CN112712899A - Data analysis method based on primary liver cancer big data and storage medium - Google Patents
Data analysis method based on primary liver cancer big data and storage medium Download PDFInfo
- Publication number
- CN112712899A CN112712899A CN202110047657.3A CN202110047657A CN112712899A CN 112712899 A CN112712899 A CN 112712899A CN 202110047657 A CN202110047657 A CN 202110047657A CN 112712899 A CN112712899 A CN 112712899A
- Authority
- CN
- China
- Prior art keywords
- data
- report
- patient
- image
- nodule
- 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.)
- Pending
Links
- 201000007270 liver cancer Diseases 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000007405 data analysis Methods 0.000 title claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 71
- 238000007689 inspection Methods 0.000 claims abstract description 55
- 102000013529 alpha-Fetoproteins Human genes 0.000 claims abstract description 33
- 108010026331 alpha-Fetoproteins Proteins 0.000 claims abstract description 33
- 238000007781 pre-processing Methods 0.000 claims abstract description 24
- 238000012360 testing method Methods 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 19
- 208000014018 liver neoplasm Diseases 0.000 claims description 31
- 238000012216 screening Methods 0.000 claims description 20
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 12
- 201000010099 disease Diseases 0.000 claims description 11
- 238000002604 ultrasonography Methods 0.000 claims description 11
- 238000001514 detection method Methods 0.000 claims description 10
- 230000009191 jumping Effects 0.000 claims description 9
- 206010028980 Neoplasm Diseases 0.000 claims description 8
- 238000001914 filtration Methods 0.000 claims description 7
- 238000004140 cleaning Methods 0.000 claims description 6
- 210000002966 serum Anatomy 0.000 claims description 4
- 239000007787 solid Substances 0.000 claims description 4
- 238000012916 structural analysis Methods 0.000 claims description 4
- 102100023635 Alpha-fetoprotein Human genes 0.000 claims description 3
- 230000003796 beauty Effects 0.000 claims description 3
- 238000013524 data verification Methods 0.000 claims description 3
- 238000012552 review Methods 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 abstract description 18
- 206010073071 hepatocellular carcinoma Diseases 0.000 abstract description 8
- 231100000844 hepatocellular carcinoma Toxicity 0.000 abstract description 7
- 239000000284 extract Substances 0.000 abstract description 2
- 238000010606 normalization Methods 0.000 abstract 1
- 230000002159 abnormal effect Effects 0.000 description 11
- 230000008569 process Effects 0.000 description 7
- 238000012163 sequencing technique Methods 0.000 description 4
- 201000011510 cancer Diseases 0.000 description 3
- 238000013399 early diagnosis Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 210000004185 liver Anatomy 0.000 description 3
- 230000002980 postoperative effect Effects 0.000 description 3
- 241000711549 Hepacivirus C Species 0.000 description 2
- 238000003556 assay Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000002607 contrast-enhanced ultrasound Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 206010009208 Cirrhosis alcoholic Diseases 0.000 description 1
- 206010011732 Cyst Diseases 0.000 description 1
- 241000700721 Hepatitis B virus Species 0.000 description 1
- 210000001015 abdomen Anatomy 0.000 description 1
- 238000002679 ablation Methods 0.000 description 1
- 208000010002 alcoholic liver cirrhosis Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- BFPSDSIWYFKGBC-UHFFFAOYSA-N chlorotrianisene Chemical compound C1=CC(OC)=CC=C1C(Cl)=C(C=1C=CC(OC)=CC=1)C1=CC=C(OC)C=C1 BFPSDSIWYFKGBC-UHFFFAOYSA-N 0.000 description 1
- 230000007882 cirrhosis Effects 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 208000031513 cyst Diseases 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000002592 echocardiography Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 201000007450 intrahepatic cholangiocarcinoma Diseases 0.000 description 1
- 208000019423 liver disease Diseases 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 230000036210 malignancy Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 208000008338 non-alcoholic fatty liver disease Diseases 0.000 description 1
- 206010053219 non-alcoholic steatohepatitis Diseases 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 206010036067 polydipsia Diseases 0.000 description 1
- 238000011470 radical surgery Methods 0.000 description 1
- 238000001959 radiotherapy Methods 0.000 description 1
- 238000002271 resection Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
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
- 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
-
- 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
- G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
Landscapes
- Public Health (AREA)
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The application relates to the technical field of big data processing, in particular to a data analysis method and a storage medium based on primary liver cancer big data. The method comprises the following steps: acquiring analysis data from an electronic medical record system, an image acquisition and output system and an inspection information system; preprocessing population information to obtain patient index number information; preprocessing the image report and performing structured analysis processing to obtain the attribute of the nodule; preprocessing the inspection report to obtain alpha fetoprotein inspection data; summarizing patient index number information, an image report and alpha fetoprotein test data to obtain summarized data; and analyzing the summarized data according to a preset rule to obtain a patient condition analysis result. The technical scheme of the invention extracts data in different systems for normalization and unification, screens out key index information, analyzes the hepatocellular carcinoma condition of the patient according to a preset rule and gives diagnosis and treatment suggestions, thereby improving the working efficiency and the diagnosis accuracy.
Description
Technical Field
The application relates to the technical field of big data processing, in particular to a data analysis method and a storage medium based on primary liver cancer big data.
Background
Hepatocellular carcinoma (liver cancer) is the 4 th most common malignant tumor in China, and the 2 nd malignant tumor is the death rate of diseases. Surgical resection is the first choice for treating liver cancer, but liver cancer patients usually have no abnormal clinical manifestations in the early stage, and the diagnosis is often developed to the middle and late stage, and only 15% -30% of patients can be treated by radical surgery. Screening of high risk group of liver cancer is helpful for early detection, early diagnosis and early treatment of liver cancer, and is the key for improving curative effect of liver cancer. The number of people at high risk for liver cancer in China, including Hepatitis B Virus (HBV) and/or Hepatitis C Virus (HCV) infection, excessive drinking, non-alcoholic steatohepatitis and cirrhosis caused by various other reasons, exceeds 1 hundred million. In order to improve the early diagnosis rate of liver cancer, the primary liver cancer diagnosis and treatment standard (2019), which is a standard, is applied to the screening process of high risk groups of liver cancer.
However, when patients with high risk of liver cancer see other departments, doctors in non-liver specialties have different degrees of knowledge on the liver cancer screening process and diagnosis standard. Similarly, the primary doctor may have errors in the screening and diagnosis process of the liver cancer patient due to the lack of knowledge updating channel, insufficient diagnosis experience of the specialized diseases and the like, which affects the early diagnosis of the patient. In addition, in the face of an increasing number of high risk groups of liver cancer, the working pressure of specialists in liver disease is high.
With the rapid development of big data related technologies, artificial intelligence innovation technologies and liver cancer standard diagnosis paths are fused with each other, liver cancer pathological data are automatically analyzed based on a primary liver cancer big data Platform (PLCBD), and an analysis model with high liver cancer screening efficiency and accurate analysis results is researched to serve as an auxiliary diagnosis and treatment tool, so that the problem that improvement of liver cancer curative effect needs to be solved urgently is solved.
Disclosure of Invention
The invention aims to provide a data analysis method and a storage medium based on primary liver cancer big data, and the method and the storage medium rely on a primary liver cancer big data platform to capture and standardize data of an electronic medical record system, an imaging system and an inspection system in real time, obtain indexes required by liver cancer diagnosis and realize automatic screening and analysis of liver cancer.
In order to solve the technical problems, the invention provides a data analysis method based on primary liver cancer big data, which comprises the following steps:
step 3, preprocessing the image report; the image report comprises an ultrasonic report, a CT report, a magnetic resonance report and a Pumei magnetic resonance report;
step 5, preprocessing the inspection report to obtain alpha fetoprotein inspection data;
and 7, analyzing the summarized data according to a preset rule to obtain an illness state analysis result of each patient.
Further, the preprocessing the outpatient service population information and the hospitalization population information to obtain the patient index number information comprises the following steps:
and 24, associating the unique index number of the patient with the clinical visit information to form patient index number information.
Further, the preprocessing the image report includes the following steps:
and step 33, splitting the image report into an ultrasonic report, a CT report, a magnetic resonance report and a Pumei display magnetic resonance report according to the scanning mode of the image report.
Further, the structural analysis processing of the image report to obtain the nodule attribute comprises the following steps:
Further, the preprocessing the test report to obtain alpha-fetoprotein test data comprises the following steps:
further, the analyzing the summarized data according to a preset rule to obtain an analysis result of the disease condition of each patient includes the following steps:
step 701, judging whether the patient has image data, if so, executing step 702, otherwise, skipping to step 703 to continue executing;
and step 722, returning an analysis result, and ending the analysis flow.
Further, the data analysis method based on the primary liver cancer big data comprises the following steps:
and step 8, displaying the disease condition analysis result on a terminal interface.
Accordingly, the present application also provides a computer readable storage medium storing one or more programs, which are executable by one or more processors to implement the steps of the data analysis method based on primary liver cancer big data according to any one of claims 1 to 7.
Different from the prior art, the technical scheme of the invention has the following beneficial effects:
1. the data of the patients dispersed in different systems is extracted, the data is normalized and unified, key index information required by subsequent analysis is screened out, a primary liver cancer big data platform is established, and perfect and accurate original data are provided for hepatocellular carcinoma automatic analysis.
2. According to the preset analysis and judgment rules, the hepatocellular carcinoma disease condition of each patient can be efficiently and automatically analyzed and diagnosis and treatment guide suggestions are given based on the established primary liver cancer data platform, a doctor is assisted in treatment and diagnosis of liver cancer, the working efficiency can be improved, and the diagnosis accuracy can be improved.
Drawings
FIG. 1 is a flow chart of the steps of the data analysis method based on the primary liver cancer big data of the present invention.
FIG. 2 is a flowchart illustrating the steps of pre-processing the outpatient population information and the hospitalization population information to obtain the patient index number information according to the present invention.
FIG. 3 is a flow chart of the steps of preprocessing the image report according to the present invention.
FIG. 4 is a flowchart illustrating the steps of performing a structural analysis process on the image report to obtain nodule attributes according to the present invention.
FIG. 5 is a flow chart of the steps of the present invention for preprocessing the assay report to obtain alpha-fetoprotein assay data.
FIG. 6 is a flowchart illustrating the steps of analyzing the summarized data according to a predetermined rule to obtain the disease analysis result of each patient according to the present invention.
FIG. 7 is a diagram showing the analysis result of the disease condition of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, it is a flow chart of the steps of the data analysis method based on the primary liver cancer big data of the present invention, comprising the following steps:
In a medical information system, patient data are usually distributed in different subsystems, for example, patient outpatient information is in an electronic medical record system EMR, data of various image examinations performed by a patient are stored in an image acquisition and output system PACS, data of various tests performed by a patient are stored in a laboratory examination information system LIS, and the data are used for analysis, and the data distributed in different systems are firstly extracted to a unified platform, and the general steps include: 1. the method comprises the steps of firstly obtaining database accounts or hot backup database accounts of an electronic medical record system EMR, an image acquisition and output system PACS and a laboratory examination information system LIS, and giving access rights of the accounts to population information, clinical doctor information, inspection reports and image reports. 2. A database system (namely a target database) with the same type AS a source database is constructed, and a local backup TABLE is created by the required data through CREATE TABLE AS SELECT (hereinafter abbreviated AS CTAS) of structured query language SQL. Preferably, to avoid impact on the business system, the frequency of queries to create the temporary table and the time access limitations will be limited. The data query backup frequency is 30 minutes of update by default, and the data query time range except the population information is limited to be 180 days. 3. When the data types of different databases are different and the data types which cannot be read during query are used, format conversion is carried out by using a data type conversion function CONVERT, so that the content of the data is not lost when the databases are synchronous. 4. Different types of databases including SQL Server, MySQL and the like are realized by utilizing database synchronization technology, such as Oracle GateWay, Oracle golden GateWay, ODBC and the like, so as to realize uniform query on a target database system. 5. And (3) creating a data backup table of population information, clinical clinic visit information, inspection reports and image reports on the target database by adopting a CTAS (simultaneous localization and mapping) method.
in order to ensure the accuracy of the patient information and avoid various abnormal problems generated in the subsequent analysis process, the data of the outpatient service population information and the inpatient service population information needs to be verified, and abnormal data in the outpatient service population information is filtered. In particular, critical information processing for demographic information typically includes: name processing, namely clearing numbers, spaces and special characters; birthday processing, namely converting the date in the character string format into a unified date format by using a to _ date function, marking the date as abnormal if the type cannot be converted, and marking the record of which the birthday is less than 1900 years or more than the current date as abnormal; sex processing, namely, the sex of the numerical type is transferred to a male or a female, and other types which cannot be transferred are marked as abnormal; processing the identity card, wherein the identity card should meet 15-bit and 18-bit identity card numbers, address coding, date of birth and check bit check, and if the identity card information is empty or does not meet a check mechanism, marking the record as abnormal; the treatment number is processed, and the data which does not conform to the treatment number format specification is marked as abnormal; checking whether the sex, the birthday and the identity card of the patient are consistent, if so, giving priority to the identity card information and recording the abnormity; exception handling, namely emptying the field marked as exception and reserving other fields of the record; and (4) processing repeated personal information, and sequencing and combining repeated population information by using a field similarity matching method (Smith-Waterman algorithm, editing distance and Cosine similarity function). After the series of processing, the obtained outpatient service population information table and inpatient service population information table include the number of treatment (outpatient service number, inpatient service number), name, birthday, sex, birthday, identity card, hospital of treatment, type of treatment, and the like.
the main indices used vary from one medical subsystem to another, such as the inability to use a hospital number to query a patient for a laboratory examination at an outpatient clinic. Therefore, in the embodiment of the invention, the main index is established as the unique identifier of the patient, the databases of the patients in different information systems are effectively associated together, and the consistency of the personal information of the patients in the various medical information systems and the information sharing during outpatient service or hospitalization are ensured. In particular, the patient primary index (EMPI) is created by using the population information through a Hash algorithm.
And 24, associating the unique index number of the patient with the clinical visit information to form patient index number information, wherein the content of the patient index number information comprises the EMPI, the visit number, the name, the ID card, the gender, the birthday and the admission date.
Step 3, preprocessing the image report; the image report comprises an ultrasonic report, a CT report, a magnetic resonance report and a Pumei magnetic resonance report; referring to fig. 3, a flowchart of the preprocessing step for the image report according to the present invention includes the following steps:
And step 33, splitting the image report into an ultrasonic report, a CT report, a magnetic resonance report and a Pumei display magnetic resonance report according to the scanning mode of the image report.
Step 5, preprocessing the inspection report to obtain alpha fetoprotein inspection data; fig. 5 is a flowchart of the steps of preprocessing the test report to obtain alpha-fetoprotein test data according to the present invention, which includes the following steps:
The method extracts the data of the patient dispersed in different systems, and arranges and unifies the data, screens out the key index information required by the subsequent analysis, establishes a primary liver cancer big data platform, and provides perfect and accurate original data for the automatic analysis of the hepatocellular carcinoma.
And 7, analyzing the summarized data according to a preset rule to obtain an illness state analysis result of each patient. Fig. 6 is a flowchart illustrating the steps of analyzing the summarized data according to the preset rule to obtain the disease analysis result of each patient according to the present invention, which includes the following steps:
step 701, judging whether the patient has image data, if so, executing step 702, otherwise, skipping to step 703 to continue executing;
and step 722, returning an analysis result, and ending the analysis flow.
According to the technical scheme, the method can efficiently and automatically analyze the hepatocellular carcinoma condition of each patient and give diagnosis and treatment guide suggestions based on the established primary liver cancer data platform according to the preset analysis and judgment rules, assists doctors in diagnosing and treating liver cancer, and can improve the working efficiency and the diagnosis accuracy.
Preferably, on the basis of obtaining the analysis result, in order to facilitate the query of medical staff and patients, the data analysis method based on primary liver cancer big data according to the present application may further include the following steps:
and step 8, displaying the disease analysis result on a terminal interface, wherein the display interface is shown in fig. 7.
In a preferred embodiment, the present disclosure further provides a computer-readable storage medium, where one or more programs are stored, and the one or more programs are executable by one or more processors to implement any of the above steps of the data analysis method based on primary liver cancer big data.
The above embodiments are merely illustrative of the technical solutions of the present invention, and the present invention is not limited to the above embodiments, and any modifications or alterations according to the principles of the present invention should be within the protection scope of the present invention.
Claims (8)
1. The data analysis method based on the primary liver cancer big data is characterized by comprising the following steps:
step 1, acquiring analysis data from an electronic medical record system, an image acquisition and output system and a laboratory examination information system; the analysis data comprises population information, clinical clinic visit information, inspection reports and image reports; the population information comprises outpatient service population information and hospitalization population information;
step 2, preprocessing the outpatient population information and the hospitalization population information to obtain unique index number information of the patient;
step 3, preprocessing the image report; the image report comprises an ultrasonic report, a CT report, a magnetic resonance report and a Pumei magnetic resonance report;
step 4, performing structured analysis processing on the image report to obtain a nodule attribute; the nodule attributes include a nodule property, a nodule feature, and a nodule size;
step 5, preprocessing the inspection report to obtain alpha fetoprotein inspection data;
step 6, summarizing the patient index number information, the image report and the alpha fetoprotein test data to obtain summarized data;
and 7, analyzing the summarized data according to a preset rule to obtain an illness state analysis result of each patient.
2. The data analysis method based on primary liver cancer big data as claimed in claim 1, wherein the preprocessing of the outpatient service population information and the hospitalization population information to obtain the patient index number information comprises the following steps:
step 21, carrying out data verification on the outpatient service population information and the hospitalization population information;
step 22, combining the outpatient service population information and the hospitalization population information and filtering repeated data;
step 23, creating a unique index number for each patient by adopting a Hash algorithm;
and 24, associating the unique index number of the patient with the clinical visit information to form patient index number information.
3. The method for analyzing data based on primary liver cancer big data as claimed in claim 1, wherein said preprocessing said image report comprises the following steps:
step 31, cleaning up error data in the image report;
step 32, screening and filtering the data in the image report;
and step 33, splitting the image report into an ultrasonic report, a CT report, a magnetic resonance report and a Pumei display magnetic resonance report according to the scanning mode of the image report.
4. The data analysis method based on primary liver cancer big data as claimed in claim 1, wherein said structural analysis processing of said image report to obtain nodule attributes comprises the following steps:
step 41, analyzing the properties and characteristics of the nodules from the inspection results of the image reports;
step 42, analyzing the size of the nodule from the inspection result of the image report;
step 43, the nodule properties, the nodule characteristics and the nodule size are associated and saved into the image report.
5. The method for analyzing data based on primary liver cancer big data as claimed in claim 1, wherein said preprocessing said test report to obtain alpha fetoprotein test data comprises the following steps:
step 51, cleaning up error data in the inspection report;
and step 52, screening the data in the test report to filter out the alpha-fetoprotein test data.
6. The method for analyzing data based on primary liver cancer big data according to claim 1, wherein the step of analyzing the summarized data according to a preset rule to obtain the disease analysis result of each patient comprises the following steps:
step 701, judging whether the patient has image data, if so, executing step 702, otherwise, skipping to step 703 to continue executing;
step 702, judging whether a solid nodule exists according to the nodule property of the image data, if so, skipping to step 709 to continue execution, otherwise, skipping to step 704 to continue execution;
step 703, setting the analysis result as a patient recommended to carry out ultrasonic examination, and jumping to step 722 to continue execution;
step 704, judging whether the alpha fetoprotein detection result is positive, if so, executing step 705, otherwise, skipping to step 708 to continue executing;
step 705, judging whether the MRI image and the CT image are finished, if so, executing step 706, otherwise, skipping to step 707 to continue executing;
step 706, setting the analysis result to suggest that the patient carries out alpha fetoprotein detection and image follow-up for 2-3 months, and skipping to step 722 to continue execution;
step 707, setting the analysis result to suggest that the patient performs the review after completing the MRI image and the CT image, and skipping to step 722 to continue the execution;
step 708, setting the analysis result to suggest the patient to follow-up for 6 months, and jumping to point to step 722 to continue execution;
step 709, counting the maximum diameters of all the image nodules, if the maximum diameter of the nodule is larger than 2 cm, executing step 710, otherwise, executing step 713;
step 710, calculating the total number of the image examinations of the patient with the typical characteristics of liver cancer, if the total number is greater than or equal to 1 item, executing step 711, otherwise executing step 712;
step 711, setting the analysis result as primary liver cancer, and skipping to step 722 to continue execution;
step 712, counting the total number of the benign features of the patient nodule, and skipping to step 714 to continue execution;
step 713, calculating the total number of the image examinations of the patient with the typical characteristics of the liver cancer, if the total number is greater than or equal to 2 items, skipping to step 711 to continue execution, otherwise, skipping to step 712 to continue execution;
step 714, judging whether the benign tumor feature quantity is greater than or equal to 1 item, if so, executing step 715, otherwise, executing step 716;
step 715, setting the analysis result as benign tumor, recommending the patient to carry out 1 time of ultrasonic and serum AFP detection every 6 months, and skipping to step 722 to continue execution;
step 716, determining whether the enhanced MRI check is completed, if not, executing step 717, otherwise, executing step 718;
step 717, setting the analysis result as a patient recommendation for carrying out the enhanced MRI examination, and skipping to step 722 for continuing execution;
step 718, determining whether the ultrasound contrast examination is completed, if so, executing step 719, otherwise, executing step 721;
step 719, judging whether the EOB-MRI check is completed, if so, executing step 706, otherwise, executing step 720;
step 720, setting an analysis result to suggest that the patient carries out a general beauty examination, and jumping to step 722 to continue execution;
step 721, setting the analysis result as suggesting the patient to carry out the ultrasound contrast examination, and skipping to step 722 to continue execution;
and step 722, returning an analysis result, and ending the analysis flow.
7. The data analysis method based on the primary liver cancer big data, according to claim 1, is characterized by comprising the following steps:
and step 8, displaying the disease condition analysis result on a terminal interface.
8. A computer readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to perform the steps of the method for primary liver cancer big data based data analysis according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110047657.3A CN112712899A (en) | 2021-01-14 | 2021-01-14 | Data analysis method based on primary liver cancer big data and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110047657.3A CN112712899A (en) | 2021-01-14 | 2021-01-14 | Data analysis method based on primary liver cancer big data and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112712899A true CN112712899A (en) | 2021-04-27 |
Family
ID=75549028
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110047657.3A Pending CN112712899A (en) | 2021-01-14 | 2021-01-14 | Data analysis method based on primary liver cancer big data and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112712899A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019076830A1 (en) * | 2017-10-16 | 2019-04-25 | Biopredictive | Method of prognosis and follow up of primary liver cancer |
| CN110993098A (en) * | 2019-12-06 | 2020-04-10 | 高春芳 | Establishment and application of novel blood multi-index liver cancer diagnosis model (GAP-TALAD) |
| CN111681737A (en) * | 2020-05-07 | 2020-09-18 | 陈�峰 | Structured report system and method for constructing liver cancer image database |
-
2021
- 2021-01-14 CN CN202110047657.3A patent/CN112712899A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019076830A1 (en) * | 2017-10-16 | 2019-04-25 | Biopredictive | Method of prognosis and follow up of primary liver cancer |
| CN110993098A (en) * | 2019-12-06 | 2020-04-10 | 高春芳 | Establishment and application of novel blood multi-index liver cancer diagnosis model (GAP-TALAD) |
| CN111681737A (en) * | 2020-05-07 | 2020-09-18 | 陈�峰 | Structured report system and method for constructing liver cancer image database |
Non-Patent Citations (4)
| Title |
|---|
| 中华人民共和国国家卫生健康委员会医政医管局: "原发性肝癌诊疗规范(2019年版)", 《中国实用外科杂志》 * |
| 刘英平等: "甲胎蛋白与癌胚抗原在原发性肝癌与转移性肝癌鉴别诊断中的意义分析", 《中国实验诊断学》 * |
| 王垒 等: "原发性肝癌大数据建设初步探索", 《中华肝胆外科杂志》 * |
| 王垒等: "基于大数据平台的肝细胞癌自动化中国分期模型研究", 《中华肝脏外科手术学电子杂志》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wu et al. | Comparison of chest radiograph interpretations by artificial intelligence algorithm vs radiology residents | |
| CN108831559B (en) | Chinese electronic medical record text analysis method and system | |
| CN110335665B (en) | Image searching method and system applied to medical image auxiliary diagnosis analysis | |
| JP5952835B2 (en) | Imaging protocol updates and / or recommenders | |
| CN112712879B (en) | Information extraction method, device, equipment and storage medium for medical image report | |
| CN112614565A (en) | Traditional Chinese medicine classic famous prescription intelligent recommendation method based on knowledge-graph technology | |
| US20210057058A1 (en) | Data processing method, apparatus, and device | |
| Bai et al. | Thyroid nodules risk stratification through deep learning based on ultrasound images | |
| US11062448B2 (en) | Machine learning data generation support apparatus, operation method of machine learning data generation support apparatus, and machine learning data generation support program | |
| US20220285011A1 (en) | Document creation support apparatus, document creation support method, and program | |
| CN111061835B (en) | Query method and device, electronic equipment and computer readable storage medium | |
| EP2656243B1 (en) | Generation of pictorial reporting diagrams of lesions in anatomical structures | |
| Yamashita et al. | Automated identification and measurement extraction of pancreatic cystic lesions from free-text radiology reports using natural language processing | |
| Zhang et al. | Comparison of chest radiograph captions based on natural language processing vs completed by radiologists | |
| Tabatabaei et al. | Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach | |
| Yurasakpong et al. | Anatomical variants identified on chest computed tomography of 1000+ COVID‐19 patients from an open‐access dataset | |
| JP7473314B2 (en) | Medical information management device and method for adding metadata to medical reports | |
| Chen et al. | Automatically structuring on Chinese ultrasound report of cerebrovascular diseases via natural language processing | |
| Zhang et al. | Utility of a rule-based algorithm in the assessment of standardized reporting in PI-RADS | |
| US11978274B2 (en) | Document creation support apparatus, document creation support method, and document creation support program | |
| Smischney et al. | Retrospective derivation and validation of a search algorithm to identify emergent endotracheal intubations in the intensive care unit | |
| CN112712899A (en) | Data analysis method based on primary liver cancer big data and storage medium | |
| CN114201613A (en) | Test question generation method, test question generation device, electronic device, and storage medium | |
| Wang et al. | Deep learning for automating the organization of institutional dermatology image stores | |
| Tafavvoghi et al. | Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210427 |
|
| RJ01 | Rejection of invention patent application after publication |