data-factory-airflow-dags | DAGs adapting the MRI preprocessing pipeline to Airflow | Data Labeling library
kandi X-RAY | data-factory-airflow-dags Summary
kandi X-RAY | data-factory-airflow-dags Summary
data-factory-airflow-dags is a Python library typically used in Artificial Intelligence, Data Labeling applications. data-factory-airflow-dags has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
DAGs adapting the MRI preprocessing pipeline to Airflow
DAGs adapting the MRI preprocessing pipeline to Airflow
Support
Quality
Security
License
Reuse
Support
data-factory-airflow-dags has a low active ecosystem.
It has 2 star(s) with 1 fork(s). There are 3 watchers for this library.
It had no major release in the last 6 months.
There are 2 open issues and 0 have been closed. There are 14 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of data-factory-airflow-dags is current.
Quality
data-factory-airflow-dags has no bugs reported.
Security
data-factory-airflow-dags has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
data-factory-airflow-dags is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
data-factory-airflow-dags releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi has reviewed data-factory-airflow-dags and discovered the below as its top functions. This is intended to give you an instant insight into data-factory-airflow-dags implemented functionality, and help decide if they suit your requirements.
- Generate a pipeline step for the given workflow .
- Define a DAG for pre - process images .
- Convert DICOM to Nifti format .
- Construct an MPM pipeline step .
- Create a DAG for creating DICOM files .
- Generate a DAG for a given dataset .
- Define a DAG of files in the dataset .
- Rebuild a pipeline step .
- Define a DAG for failed processing .
- Default configuration for a neuromorphometric pipeline .
Get all kandi verified functions for this library.
data-factory-airflow-dags Key Features
No Key Features are available at this moment for data-factory-airflow-dags.
data-factory-airflow-dags Examples and Code Snippets
No Code Snippets are available at this moment for data-factory-airflow-dags.
Community Discussions
Trending Discussions on Data Labeling
QUESTION
How can I do this split process in Python?
Asked 2021-Dec-30 at 14:06
I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.
What I've done so far is make this representation with the same enumerator class.
A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?
...ANSWER
Answered 2021-Dec-30 at 13:57Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:
QUESTION
Replacing a character with a space and dividing the string into two words in R
Asked 2020-Nov-18 at 07:32
I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.
In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space
i have this:
...ANSWER
Answered 2020-Nov-16 at 07:24Try something like:
QUESTION
Azure ML FileDataset registers, but cannot be accessed for Data Labeling project
Asked 2020-Oct-28 at 20:31
Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.
Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.
Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.
...ANSWER
Answered 2020-Oct-27 at 22:39Is the data behind virtual network by any chance?
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install data-factory-airflow-dags
Create the following pools:.
Create the following pools: image_preprocessing with N slots, where N is less than the number of vCPUs available on the machine remote_file_copy with N slots, where N should be 1 or 2 to avoid saturating network IO
In Airflow config file, add a [spm] section with the following entries: SPM_DIR: path to the root folder of SPM 12.
In Airflow config file, add a [mipmap] section with the following entries (required only if MipMap is used for ETL): DB_CONFIG_FILE: path to the configuration file used by MipMap to connect to its work database.
In Airflow config file, add a [data-factory] section with the following entries: DATASETS: comma separated list of datasets to process. Each dataset is configured using a [<dataset>] section in the config file EMAIL_ERRORS_TO: email address to send errors to SLACK_CHANNEL: optional, Slack channel to use to send status messages SLACK_CHANNEL_USER: optional, user to post as in Slack SLACK_TOKEN: optional, authorisation token for Slack DATA_CATALOG_SQL_ALCHEMY_CONN: connection URL to the Data catalog database tracking artifacts generated by the MRI pipelines. I2B2_SQL_ALCHEMY_CONN: connection URL to the I2B2 database storing all the MRI pipelines results.
For each dataset, add a [data-factory:<dataset>] section, replacing <dataset> with the name of the dataset and define the following entries: DATASET_LABEL: Name of the dataset
For each dataset, configure the [data-factory:<dataset>:reorganisation] section if you need to reorganise an input folder containing only files or folders that will be split into several folders, one per visit for example: INPUT_FOLDER: Folder containing the original imaging data to process. This data should have been already anonymised by a tool INPUT_FOLDER_DEPTH: depth of folders to explore while scanning the original imaging data to process. INPUT_CONFIG: List of flags defining how incoming imaging data are organised, values are defined below in the preprocessing section. MAX_ACTIVE_RUNS: maximum number of reorganisation tasks in parallel FOLDER_FILTER: regex that describes acceptable folder names. Folders that does not fully match it will be discarded. PIPELINES: List of pipelines to execute. Values are copy_to_local: if used, input data are first copied to a local folder to speed-up processing. dicom_reorganise: output_folder: output folder that will contain the reorganised data. output_folder_structure: description of the desired folder organisation. E.g. '#PatientID/#StudyID/#SeriesDescription/#SeriesNumber' docker_image: organiser docker image. docker_input_dir: docker input volume for the organiser (path inside the container). docker_output_dir: docker output volume for the organiser (path inside the container). allowed_field_values: list of fields with restricted set of values used to filter out unwanted images, e.g. FIELD=VALUE1,VALUE2,VALUE3 [FIELD2=VALUE1,VALUE2 ...] nifti_reorganise: output_folder: output folder that will contain the reorganised data. output_folder_structure: description of the desired folder organisation. E.g. '#PatientID/#StudyID/#SeriesDescription/#SeriesNumber' docker_image: organiser docker image. docker_input_dir: docker input volume for the organiser (path inside the container). docker_output_dir: docker output volume for the organiser (path inside the container). trigger_preprocessing: scan the current folder and triggers preprocessing of images on each folder discovered trigger_ehr: scan the current folder and triggers importation of EHR data on each folder discovered
If trigger_preprocessing is used, configure the [data-factory:<dataset>:reorganisation:trigger_preprocessing] section: DEPTH: depth of folders to explore when triggering importation of EHR data
If trigger_ehr is used, configure the [data-factory:<dataset>:reorganisation:trigger_ehr] section: DEPTH: depth of folders to explore when triggering preprocessing
For each dataset, now configure the [data-factory:<dataset>:preprocessing] section: INPUT_FOLDER: Folder containing the original imaging data to process. This data should have been already anonymised by a tool. Not required when the reorganisation pipelines have been used before. INPUT_CONFIG: List of flags defining how incoming imaging data are organised, values are boost: (optional) When enabled, we consider that all the files from a same folder share the same meta-data. The processing is (about 2 times) faster. This option is enabled by default. session_id_by_patient: Rarely, a data set might use study IDs which are unique by patient (not for the whole study). E.g.: LREN data. In such a case, you have to enable this flag. This will use PatientID + StudyID as a session ID. visit_id_in_patient_id: Rarely, a data set might mix patient IDs and visit IDs. E.g. : LREN data. In such a case, you have to enable this flag. This will try to split PatientID into VisitID and PatientID. visit_id_from_path: Enable this flag to get the visit ID from the folder hierarchy instead of DICOM meta-data (e.g. can be useful for PPMI). repetition_from_path: Enable this flag to get the repetition ID from the folder hierarchy instead of DICOM meta-data (e.g. can be useful for PPMI). MAX_ACTIVE_RUNS: maximum number of folders containing scans to pre-process in parallel MIN_FREE_SPACE: minimum percentage of free space available on local disk MISC_LIBRARY_PATH: path to the Misc&Libraries folder for SPM pipelines. PIPELINES_PATH: path to the root folder containing the Matlab scripts for the pipelines PROTOCOLS_DEFINITION_FILE: path to the default protocols definition file defining the protocols used on the scanner. SCANNERS: List of methods describing how the preprocessing data folder is scanned for new work, values are continuous: input folder is scanned frequently for new data. Sub-folders should contain a .ready file to indicate that processing can be performed on that folder. daily: input folder contains a sub-folder for the year, this folder contains daily sub-folders for each day of the year (format yyyyMMdd). Those daily sub-folders in turn contain the folders for each scan to process. once: input folder contains a set of sub-folders each containing a scan to process. PIPELINES: List of pipelines to execute. Values are copy_to_local: if used, input data are first copied to a local folder to speed-up processing. dicom_to_nifti: convert all DICOM files to Nifti format. mpm_maps: computes the Multiparametric Maps (MPMs) and brain segmentation in different tissue maps. neuro_morphometric_atlas: computes an individual Atlas based on the NeuroMorphometrics Atlas. export_features: exports neuroimaging features stored in CSV files to the I2B2 database catalog_to_i2b2: exports meta-data from the data catalog to the I2B2 database.
If copy_to_local is used, configure the [data-factory:<dataset>:preprocessing:copy_to_local] section: OUTPUT_FOLDER: destination folder for the local copy
If dicom_to_nifti is used or required (when DICOM images are used as input), configure the [data-factory:<dataset>:preprocessing:dicom_to_nifti] section: OUTPUT_FOLDER: destination folder for the Nifti images BACKUP_FOLDER: backup folder for the Nitfi images SPM_FUNCTION: SPM function called. Default to 'DCM2NII_LREN' PIPELINE_PATH: path to the folder containing the SPM script for this pipeline. Default to [data-factory:<dataset>:preprocessing]PIPELINES_PATH + '/Nifti_Conversion_Pipeline' MISC_LIBRARY_PATH: path to the Misc&Libraries folder for SPM pipelines. Default to MISC_LIBRARY_PATH value in [data-factory:<dataset>:preprocessing] section. PROTOCOLS_DEFINITION_FILE: path to the Protocols definition file defining the protocols used on the scanner. Default to PROTOCOLS_DEFINITION_FILE value in [data-factory:<dataset>:preprocessing] section. DCM2NII_PROGRAM: Path to DCM2NII program. Default to [data-factory:<dataset>:preprocessing]PIPELINES_PATH + '/dcm2nii'
If mpm_maps is used, configure the [data-factory:<dataset>:preprocessing:mpm_maps] section: OUTPUT_FOLDER: destination folder for the MPMs and brain segmentation BACKUP_FOLDER: backup folder for the MPMs and brain segmentation SPM_FUNCTION: SPM function called. Default to 'Preproc_mpm_maps' PIPELINE_PATH: path to the folder containing the SPM script for this pipeline. Default to [data-factory:<dataset>:preprocessing]PIPELINES_PATH + '/MPMs_Pipeline' MISC_LIBRARY_PATH: path to the Misc&Libraries folder for SPM pipelines. Default to MISC_LIBRARY_PATH value in [data-factory:<dataset>:preprocessing] section. PROTOCOLS_DEFINITION_FILE: path to the Protocols definition file defining the protocols used on the scanner. Default to PROTOCOLS_DEFINITION_FILE value in [data-factory:<dataset>:preprocessing] section.
If neuro_morphometric_atlas is used, configure the [data-factory:<dataset>:preprocessing:neuro_morphometric_atlas] section: OUTPUT_FOLDER: destination folder for the Atlas File, the volumes of the Morphometric Atlas structures (.txt), the csv file containing the volume, and globals plus Multiparametric Maps (R2*, R1, MT, PD) for each structure defined in the Subject Atlas. BACKUP_FOLDER: backup folder for the Atlas File, the volumes of the Morphometric Atlas structures (.txt), the csv file containing the volume, and globals plus Multiparametric Maps (R2*, R1, MT, PD) for each structure defined in the Subject Atlas. SPM_FUNCTION: SPM function called. Default to 'NeuroMorphometric_pipeline' PIPELINE_PATH: path to the folder containing the SPM script for this pipeline. Default to [data-factory:<dataset>:preprocessing]PIPELINES_PATH + '/NeuroMorphometric_Pipeline/NeuroMorphometric_tbx/label' MISC_LIBRARY_PATH: path to the Misc&Libraries folder for SPM pipelines. Default to MISC_LIBRARY_PATH value in [data-factory:<dataset>:preprocessing] section. PROTOCOLS_DEFINITION_FILE: path to the Protocols definition file defining the protocols used on the scanner. Default to PROTOCOLS_DEFINITION_FILE value in [data-factory:<dataset>:preprocessing] section. TPM_TEMPLATE: Path to the the template used for segmentation step in case the image is not segmented. Default to SPM_DIR + 'tpm/nwTPM_sl3.nii'
For each dataset, now configure the [data-factory:<dataset>:ehr] section: INPUT_FOLDER: Folder containing the original EHR data to process. This data should have been already anonymised by a tool INPUT_FOLDER_DEPTH: When a once scanner is used, indicates the depth of folders to traverse before reaching EHR data. Default to 1. MIN_FREE_SPACE: minimum percentage of free space available on local disk SCANNERS: List of methods describing how the EHR data folder is scanned for new work, values are daily: input folder contains a sub-folder for the year, this folder contains daily sub-folders for each day of the year (format yyyyMMdd). Those daily sub-folders in turn contain the EHR files in CSV format to process. once: input folder contains the EHR files in CSV format to process. PIPELINES: List of pipelines to execute. Values are map_ehr_to_i2b2: .
Configure the [data-factory:<dataset>:ehr:map_ehr_to_i2b2] section: DOCKER_IMAGE: Docker image of the tool that maps EHR data to an I2B2 schema.
Configure the [data-factory:<dataset>:ehr:version_incoming_ehr] section: OUTPUT_FOLDER: output folder used to store versioned EHR data.
Create the following pools: image_preprocessing with N slots, where N is less than the number of vCPUs available on the machine remote_file_copy with N slots, where N should be 1 or 2 to avoid saturating network IO
In Airflow config file, add a [spm] section with the following entries: SPM_DIR: path to the root folder of SPM 12.
In Airflow config file, add a [mipmap] section with the following entries (required only if MipMap is used for ETL): DB_CONFIG_FILE: path to the configuration file used by MipMap to connect to its work database.
In Airflow config file, add a [data-factory] section with the following entries: DATASETS: comma separated list of datasets to process. Each dataset is configured using a [<dataset>] section in the config file EMAIL_ERRORS_TO: email address to send errors to SLACK_CHANNEL: optional, Slack channel to use to send status messages SLACK_CHANNEL_USER: optional, user to post as in Slack SLACK_TOKEN: optional, authorisation token for Slack DATA_CATALOG_SQL_ALCHEMY_CONN: connection URL to the Data catalog database tracking artifacts generated by the MRI pipelines. I2B2_SQL_ALCHEMY_CONN: connection URL to the I2B2 database storing all the MRI pipelines results.
For each dataset, add a [data-factory:<dataset>] section, replacing <dataset> with the name of the dataset and define the following entries: DATASET_LABEL: Name of the dataset
For each dataset, configure the [data-factory:<dataset>:reorganisation] section if you need to reorganise an input folder containing only files or folders that will be split into several folders, one per visit for example: INPUT_FOLDER: Folder containing the original imaging data to process. This data should have been already anonymised by a tool INPUT_FOLDER_DEPTH: depth of folders to explore while scanning the original imaging data to process. INPUT_CONFIG: List of flags defining how incoming imaging data are organised, values are defined below in the preprocessing section. MAX_ACTIVE_RUNS: maximum number of reorganisation tasks in parallel FOLDER_FILTER: regex that describes acceptable folder names. Folders that does not fully match it will be discarded. PIPELINES: List of pipelines to execute. Values are copy_to_local: if used, input data are first copied to a local folder to speed-up processing. dicom_reorganise: output_folder: output folder that will contain the reorganised data. output_folder_structure: description of the desired folder organisation. E.g. '#PatientID/#StudyID/#SeriesDescription/#SeriesNumber' docker_image: organiser docker image. docker_input_dir: docker input volume for the organiser (path inside the container). docker_output_dir: docker output volume for the organiser (path inside the container). allowed_field_values: list of fields with restricted set of values used to filter out unwanted images, e.g. FIELD=VALUE1,VALUE2,VALUE3 [FIELD2=VALUE1,VALUE2 ...] nifti_reorganise: output_folder: output folder that will contain the reorganised data. output_folder_structure: description of the desired folder organisation. E.g. '#PatientID/#StudyID/#SeriesDescription/#SeriesNumber' docker_image: organiser docker image. docker_input_dir: docker input volume for the organiser (path inside the container). docker_output_dir: docker output volume for the organiser (path inside the container). trigger_preprocessing: scan the current folder and triggers preprocessing of images on each folder discovered trigger_ehr: scan the current folder and triggers importation of EHR data on each folder discovered
If trigger_preprocessing is used, configure the [data-factory:<dataset>:reorganisation:trigger_preprocessing] section: DEPTH: depth of folders to explore when triggering importation of EHR data
If trigger_ehr is used, configure the [data-factory:<dataset>:reorganisation:trigger_ehr] section: DEPTH: depth of folders to explore when triggering preprocessing
For each dataset, now configure the [data-factory:<dataset>:preprocessing] section: INPUT_FOLDER: Folder containing the original imaging data to process. This data should have been already anonymised by a tool. Not required when the reorganisation pipelines have been used before. INPUT_CONFIG: List of flags defining how incoming imaging data are organised, values are boost: (optional) When enabled, we consider that all the files from a same folder share the same meta-data. The processing is (about 2 times) faster. This option is enabled by default. session_id_by_patient: Rarely, a data set might use study IDs which are unique by patient (not for the whole study). E.g.: LREN data. In such a case, you have to enable this flag. This will use PatientID + StudyID as a session ID. visit_id_in_patient_id: Rarely, a data set might mix patient IDs and visit IDs. E.g. : LREN data. In such a case, you have to enable this flag. This will try to split PatientID into VisitID and PatientID. visit_id_from_path: Enable this flag to get the visit ID from the folder hierarchy instead of DICOM meta-data (e.g. can be useful for PPMI). repetition_from_path: Enable this flag to get the repetition ID from the folder hierarchy instead of DICOM meta-data (e.g. can be useful for PPMI). MAX_ACTIVE_RUNS: maximum number of folders containing scans to pre-process in parallel MIN_FREE_SPACE: minimum percentage of free space available on local disk MISC_LIBRARY_PATH: path to the Misc&Libraries folder for SPM pipelines. PIPELINES_PATH: path to the root folder containing the Matlab scripts for the pipelines PROTOCOLS_DEFINITION_FILE: path to the default protocols definition file defining the protocols used on the scanner. SCANNERS: List of methods describing how the preprocessing data folder is scanned for new work, values are continuous: input folder is scanned frequently for new data. Sub-folders should contain a .ready file to indicate that processing can be performed on that folder. daily: input folder contains a sub-folder for the year, this folder contains daily sub-folders for each day of the year (format yyyyMMdd). Those daily sub-folders in turn contain the folders for each scan to process. once: input folder contains a set of sub-folders each containing a scan to process. PIPELINES: List of pipelines to execute. Values are copy_to_local: if used, input data are first copied to a local folder to speed-up processing. dicom_to_nifti: convert all DICOM files to Nifti format. mpm_maps: computes the Multiparametric Maps (MPMs) and brain segmentation in different tissue maps. neuro_morphometric_atlas: computes an individual Atlas based on the NeuroMorphometrics Atlas. export_features: exports neuroimaging features stored in CSV files to the I2B2 database catalog_to_i2b2: exports meta-data from the data catalog to the I2B2 database.
If copy_to_local is used, configure the [data-factory:<dataset>:preprocessing:copy_to_local] section: OUTPUT_FOLDER: destination folder for the local copy
If dicom_to_nifti is used or required (when DICOM images are used as input), configure the [data-factory:<dataset>:preprocessing:dicom_to_nifti] section: OUTPUT_FOLDER: destination folder for the Nifti images BACKUP_FOLDER: backup folder for the Nitfi images SPM_FUNCTION: SPM function called. Default to 'DCM2NII_LREN' PIPELINE_PATH: path to the folder containing the SPM script for this pipeline. Default to [data-factory:<dataset>:preprocessing]PIPELINES_PATH + '/Nifti_Conversion_Pipeline' MISC_LIBRARY_PATH: path to the Misc&Libraries folder for SPM pipelines. Default to MISC_LIBRARY_PATH value in [data-factory:<dataset>:preprocessing] section. PROTOCOLS_DEFINITION_FILE: path to the Protocols definition file defining the protocols used on the scanner. Default to PROTOCOLS_DEFINITION_FILE value in [data-factory:<dataset>:preprocessing] section. DCM2NII_PROGRAM: Path to DCM2NII program. Default to [data-factory:<dataset>:preprocessing]PIPELINES_PATH + '/dcm2nii'
If mpm_maps is used, configure the [data-factory:<dataset>:preprocessing:mpm_maps] section: OUTPUT_FOLDER: destination folder for the MPMs and brain segmentation BACKUP_FOLDER: backup folder for the MPMs and brain segmentation SPM_FUNCTION: SPM function called. Default to 'Preproc_mpm_maps' PIPELINE_PATH: path to the folder containing the SPM script for this pipeline. Default to [data-factory:<dataset>:preprocessing]PIPELINES_PATH + '/MPMs_Pipeline' MISC_LIBRARY_PATH: path to the Misc&Libraries folder for SPM pipelines. Default to MISC_LIBRARY_PATH value in [data-factory:<dataset>:preprocessing] section. PROTOCOLS_DEFINITION_FILE: path to the Protocols definition file defining the protocols used on the scanner. Default to PROTOCOLS_DEFINITION_FILE value in [data-factory:<dataset>:preprocessing] section.
If neuro_morphometric_atlas is used, configure the [data-factory:<dataset>:preprocessing:neuro_morphometric_atlas] section: OUTPUT_FOLDER: destination folder for the Atlas File, the volumes of the Morphometric Atlas structures (.txt), the csv file containing the volume, and globals plus Multiparametric Maps (R2*, R1, MT, PD) for each structure defined in the Subject Atlas. BACKUP_FOLDER: backup folder for the Atlas File, the volumes of the Morphometric Atlas structures (.txt), the csv file containing the volume, and globals plus Multiparametric Maps (R2*, R1, MT, PD) for each structure defined in the Subject Atlas. SPM_FUNCTION: SPM function called. Default to 'NeuroMorphometric_pipeline' PIPELINE_PATH: path to the folder containing the SPM script for this pipeline. Default to [data-factory:<dataset>:preprocessing]PIPELINES_PATH + '/NeuroMorphometric_Pipeline/NeuroMorphometric_tbx/label' MISC_LIBRARY_PATH: path to the Misc&Libraries folder for SPM pipelines. Default to MISC_LIBRARY_PATH value in [data-factory:<dataset>:preprocessing] section. PROTOCOLS_DEFINITION_FILE: path to the Protocols definition file defining the protocols used on the scanner. Default to PROTOCOLS_DEFINITION_FILE value in [data-factory:<dataset>:preprocessing] section. TPM_TEMPLATE: Path to the the template used for segmentation step in case the image is not segmented. Default to SPM_DIR + 'tpm/nwTPM_sl3.nii'
For each dataset, now configure the [data-factory:<dataset>:ehr] section: INPUT_FOLDER: Folder containing the original EHR data to process. This data should have been already anonymised by a tool INPUT_FOLDER_DEPTH: When a once scanner is used, indicates the depth of folders to traverse before reaching EHR data. Default to 1. MIN_FREE_SPACE: minimum percentage of free space available on local disk SCANNERS: List of methods describing how the EHR data folder is scanned for new work, values are daily: input folder contains a sub-folder for the year, this folder contains daily sub-folders for each day of the year (format yyyyMMdd). Those daily sub-folders in turn contain the EHR files in CSV format to process. once: input folder contains the EHR files in CSV format to process. PIPELINES: List of pipelines to execute. Values are map_ehr_to_i2b2: .
Configure the [data-factory:<dataset>:ehr:map_ehr_to_i2b2] section: DOCKER_IMAGE: Docker image of the tool that maps EHR data to an I2B2 schema.
Configure the [data-factory:<dataset>:ehr:version_incoming_ehr] section: OUTPUT_FOLDER: output folder used to store versioned EHR data.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
Find more information at:
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
