Intelligent management platform and management method for patient response data after medication
By designing an intelligent management platform for patient response data after medication, the shortcomings in the management and evaluation of patient response data during CSE treatment in the existing technology are solved, and intelligent management of patient response data and analysis of treatment effects are realized, and clear evaluation indicators are provided.
Patent Information
- Application Number
- CN202411890153.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-20
- Publication Date
- 2025-04-15
- Estimated Expiration
- 2044-12-20
AI Technical Summary
The prior art lacks the management and evaluation methods of patient response data during the treatment of status epilepsy (CSE), making it difficult to effectively evaluate the treatment effect.
An intelligent management platform for patient response data after medication is designed, including a patient data collection module, a patient response data collection module, a patient data analysis module, a patient data management module and a patient medication management module. Through these modules, the basic parameters and response data of patients are collected, analyzed and managed, the patient's treatment data prediction value is calculated, and treatment suggestions are provided.
The intelligent management of patient response data and the analysis of treatment effects are realized, and clear evaluation indicators are provided, ensuring the accurate assessment of patient status and effective analysis of treatment effects.
Smart Images
Figure CN119833052A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of patient reaction data management and analysis, and in particular to an intelligent management platform and management method for patient reaction data after medication. Background Art
[0002] Status epilepticus (SE) is a common acute and critical disease of the nervous system. Its seizure types are complex and require comprehensive treatment. It often leads to severe neurological deficits and has a high disability and mortality rate. The incidence rate is about 0.3% to 0.8% in the general population and 2.6% to 6% in epilepsy patients, with an annual total incidence rate of about 12.6 / 100,000. Among different types of SE, convulsive status epilepticus (CSE) is the most acute and severe, accounting for the highest proportion of all SE cases, about 45% to 74%. It is manifested as persistent limb rigidity, clonus or continuous clonus, accompanied by impaired consciousness. According to foreign reports, the mortality rate of CSE is about 3.45% to 39%. At present, there is no means of managing and evaluating patient response data in the treatment of CSE using Acorus calamus and Curcuma chinensis. Summary of the invention
[0003] 1. Technical issues to be resolved
[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent management platform and method for patient reaction data after medication, which has the advantages of managing patient reaction data and analyzing treatment effects, and solves the above-mentioned technical problems.
[0005] (II) Technical solution
[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent management platform for patient reaction data after medication, comprising a patient data acquisition module, a patient reaction data collection module, a patient data analysis module, a patient data management module and a patient medication management module;
[0007] The patient data collection module is used to collect basic parameters of patients, including age, gender and etiology type. The patient data collection module divides all patients into different groups based on age and gender, and calculates the proportion of different etiology types in each group, and obtains the weight of the basic parameters of patients by synthesizing the proportion of different etiology types, and sends the obtained weight of the basic parameters of patients to the patient data analysis module;
[0008] The patient response data collection module is used to collect data during the treatment of status epilepticus, including the proportion of mSTESS scores ≤ 2 points within 24 hours of enrollment, the termination time of CSE attacks, the incidence of RSE, the incidence of super-RSE, the incidence of Todd's palsy, the number of mechanical ventilation during hospitalization, the number of anesthetic drugs used during hospitalization, and the frequency of epileptic seizures from the termination of CSE to 30 days after randomization, and calculate the patient response data weight based on the data during the treatment of status epilepticus, and the patient response data collection module sends the calculated patient response data weight to the patient data analysis module;
[0009] The patient data analysis module calculates the patient treatment data prediction value based on the patient basic parameter weight and the patient response data weight, and sends the patient treatment data prediction value to the patient data management module;
[0010] The patient data management module includes a doctor port and a patient port, wherein the doctor port and the patient port are used to display the calculated patient treatment data prediction value and all data in the patient data collection module and the patient response data collection module to the doctor and the patient, and the doctor port is also used for the doctor to provide treatment suggestions to the patient based on all data in the patient data collection module and the patient response data collection module and the patient treatment data prediction value, and the patient port is also used for the patient to view the treatment suggestions given by the doctor, and after the patient is transferred to another hospital for treatment or the treatment is completed, the stored data is deleted after the patient data is sent to the patient;
[0011] There is a data connection between the patient medication management module and the patient port in the patient data management module, and the patient's medication treatment time is recorded while reminding the patient to take the medicine on time.
[0012] As a preferred technical solution of the present invention, the patient data collection module collects the patient's basic parameters including age, gender and etiology type. The specific expression is as follows:
[0013]
[0014] Among them, HZSJ represents the patient dataset, They represent the number storage sets of patients 1 to N, respectively. Patient n is the nth patient among patients 1 to N. The data expression stored in the number storage set of patient n is as follows:
[0015]
[0016] Among them, NL n represents the age of patient n, XB n Indicates the gender of patient n, BY n Represents the etiology type of patient n.
[0017] As a preferred technical solution of the present invention, the patient data collection module calculates the proportion of each group of different etiology types, and comprehensively obtains the proportion of different etiology types to obtain the specific expression of the patient's basic parameter weight as follows:
[0018]
[0019] Among them, HZJC n represents the patient basic parameter weight of patient n, represents the total number of causes of patient N in the corresponding age group, N all represents the total number of all causes, Represents the total number of causes of patient n that occur in their corresponding gender group.
[0020] As a preferred technical solution of the present invention, the specific expression of the data collected by the patient response data collection module during the treatment of status epilepticus is as follows:
[0021] [mSTESS,t n ,RSE,super-RSE,Todd's,TQCS n ,MZCS n ,FZPL n ]
[0022] Among them, mSTESS refers to the proportion of patients with mSTESS score ≤ 2 points within 24 hours after enrollment, t n represents the end time of CSE episode of patient n, RSE represents the incidence of RSE, super-RSE represents the incidence of super-RSE, Todd's represents the incidence of Todd's palsy, TQCS represents the incidence of n represents the number of mechanical ventilation occurrences during hospitalization of patient n, MZCS n represents the number of times patient n used anesthetic drugs during hospitalization, FZPL n represents the frequency of epileptic seizures in patient n from the end of CSE to 30 days after randomization.
[0023] As a preferred technical solution of the present invention, the patient response data collection module calculates the patient response data weight based on the data during the treatment of status epilepticus as follows:
[0024] HkDJ n =t n (mSTESS+RSE+super-RSE+Todd's+FZPL n )+TQCS n +MZCS n
[0025] Among them, mSTESS refers to the proportion of patients with mSTESS score ≤ 2 points within 24 hours after enrollment, t n represents the end time of CSE episode of patient n, RSE represents the incidence of RSE, super-RSE represents the incidence of super-RSE, Todd's represents the incidence of Todd's palsy, TQCS represents the incidence of n represents the number of mechanical ventilation occurrences during hospitalization of patient n, MZCS n represents the number of times patient n used anesthetic drugs during hospitalization, FZPL n represents the frequency of epileptic seizures in patient n from the end of CSE to 30 days after randomization, HZFY n Represents the patient response data weight for patient n.
[0026] As a preferred technical solution of the present invention, the specific expression of the patient treatment data prediction value calculated by the patient data analysis module based on the patient basic parameter weight and the patient response data weight is as follows:
[0027]
[0028] Among them, HZFY n represents the patient response data weight of patient n, HZJC n represents the patient basic parameter weight of patient n, HZYC n represents the predicted value of patient treatment data, and e represents a natural constant.
[0029] As a preferred technical solution of the present invention, the doctor port and the patient port are both provided with access rights, and both can be operated after being authorized by the doctor or the patient.
[0030] As a preferred technical solution of the present invention, the time and frequency of reminding the patient to take medicine on time in the patient medication management module are based on the data sent from the doctor port to the patient port in the patient data management module.
[0031] As a preferred technical solution of the present invention, the patient medication management module also includes calculating the patient's medication compliance:
[0032]
[0033] Among them, YCD represents the patient's medication compliance, ZQCS represents the exact number of times the patient takes the medication, and FYCS represents the total number of times the patient takes the medication.
[0034] The present invention also provides a method for intelligent management of patient reaction data after medication, based on the above-mentioned intelligent management platform for patient reaction data after medication, comprising the following steps:
[0035] Step 1: Collect the basic parameters of the patient and obtain the weights of the basic parameters of the patient;
[0036] Step 2: Collect data from patients during the treatment of status epilepticus and calculate the weight of patient response data;
[0037] Step 3: Calculate the predicted value of the patient treatment data based on the patient's basic parameter weights and the patient's response data weights;
[0038] Step 4: The patient receives treatment suggestions provided to the patient based on all data in the patient data collection module, the patient response data collection module and the patient treatment data prediction value output by the doctor through the doctor port at the patient port;
[0039] Step 5: After the patient is transferred to another hospital for treatment or the treatment is completed, the stored data is deleted after the patient data is sent to the patient.
[0040] Compared with the prior art, the present invention provides an intelligent management platform and management method for patient reaction data after medication, which has the following beneficial effects:
[0041] The present invention collects basic parameters of patients and obtains weights of basic parameters of patients, collects data of patients during the treatment of status epilepticus, calculates weights of patient response data, and calculates predicted values of patient treatment data based on the weights of basic parameters of patients and the weights of patient response data. The patient receives treatment suggestions output by the doctor through the doctor port based on the patient data acquisition module, all data in the patient response data collection module and predicted values of patient treatment data, thereby providing clear evaluation indicators for the specific status of patients after taking the modified Acorus calamus and Curcuma decoction, ensuring the management of patient response data and analyzing the treatment effect. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] Figure 1 It is a schematic diagram of the system framework of the present invention;
[0043] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION
[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0045] The drugs taken by the patients in this article are: in addition to the standardized western medicine treatment of CSE, oral or nasogastric administration of the modified prescription of Acorus calamus and Curcuma Root Decoction, the drug composition: Acorus calamus 9g, Curcuma Root 6g, stir-fried Gardenia 9g, fresh bamboo leaf 9g, Paeonia suffruticosa 9g, Forsythia suspensa 6g, and Bamboo Sap 15g, which are modified according to the patient's condition. Drug addition and subtraction scheme: add Gastrodia elata, Uncaria rhynchophylla, and Antelope horn for liver fire; remove Gardenia elata and add Atractylodes macrocephala and Poria cocos for phlegm and abdominal fullness; add Bupleurum chinense, Scutellaria baicalensis, Rhubarb, and Sodium Sulfate for phlegm and heat. The Chinese medicine is made into a decoction-free granule. Take 1 set per day, twice a day, from the start of enrollment to the 7th day.
[0046] See also Figure 1 - Figure 2 , an intelligent management platform for patient reaction data after medication, including a patient data acquisition module, a patient reaction data collection module, a patient data analysis module, a patient data management module and a patient medication management module.
[0047] The patient data collection module is used to collect basic parameters of patients, including age, gender and etiology type. The patient data collection module divides all patients into different groups based on age and gender, and calculates the proportion of different etiology types in each group. The proportion of different etiology types is combined to obtain the weight of the basic parameters of the patients, and the obtained weight of the basic parameters of the patients is sent to the patient data analysis module. The specific expressions for collecting basic parameters of patients including age, gender and etiology type by the patient data collection module are as follows:
[0048]
[0049] Among them, HZSJ represents the patient dataset, They represent the number storage sets of patients 1 to N, respectively. Patient n is the nth patient among patients 1 to N. The data expression stored in the number storage set of patient n is as follows:
[0050]
[0051] Among them, NL n represents the age of patient n, XB n Indicates the gender of patient n, BY n Represents the etiology type of patient n. The patient data collection module calculates the proportion of each group of different etiology types, and synthesizes the proportions of different etiology types to obtain the specific expression of the patient's basic parameter weight as follows:
[0052]
[0053] Among them, HZJC n represents the patient basic parameter weight of patient n, N represents the total number of causes of patient n in the corresponding age group. all represents the total number of all causes, Represents the total number of causes of patient n that occur in their corresponding gender group.
[0054] The patient response data collection module is used to collect data on the treatment of status epilepticus, including the proportion of mSTESS scores ≤ 2 points within 24 hours of enrollment, the termination time of CSE, the incidence of RSE, the incidence of super-RSE, the incidence of Todd's palsy, the number of mechanical ventilation during hospitalization, the number of anesthetic drugs used during hospitalization, and the frequency of epileptic seizures from the termination of CSE to 30 days of randomization. The patient response data weight is calculated based on the data of the patient during the treatment of status epilepticus. The patient response data collection module sends the calculated patient response data weight to the patient data analysis module. The specific expression of the patient response data collection module to collect the patient's data during the treatment of status epilepticus is as follows:
[0055] [mSTESS,t n ,RSE,super-RSE,Todd's,TQCS n ,MZCS n ,FZPL n ]
[0056] Among them, mSTESS refers to the proportion of patients with mSTESS score ≤ 2 points within 24 hours after enrollment, t n represents the end time of CSE episode of patient n, RSE represents the incidence of RSE, super-RSE represents the incidence of super-RSE, Todd's represents the incidence of Todd's palsy, TQCS represents the incidence of n represents the number of mechanical ventilation occurrences during hospitalization of patient n, MZCS n represents the number of times patient n used anesthetic drugs during hospitalization, FZPL n It represents the epileptic seizure frequency of patient n from the end of CSE to 30 days of randomization. The patient response data collection module calculates the patient response data weight based on the data of the patient during the treatment of status epilepticus as follows:
[0057] HkDJ n =t n (mSTESS+RSE+super-RSE+Todd's+FZPL n )+TQCS n +MZCS n
[0058] Among them, mSTESS refers to the proportion of patients with mSTESS score ≤ 2 points within 24 hours after enrollment, tn represents the end time of CSE episode of patient n, RSE represents the incidence of RSE, super-RSE represents the incidence of super-RSE, Todd's represents the incidence of Todd's palsy, TQCS represents the incidence of n represents the number of mechanical ventilation occurrences during hospitalization of patient n, MZCS n represents the number of times patient n used anesthetic drugs during hospitalization, FZPL n represents the frequency of epileptic seizures in patient n from the end of CSE to 30 days after randomization, HZFY n Represents the patient response data weight for patient n.
[0059] The patient data analysis module calculates the patient treatment data prediction value based on the patient basic parameter weight and the patient response data weight, and sends the patient treatment data prediction value to the patient data management module. The specific expression of the patient treatment data prediction value calculated by the patient data analysis module based on the patient basic parameter weight and the patient response data weight is as follows:
[0060]
[0061] Among them, HZFY n represents the patient response data weight of patient n, HZJC n represents the patient basic parameter weight of patient n, HZYC n represents the predicted value of patient treatment data, and e represents a natural constant.
[0062] The patient data management module includes a doctor port and a patient port. The doctor port and the patient port are used to display the calculated patient treatment data prediction values and all data in the patient data collection module and the patient response data collection module to the doctor and the patient. The doctor port is also used for the doctor to provide treatment suggestions to the patient based on all data in the patient data collection module, the patient response data collection module and the patient treatment data prediction values. The patient port is also used for the patient to view the treatment suggestions given by the doctor, and after the patient is transferred to another hospital for treatment or the treatment is completed, the stored data is deleted after the patient data is sent to the patient. Among them, the doctor port and the patient port are both set with access rights, and operations can only be performed after authorization by the doctor or the patient. The time and number of times the patient is reminded to take medicine on time in the patient medication management module are based on the data sent from the doctor port to the patient port in the patient data management module.
[0063] There is a data connection between the patient medication management module and the patient port in the patient data management module, and the patient's medication treatment time is recorded while reminding the patient to take the medicine on time. The patient medication management module also includes the calculation of the patient's medication compliance:
[0064]
[0065] Among them, YCD represents the patient's medication compliance, ZQCS represents the exact number of times the patient takes the medication, and FYCS represents the total number of times the patient takes the medication. For patients with low compliance, doctors will remind them through the doctor port.
[0066] Example:
[0067] This embodiment explains the calculation process above, where mSTESS = 0.7, RSE = 0.14, super-RSE = 0.01, Todd's = 0.13, TQCS n =0,MZCS n =1,FZPL n =0.1, t n =1.5, at this time, HZFY n =t n (mSTESS+RSE+super-RSE+Todd's+FZPL n )+TQCS n +MZCS n =1.5*(0.7+0.14+0.01+0.13+0.1)+0+1=2.62, The calculated Therefore, there are clear evaluation indicators for the specific condition of patients after taking the modified Acorus calamus and Curcuma Decoction, thereby ensuring the management of patient response data and analyzing the treatment effect.
[0068] The present invention also provides a method for intelligent management of patient reaction data after medication, based on the above-mentioned intelligent management platform for patient reaction data after medication, comprising the following steps:
[0069] Step 1: Collect the basic parameters of the patient and obtain the weights of the basic parameters of the patient;
[0070] Step 2: Collect data from patients during the treatment of status epilepticus and calculate the weight of patient response data;
[0071] Step 3: Calculate the predicted value of the patient treatment data based on the patient's basic parameter weights and the patient's response data weights;
[0072] Step 4: The patient receives treatment suggestions provided to the patient based on all data in the patient data collection module, the patient response data collection module and the patient treatment data prediction value output by the doctor through the doctor port at the patient port;
[0073] Step 5: After the patient is transferred to another hospital for treatment or the treatment is completed, the stored data is deleted after the patient data is sent to the patient.
[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. An intelligent management platform for patient response data after medication, characterized by: It includes a patient data collection module, a patient response data collection module, a patient data analysis module, a patient data management module and a patient medication management module; The patient data collection module is used to collect basic parameters of patients, including age, gender and etiology type. The patient data collection module divides all patients into different groups based on age and gender, and calculates the proportion of different etiology types in each group, and obtains the weight of the basic parameters of patients by synthesizing the proportion of different etiology types, and sends the obtained weight of the basic parameters of patients to the patient data analysis module; The patient response data collection module is used to collect data during the treatment of status epilepticus, including the proportion of mSTESS scores ≤ 2 points within 24 hours of enrollment, the termination time of CSE attacks, the incidence of RSE, the incidence of super-RSE, the incidence of Todd's palsy, the number of mechanical ventilation during hospitalization, the number of anesthetic drugs used during hospitalization, and the frequency of epileptic seizures from the termination of CSE to 30 days after randomization, and calculate the patient response data weight based on the data during the treatment of status epilepticus, and the patient response data collection module sends the calculated patient response data weight to the patient data analysis module; The patient data analysis module calculates the patient treatment data prediction value based on the patient basic parameter weight and the patient response data weight, and sends the patient treatment data prediction value to the patient data management module; The patient data management module includes a doctor port and a patient port, wherein the doctor port and the patient port are used to display the calculated patient treatment data prediction value and all data in the patient data collection module and the patient response data collection module to the doctor and the patient, and the doctor port is also used for the doctor to provide treatment suggestions to the patient based on all data in the patient data collection module and the patient response data collection module and the patient treatment data prediction value, and the patient port is also used for the patient to view the treatment suggestions given by the doctor, and after the patient is transferred to another hospital for treatment or the treatment is completed, the stored data is deleted after the patient data is sent to the patient; There is a data connection between the patient medication management module and the patient port in the patient data management module, and the patient's medication treatment time is recorded while reminding the patient to take the medicine on time.
2. The intelligent management platform for patient response data after medication according to claim 1, characterized in that: The specific expression of the patient data collection module collecting the patient's basic parameters including age, gender and etiology type is as follows: Among them, HZSJ represents the patient dataset, They represent the number storage sets of patients 1 to N, respectively. Patient n is the nth patient among patients 1 to N. The data expression stored in the number storage set of patient n is as follows: Among them, NL n represents the age of patient n, XB n Indicates the gender of patient n, BY n Represents the etiology type of patient n.
3. The intelligent management platform for patient response data after medication according to claim 2 is characterized by: The patient data collection module calculates the proportion of each group of different etiology types, and comprehensively obtains the proportion of different etiology types to obtain the specific expression of the patient's basic parameter weight as follows: Among them, HZJC n represents the patient basic parameter weight of patient n, N represents the total number of causes of patient n in the corresponding age group. all represents the total number of all causes, Represents the total number of causes of patient n that occur in their corresponding gender group.
4. The intelligent management platform for patient response data after medication according to claim 3 is characterized by: The specific expression of the data collected by the patient response data collection module during the treatment of status epilepticus is as follows: [mSTESS,t n ,RSE,super-RSE,Todd’s,TQCS n ,MZCS n ,FZPL n ] Among them, mSTESS refers to the proportion of patients with mSTESS score ≤ 2 points within 24 hours after enrollment, t n represents the end time of CSE episode of patient n, RSE represents the incidence of RSE, super-RSE represents the incidence of super-RSE, Todd's represents the incidence of Todd's palsy, TQCS represents the incidence of n represents the number of mechanical ventilation occurrences during hospitalization of patient n, MZCS n represents the number of times patient n used anesthetic drugs during hospitalization, FZPL n represents the frequency of epileptic seizures in patient n from the end of CSE to 30 days after randomization.
5. The intelligent management platform for patient response data after medication according to claim 4 is characterized by: The specific expression of the patient response data weight obtained by the patient response data collection module based on the data during the treatment of status epilepticus is as follows: HZFY n =t n (mSTESS+RSE+super-RSE+Todd’s+FZPL n )+TQCS n +MZCS n Among them, mSTESS refers to the proportion of patients with mSTESS score ≤ 2 points within 24 hours after enrollment, t n represents the end time of CSE episode of patient n, RSE represents the incidence of RSE, super-RSE represents the incidence of super-RSE, Todd's represents the incidence of Todd's palsy, TQCS represents the incidence of n represents the number of mechanical ventilation occurrences during hospitalization of patient n, MZCS n represents the number of anesthetic drugs used by patient n during hospitalization, PZPL n represents the frequency of epileptic seizures in patient n from the end of CSE to 30 days after randomization, HZFY n Represents the patient response data weight for patient n.
6. The intelligent management platform for patient response data after medication according to claim 5, characterized in that: The specific expression of the patient treatment data prediction value calculated by the patient data analysis module based on the patient basic parameter weight and the patient response data weight is as follows: Among them, HZYC n represents the patient response data weight of patient n, HZJC n represents the patient basic parameter weight of patient n, HZYC n represents the predicted value of patient treatment data, and e represents a natural constant.
7. The intelligent management platform for patient response data after medication according to claim 1, characterized in that: The doctor port and the patient port are both provided with access rights, and can be operated only after authorization by the doctor or the patient.
8. The intelligent management platform for patient response data after medication according to claim 1, characterized in that: The time and frequency of reminding the patient to take medicine on time in the patient medication management module are based on the data sent from the doctor port to the patient port in the patient data management module.
9. The intelligent management platform for patient response data after medication according to claim 1, characterized in that: The patient medication management module also includes calculating the patient's medication compliance: Among them, YCD represents the patient's medication compliance, ZQCS represents the exact number of times the patient takes the medication, and FYCS represents the total number of times the patient takes the medication.
10. A method for intelligent management of patient reaction data after medication, applied to the intelligent management platform for patient reaction data after medication as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect the basic parameters of the patient and obtain the weights of the basic parameters of the patient; Step 2: Collect data from patients during the treatment of status epilepticus and calculate the weight of patient response data; Step 3: Calculate the predicted value of the patient treatment data based on the patient's basic parameter weights and the patient's response data weights; Step 4: The patient receives treatment suggestions provided to the patient based on all data in the patient data collection module, the patient response data collection module and the patient treatment data prediction value output by the doctor through the doctor port at the patient port; Step 5: After the patient is transferred to another hospital for treatment or the treatment is completed, the stored data is deleted after the patient data is sent to the patient.
Citation Information
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Epileptic seizure recording and management patient equipment, server, medical equipment, and system
CN105069729A