An intelligent management platform and management method for patient response data after medication

Abstract

The present invention relates to the technical field of patient response data management and analysis, and discloses an intelligent management platform and management method for patient response data after medication, which includes a patient data acquisition module, a patient response data collection module, a patient data analysis module, a patient data management module, and a patient medication management module. The platform collects data from patients during the treatment of status epilepticus through the weight of patient basic parameters, calculates the weight of patient response data, and obtains the patient treatment data prediction value based on the patient basic parameter weight and the patient response data weight. The patient receives treatment advice provided to the patient by the doctor through the doctor port based on all data in the patient data acquisition module, the patient response data collection module, and the patient treatment data prediction value, thereby providing clear evaluation indicators for the specific status of the patient after taking the modified recipe of Changpu Yujin Decoction, ensuring the management of patient response data, and analyzing the treatment effect.

Description

Technical Field

[0001] The present invention relates to the technical field of patient reaction data management and analysis, and specifically to an intelligent management platform and management method for patient reaction data after medication. Background Art

[0002] Status epilepticus (SE) is a common, critical neurological emergency. Its complex seizure patterns require comprehensive treatment, often leading to severe neurological deficits and high rates of disability and mortality. Its incidence is approximately 0.3% to 0.8% in the general population and 2.6% to 6% in patients with epilepsy, with an annual incidence of approximately 12.6 per 100,000 people. Among the different types of SE, convulsive status epilepticus (CSE) is the most acute and severe, accounting for the highest proportion of SE cases, approximately 45% to 74%. It manifests as persistent limb rigidity, clonus, or sustained clonus, accompanied by impaired consciousness. International reports suggest a mortality rate of approximately 3.45% to 39% for CSE. Currently, there are no existing methods for managing and evaluating patient response data during the treatment of CSE using Changpu Yujin Decoction. Summary of the Invention

[0003] (1) Technical problems solved

[0004] In response to the shortcomings of the existing technology, the present invention provides an intelligent management platform and management method for patient reaction data after medication, which has the advantages of managing patient reaction data and analyzing treatment effects, and solves the above technical problems.

[0005] (2) Technical solution

[0006] To achieve the above-mentioned object, 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 patient parameters, 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 obtained proportions of different etiology types are combined to obtain the patient basic parameter weights, and the obtained patient basic parameter weights are sent 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 patients with a mSTESS score ≤ 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 events during hospitalization, the number of anesthetic drug use 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. 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, which are used to display the calculated patient treatment data prediction value and all data in the patient data acquisition module and the patient response data collection module to the doctor and the patient. The doctor port is also used to allow the doctor to provide treatment suggestions to the patient based on all data in the patient data acquisition module, the patient response data collection module and the patient treatment data prediction value. 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 after the treatment is completed, the patient data is sent to the patient and the stored data is deleted;

[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 acquisition 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 stored in the number storage set of patient n is expressed as follows:

[0015]

[0016] Among them, NL n represents the age of patient n, XB n Indicates the gender of patient n, BY n Indicates the etiology type of patient n.

[0017] As a preferred technical solution of the present invention, the patient data acquisition 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 basic parameter weight as follows:

[0018]

[0019] Among them, HZJC n represents the patient basic parameter weight of patient n, The total number of causes of patient N in the corresponding age group, N all represents the total number of all causes, It represents the total number of occurrences of the cause of patient n in the 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 mSTESS scores ≤ 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 episodes 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 mSTESS scores ≤ 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 episodes 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 patient data analysis module calculates the patient treatment data prediction value based on the patient basic parameter weight and the patient response data weight 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, both the doctor port and the patient port are provided with access rights, and both can be operated only after authorization 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 further 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 patient's basic parameters and obtain the patient's basic parameter weights;

[0036] Step 2: Collect data from patients during the treatment of status epilepticus and calculate the weight of the patient response data;

[0037] Step 3: Calculate the predicted value of the patient's treatment data based on the patient's basic parameter weights and the patient's response data weights;

[0038] Step 4: The patient receives treatment recommendations provided by the doctor through the doctor's port based on all data in the patient data acquisition module, the patient response data collection module, and the patient treatment data prediction value.

[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 existing technology, the present invention provides an intelligent management platform and 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 during the treatment of status epilepticus, and calculates weights of patient response data, and calculates predicted values ​​of patient treatment data based on the weights of basic parameters and 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 the patient 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 Schematic diagram of the system framework of the present invention;

[0043] Figure 2 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0045] In this study, patients received a modified prescription of Changpu Yujin Tang (Cypress Curcuma Decoction) in addition to standard Western medical treatment for CSE. The prescription consisted of: 9g of Acorus calamus, 6g of Curcuma zedoaria, 9g of stir-fried Gardenia jasminoides, 9g of fresh bamboo leaves, 9g of Paeonia suffruticosa, 6g of Forsythia suspensa, and 15g of Dangzhuli (Light Bamboo Liquor). The prescription was modified based on the patient's condition. For excessive liver fire, Gastrodia elata, Uncaria rhynchophylla, and Antelope horn were added; for phlegm obstructing the orifices, Gardenia jasminoides were removed and Atractylodes macrocephala and Poria cocos were added; for phlegm-heat and abdominal fullness, Bupleurum chinense, Scutellaria baicalensis, Rhubarb, and Sodium sulfate were added. The Chinese medicine was prepared into a decoction-free granule. One dose was taken twice daily from enrollment until day 7.

[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 patient parameters, 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 obtained proportions of different etiology types are combined to obtain the patient basic parameter weights, and the obtained patient basic parameter weights are sent to the patient data analysis module. The specific expressions for collecting patient basic parameters 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 stored in the number storage set of patient n is expressed 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 combines the obtained proportions of different etiology types to obtain the specific expression of the patient 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 their corresponding age group. all represents the total number of all causes, It represents the total number of occurrences of the cause of patient n in the 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 patients with 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 ventilations during hospitalization, the number of anesthetics used during hospitalization, and the frequency of epileptic seizures from the termination of CSE to 30 days after randomization. The patient response data collection module calculates the patient response data weight based on the data 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 for the data collected by the patient response data collection module 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 mSTESS scores ≤ 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 episodes during hospitalization of patient n, MZCS n represents the number of times patient n used anesthetic drugs during hospitalization, FZPL n The specific expression for the patient response data weight calculated by the patient response data collection module based on the data during the treatment of status epilepticus is 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 mSTESS scores ≤ 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 episodes 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 weights and the patient response data weights, 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 weights and the patient response data weights 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 acquisition module and the patient response data collection module to doctors and patients. The doctor port is also used to allow doctors to provide treatment recommendations to patients based on all data in the patient data acquisition module, the patient response data collection module and the patient treatment data prediction values. The patient port is also used for patients to view the treatment recommendations given by doctors, 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 can only be operated after authorization by the doctor or patient. The time and number of times that patients are 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. It records the patient's medication treatment time and reminds the patient to take the medicine on time. The patient medication management module also includes 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 example illustrates 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 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 patient's basic parameters and obtain the patient's basic parameter weights;

[0070] Step 2: Collect data from patients during the treatment of status epilepticus and calculate the weight of the patient response data;

[0071] Step 3: Calculate the predicted value of the patient's treatment data based on the patient's basic parameter weights and the patient's response data weights;

[0072] Step 4: The patient receives treatment recommendations provided by the doctor through the doctor's port based on all data in the patient data acquisition module, the patient response data collection module, and the patient treatment data prediction value.

[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] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the 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 patient data collection module, patient response data collection module, patient data analysis module, patient data management module and patient medication management module; The patient data collection module is used to collect basic patient parameters, 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 obtained proportions of different etiology types are combined to obtain the patient basic parameter weights, and the obtained patient basic parameter weights are sent 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 patients with a mSTESS score ≤ 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 events during hospitalization, the number of anesthetic drug use 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. 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, which are used to display the calculated patient treatment data prediction value and all data in the patient data acquisition module and the patient response data collection module to the doctor and the patient. The doctor port is also used to allow the doctor to provide treatment suggestions to the patient based on all data in the patient data acquisition module, the patient response data collection module and the patient treatment data prediction value. 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 after the treatment is completed, the patient data is sent to the patient and the stored data is deleted; 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 data collection module calculates the proportion of each group of different etiology types, and combines the obtained proportions of different etiology types to obtain the specific expression of the patient 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 their corresponding age group. all represents the total number of all causes, It represents the total number of occurrences of the cause of patient n in the corresponding gender group; The patient response data collection module calculates the patient response data weight based on the data during the treatment of status epilepticus 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 mSTESS scores ≤ 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 episodes 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 of patient n; The specific expression for the patient treatment data prediction value calculated by the patient data analysis module based on the patient basic parameter weights and the patient response data weights is as follows: 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.

2. The intelligent management platform for patient response data after medication according to claim 1, characterized in that: The patient data collection module collects the patient's basic parameters including age, gender and etiology type. The specific expression 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 stored in the number storage set of patient n is expressed as follows: Among them, NL n represents the age of patient n, XB n Indicates the gender of patient n, BY n Indicates the etiology type of patient n.

3. The intelligent management platform for patient response data after medication according to claim 2, characterized in that: 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 mSTESS scores ≤ 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 episodes 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.

4. 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 only be operated after authorization by the doctor or the patient.

5. The intelligent management platform for patient response data after medication according to claim 1 is characterized by: 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.

6. 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.

7. A method for intelligent management of patient reaction data after medication, applied to the intelligent management platform for patient reaction data after medication according to any one of claims 1 to 6, characterized in that: The following steps are involved: Step 1: Collect the patient's basic parameters and obtain the patient's basic parameter weights; Step 2: Collect data from patients during the treatment of status epilepticus and calculate the weight of the patient response data; Step 3: Calculate the predicted value of the patient's treatment data based on the patient's basic parameter weights and the patient's response data weights; Step 4: The patient receives treatment recommendations provided by the doctor through the doctor's port based on all data in the patient data acquisition module, the patient response data collection module, and the patient treatment data prediction value. 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.

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