CN113990426A - Medical risk assessment system and method - Google Patents

Abstract

Translated fromChinese

本发明提供一种医疗风险评估系统及方法，包括：病人管理模块，用于存储病人带有编码的身份信息；电子病历模块，用于存储病人的电子病历信息；数据模块，用于根据病人的编码来获取对应的电子病历信息；检测模块，用于检测治疗中病人的生理指标；预处理模块，用于处理得到高风险生理指标的实时数值；第一分析模块，用于建立第一风险评估模型并根据待治疗病人的初始病症处理得到多个治疗方案；第二分析模块，用于建立第二风险评估模型并根据输入的高风险生理指标的实时数值处理得到数值所处区间及比重。有益效果是本系统及方法对于待治疗和治疗中病人分别进行治疗方案的风险评估，得出的风险评估结果能够有效帮助医生选择或更换最佳治疗方案。

The present invention provides a medical risk assessment system and method, comprising: a patient management module for storing coded identity information of a patient; an electronic medical record module for storing the electronic medical record information of the patient; a data module for storing the patient's electronic medical record information; coding to obtain corresponding electronic medical record information; a detection module, used to detect the physiological indicators of patients under treatment; a preprocessing module, used to process and obtain real-time values of high-risk physiological indicators; a first analysis module, used to establish a first risk assessment The model is processed to obtain multiple treatment plans according to the initial symptoms of the patient to be treated; the second analysis module is used to establish a second risk assessment model and obtain the interval and proportion of the value according to the real-time numerical processing of the input high-risk physiological index. The beneficial effect is that the system and the method respectively carry out risk assessment of the treatment plan for the patient to be treated and the patient under treatment, and the obtained risk assessment result can effectively help the doctor to choose or replace the best treatment plan.

Description

Medical risk assessment system and method

Technical Field

The invention relates to the technical field of medical treatment, in particular to a medical risk assessment system and a medical risk assessment method.

Background

With the rapid development of science and technology, the medical industry increasingly applies big data technology, carries out directional analysis by collecting a large amount of patient information, and obtains the risks of patients of the same type in the treatment process and after treatment by analyzing the patient information of a certain type.

The conventional risk assessment system only carries out directional assessment on a certain type of patients, is only limited to carry out risk assessment on medical events in the treatment process, cannot provide treatment scheme risk assessment on the patients in the treatment process and treatment scheme risk assessment before treatment, has one-sidedness and single function, and cannot provide risk assessment of the whole treatment process for the patients.

Disclosure of Invention

In view of the problems in the prior art, the present invention provides a medical risk assessment system for performing medical risk assessment on a patient to be treated and a patient under treatment, the medical risk assessment system comprising:

a patient management module for storing coded identity information for a plurality of past patients, a plurality of said patients to be treated and a plurality of said patients under treatment;

the electronic medical record module is used for storing the electronic medical record information of each past patient, each patient to be treated and each patient in treatment;

the data module is respectively connected with the patient management module and the electronic medical record module and used for acquiring corresponding electronic medical record information according to the codes of the previous patients, the patients to be treated and the patients in treatment, and counting the age, the initial symptoms, the disease types, the adopted treatment scheme, the adverse symptoms in the treatment process and a plurality of physiological indexes recorded in the treatment process in the electronic medical record information;

the detection module is used for detecting and outputting a plurality of physiological indexes of the patient in each treatment in real time;

the pretreatment module is connected with the detection module and is used for pretreating each physiological index of the patient in each treatment to obtain a real-time numerical value of a high-risk physiological index;

the first analysis module is connected with the data module and used for establishing a first risk assessment model according to the age, initial symptoms and disease types of each previous patient, the adopted treatment scheme and the adverse symptoms in the treatment process, and inputting the initial symptoms of each patient to be treated into the first risk assessment model to be processed to obtain a plurality of treatment schemes, a plurality of adverse symptoms in the treatment process of each treatment scheme and the occurrence probability of each adverse symptom so as to assist a doctor in medical risk assessment;

and the second risk evaluation model is used for setting a plurality of intervals for the numerical values of the physiological indexes, counting the number of the numerical values of the physiological indexes and processing the numerical values to obtain the proportion of all the intervals, and processing the interval in which the real-time numerical value of the high-risk physiological index is located and the corresponding proportion according to the input real-time numerical value of the high-risk physiological index so as to assist a doctor in medical risk evaluation.

Preferably, the first analysis module comprises:

a first model unit for establishing the first risk assessment model according to the age, initial condition, disease type, treatment scheme and adverse symptoms during treatment of each previous patient;

and the first analysis unit is connected with the first model unit and used for inputting the initial symptoms of the patients to be treated into the first risk assessment model and processing the initial symptoms to obtain a plurality of treatment schemes, a plurality of adverse symptoms appearing in the treatment process of each treatment scheme and the occurrence probability of each adverse symptom.

Preferably, the preprocessing module comprises:

the first processing unit is used for processing the real-time numerical value of each physiological index of the patient in each treatment and the preset standard numerical value of each physiological index to obtain a plurality of difference values;

the storage unit is used for storing a low risk interval, a medium risk interval and a high risk interval, wherein the upper limit value of the low risk interval is not more than the lower limit value of the medium risk interval, and the upper limit value of the medium risk interval is not more than the lower limit value of the high risk interval;

the second processing unit is respectively connected with the first processing unit and the storage unit and used for judging whether each difference value belongs to the low-risk interval or the medium-risk interval or the high-risk interval, and when the difference value belongs to the low-risk interval, the physiological index corresponding to the difference value is judged to be a low-risk physiological index;

when the difference value belongs to the intermediate risk interval, judging that the physiological index corresponding to the difference value is an intermediate risk physiological index;

and when the difference belongs to the high-risk interval, judging that the physiological index corresponding to the difference is a high-risk physiological index and outputting a real-time numerical value corresponding to the high-risk physiological index.

Preferably, the second analysis module comprises:

the second risk assessment model is set up into a plurality of intervals according to the values of the physiological indexes recorded in the previous patient treatment process, the number of the physiological index values is counted, and the proportion of each interval in all the intervals is obtained through processing;

and the second analysis unit is connected with the second model unit and used for inputting the real-time value of the high-risk physiological index into the second risk assessment model and processing the real-time value to obtain the interval where the real-time value of the high-risk physiological index is located and the corresponding proportion.

Preferably, the medical risk assessment method is applied to the medical risk assessment system, and specifically includes the following steps:

step S1, the medical risk assessment system obtains corresponding electronic medical record information according to the codes of the previous patients, the patients to be treated, and the patients under treatment, and counts the age, initial condition, disease type, treatment plan, adverse symptoms during treatment, and physiological indexes recorded during treatment in the electronic medical record information, and determines whether the patients are the patients under treatment:

if yes, go to step S2;

if not, go to step S3;

step S2, the medical risk assessment system inputs the initial condition of each patient to be treated into the first risk assessment model configured in advance, and processes the initial condition to obtain a plurality of treatment plans, a plurality of adverse symptoms occurring in the treatment process of each treatment plan, and the occurrence probability of each adverse symptom, so as to assist a doctor in performing medical risk assessment;

step S3, the medical risk assessment system inputs the real-time value of the high-risk physiological index into the second risk assessment model configured in advance, and processes the real-time value to obtain an interval where the real-time value of the high-risk physiological index is located and a corresponding proportion, so as to assist a doctor in performing medical assessment.

Preferably, the step S2 includes:

step S21, the medical risk assessment system inputs the initial condition of each patient to be treated into the first risk assessment model, and the first risk assessment model sequentially screens a plurality of treatment plans, a plurality of adverse symptoms occurring in the treatment process of each treatment plan, and the occurrence probability of each adverse symptom according to the age, the disease type, and the initial condition of each patient to be treated;

step S22, the medical risk assessment system compares the occurrence probability of each adverse symptom with a preset threshold, and outputs the corresponding adverse symptom and the occurrence probability when the occurrence probability of the adverse symptom is greater than the preset threshold.

Preferably, if the medical risk assessment system stores a low risk section, a medium risk section, and a high risk section in advance, the upper limit value of the low risk section is not greater than the lower limit value of the medium risk section, and the upper limit value of the medium risk section is not greater than the lower limit value of the high risk section, the step S3 further includes:

step S31, the medical risk assessment system processes the real-time values of the physiological indexes of the patients in the treatment and the preset standard values of the physiological indexes to obtain a plurality of difference values;

step S32, the medical risk assessment system determines whether each difference belongs to the high risk section:

if yes, judging that the physiological index corresponding to the difference value is a high-risk physiological index, acquiring a real-time numerical value corresponding to the high-risk physiological index, and turning to the step S33;

if not, exiting;

step S33, the medical risk assessment system inputs the real-time value of the high-risk physiological index into the second risk assessment model, and processes the real-time value to obtain an interval and a corresponding proportion of the real-time value of the high-risk physiological index, and a proportion of a previous patient in the interval in which a treatment plan is changed.

The technical scheme has the following advantages or beneficial effects: the system and the method respectively carry out the risk assessment of the treatment scheme on the patient to be treated and the patient in treatment, and the obtained risk assessment result can effectively help a doctor to select or replace the optimal treatment scheme.

Drawings

FIG. 1 is a block diagram of the system according to the preferred embodiment of the present invention;

FIG. 2 is a flow chart of the steps of the method according to the preferred embodiment of the present invention;

FIG. 3 is a flowchart illustrating the detailed procedure of step S2 according to the preferred embodiment of the present invention;

FIG. 4 is a flowchart illustrating the step S3 according to the preferred embodiment of the present invention.

Detailed Description

The invention is described in detail below with reference to the figures and specific embodiments. The present invention is not limited to the embodiment, and other embodiments may be included in the scope of the present invention as long as the gist of the present invention is satisfied.

In accordance with the above-mentioned problems of the prior art, there is provided a medical risk assessment system for performing medical risk assessment on a patient to be treated and a patient under treatment, as shown in fig. 1, the medical risk assessment system comprising:

apatient management module 1 for storing identity information with codes of a plurality of past patients, a plurality of patients to be treated and a plurality of patients under treatment;

an electronicmedical record module 2, which is used for storing the electronic medical record information of each previous patient, each patient to be treated and each patient in treatment;

the data module 3 is respectively connected with thepatient management module 1 and the electronicmedical record module 2 and is used for acquiring corresponding electronic medical record information according to the codes of the previous patients, the patients to be treated and the patients in treatment, and counting the age, the initial symptoms and the disease types in the electronic medical record information, the adopted treatment scheme, the adverse symptoms in the treatment process and a plurality of physiological indexes recorded in the treatment process;

adetection module 4 for detecting and outputting multiple physiological indexes of patients in each treatment in real time;

thepretreatment module 5 is connected with thedetection module 4 and is used for pretreating all physiological indexes of patients in all treatments to obtain real-time numerical values of high-risk physiological indexes;

thefirst analysis module 6 is connected with the data module 3 and used for establishing a first risk assessment model according to the age, initial symptoms and disease types of previous patients, the adopted treatment scheme and adverse symptoms in the treatment process, and inputting the initial symptoms of the patients to be treated into the first risk assessment model to be processed to obtain a plurality of treatment schemes, a plurality of adverse symptoms in the treatment process of each treatment scheme and the occurrence probability of each adverse symptom so as to assist doctors in medical risk assessment;

and the second analysis module 7 is respectively connected with the data module 3 and thepreprocessing module 5 and is used for establishing a second risk assessment model according to the physiological indexes recorded in the previous treatment process of the patient, the second risk assessment model sets a plurality of intervals for the values of the physiological indexes, the number of the values of the physiological indexes is counted and the proportion of the intervals in all the intervals is obtained through processing, and the second risk assessment model processes the intervals where the real-time values of the high-risk physiological indexes are located and the corresponding proportion according to the input real-time values of the high-risk physiological indexes so as to assist a doctor in medical risk assessment.

Specifically, in this embodiment, considering that a patient to be treated and a patient under treatment exist in a daily medical event, and therefore, medical risk assessment needs to be performed on both the patient to be treated and the patient under treatment, for the patient under treatment, the system can give a plurality of treatment plans suitable for the patient under treatment, a plurality of adverse symptoms occurring in the treatment process of each treatment plan, and occurrence probabilities of the adverse symptoms according to the first risk assessment model and the initial condition of the patient under treatment, a doctor selects a treatment plan most suitable for the patient under treatment and can make corresponding plan preparation for the adverse symptoms that may occur in the future, for the patient under treatment, the system gives an interval and a specific gravity of a value according to the second risk assessment model and a real-time value of a high-risk physiological index of the patient under treatment, and if the real-time value is in a higher interval and the specific gravity of the interval is smaller, the doctor needs to consider whether to adjust the treatment plan of the patient under treatment, and if the real-time value is in the middle and lower intervals and the proportion of the interval is large, the doctor does not need to adjust the treatment plan of the patient under treatment.

In a preferred embodiment of the invention, thefirst analysis module 6 comprises:

afirst model unit 61 for establishing a first risk assessment model according to the age, initial condition, disease type, treatment plan and adverse symptoms occurring during treatment of each previous patient;

and thefirst analysis unit 62 is connected with thefirst model unit 61 and is used for inputting the initial symptoms of each patient to be treated into the first risk assessment model and processing the initial symptoms to obtain a plurality of treatment schemes, a plurality of adverse symptoms appearing in the treatment process of each treatment scheme and the occurrence probability of each adverse symptom.

Specifically, in this embodiment, the system uses big data technology to collect the age, initial condition, and disease type of the previous patient, adopt the treatment scheme, and the adverse symptoms occurring during the treatment process, and integrate the data, and uses the age, initial condition, and disease type of the previous patient as the data in the first risk assessment model input set, uses the treatment scheme adopted by the previous patient, and the adverse symptoms occurring during the treatment process as the data in the first risk assessment model output set, and associates the input set with the output set with the data, and the treatment scheme can be obtained by inputting the initial condition, age, and disease type of the patient to be treated to the first risk assessment model.

In a preferred embodiment of the present invention, thepreprocessing module 5 includes:

afirst processing unit 51, for processing the real-time values of the physiological indexes of the patients under treatment and the preset standard values of the physiological indexes to obtain a plurality of difference values;

astorage unit 52, configured to store a low risk interval, a medium risk interval, and a high risk interval, where an upper limit value of the low risk interval is not greater than a lower limit value of the medium risk interval, and an upper limit value of the medium risk interval is not greater than a lower limit value of the high risk interval;

thesecond processing unit 53 is respectively connected with thefirst processing unit 51 and thestorage unit 52, and is used for judging whether each difference value belongs to a low risk interval or a medium risk interval or a high risk interval, and when the difference value belongs to the low risk interval, judging the physiological index corresponding to the difference value as a low risk physiological index;

when the difference value belongs to the medium risk interval, judging the physiological index corresponding to the difference value as a medium risk physiological index;

and when the difference belongs to the high-risk interval, judging the physiological index corresponding to the difference as the high-risk physiological index and outputting a real-time value corresponding to the high-risk physiological index.

Specifically, in this embodiment, in consideration of the fact that the hospital needs to regularly acquire the physiological indexes of the patient during treatment in the actual application scenario, the detection results presented to the doctor and the patient during treatment often have a plurality of pages, and many normal or slightly changed physiological indexes exist in the pages, so that the system distinguishes the physiological indexes by setting a low risk interval, a medium risk interval and a high risk interval, and only extracts the physiological indexes in the high risk interval, thereby greatly reducing the viewing time of the doctor and the patient to be treated.

In a preferred embodiment of the invention, the second analysis module 7 comprises:

asecond model unit 71, configured to establish a second risk assessment model according to each physiological index recorded in each previous patient treatment process, where the second risk assessment model sets multiple intervals for the value of each physiological index, counts the number of the values of each physiological index, and processes the number to obtain the specific gravity of each interval in all the intervals;

and thesecond analysis unit 72 is connected with thesecond model unit 71 and is used for inputting the real-time value of the high-risk physiological index into the second risk assessment model and processing the interval in which the real-time value of the high-risk physiological index is located and the corresponding proportion.

Specifically, in this embodiment, the system uses a big data technology to collect various physiological indexes recorded in the previous treatment process of the patient, and integrates the data, and uses the values of the various physiological indexes recorded in the previous treatment process of the patient as data in the input set of the second risk assessment model, uses the interval in which the values are located and the corresponding specific gravity as data in the output set of the second risk assessment model, and associates the input set with the output set, and the interval and the specific gravity described by the values can be obtained by outputting the real-time values of the patient in treatment to the second risk assessment model.

In a preferred embodiment of the present invention, a medical risk assessment method is applied to a medical risk assessment system, as shown in fig. 2, and specifically includes the following steps:

step S1, the medical risk assessment system obtains the corresponding electronic medical record information according to the codes of the previous patients, the patients to be treated and the patients in treatment, and counts the age, the initial symptoms and the disease types in the electronic medical record information, adopts the treatment scheme, the adverse symptoms in the treatment process and a plurality of physiological indexes recorded in the treatment process to judge whether the patients are the patients to be treated:

if yes, go to step S2;

if not, go to step S3;

step S2, the medical risk assessment system inputs the initial symptoms of each patient to be treated into a first risk assessment model which is configured in advance, and a plurality of treatment schemes, a plurality of adverse symptoms which appear in the treatment process of each treatment scheme and the occurrence probability of each adverse symptom are obtained through processing, so that doctors are assisted in medical risk assessment;

step S3, the medical risk assessment system inputs the real-time value of the high-risk physiological index into a second risk assessment model configured in advance, and processes the interval and the corresponding proportion where the real-time value of the high-risk physiological index is located, so as to assist a doctor in performing medical assessment.

In a preferred embodiment of the present invention, as shown in fig. 3, step S2 includes:

step S21, the medical risk assessment system inputs the initial symptoms of each patient to be treated into a first risk assessment model, and the first risk assessment model sequentially screens a plurality of treatment schemes, a plurality of adverse symptoms occurring in the treatment process of each treatment scheme and the occurrence probability of each adverse symptom according to the age, the disease type and the initial symptoms of each patient to be treated;

step S22, the medical risk assessment system compares the occurrence probability of each adverse symptom with a preset threshold, and outputs a corresponding adverse symptom and occurrence probability when the occurrence probability of the adverse symptom is greater than the preset threshold.

Specifically, in this embodiment, in an actual application scenario, the first risk assessment model is first screened according to the age of the current patient to be treated, data of the age range of the current patient to be treated can be obtained by processing through a set age range, then screened according to the disease type of the current patient to be treated, and then screened according to the initial condition of the current patient to be treated, and finally screened to obtain a treatment scheme meeting the condition of the patient to be treated by performing similarity calculation on the initial condition (including data of all initial conditions of the current patient to be treated or only one non-compliant data of the initial condition of the current patient to be treated).

In a preferred embodiment of the present invention, step S3 includes:

the medical risk assessment system counts the number of the previous patients in each interval who change the treatment scheme and processes to obtain the proportion of the previous patients in each interval who change the treatment scheme.

Specifically, in this embodiment, on the basis of the interval and the specific gravity provided for the doctor with real-time numerical values, the number of people who change the treatment plan for the past patient in each interval is counted, and the specific gravity of the past patient who changes the treatment plan in each interval is obtained through processing, and the doctor visually sees the specific gravity of the past patient who changes the treatment plan to more effectively judge whether the treatment plan needs to be adjusted.

In a preferred embodiment of the present invention, the medical risk assessment system pre-stores a low risk interval, a medium risk interval and a high risk interval, an upper limit of the low risk interval is not greater than a lower limit of the medium risk interval, and an upper limit of the medium risk interval is not greater than a lower limit of the high risk interval, as shown in fig. 4, step S3 further includes:

step S31, the medical risk assessment system processes the real-time values of the physiological indexes of the patients in the treatment and the preset standard values of the physiological indexes to obtain a plurality of difference values;

step S32, the medical risk assessment system determines whether each difference belongs to a high risk interval:

if yes, the physiological index corresponding to the difference value is judged to be a high-risk physiological index, a real-time numerical value corresponding to the high-risk physiological index is obtained, and the step S33 is switched to;

if not, exiting;

step S33, the medical risk assessment system inputs the real-time value of the high-risk physiological index into the second risk assessment model and processes the interval and the corresponding proportion of the real-time value of the high-risk physiological index, and the proportion of the previous patient in the interval with the treatment plan replaced.

While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.

Claims (8)

Translated fromChinese

1.一种医疗风险评估系统，其特征在于，用于针对待治疗病人及治疗中病人进行医疗风险评估，则所述医疗风险评估系统包括:1. a medical risk assessment system, is characterized in that, for the patient to be treated and the patient in the treatment to carry out medical risk assessment, then described medical risk assessment system comprises:一病人管理模块，用于存储多个以往病人、多个所述待治疗病人和多个所述治疗中病人带有编码的身份信息；a patient management module, used for storing coded identity information of a plurality of past patients, a plurality of the patients to be treated and a plurality of the patients under treatment;一电子病历模块，用于存储各所述以往病人、各所述待治疗病人和各所述治疗中病人的电子病历信息；an electronic medical record module for storing electronic medical record information of each of the past patients, each of the patients to be treated and each of the patients under treatment;一数据模块，分别连接所述病人管理模块和所述电子病历模块，用于根据各所述以往病人、各所述待治疗病人和各所述治疗中病人的编码来获取对应的电子病历信息，并统计各所述电子病历信息中的年龄、初始病症、疾病类型、采用治疗方案、治疗过程中出现的不良症状和治疗过程中记录的多项生理指标；a data module, respectively connected to the patient management module and the electronic medical record module, for acquiring corresponding electronic medical record information according to the codes of each of the past patients, each of the patients to be treated and each of the patients under treatment, And count the age, initial symptoms, disease types, treatment plans adopted, adverse symptoms during treatment, and a number of physiological indicators recorded during treatment in each of the electronic medical record information;一检测模块，用于实时检测各所述治疗中病人的多项生理指标并输出；A detection module, used for real-time detection and output of a plurality of physiological indicators of the patients under treatment;一预处理模块，连接所述检测模块，用于对各所述治疗中病人的各所述生理指标进行预处理得到高风险生理指标的实时数值；a preprocessing module, connected to the detection module, for preprocessing each of the physiological indicators of each of the patients under treatment to obtain real-time values of high-risk physiological indicators;一第一分析模块，连接所述数据模块，用于根据各所述以往病人的年龄、初始病症、疾病类型、采用治疗方案及治疗过程中出现的不良症状建立一第一风险评估模型，并将各所述待治疗病人的初始病症输入至所述第一风险评估模型处理得到多个治疗方案、各所述治疗方案治疗过程中出现的多个不良症状及各所述不良症状的发生概率，以辅助医生进行医疗风险评估；A first analysis module, connected to the data module, for establishing a first risk assessment model according to the age, initial symptoms, disease types, treatment plans and adverse symptoms occurring in the treatment process of each of the previous patients, and assigning The initial symptoms of each of the patients to be treated are input into the first risk assessment model for processing to obtain a plurality of treatment plans, a plurality of adverse symptoms that occur during the treatment of each of the treatment plans, and the probability of occurrence of each of the adverse symptoms, to obtain Assist doctors in medical risk assessment;一第二分析模块，分别连接所述数据模块和所述预处理模块，用于根据各所述以往病人治疗过程中记录的各所述生理指标建立一第二风险评估模型，所述第二风险评估模型对各所述生理指标的数值设置多个区间，统计各所述生理指标数值的数量并处理得到各所述区间在所有区间中的比重，所述第二风险评估模型根据输入的所述高风险生理指标的实时数值处理得到所述高风险生理指标的实时数值所处的区间及对应的比重，以辅助医生进行医疗风险评估。a second analysis module, respectively connected to the data module and the preprocessing module, for establishing a second risk assessment model according to each of the physiological indicators recorded during the treatment of each of the previous patients, the second risk The evaluation model sets a plurality of intervals for the values of each of the physiological indicators, counts the number of the values of the physiological indicators and processes to obtain the proportion of each of the intervals in all intervals, and the second risk assessment model is based on the input of the The real-time numerical processing of the high-risk physiological index obtains the interval in which the real-time numerical value of the high-risk physiological index is located and the corresponding proportion, so as to assist the doctor in medical risk assessment.2.根据权利要求1所述的医疗风险评估系统，其特征在于，所述第一分析模块包括：2. The medical risk assessment system according to claim 1, wherein the first analysis module comprises:一第一模型单元，用于根据各所述以往病人的年龄、初始病症、疾病类型、采用治疗方案及治疗过程中出现的不良症状建立所述第一风险评估模型；a first model unit, used for establishing the first risk assessment model according to the age, initial condition, disease type, treatment plan adopted and adverse symptoms occurring during the treatment of each of the previous patients;一第一分析单元，连接所述第一模型单元，用于将各所述待治疗病人的初始病症输入至所述第一风险评估模型并处理得到多个治疗方案、各所述治疗方案治疗过程中出现的多个不良症状及各所述不良症状的发生概率。A first analysis unit, connected to the first model unit, for inputting the initial symptoms of each of the patients to be treated into the first risk assessment model and processing to obtain a plurality of treatment plans, the treatment process of each of the treatment plans A number of adverse symptoms appearing in and the probability of occurrence of each of the adverse symptoms.3.根据权利要求1所述的医疗风险评估系统，其特征在于，所述预处理模块包括：3. The medical risk assessment system according to claim 1, wherein the preprocessing module comprises:一第一处理单元，用于根据各所述治疗中病人的各所述生理指标的实时数值和预先设置的各所述生理指标的标准数值处理得到多个差值；a first processing unit, configured to process and obtain a plurality of difference values according to the real-time value of each of the physiological indicators of each patient under treatment and the preset standard value of each of the physiological indicators;一存储单元，用于存储低风险区间、中风险区间和高风险区间，所述低风险区间的上限值不大于所述中风险区间的下限值，所述中风险区间的上限值不大于所述高风险区间的下限值；a storage unit for storing a low risk interval, a medium risk interval and a high risk interval, the upper limit of the low risk interval is not greater than the lower value of the medium risk interval, and the upper limit of the medium risk interval is not greater than greater than the lower limit of the high-risk interval;一第二处理单元，分别连接所述第一处理单元和所述存储单元，用于判断各所述差值属于所述低风险区间或所述中风险区间或所述高风险区间，当所述差值属于所述低风险区间时，判定所述差值对应的所述生理指标为低风险生理指标；a second processing unit, connected to the first processing unit and the storage unit, respectively, and configured to determine that each of the differences belongs to the low-risk interval, the medium-risk interval, or the high-risk interval. When the difference value belongs to the low-risk interval, it is determined that the physiological index corresponding to the difference value is a low-risk physiological index;当所述差值属于所述中风险区间时，判定所述差值对应的所述生理指标为中风险生理指标；When the difference value belongs to the medium risk interval, it is determined that the physiological index corresponding to the difference value is a medium risk physiological index;当所述差值属于所述高风险区间时，判定所述差值对应的所述生理指标为高风险生理指标并输出所述高风险生理指标对应的实时数值。When the difference value belongs to the high-risk interval, it is determined that the physiological index corresponding to the difference value is a high-risk physiological index, and a real-time value corresponding to the high-risk physiological index is output.4.根据权利要求1所述的医疗风险评估系统，其特征在于，所述第二分析模块包括：4. The medical risk assessment system according to claim 1, wherein the second analysis module comprises:一第二模型单元，用于根据各所述以往病人治疗过程中记录的各所述生理指标建立所述第二风险评估模型，所述第二风险评估模型对各所述生理指标的数值设置多个区间，统计各所述生理指标数值的数量并处理得到各所述区间在所有区间中的比重；a second model unit, configured to establish the second risk assessment model according to each of the physiological indicators recorded during the treatment of each of the patients in the past, and the second risk assessment model sets a number of values for each of the physiological indicators. each interval, count the number of each of the physiological index values and process to obtain the proportion of each of the intervals in all the intervals;一第二分析单元，连接所述第二模型单元，用于将所述高风险生理指标的实时数值输入所述第二风险评估模型并处理得到所述高风险生理指标的实时数值所处的区间及对应的比重。A second analysis unit, connected to the second model unit, for inputting the real-time value of the high-risk physiological index into the second risk assessment model and processing to obtain the interval in which the real-time value of the high-risk physiological index is located and the corresponding proportion.5.一种医疗风险评估方法，其特征在于，应用于如权利要求1-4中任意一项所述的医疗风险评估系统，具体包括以下步骤：5. a medical risk assessment method, is characterized in that, is applied to the medical risk assessment system as described in any one in claim 1-4, specifically comprises the following steps:步骤S1，所述医疗风险评估系统根据各所述以往病人、各所述待治疗病人和各所述治疗中病人的编码来获取对应的电子病历信息，并统计各所述电子病历信息中的年龄、初始病症、疾病类型、采用治疗方案、治疗过程中出现的不良症状和治疗过程中记录的多项生理指标，判断所述病人是否为待治疗病人：Step S1, the medical risk assessment system obtains corresponding electronic medical record information according to the codes of each of the past patients, each of the patients to be treated, and each of the patients under treatment, and counts the age in each of the electronic medical record information. , initial symptoms, disease types, treatment plans, adverse symptoms that occurred during treatment, and multiple physiological indicators recorded during treatment to determine whether the patient is a patient to be treated:若是，转向步骤S2；If yes, go to step S2;若否，转向步骤S3；If not, turn to step S3;步骤S2，所述医疗风险评估系统将各所述待治疗病人的初始病症输入至预先配置的所述第一风险评估模型处理得到多个治疗方案、各所述治疗方案治疗过程中出现的多个不良症状及各所述不良症状的发生概率，以辅助医生进行医疗风险评估；Step S2, the medical risk assessment system inputs the initial symptoms of each of the patients to be treated into the preconfigured first risk assessment model for processing to obtain multiple treatment plans, and multiple treatment plans that occur during the treatment of each of the treatment plans. Adverse symptoms and the probability of occurrence of each said adverse symptoms to assist doctors in medical risk assessment;步骤S3，所述医疗风险评估系统将高风险生理指标的实时数值输入至预先配置的所述第二风险评估模型处理得到所述高风险生理指标的实时数值所处的区间及对应的比重，以辅助医生进行医疗评估。Step S3, the medical risk assessment system inputs the real-time value of the high-risk physiological index into the pre-configured second risk assessment model to obtain the interval and the corresponding proportion of the real-time value of the high-risk physiological index, so as to obtain the real-time value of the high-risk physiological index. Assisting physicians with medical assessments.6.根据权利要求5所述的医疗风险评估方法，其特征在于，所述步骤S2包括：6. The medical risk assessment method according to claim 5, wherein the step S2 comprises:步骤S21，所述医疗风险评估系统将各所述待治疗病人的初始病症输入所述第一风险评估模型，所述第一风险评估模型根据各所述待治疗病人的年龄、疾病类型、初始病症依次进行筛选得到多个治疗方案、各所述治疗方案治疗过程中出现的多个不良症状及各所述不良症状的发生概率；In step S21, the medical risk assessment system inputs the initial symptoms of each of the patients to be treated into the first risk assessment model, and the first risk assessment model is based on the age, disease type, and initial symptoms of each of the patients to be treated. Perform screening in sequence to obtain a plurality of treatment plans, a plurality of adverse symptoms occurring during the treatment of each of the treatment plans, and the probability of occurrence of each of the adverse symptoms;步骤S22，所述医疗风险评估系统将各所述不良症状的发生概率与一预设阈值进行比较，并在所述不良症状的发生概率大于所述预设阈值时输出对应的所述不良症状及发生概率。Step S22, the medical risk assessment system compares the occurrence probability of each adverse symptom with a preset threshold, and outputs the corresponding adverse symptom and the corresponding adverse symptom when the occurrence probability of the adverse symptom is greater than the preset threshold. probability of occurrence.7.根据权利要求5所述的医疗风险评估方法，其特征在于，所述步骤S3中包括：7. The medical risk assessment method according to claim 5, wherein the step S3 comprises:所述医疗风险评估系统统计各所述区间中的各所述以往病人更换治疗方案的人数并处理得到各所述区间中更换治疗方案的以往病人的比重。The medical risk assessment system counts the number of patients who have changed the treatment plan in each of the intervals, and processes to obtain the proportion of the past patients who changed the treatment plan in each of the intervals.8.根据权利要求7所述的医疗风险评估方法，其特征在于，所述医疗风险评估系统预先存储有低风险区间、中风险区间和高风险区间，所述低风险区间的上限值不大于所述中风险区间的下限值，所述中风险区间的上限值不大于所述高风险区间的下限值，则所述步骤S3还包括：8. The medical risk assessment method according to claim 7, wherein the medical risk assessment system pre-stores a low-risk interval, a medium-risk interval and a high-risk interval, and the upper limit of the low-risk interval is not greater than The lower limit of the medium risk interval, the upper limit of the medium risk interval is not greater than the lower limit of the high risk interval, then the step S3 further includes:步骤S31，所述医疗风险评估系统根据各所述治疗中病人的各所述生理指标的实时数值和预先设置的各所述生理指标的标准数值处理得到多个差值；Step S31, the medical risk assessment system obtains a plurality of difference values by processing according to the real-time value of each of the physiological indicators of each of the patients under treatment and the preset standard value of each of the physiological indicators;步骤S32，所述医疗风险评估系统判断各所述差值是否属于所述高风险区间：Step S32, the medical risk assessment system determines whether each of the differences belongs to the high-risk interval:若是，判定所述差值对应的所述生理指标为高风险生理指标，获取所述高风险生理指标对应的实时数值并转向步骤S33；If so, determine that the physiological index corresponding to the difference is a high-risk physiological index, obtain the real-time value corresponding to the high-risk physiological index, and turn to step S33;若否，退出；if not, exit;步骤S33，所述医疗风险评估系统将所述高风险生理指标的实时数值输入所述第二风险评估模型并处理得到所述高风险生理指标的实时数值所处的区间和对应的比重，以及所处区间中更换治疗方案的以往病人的比重。Step S33, the medical risk assessment system inputs the real-time value of the high-risk physiological index into the second risk assessment model and processes to obtain the interval and corresponding proportion of the real-time value of the high-risk physiological index, and The proportion of previous patients who changed treatment regimens in the interval.

Images