the treatment plan is needed.

6. The automatic nursing grading method of intelligent nursing according to claim 5 is characterized by: The calculation formula of the complication interference score is: ； in, is the complication interference score, is the total number of complication assessment indicators for patients, It is The weight value of the complication assessment index is It is The scores of the complication assessment indicators are scored according to the severity of the patient's condition, with 1 point for mild, 2 points for moderate, 3 points for severe, and 4 points for extremely severe.

7. The automatic nursing grading method of intelligent nursing according to claim 6 is characterized by: In step 4, the calculation process of the nursing demand evaluation index includes: Summarize the patient's nursing status data series, including assessment indicators of vital signs, nutritional status, disease status, and emotional state, and extract complication assessment results, including complication type, severity, and impact on the patient's life; According to clinical experience and guidelines, weights are assigned to different nursing status assessment indicators and complication severity, and a unified scoring standard is recommended to convert the data of each assessment indicator into a quantitative score; According to the scoring criteria and weights, calculate the patient's nursing needs evaluation index, analyze the nursing needs evaluation index, and determine the patient's nursing urgency; According to the nursing needs evaluation index, patients are divided into different risk levels, namely low risk level, medium risk level and high risk level, and a risk evaluation threshold associated with the nursing needs evaluation index is matched for each risk level; Based on the patient's nursing needs evaluation index and urgency, an individualized nursing plan is developed for each risk level, and nursing resources are allocated in priority according to the patient's nursing urgency.

8. The automatic nursing grading method of intelligent nursing according to claim 7 is characterized by: The calculation formula of the nursing demand evaluation index is: ； in, is the nursing needs assessment index. is the total number of nursing status assessment indicators, It is The weight value of the nursing status evaluation index, It is The scores of the nursing status assessment indicators are is the maximum score of the nursing status assessment indicator, which is used to standardize the score. is the complication interference score, is the maximum value of the complication interference score, which is used to standardize the interference score. The value range is between 0 and 1. The closer the value is to 1, the higher the patient's care needs and the higher the urgency. The closer the value is to 0, the lower the patient's care needs and the lower the urgency.

9. The automatic nursing grading method of intelligent nursing according to claim 8 is characterized by: The multiple risk levels correspond to the multiple risk assessment thresholds, and the corresponding relationship between each risk level and the risk assessment threshold is as follows: Low risk level ; Medium risk level ; High risk level ; in, Nursing needs assessment index, is the upper threshold of the low risk level and the lower threshold of the medium risk level, is the upper threshold of the medium risk level and the lower threshold of the high risk level, , .

10. The automatic nursing grading method of intelligent nursing according to claim 9, characterized in that: In step 5, the output process of nursing advice includes: Combine the relevant data of the patient's nursing status data series and the complication-related data to calculate the nursing needs evaluation index to determine the risk level of the patient's current nursing; Output corresponding nursing recommendations based on the patient's current risk level of care; Prioritize nursing resources based on the patient's care urgency, regularly monitor the patient's care status and changes in complications, and update the nursing needs evaluation index based on the latest data to adjust the nursing plan to adapt to the patient's changes.

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