CN115954107B - Method and device for analyzing clinical test data of primary cholangitis - Google Patents
Method and device for analyzing clinical test data of primary cholangitis Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and a device for analyzing clinical test data of primary cholangitis. The method comprises the following steps: clinical data of the primary cholangitis patient in the disease progress process is obtained from a preset database, and the clinical data are screened based on preset key index item data to obtain sample clinical data; clustering the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results; and carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result. According to the technical scheme, the problem that the application of clinical data analysis of primary cholangitis patients is less and deep is solved, the full mining and analysis of the clinical data of PBC patients can be realized, and data support is provided for the clinical manifestation classification and prognosis judgment of the PBC patients.
Description
Technical Field
The embodiment of the invention relates to the technical field of clinical data processing, in particular to a method and a device for analyzing clinical test data of primary cholangitis.
Background
Primary cholangitis (primary biliary cholangitis, PBC) is an autoimmune liver disease of unknown cause. The PBC onset crowd is mainly middle-aged and elderly women, and the onset is not limited by regions and ethnicity. Abnormalities in the clinical examination of PBC patients occur in the values of the relevant indicators, such as elevated serum alkaline phosphatase (ALP), elevated aspartate Aminotransferase (AST) and alanine Aminotransferase (ALT), elevated serum immunoglobulins, mainly elevated immunoglobulin M (ImmunoglobulinM, igM) and positive serum anti-mitochondrial antibodies (antimitochondrial antibodies, AMAs), etc. Among them, AMAs are marker antibodies for serodiagnosis of PBC, and antinuclear antibodies (antinuclear antibody, ANA) can be detected in serum by indirect immunofluorescence, and these clinical data may have diagnostic and prognostic value. If the disease condition and the prognosis diversity of the PBC patients can be determined, accurate prediction or assessment of the prognosis of the PBC patients is of great significance for further clinical treatment follow-up. However, there is currently no simple and effective method for distinguishing clinical features and accurately judging prognosis of PBC patients, except for clinical and pathological stages of PBC.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for analyzing primary cholangitis clinical test data, which can fully mine and analyze clinical data of PBC patients and provide data support for clinical manifestation classification and prognosis judgment of the PBC patients.
According to an aspect of the present invention, there is provided a method for analyzing clinical test data of primary cholangitis, the method comprising:
clinical data of the primary cholangitis patient in the disease progress process is obtained from a preset database, and the clinical data are screened based on preset key index item data to obtain sample clinical data;
clustering the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results;
and carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result.
According to another aspect of the present invention, there is provided a primary cholangitis clinical test data analysis apparatus, the apparatus comprising:
the sample data acquisition module is used for acquiring clinical data of the primary cholangitis patient in the disease progress process from a preset database, and screening the clinical data based on preset key index item data to obtain sample clinical data;
The sample data clustering module is used for clustering the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results;
and the sample data analysis module is used for carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result.
According to another aspect of the present invention, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the primary cholangitis clinical test data analysis method of any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to perform the method of analysis of primary cholangitis clinical test data of any of the embodiments of the present invention.
According to the technical scheme, clinical data in the disease progress process of the PBC patient is obtained from the preset database, and the clinical data are screened based on the preset key index item data, so that sample clinical data are obtained; clustering the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results; and carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result. The technical scheme of the embodiment of the invention solves the problems of less and insufficient application of the clinical data analysis of the PBC patients, can realize the full mining and analysis of the clinical data of the PBC patients, and provides data support for the clinical manifestation classification and prognosis judgment of the PBC patients.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and 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 for analyzing primary cholangitis clinical test data provided by an embodiment of the present invention;
FIG. 2 is a flow chart of another method for analyzing primary cholangitis clinical test data provided by an embodiment of the present invention;
FIG. 3 is a flow chart of yet another method for analyzing primary cholangitis clinical test data provided by an embodiment of the present invention;
FIG. 4 is a flow chart of a specific primary cholangitis clinical test data analysis method provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of a method for statistical analysis of clinical data of primary cholangitis according to an embodiment of the present invention;
FIG. 6 is a block diagram of a primary cholangitis clinical test data analysis device according to an embodiment of the present invention;
fig. 7 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It is noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and in the foregoing figures, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
In the technical scheme of the embodiment of the invention, the aspects of the related personal information of the user, such as acquisition, collection, updating, analysis, processing, use, transmission, storage and the like, all conform to the rules of relevant laws and regulations, are used for legal purposes, and do not violate the popular public order. Necessary measures are taken for the personal information of the user, illegal access to the personal information data of the user is prevented, and the personal information security, network security and national security of the user are maintained.
Fig. 1 is a flowchart of a method for analyzing primary cholangitis clinical test data according to an embodiment of the present invention, where the embodiment is applicable to a scenario of PBC clinical test data analysis, and is more applicable to a scenario of implementing PBC clinical test data analysis based on clinical data and disease progress. The method can be performed by a primary cholangitis clinical test data analysis device, which can be implemented in hardware and/or software, and can also be configured in an electronic device.
As shown in fig. 1, the method for analyzing clinical test data of primary cholangitis comprises the following steps:
s110, acquiring clinical data of the primary cholangitis patient in the disease progress process from a preset database, and screening the clinical data based on preset key index item data to obtain sample clinical data.
Wherein the preset database is used for storing data generated at each stage of the medical activity. The preset database may be associated with a hospital information system (Hospital Information System, HIS) and/or laboratory (clinical laboratory) information system (Laboratory Information System, LIS) to collect, store, process, extract, transmit, aggregate, etc. data generated at various stages of a medical activity, thereby providing comprehensive automated management and various services for the overall operation of the hospital. Clinical data includes clinical symptom description data, patient characteristic attribute data, and test data.
The test data includes clinical test results during each course of disease progression in the PBC patient, for example, test results that may be autoantibodies, and the like. Autoantibodies are antibodies against self tissues, organs, cells and cellular components, and PBC-related autoantibodies include ANA, AMA and/or AMA-M2, ACA and/or anti-CENP-B antibodies, anti-gp 210 antibodies, anti-sp 100 antibodies, anti-Ro 52 antibodies, anti-SSA antibodies, anti-SSB antibodies, etc. The detection results of autoantibodies include qualitative results (positive or negative) and semi-quantitative or quantitative values converted from antibody titer or concentration.
Wherein the predetermined key index items comprise a plurality of predetermined classes of antinuclear antibodies associated with diagnosis and prognosis of PBC. Specifically, the preset key index items are an Anti-nuclear antibody (ANA), an Anti-mitochondrial antibody (AMA) and/or an Anti-AMA-M2 antibody, an Anti-centromere antibody (Anti-centromere antibody, ACA) and/or an Anti-centromere protein B (Centromere protein B, CENP-B) antibody, an Anti-Ro 52 antibody, an Anti-SSA antibody, an Anti-SSB antibody (Anti-mononuclear antibody), an Anti-Smith (Sm) antibody, an Anti-ribonucleoprotein (nRNP) antibody, an Anti-double-stranded DNA (dsDNA) antibody, an Anti-ribosomal P protein (Rib) antibody, an Anti-histone (His) antibody, an Anti-Nuk antibody, an Anti-Scl-70 antibody, an Anti-Jol antibody, an Anti-gp 210 antibody, an Anti-sp 100 antibody, an Anti-Soluble Liver Antigen (SLA) antibody, an Anti-hepatorenal microsomal type 1 antibody (LKM-1) and an Anti-hepatocyte cytoplasmic type 1 (Lc 1) antibody, and a total of 19 autoantibodies. The anti-nuclear antibody is a generic term for autoantibodies targeting various components of eukaryotic cells, and is total ANA detected by indirect immunofluorescence. ANA has a plurality of target antigens, each of which corresponds to a different autoantibody, forming an antinuclear antibody profile. The antinuclear antibody profile is subordinate to the antinuclear antibody total antibody, and ANA positive does not represent an antibody which is necessarily positive in the existing antinuclear antibody profile. ANA, AMA and ACA were detected by indirect immunofluorescence and the remaining antibodies by immunoblotting and ELISA (enzyme linked immunosorbent assay, ELISA).
Further, screening clinical data based on preset key index item data to obtain sample clinical data, including: and selecting data which comprises all preset key index items and is valid to be used as sample clinical data from the clinical data.
Specifically, firstly, obtaining the test results of blood routine, biochemical index, virology marker, autoantibody and the like in the progress of each disease of PBC patients from HIS and/or LIS; and then, selecting data which contains all preset key index items and is valid to be used as sample clinical data from the clinical data based on the preset key index item data.
S120, clustering the test data in the sample clinical data by stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results.
The preset hierarchical clustering algorithm is a BIRCH two-step clustering method. The BIRCH two-step clustering method is an improvement on the BIRCH algorithm, and a mechanism for automatically determining the number of clusters is added for clustering multiple attribute data sets. A hierarchical nested cluster tree is created by computing the similarity between different classes of data points, the original data points of the different classes being the lowest level of the tree, the top level of the tree being the root node of a cluster.
Specifically, the BIRCH two-step clustering method is divided into two stages:
1. pre-clustering (pre-clustering) phase.
Specifically, data points corresponding to test data in sample clinical data are read one by one, and clustering characteristic trees (Cluster Feature tree/CF tree) are generated, and meanwhile, data points of dense areas are clustered in advance to form a plurality of sub-clusters.
2. A clustering (pre-clustering) stage.
And merging the sub-clusters by using a condensation method (agglomerative hierarchical clustering method) by taking the sub-clusters as objects according to the result of the pre-clustering stage until the number of target clusters is reached.
Specifically, first, sample clinical data is staged, for example, the sample clinical data may be staged according to distance, density, connectivity, or the like between the sample clinical data; and then inputting the test data in the sample clinical data after the stage division into a preset hierarchical clustering algorithm for clustering to obtain a clustering result of the test data in the corresponding stage.
S130, carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result.
The clinical data may be clinical diagnosis results related to sample test data in the clustered results, for example, clinical data such as blood routine, biochemical index, virologic markers, and the like. Wherein the biochemical index comprises liver function indexes such as total protein, albumin, globulin, albumin ratio, total bilirubin, transaminase, direct bilirubin, indirect bilirubin and the like, and blood lipid indexes such as total cholesterol, triglyceride, high density lipoprotein, apolipoprotein, fasting blood glucose, renal function, uric acid, lactate dehydrogenase, creatine myoenzyme and the like; the virology marker comprises indicators such as hepatitis A antibody, five items of hepatitis B, hepatitis C antibody, hepatitis E antibody and the like.
The statistical analysis includes establishing mathematical model and performing mathematical statistics and analysis on the data. The statistical analysis method comprises the following steps: frequency analysis, data exploration, cross-table analysis, chi-square test, T test, variance analysis, regression analysis, factor analysis and the like.
Specifically, according to the characteristics of sample clinical data corresponding to the test data in the clustering result, one or more statistical analysis methods are selected, a mathematical model is established by using a mathematical mode, mathematical statistics and analysis are carried out on the data, a target analysis result is obtained, and the clinical data, disease characteristics and the like can be combined to better reflect the relation between the test data and the clinical data.
According to the technical scheme, clinical data in the disease progress process of the PBC patient is obtained from the preset database, and the clinical data are screened based on the preset key index item data, so that sample clinical data are obtained; clustering the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results; and carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result. According to the technical scheme, the PBC patient clinical test data are fully mined and analyzed, so that people can know the characteristics and heterogeneity of the PBC diseases, a clinician can quickly identify the characteristics of the diseases of the patient on the basis of comprehensively analyzing the clinical test results, and a decision basis is provided for diagnosis, typing, treatment and prognosis judgment of the diseases.
Fig. 2 is a flowchart of another method for analyzing clinical test data of primary cholangitis, which is provided in the embodiment of the present invention, and the method for analyzing clinical test data of primary cholangitis in this embodiment and the method for analyzing clinical test data of primary cholangitis in the above embodiment belong to the same inventive concept, and a process of clustering test data in sample clinical data in stages by using a preset hierarchical clustering algorithm to obtain a plurality of clustering results is further described on the basis of the above embodiment. The method can be performed by a primary cholangitis clinical test data analysis device, which can be implemented by software and/or hardware, and integrated into an electronic device with application development functions.
As shown in fig. 2, the method for analyzing clinical test data of primary cholangitis comprises the following steps:
s210, acquiring clinical data of the PBC patient in the disease progress process from a preset database, and screening the clinical data based on preset key index item data to obtain sample clinical data.
S220, pre-grouping the sample clinical data by adopting the log-likelihood distance among all the test data to obtain a corresponding pre-grouping result.
Assuming that the sample clinical data is divided into a plurality of clusters, one of the clusters includes two types of test data, and the calculation of the log-likelihood distance between the two types of test data includes: firstly, respectively calculating the log-likelihood estimation before combining the two types of test data and the combined log-likelihood estimation; and then, calculating the difference between the log-likelihood estimation before and after combination, namely the log-likelihood distance between the two types of test data.
Specifically, the log likelihood distance between every two pieces of test data in the sample clinical data is calculated, and the sample clinical data is pre-grouped based on the log likelihood distance to obtain a pre-grouping result.
S230, based on the pre-grouping result, performing balanced iterative clustering to obtain a plurality of clustering results.
The balanced iterative clustering (Balanced Iterative Reducing and Clustering using Hierarchies, BIRCH) also uses a hierarchical method of balanced iterative protocols and clustering, and can be performed by only scanning the data set to be clustered. After scanning the data set, a CF tree stored in the memory is created, which can be regarded as multi-layer compression of the data. The CF Tree only stores the CF node and the corresponding pointer, and all samples are on the disk, so that the memory can be saved. Wherein each node of the cluster feature tree has cluster features, including leaf nodes as well as cluster features, each cluster feature being a triplet, which can be represented by (N, LS, SS). Where N represents the number of sample data owned by this cluster feature; LS is the sum of the individual feature attribute values of the sample data owned in this cluster feature; SS represents the sum of squares of feature dimensions of the sample data that is owned in this cluster feature. The establishment of the cluster feature tree comprises the following steps:
1. Parameters of CF Tree are defined.
Specifically, a maximum CF number B of the internal node, a maximum CF number L of the leaf node, and a maximum sample radius threshold T of each CF of the leaf node are defined.
2. And establishing a CF Tree.
Specifically, the first sample clinical data is read in from the pre-grouping result, and is put into a new CF triplet a, n=1 of the triplet, and the new CF is put into the root node; continuing to read in the second sample clinical data, finding that the second sample clinical data and the first sample clinical data A are in the hypersphere range with the radius of T, namely, the second sample clinical data and the first sample clinical data belong to one CF, adding a second point into the CF triplet A, and updating the value of the triplet A at the moment, wherein N=2 in the triplet A; at this point, the third node is reached, but this node cannot be incorporated into the supersphere formed by the immediately preceding node, i.e. a new CF triplet B is needed to accommodate this new value, where the root node has two CF triples a and B.
3. And traversing sample clinical data corresponding to the pre-grouping result to establish a CF Tree.
The flow of BIRCH algorithm:
1. and sequentially reading all clinical data of the pre-grouping result samples, and establishing a CF Tree in the memory.
2. CF tree pretreatment.
Specifically, setting a sample number threshold, and removing tree nodes with the number of sample clinical data smaller than the sample number threshold; a sample merge threshold is set, and tuples with a merged hypersphere distance less than the sample merge threshold.
3. And clustering all the CF tuples by using a clustering algorithm. This has the advantage that it eliminates unreasonable tree structures due to the order of sample clinical data read-in, and some tree structure splits due to the limitation of the number of nodes CF.
4. And clustering all sample points according to distance by using centroids of all CF nodes of the CF Tree generated by the step 3 as initial centroid points to obtain a clustering result.
The method further comprises the steps of:
s240, evaluating a plurality of clustering results by adopting a preset clustering result evaluation algorithm to obtain a clustering evaluation result.
The clustering result evaluation algorithm is used for evaluating the clustering result and measuring the quality of the clustering result. The evaluation algorithm of the clustering result can be classified into an internal evaluation (internal evaluation) algorithm and an external evaluation (external evaluation) algorithm. The external evaluation algorithm is used to evaluate the clustering result with a known real label (group score), for example, by Purity (Purity), rand Index (RI), F value (F-score), and adjustment of Rand coefficient (Adjusted Rand Index, ARI), etc. The internal evaluation algorithm is used for completely marking no data, and is only evaluated according to the clustering result, for example, the contour coefficient (Silhouette Coefficient) and the Calinski-Harabasz Index (Calinski-Harabasz Index) are used for evaluation.
Optionally, performing the clustering result evaluation based on the contour coefficient includes: and calculating a contour coefficient detection value S (i) of the sample point. Specifically, calculating a corresponding contour coefficient detection value S (i) of an ith sample through a formula (1), and assuming that a clustering result comprises N clusters, wherein a (i) is an average distance from the ith sample point to other sample points in the cluster corresponding to the sample point, b (i) is an average distance from the ith sample point to other (N-1) clusters, and taking the contour coefficient detection value S (i) of the sample point as a clustering evaluation result through an average distance from the ith sample point to all sample points in the cluster.
S250, correcting the plurality of clustering results according to the clustering evaluation result to obtain a final clustering result.
Optionally, an evaluation threshold is set, and the clustering result is corrected. If S (i) is greater than the evaluation threshold, indicating that the cluster exists in the clustering result; if S (i) is not greater than the evaluation threshold, it indicates that the cluster does not exist. And (3) taking the clustering result with the S (i) larger than the evaluation threshold value as a final clustering result.
S260, carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result.
According to the technical scheme, clinical data in the disease progress process of the PBC patient is obtained from the preset database, and the clinical data are screened based on the preset key index item data, so that sample clinical data are obtained; pre-grouping the sample clinical data by adopting the log likelihood distance among all the test data to obtain a corresponding pre-grouping result; based on the pre-grouping result, performing balanced iterative clustering to obtain a plurality of clustering results; evaluating a plurality of clustering results by adopting a preset clustering result evaluation algorithm to obtain a clustering evaluation result; correcting the plurality of clustering results according to the clustering evaluation result to obtain a final clustering result; and carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result. According to the technical scheme provided by the embodiment of the invention, the clustering result is evaluated, and the clustering result is corrected according to the clustering evaluation result, so that the problem that the clinical data analysis of the PBC patient is less and not deep enough is solved, the clinical data of the PBC patient can be fully mined and analyzed, and the data support is provided for the clinical manifestation classification and prognosis judgment of the PBC patient.
Fig. 3 is a flowchart of another method for analyzing clinical test data of primary cholangitis, which is provided in the embodiment of the present invention, and the PBC clinical test data analysis method in the embodiment belongs to the same inventive concept, and further describes a process of performing statistical analysis on sample clinical data corresponding to test data in each clustering result based on the embodiment. The method can be performed by a primary cholangitis clinical test data analysis device, which can be implemented by software and/or hardware, and integrated into an electronic device with application development functions.
As shown in fig. 3, the method for analyzing clinical test data of primary cholangitis comprises the following steps:
s310, acquiring clinical data of the PBC patient in the disease progress process from a preset database, and screening the clinical data based on preset key index item data to obtain sample clinical data.
S320, clustering the test data in the sample clinical data by stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results.
S330, carrying out statistical analysis on at least one statistical item of sex distribution, age distribution, clinical characteristics, complications, positive antibody categories and clinical endpoint events according to sample clinical data corresponding to the test data in each clustering result to obtain a target statistical result.
Complications are one or more diseases or clinical states that exist simultaneously with the primary disease and are independent of each other. PBC may be accompanied by ascites, portal hypertension, liver failure, and other complications such as liver failure and symptoms related to cirrhosis in late stage, and liver cancer may be accompanied by. In addition, complications of PBC may include one or more autoimmune diseases, such as sjogren's syndrome, thyroiditis, rheumatoid arthritis, systemic sclerosis, and systemic lupus erythematosus, among others.
Clinical endpoint events mainly refer to liver disease-related death and liver cirrhosis decompensation (ascites, upper gastrointestinal hemorrhage and/or hepatic encephalopathy), liver cancer, liver transplantation or liver disease-related death, etc.
It can be understood that, because the data volume of the PBC related clinical data is large, the purpose of estimating the population is achieved by carrying out statistical analysis on the sample clinical data, and the influence of the statistical term on the PBC is more intuitively shown.
Further, the method comprises the following steps:
s340, acquiring PBC clinical data to be analyzed.
S350, determining classification results and prognosis characteristics corresponding to the PBC clinical data to be analyzed according to target analysis results corresponding to the clustering results.
Specifically, first, obtaining PBC clinical data to be analyzed; then, determining a classification result corresponding to the PBC clinical data to be analyzed according to the corresponding relation between each clustering result and the target analysis result; and determining the prognosis characteristics of the PBC clinical data to be analyzed according to the prognosis characteristics corresponding to the clustering results in the target statistical results.
In a specific embodiment, fig. 4 is a flowchart of a specific method for analyzing clinical data of primary cholangitis according to an embodiment of the present invention, and as shown in fig. 4, the method for analyzing clinical data of primary cholangitis includes the following steps:
s410, acquiring clinical data of the PBC patient.
Specifically, clinical data of PBC patients are extracted from hospital medical information systems (HIS system and LIS system), retrospective study queues are established, and baseline and follow-up data including discharge diagnosis, demographic data, medical history, physical examination results, complications, laboratory detection results (including blood routine, biochemical indexes, virological markers and autoantibody results) and the like are recorded. .
S420, determining sample clinical data based on the autoantibody type of the clinical data.
The clinical data of the PBC patients with complete data and 19 autoantibody detection contained in the clinical data are taken as sample clinical data, and sample clinical data with sample size of 537 is obtained.
S430, pre-grouping the sample clinical data by adopting the log-likelihood distance to obtain a pre-grouping result.
To reduce the distance between all possible clusters, 19 autoantibodies were pre-grouped. Specifically, a contour coefficient detection value S (i) of the sample point is calculated, and a clustering result of S (i) >0.5 is reserved as a pre-grouping result.
S440, based on the pre-grouping result, carrying out cluster analysis on the sample clinical data through a BIRCH algorithm to obtain a plurality of clustering results.
And (3) carrying out cluster analysis on sample clinical data with the sample size of 537 by adopting a BIRCH two-step clustering algorithm, converting the corresponding autoantibody result into two classification variable negative and positive, and carrying out cluster analysis to obtain five clustering results (refer to figure 5). The sample size corresponding to the cluster 1 is 107, the sample size corresponding to the cluster 2 is 120, the sample size corresponding to the cluster 3 is 125, the sample size corresponding to the cluster 4 is 101, and the sample size corresponding to the cluster 5 is 84; meanwhile, corresponding numbers are generated according to the clustering result and are used for representing the clustering types corresponding to the sample clinical data and the corresponding patients.
S450, carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result.
Baseline demographics, clinical symptom signs, complications, laboratory detection indexes, follow-up visits and clinical endpoint events of sample clinical data corresponding to the test data in the five clusters are compared, survival analysis and comparison are carried out on different clusters, clinical characteristics of different autoantibody clusters are described, and target analysis results corresponding to the clusters are obtained (refer to figure 5).
S460, determining classification results and prognosis characteristics corresponding to the PBC clinical data to be analyzed according to target analysis results corresponding to the clustering results.
Specifically, according to the target analysis results corresponding to the five clusters, the classification results and the prognosis characteristics corresponding to the PBC clinical data to be analyzed are determined (refer to FIG. 5).
According to the technical scheme, clinical data in the disease progress process of the PBC patient is obtained from the preset database, and the clinical data are screened based on the preset key index item data, so that sample clinical data are obtained; clustering the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results; carrying out statistical analysis on at least one statistical item of sex distribution, age distribution, clinical characteristics, complications, positive antibody categories and clinical endpoint events according to sample clinical data corresponding to the test data in each clustering result to obtain a target statistical result; acquiring PBC clinical data to be analyzed; and determining a classification result and a prognosis characteristic corresponding to the PBC clinical data to be analyzed according to the target analysis result corresponding to each clustering result. According to the technical scheme provided by the embodiment of the invention, the sample clinical data and the clinical data are statistically analyzed based on the clustering result, so that the classification result and the prognosis characteristic of the PBC clinical test data to be analyzed can be directly obtained, the problem that the application of the PBC patient clinical data analysis is less and deep enough is solved, the full mining and analysis of the PBC patient clinical data can be realized, and the data support is provided for the clinical manifestation classification and the prognosis judgment of the PBC patient.
Fig. 6 is a block diagram of a primary cholangitis clinical test data analysis device according to an embodiment of the present invention, where the embodiment is applicable to a scenario of PBC clinical test data analysis, and is more applicable to a scenario of implementing PBC clinical test data analysis based on clinical data and disease progress. The apparatus may be implemented in hardware and/or software, and integrated into a computer device having application development functionality.
As shown in fig. 6, the primary cholangitis clinical test data analysis device includes: a sample data acquisition module 601, a sample data clustering module 602, and a sample data analysis module 603.
The sample data acquisition module 601 is configured to acquire clinical data in a disease progress process of a PBC patient from a preset database, and screen the clinical data based on preset key index item data to obtain sample clinical data; the sample data clustering module 602 is configured to cluster the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results; the sample data analysis module 603 is configured to perform statistical analysis on sample clinical data corresponding to the test data in each cluster result, so as to obtain a target analysis result.
According to the technical scheme, clinical data in the disease progress process of the PBC patient is obtained from the preset database, and the clinical data are screened based on the preset key index item data, so that sample clinical data are obtained; clustering the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results; and carrying out statistical analysis on sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result. The technical scheme of the embodiment of the invention solves the problems of less and insufficient application of the clinical data analysis of the PBC patients, can realize the full mining and analysis of the clinical data of the PBC patients, and provides data support for the clinical manifestation classification and prognosis judgment of the PBC patients.
Optionally, the sample data obtaining module 601 is configured to: and selecting data which comprises all preset key index items and is valid to be used as sample clinical data from the clinical data.
Optionally, the sample data clustering module 602 is configured to:
pre-grouping the sample clinical data by adopting the log likelihood distance among the sample test data to obtain a corresponding pre-grouping result;
Based on the pre-grouping result, performing balanced iterative clustering to obtain a plurality of clustering results.
Optionally, the sample data clustering module 602 is configured to:
evaluating a plurality of clustering results by adopting a preset clustering result evaluation algorithm to obtain a clustering evaluation result;
and correcting the plurality of clustering results according to the clustering evaluation result to obtain a final clustering result.
Optionally, the sample data analysis module 603 is further configured to: and carrying out statistical analysis on at least one statistical item of sex distribution, age distribution, clinical characteristics, complications, positive antibody categories and clinical endpoint events according to sample clinical data corresponding to the test data in each clustering result.
Optionally, the apparatus further comprises a clinical test data analysis module for:
acquiring PBC clinical data to be analyzed;
and determining a classification result and a prognosis characteristic corresponding to the PBC clinical data to be analyzed according to the target analysis result corresponding to each clustering result.
The PBC clinical examination data analysis device provided by the embodiment of the invention can execute the PBC clinical examination data analysis method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 7 is a block diagram of an electronic device according to an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, or other appropriate computers. The electronic device may also represent various forms of mobile apparatus, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), or other similar computing devices. The components shown herein, their connection relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the invention described and/or claimed herein.
As shown in fig. 7, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard or a mouse; an output unit 17 such as various types of displays or speakers, etc.; a storage unit 18 such as a magnetic disk or an optical disk; and a communication unit 19 such as a network card, modem or wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), any suitable processor, controller or microcontroller, and the like. The processor 11 performs the various methods and processes described above, such as the PBC clinical test data analysis method.
In some embodiments, the PBC clinical test data analysis method may be implemented as a computer program, which is tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the PBC clinical test data analysis method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the PBC clinical test data analysis method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine or partly on the machine, partly on the machine and partly on a remote machine or entirely on the remote machine or server as a stand-alone software package.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display ) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (7)
1. A method for analyzing clinical test data of primary cholangitis, comprising:
clinical data of the primary cholangitis patient in the disease progress process is obtained from a preset database, and the clinical data are screened based on preset key index item data to obtain sample clinical data;
clustering the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results;
Carrying out statistical analysis on the sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result;
the step of clustering the test data in the sample clinical data by stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results comprises the following steps:
pre-grouping the sample clinical data by adopting the log-likelihood distance among the test data to obtain a corresponding pre-grouping result;
based on the pre-grouping result, performing balanced iterative clustering to obtain a plurality of clustering results;
evaluating the plurality of clustering results by adopting a preset clustering result evaluation algorithm to obtain a clustering evaluation result;
correcting the plurality of clustering results according to the clustering evaluation result to obtain a final clustering result;
the method for evaluating the plurality of clustering results by adopting a preset clustering result evaluation algorithm to obtain a clustering evaluation result comprises the following steps: calculating a contour coefficient detection value of a sample point;
correspondingly, the correcting the plurality of clustering results according to the clustering evaluation result to obtain a final clustering result comprises the following steps:
if the contour coefficient detection value is larger than the evaluation threshold value, the cluster exists in the clustering result;
If the contour coefficient detection value is not greater than the evaluation threshold value, the clustering result is indicated to have the cluster;
taking the clustering result of which the contour coefficient detection value is larger than the evaluation threshold value as a final clustering result;
the preset hierarchical clustering algorithm is a BIRCH two-step clustering method, and the clustering of the test data in the sample clinical data by stages is performed by adopting the preset hierarchical clustering algorithm to obtain a plurality of clustering results, and the method comprises the following steps:
staging the sample clinical data according to distance, density or connectivity between the sample clinical data;
for sample clinical data after being staged, data points corresponding to the test data in the sample clinical data are read one by one, and clustering characteristic trees are generated, and meanwhile data points of dense areas are clustered in advance to form a plurality of sub-clusters, so that a pre-clustering stage result is obtained;
and merging the sub-clusters by using a condensation method according to the result of the pre-clustering stage by taking the sub-clusters as objects until the number of the target clusters is reached.
2. The method of claim 1, wherein the screening the clinical data based on the preset key indicator item data to obtain sample clinical data comprises:
And selecting data which comprises all preset key index items and is valid to be used as the sample clinical data from the clinical data.
3. The method of claim 2, wherein the predetermined key indicators comprise a plurality of predetermined classes of antinuclear antibodies associated with diagnosis and prognosis of primary biliary cholangitis.
4. The method of claim 1, wherein said statistically analyzing the sample clinical data corresponding to the test data in each cluster result comprises:
and carrying out statistical analysis on at least one statistical item of sex distribution, age distribution, clinical characteristics, complications, positive antibody categories and clinical endpoint events according to the sample clinical data corresponding to the test data in each clustering result.
5. The method according to any one of claims 1-4, further comprising:
acquiring clinical data of primary cholangitis to be analyzed;
and determining a classification result and a prognosis characteristic corresponding to the clinical data of the primary cholangitis to be analyzed according to the target analysis result corresponding to each clustering result.
6. The method according to any one of claims 1-4, wherein the preset key indicator term data comprises: anti-nuclear, anti-mitochondrial and/or anti-AMA-M2 antibodies, anti-centromere antibodies and/or anti-centromere protein B antibodies, anti-Ro 52 antibodies, anti-SSA antibodies, anti-SS-B antibodies, anti-Smith antibodies, anti-ribonucleoprotein antibodies, anti-double stranded DNA antibodies, anti-ribosomal P protein antibodies, anti-histone antibodies, anti-Nuk antibodies, anti-Scl-70 antibodies, anti-Jol antibodies, anti-gp 210 antibodies, anti-sp 100 antibodies, anti-soluble liver antigen antibodies, anti-hepatorenal microsomal antibodies, and anti-hepatocyte cytoplasmic type 1 antibodies.
7. A primary cholangitis clinical test data analysis device, comprising:
the sample data acquisition module is used for acquiring clinical data of the primary cholangitis patient in the disease progress process from a preset database, and screening the clinical data based on preset key index item data to obtain sample clinical data;
the sample data clustering module is used for clustering the test data in the sample clinical data in stages by adopting a preset hierarchical clustering algorithm to obtain a plurality of clustering results;
The sample data analysis module is used for carrying out statistical analysis on the sample clinical data corresponding to the test data in each clustering result to obtain a target analysis result;
the sample data clustering module is further configured to:
pre-grouping the sample clinical data by adopting the log-likelihood distance among the test data to obtain a corresponding pre-grouping result;
based on the pre-grouping result, performing balanced iterative clustering to obtain a plurality of clustering results;
evaluating the plurality of clustering results by adopting a preset clustering result evaluation algorithm to obtain a clustering evaluation result;
correcting the plurality of clustering results according to the clustering evaluation result to obtain a final clustering result;
the sample data clustering module comprises a contour coefficient determining unit and a clustering result correcting unit,
the contour coefficient determining unit is used for: calculating a contour coefficient detection value of a sample point;
the clustering result correction unit is used for:
if the contour coefficient detection value is larger than the evaluation threshold value, the cluster exists in the clustering result;
if the contour coefficient detection value is not greater than the evaluation threshold value, the clustering result is indicated to have the cluster;
Taking the clustering result of which the contour coefficient detection value is larger than the evaluation threshold value as a final clustering result;
the preset hierarchical clustering algorithm is a BIRCH two-step clustering method, and the sample data clustering module is used for:
staging the sample clinical data according to distance, density or connectivity between the sample clinical data;
for sample clinical data after being staged, data points corresponding to the test data in the sample clinical data are read one by one, and clustering characteristic trees are generated, and meanwhile data points of dense areas are clustered in advance to form a plurality of sub-clusters, so that a pre-clustering stage result is obtained;
and merging the sub-clusters by using a condensation method according to the result of the pre-clustering stage by taking the sub-clusters as objects until the number of the target clusters is reached.
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