CN116580798B - Research method for dynamic and accurate clinical test selection of subject medicine - Google Patents
Research method for dynamic and accurate clinical test selection of subject medicine Download PDFInfo
- Publication number
- CN116580798B CN116580798B CN202310854226.7A CN202310854226A CN116580798B CN 116580798 B CN116580798 B CN 116580798B CN 202310854226 A CN202310854226 A CN 202310854226A CN 116580798 B CN116580798 B CN 116580798B
- Authority
- CN
- China
- Prior art keywords
- patient
- information
- disease
- hospital
- dynamic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 239000003814 drug Substances 0.000 title claims abstract description 33
- 238000011160 research Methods 0.000 title claims abstract description 17
- 238000012360 testing method Methods 0.000 title abstract description 10
- 201000010099 disease Diseases 0.000 claims abstract description 139
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 139
- 238000012216 screening Methods 0.000 claims abstract description 27
- 238000004458 analytical method Methods 0.000 claims abstract description 23
- 238000011156 evaluation Methods 0.000 claims abstract description 14
- 229940079593 drug Drugs 0.000 claims abstract description 12
- 238000003745 diagnosis Methods 0.000 claims description 10
- 230000008859 change Effects 0.000 claims description 9
- 230000010354 integration Effects 0.000 claims description 5
- 238000012790 confirmation Methods 0.000 claims description 4
- 238000007405 data analysis Methods 0.000 claims description 4
- 230000005484 gravity Effects 0.000 claims description 4
- 230000001360 synchronised effect Effects 0.000 claims description 4
- 230000007115 recruitment Effects 0.000 abstract description 23
- 230000008569 process Effects 0.000 description 9
- 238000011161 development Methods 0.000 description 7
- 230000002457 bidirectional effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000012106 screening analysis Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention discloses a research method for selecting a dynamic and accurate subject medicament clinical test, which comprises the following steps: firstly, collecting specific disease information of a patient in a hospital through a patient disease database in the hospital; then, acquiring the special disease information of the patient outside the hospital according to the follow-up disease data of the patient outside the hospital; next, comprehensively analyzing according to the special disease information in and out of the patient's hospital to obtain the condition information parameters of the patient; screening condition information parameters, and carrying out dynamic information prediction, evaluation and analysis; finally, matching the patient selection information according to the dynamic information prediction and evaluation analysis results, and screening the subjects meeting the conditions according to the selection information; according to the invention, condition information parameters are established, subject information is acquired through a dynamic algorithm, and historical body conditions, disease course data and the like of patients in a disease-specific system are utilized, so that the subject selection efficiency is improved, the recruitment accuracy is improved, and the period of clinical trials of medicines is shortened.
Description
Technical Field
The invention relates to the field of clinical trial research of medicines, in particular to a dynamic and accurate research method for clinical trial selection of a subject medicine.
Background
The clinical test of the medicine is an indispensable step for confirming the effectiveness and safety of a new medicine, and because of the specificity of methods, means and purposes of clinical research of the medicine, for example, participation of human subjects, approval of data and results of the clinical test of the medicine and the like are required, the clinical research of the medicine is different from the general scientific research, and more principles are required to be followed.
In the current clinical trial study work of medicines, the selection and recruitment of subjects are all based on invitations in patients in clinical trial institutions, or the release of recruitment information is carried out by public domain flow such as public numbers recruited by a certain subject, or a mode of placing a display rack in the clinical trial institutions.
In the clinical test process of the medicine, the progress of the selection and recruitment of the subjects determines the actual clinical application efficiency of the whole medicine and is also a link of success and failure of the production enterprise project; the traditional selection mode of the clinical trial study of the subject medicine has almost no pertinence, and the bidirectional information interaction process with the potential subject cannot be established, so that only the recruitment information can be issued unidirectionally; meanwhile, the efficiency of the selection recruitment is also different due to the number of patient group cardinalities of the clinical trial institution based on the recruitment of the patients to be treated; the existing clinical research of the medicine is mainly proposed by expert researchers of hospitals, recommended in patients with medical visits, great difficulty in recruitment of subjects occurs, the efficiency is poor, and the compliance of management is not high; resulting in inefficient recruitment of the market to the subject.
Disclosure of Invention
The invention aims to provide a research method for dynamic and accurate clinical trial selection of a subject medicament, which solves the following technical problems:
how to improve the selection efficiency of the subjects, improve the recruitment accuracy and shorten the period of clinical trial of the medicine.
The aim of the invention can be achieved by the following technical scheme:
a research method for dynamic and accurate clinical trial selection of a subject drug, the research method comprising:
s1, collecting patient disease information in a patient hospital through a patient disease database in the hospital;
s2, acquiring patient out-of-hospital specific disease information according to follow-up disease data of the patient out-of-hospital;
s3, comprehensively analyzing according to the special disease information in and out of the patient' S hospital to obtain the condition information parameters of the patient;
s4, screening condition information parameters and carrying out dynamic information prediction, evaluation and analysis;
and S5, matching the patient selection information according to the dynamic information prediction and evaluation analysis result, and screening the subjects meeting the conditions according to the selection information.
Preferably, the dynamic information prediction and evaluation analysis method in step S4 is as follows:
acquiring historical condition information parameters in a historical special department disease database;
creating a patient label from the historical condition information parameter analysis:
key fields of the patient label are set: age, sex, bmi, etc.;
setting different specific gravities of a plurality of labels of a patient;
and predicting the dynamic trend of the course of disease according to the generation condition of the patient label.
Preferably, the screening method of the condition information parameter in step S4 is as follows:
according to the formulaCalculating the prediction coefficient of the special diseases>, wherein ,/>、/>Is a weight coefficient; />For body mass index>For the patient visit times, < >>Is a patient age factor; />Is a standard body mass index; />Is a preset body mass index deviation value.
Preferably, the disease-specific prediction coefficient is calculatedWith a preset threshold (+)>，/>) And (3) performing comparison:
if it is≤/>Judging that the patient specific disease index is low;
if it is≤/>Judging that the patient specific disease index meets the requirement, and acquiring the current patient condition information parameter;
if it is＞/>And judging that the patient's specific disease index is higher.
Preferably, the process of comprehensive analysis in step S3:
synchronizing patient in-hospital specific disease information and out-of-hospital specific disease information to obtain data integration information;
analyzing the data integration information by a multidimensional data analysis method and acquiring patient condition information parameters, wherein the condition information parameters are used for predicting the incidence probability of a proprietary patient.
Preferably, the prediction process of the disease course development dynamic trend is as follows:
statistical history of the change profile of a condition information parameter over a specified period of time；
Statistically predicting a change curve of a condition information parameter over a specified period of time；
For each patient's condition information parameters, will and />Establishing in the same coordinate system;
will beAnd->Performing comparison, and calculating->And->In the overlapping region->Above->Area value +.>。
Preferably, the area value isPreset threshold value +.>Comparing, and judging the size of the patient's disease-specific dynamic trend:
if it is≥/>Judging that the dynamic trend of the patient specific disease is large, and evaluating the patient specific disease;
if it is＜/>And if the dynamic trend of the patient specific disease is smaller, updating the matching information outside the hospital of the patient.
Preferably, in step S5, the output mode of the patient selection information result is:
analyzing patient specific disease assessment information according to the patient specific disease dynamic trend size:
if it is≥/>Then get +.>And->Time point corresponding to the maximum distance point +.>Obtain->Judging whether a preset dynamic scoring result appears in the time period according to the patient's specific disease dynamic data information in the time period, and locking the patient's specific disease information data when judging that the dynamic scoring result does not appear;
setting a score corresponding to each standard according to expert selection standards, and grading the information data of the locking special diseases;
and obtaining a selection information table according to the grading ranking.
wherein ,is a preset period of time.
Preferably, the acquiring of the patient' S hospital specific information in step S1 further includes registering information of the patient through public numbers, and performing confirmation of rehabilitation in real time:
if yes, the special disease database is changed into a screening object, is determined to be in a screening state, and is updated synchronously;
if not, the patient information is determined to be the diagnosis target, the diagnosis state is determined, and the patient information is synchronously updated and exported.
Preferably, in step S2, the patient out-of-hospital specific information is confirmed by registering a public number account to confirm the diagnosis:
if yes, the patient is confirmed, and patient information matching is carried out;
if not, the patient is screened, and the screening data is uploaded to confirm the synchronous hospital information.
The invention has the beneficial effects that: the method comprises the steps of obtaining subject information through a dynamic algorithm, selecting a patient meeting the conditions as a subject through screening analysis by optimizing a screening flow according to the historical body condition, disease course data and the like of the patient in a disease-specific system and combining the disease course development characteristics of the disease; the selection and recruitment processes of the patients are optimized through the dynamic information prediction and evaluation analysis steps, the recruitment accuracy of the patients is improved, the period of clinical tests of the medicines is shortened, innovation of the medicine enterprises is facilitated, the clinical tests can be completed more rapidly, and the development of the whole industry is assisted.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of study of dynamic and accurate subject drug clinical trial selection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The traditional selection mode of the subject medicine clinical trial study is not targeted, the matched patient information is not accurate enough, the bidirectional information interaction process with potential subjects cannot be established, the scope of the general release of the recruitment information is limited, the current medicine clinical study is recommended to be selected from the patients in the treatment by hospital specialists, the efficiency of the subject recruitment is poor, and the management compliance is also not high.
In order to solve the technical problems, the invention dynamically analyzes the data information of the patient by using the principles of dynamic and accurate subject selection and recruitment and by applying big data and artificial intelligence technology and matching the requirements of researchers, efficiently selects out the subjects meeting the conditions, records the potential subject information and sends the tested invitation, thereby improving the research efficiency of the clinical test of the medicine.
Referring to fig. 1, the invention relates to a dynamic and accurate research method for selecting a subject medicament clinical test, which comprises the following steps:
s1, collecting patient disease information in a patient hospital through a patient disease database in the hospital;
s2, acquiring patient out-of-hospital specific disease information according to follow-up disease data of the patient out-of-hospital;
s3, comprehensively analyzing according to the special disease information in and out of the patient' S hospital to obtain the condition information parameters of the patient;
s4, screening condition information parameters and carrying out dynamic information prediction, evaluation and analysis;
and S5, matching the patient selection information according to the dynamic information prediction and evaluation analysis result, and screening the subjects meeting the conditions according to the selection information.
Through the technical scheme: through the development of medical informatization, acquiring the disease condition data of the patient in the hospital by a HIS, LIS, PACS system in the hospital and acquiring the external data by the application of an external patient whole course management system, the research method comprises the following specific steps: firstly, collecting specific disease information of a patient in a hospital through a patient disease database in the hospital; then, acquiring the special disease information of the patient outside the hospital according to the follow-up disease data of the patient outside the hospital; then, comprehensively analyzing according to the special disease information in and out of the patient's hospital to obtain the condition information parameters of the patient; screening condition information parameters and carrying out dynamic information prediction and evaluation analysis; and finally, matching the patient selection information according to the dynamic information prediction and evaluation analysis results, and screening the subjects meeting the conditions according to the selection information.
It should be noted that, according to the comprehensive analysis of the patient's in-hospital and out-of-hospital specific disease information, compared with the traditional independent database of specific diseases, or the slow disease management system, based on the traditional information statistics technology, the two are fused, and the comprehensive management of the patient's in-hospital and out-of-hospital is performed, thus realizing the communication of data; by integrating the data of the patient outside and crossing the hospital, the integrity and the continuity of the data are more strongly ensured.
As one embodiment of the present invention, the step S4 dynamic information prediction and evaluation analysis method comprises:
acquiring historical condition information parameters in a historical special department disease database;
creating a patient label from the historical condition information parameter analysis:
key fields of the patient label are set: age, sex, bmi, etc.;
setting different specific gravities of a plurality of labels of a patient;
and predicting the dynamic trend of the course of disease according to the generation condition of the patient label.
Through the technical scheme: acquiring historical condition information parameters in a historical special disease database, and creating a label of each patient through the historical condition information parameters, wherein the label is mainly based on disease data of the patient, such as current medical history and past history of the patient; and the patient bmi index is judged by combining personal information characteristics, living states and the like of the patient, the disease course development trend of the patient is analyzed and judged, and the indexes of specific disease information judgment corresponding to different diseases are different; creating a patient label from the historical condition information parameter analysis: key fields of the patient label are set: age, sex, bmi, etc.; setting different specific gravities of a plurality of labels of a patient; and predicting the dynamic trend of the course of disease according to the generation condition of the patient label.
As an embodiment of the present invention, the screening method of the condition information parameter in step S4 is as follows:
according to the formulaCalculating the prediction coefficient of the special diseases>, wherein ,/>、/>Is a weight coefficient; />For body mass index>For the patient visit times, < >>Is a patient age factor; />Is a standard body mass index; />Is a preset body mass index deviation value.
Through the technical scheme: screening patients meeting recruitment conditions by analyzing patient condition information parameters, specifically, by the formulaCalculating the prediction coefficient of the special diseases>, wherein ,/>、 />The weight coefficient is set according to different influence degrees of the patient condition index on the condition; />The body quality index can reflect the body quality condition of a patient; />For the patient visit times, < >>Is a patient age factor; />Is a standard body mass index; />The preset body mass index deviation value is selected according to empirical data, and will not be described herein.
Notably, are: patient age coefficientThe time period is set according to a selected section of the recruitment condition, and is determined according to whether different ages fall into a range of an age period corresponding to the recruitment condition, for example, the recruitment age period is 35 to 60 years old, a higher age coefficient is selected if the recruitment age period falls into the section, a lower age coefficient is selected if the recruitment age period does not fall into the section, and specific conditions are selected and adjusted according to the recruitment requirement, and the details are not described herein.
As one embodiment of the invention, the disease-specific prediction coefficientWith a preset threshold (+)>， />) And (3) performing comparison:
if it is≤/>Judging that the patient specific disease index is low;
if it is ≤ />Judging that the patient specific disease index meets the requirement;
if it is＞ />And judging that the patient's specific disease index is higher.
Through the technical scheme: according to the prediction coefficient of the specific diseaseJudging the special disease index condition of the patient in a size range; specifically, the calculated disease prediction coefficient +.>With a preset threshold (+)>， />) Comparing the sizes: judging if->≤ />Judging that the patient specific disease index is low; if->≤/>Judging that the patient specific disease index meets the requirement; if->＞/>And judging that the patient's specific disease index is higher. Patient specific disease information meeting the requirement of a preset range is screened to enter a dynamic information prediction evaluation stage; it should be noted that: for values not belonging to the preset threshold value ()>， />) The specific disease coefficient in the range is subjected to data archiving treatment, and the rehabilitation state of the patient is judged according to the specific disease information outside the patient hospital, so that the specific disease is convenient to treat and statistically record the data.
As an embodiment of the present invention, the process of comprehensive analysis in step S3:
synchronizing and summarizing the specific disease information of the patient in the hospital and the specific disease information of the patient outside the hospital;
analyzing the summarized specific disease information by a multidimensional data analysis method and acquiring patient condition information parameters, wherein the condition information parameters are used for predicting the incidence of the specific disease.
Through the technical scheme: by summarizing the specific disease information in the patient hospital and the specific disease information outside the patient hospital, the method in the embodiment comprises integrating the data outside the patient hospital and crossing the hospital area, different disease development information features are different, analysis of data integration information is realized through multidimensional data analysis, condition information parameters of the specific patient are obtained, the disease probability of the specific patient is predicted through the condition parameters, and the prediction accuracy of the specific patient is improved.
As one embodiment of the invention, the prediction process of the dynamic trend of the course of disease is as follows:
statistical history of the change profile of a condition information parameter over a specified period of time；
Statistically predicting a change curve of a condition information parameter over a specified period of time；
For each patient's condition information parameters, will and />Establishing in the same coordinate system;
will beAnd->Performing comparison, and calculating->And->In the overlapping region->Above->Area value of region。
Through the technical scheme: in order to accurately obtain the dynamic trend of the course of disease, the prediction process is as follows: statistical history of the change profile of a condition information parameter over a specified period of timeThe method comprises the steps of carrying out a first treatment on the surface of the Statistically predicting the variation curve of the condition information parameter in a specific period of time +.>The method comprises the steps of carrying out a first treatment on the surface of the For the condition information parameter of each patient +.> and />Establishing in the same coordinate system; will->And->Performing comparison, and calculating->And->In the overlapping region->Above->Area value +.>。
As an embodiment of the present invention, the area value isCritical index of patient specific diseasePreset threshold value corresponding to data parameter ∈>Comparing, and judging the size of the patient's disease-specific dynamic trend:
if it is≥/>Judging that the dynamic trend of the patient specific disease is large, and evaluating the patient specific disease;
if it is＜/>And if the dynamic trend of the patient specific disease is smaller, updating the matching information outside the hospital of the patient.
Through the technical scheme: by analysing area valuesThe size is used for analyzing the dynamic trend of the patient specific diseases, and specifically the area value is +.>Preset threshold value +.2 corresponding to critical data index parameter of patient specific disease>And (3) performing comparison: if->≥/>Judging that the dynamic trend of the patient specific disease is large, and evaluating and analyzing the patient specific disease condition; if->＜/>If the dynamic trend of the patient specific disease is smaller, the matching information outside the hospital of the patient is updated, the patient disease index change is analyzed by judging that the dynamic change of the patient specific disease is lower, the patient possibly has the condition of disease deterioration or recovery, and specific analysis is needed according to the critical data condition of the patient specific disease.
As one embodiment of the present invention, the output mode of the patient selection information result in step S5 is:
analyzing patient specific disease assessment information according to the patient specific disease dynamic trend size:
if it is ≥/>Then get +.>And->Time point corresponding to the maximum distance point +.>Obtain->Judging whether a preset dynamic scoring result appears in the time period according to the patient's specific disease dynamic data information in the time period, and locking the patient's specific disease information data when judging that the dynamic scoring result does not appear;
setting a score corresponding to each standard according to expert selection standards, and grading the information data of the locking special diseases;
and obtaining a selection information table according to the grading ranking.
wherein ,is a preset period of time.
Through the technical scheme: the method comprises the steps of obtaining a selection information result through analysis of the dynamic trend of the patient course development, wherein the output mode of the selection information result is as follows: setting a score value of a questionnaire form to match with a corresponding label; matching the patient to perform questionnaire answering and outputting a questionnaire static score value according to the answer result of the patient; analyzing patient specific disease assessment information according to patient specific disease dynamic trend results: analyzing patient specific disease assessment information according to the patient specific disease dynamic trend size: if it is≥ />Then get +.>And->Time point corresponding to the maximum distance point +.>Obtain->Judging whether a preset dynamic scoring result appears in the time period according to the patient's specific disease dynamic data information in the time period, and locking the patient's specific disease information data when judging that the dynamic scoring result does not appear; setting a score corresponding to each standard according to expert selection standards, and grading the information data of the locking special diseases; obtaining a selection information table according to the grading ranking; wherein (1)>Is a preset period of time.
As an embodiment of the present invention, the obtaining of the patient' S hospital specific information in step S1 further includes registering the patient with the public number, and performing confirmation of the rehabilitation in real time:
if yes, the special disease database is changed into a screening object, is determined to be in a screening state, and is updated synchronously;
if not, the patient information is determined to be the diagnosis target, the diagnosis state is determined, and the patient information is synchronously updated and exported.
Through the technical scheme: in order to ensure the accuracy of the acquisition of the hospital information, and simultaneously improve the channel communication of the acquisition of the hospital patient specific disease information, ensure the dynamic acquisition of the patient information meeting the conditions under the study recruitment conditions of the clinical trial of the medicine, scan the two-dimension code of the working room in the hospital and add the two-dimension code into the working room, register the information through the corresponding public number, and confirm the rehabilitation condition in real time: if yes, the special disease database is changed into a screening object, is determined to be in a screening state, and is updated synchronously; if not, the patient is determined to be diagnosed, and the patient information is synchronously updated and derived by combining the patient information recorded by the in-hospital diagnosis.
As an embodiment of the present invention, in step S2, the patient out-of-hospital specific disease information is confirmed by registering a public number account number:
if yes, the patient is confirmed, and patient information matching is carried out;
if not, the patient is screened, and the screening data is uploaded to confirm the synchronous hospital information.
In this embodiment, the patient is further subjected to confirmation of the diagnosis condition by acquiring the patient's out-of-hospital specific disease information, specifically by scanning two-dimensional codes of a studio or sharing the patient with a public number and registering an account number: if yes, the patient is confirmed, and patient information matching is carried out; if not, the patient is screened, and the screening data is uploaded to confirm the synchronous hospital information.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar thereto, by those skilled in the art, without departing from the principles of the invention or beyond the scope of the appended claims.
Claims (8)
1. A research method for dynamic and accurate clinical trial selection of a subject drug, the research method comprising:
s1, collecting patient disease information in a patient hospital through a patient disease database in the hospital;
s2, acquiring patient out-of-hospital specific disease information according to follow-up disease data of the patient out-of-hospital;
s3, comprehensively analyzing according to the special disease information in and out of the patient' S hospital to obtain the condition information parameters of the patient;
s4, screening condition information parameters and carrying out dynamic information prediction, evaluation and analysis;
s5, matching patient selection information according to dynamic information prediction and evaluation analysis results, and screening subjects meeting the conditions according to the selection information;
the screening method of the condition information parameters in the step S4 is as follows:
according to the formulaCalculating the prediction coefficient of the special diseases>, wherein ,/>、Is a weight coefficient; />For body mass index>For the patient visit times, < >>Is a patient age factor; />Is a standard body mass index; />A preset body mass index deviation value;
predicting the disease-specific prediction coefficientWith a preset threshold (+)>，/>) And (3) performing comparison:
if it is≤/>Judging that the patient specific disease index is low;
if it is≤/>Judging that the patient specific disease index meets the requirement;
if it is＞/>And judging that the patient's specific disease index is higher.
2. The method for studying clinical trial selection of a subject drug according to claim 1, wherein the method for predicting and evaluating the dynamic information in step S4 comprises:
acquiring historical condition information parameters in a historical special department disease database;
creating a patient label from the historical condition information parameter analysis:
key fields of the patient label are set: age, sex, bmi;
setting different specific gravities of a plurality of labels of a patient;
and predicting the dynamic trend of the course of disease according to the generation condition of the patient label.
3. The method according to claim 1, wherein the step S3 comprises the steps of:
synchronizing patient in-hospital specific disease information and out-of-hospital specific disease information to obtain data integration information;
analyzing the data integration information by a multidimensional data analysis method and acquiring patient condition information parameters, wherein the condition information parameters are used for predicting the incidence probability of a proprietary patient.
4. The method for studying clinical trial selection of a subject drug of claim 2, wherein the prediction of the dynamic trend of the course of disease is as follows:
statistical history of the change profile of a condition information parameter over a specified period of time；
Statistically predicting a change curve of a condition information parameter over a specified period of time；
For each patient's condition information parameters, will and />Establishing in the same coordinate system;
will beAnd->Performing comparison, and calculating->And->In the overlapping region->Above->Area value +.>。
5. The method for dynamically and accurately selecting and studying a clinical trial of a subject drug as claimed in claim 4, wherein the area value is determined byPreset threshold value +.>Comparing, and judging the size of the patient's disease-specific dynamic trend:
if it is≥/>Judging that the dynamic trend of the patient specific disease is large, and evaluating the patient specific disease;
if it is＜/>And if the dynamic trend of the patient specific disease is smaller, updating the matching information outside the hospital of the patient.
6. The method for studying clinical trial selection of a subject drug according to claim 5, wherein the outputting of the patient selection information in step S5 is performed in the following manner:
analyzing patient specific disease assessment information according to the patient specific disease dynamic trend size:
if it is≥/>Then get +.>And->Time point corresponding to the maximum distance point +.>Obtain->Judging whether a preset dynamic scoring result appears in the time period or not according to the patient specific disease dynamic data information in the time period, and locking the patient specific disease information data when judging that the dynamic scoring result does not appear;
setting a score corresponding to each standard according to expert selection standards, and grading the information data of the locking special diseases;
obtaining a selection information table according to the grading ranking;
wherein ,is a preset period of time.
7. The method according to claim 1, wherein in step S1, the acquisition of the patient' S hospital specific information further includes registration of the patient with a public number, and confirmation of rehabilitation in real time:
if yes, the special disease database is changed into a screening object, is determined to be in a screening state, and is updated synchronously;
if not, the patient information is determined to be the diagnosis target, the diagnosis state is determined, and the patient information is synchronously updated and exported.
8. The method for dynamically and accurately selecting and researching a clinical trial of a subject drug according to claim 1, wherein in the step S2, the diagnosis condition is confirmed by registering a public number account number with the patient' S extra-hospital specific disease information:
if yes, the patient is confirmed, and patient information matching is carried out;
if not, the patient is screened, and the screening data is uploaded to confirm the synchronous hospital information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310854226.7A CN116580798B (en) | 2023-07-13 | 2023-07-13 | Research method for dynamic and accurate clinical test selection of subject medicine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310854226.7A CN116580798B (en) | 2023-07-13 | 2023-07-13 | Research method for dynamic and accurate clinical test selection of subject medicine |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116580798A CN116580798A (en) | 2023-08-11 |
| CN116580798B true CN116580798B (en) | 2023-10-20 |
Family
ID=87541648
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310854226.7A Active CN116580798B (en) | 2023-07-13 | 2023-07-13 | Research method for dynamic and accurate clinical test selection of subject medicine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116580798B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480456A (en) * | 2017-08-22 | 2017-12-15 | 浙江大学医学院附属第医院 | clinical trial management method and system |
| CN111640475A (en) * | 2020-04-29 | 2020-09-08 | 上海米帝信息技术有限公司 | Management system for clinical test |
| CN113517044A (en) * | 2021-08-13 | 2021-10-19 | 南京焰帆生物技术有限公司 | Clinical data processing method and system for evaluating citicoline based on pharmacokinetics |
| CN115862897A (en) * | 2023-02-21 | 2023-03-28 | 江西曼荼罗软件有限公司 | Syndrome monitoring method and system based on clinical data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20230060306A (en) * | 2021-10-27 | 2023-05-04 | 주식회사 세타랩 | Clinical training field assessment system and method based on tablet PC and U-portfolio, and computer readable recoding medium thereof |
-
2023
- 2023-07-13 CN CN202310854226.7A patent/CN116580798B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480456A (en) * | 2017-08-22 | 2017-12-15 | 浙江大学医学院附属第医院 | clinical trial management method and system |
| CN111640475A (en) * | 2020-04-29 | 2020-09-08 | 上海米帝信息技术有限公司 | Management system for clinical test |
| CN113517044A (en) * | 2021-08-13 | 2021-10-19 | 南京焰帆生物技术有限公司 | Clinical data processing method and system for evaluating citicoline based on pharmacokinetics |
| CN115862897A (en) * | 2023-02-21 | 2023-03-28 | 江西曼荼罗软件有限公司 | Syndrome monitoring method and system based on clinical data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116580798A (en) | 2023-08-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP4819314B2 (en) | Diagnostic information system | |
| JP2022514162A (en) | Systems and methods for designing clinical trials | |
| US20030149597A1 (en) | System for supporting clinical decision-making | |
| US20120277623A1 (en) | Methods and Systems of Delivering a Probability of a Medical Condition | |
| KR20190132290A (en) | Method, server and program of learning a patient diagnosis | |
| US20190214134A1 (en) | System and method for automated healthcare service | |
| Singh et al. | Classification of first trimester ultrasound images using deep convolutional neural network | |
| US20080306764A1 (en) | System and Method for Complex Arena Intelligence | |
| CN116580798B (en) | Research method for dynamic and accurate clinical test selection of subject medicine | |
| Galper et al. | PRO (Patient reported outcomes) implementation: from vision to reality | |
| Bernardini et al. | A multicenter prospective study validated a nomogram to predict individual risk of dependence in ambulation after rehabilitation | |
| Nithya | Study on predictive analytics practices in health care system | |
| Nour | Artificial Intelligence (AI) for Improving Performance at the Cutting Edge of Medical Imaging | |
| Zhao et al. | Logistic model and gradient boosting machine model for physical therapy of lumbar disc herniation | |
| Lip et al. | Transforming Clinical Trials with Artificial Intelligence | |
| Priya et al. | A novel intelligent diagnosis and disease prediction algorithm in green cloud using machine learning approach | |
| Chen et al. | Design and evaluation of an intelligent physical examination system in improving the satisfaction of patients with chronic disease | |
| Zein et al. | Review of needle electromyography complications in thrombocytopenic cancer patients | |
| Holt et al. | A nationwide adaptive prediction tool for coronary heart disease prevention. | |
| Rani et al. | Machine Learning Model for Accurate Prediction of Thyroid Disease | |
| US20230245752A1 (en) | System and method for biomarker analysis in sporting, wellness and healthcare | |
| CN113870984A (en) | AI diagnosis and treatment follow-up system-based standardized diagnosis and treatment center authentication and assessment system | |
| Kwon | The utility of atrial fibrillation clinical registry data in the modelling of change in patient-reported outcomes | |
| Dhengre et al. | Investigating effect of standardized total body skin examination using sequence-networks | |
| Wang et al. | TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |