Pre-processing¶

Extract¶

Note 1: The extract module is required if the EHR data exists in a relational database

Note 2: If the EHR data is in the flat file (.csv), please skip this section and proceed to next sections

Help menu¶

To display the help menu of the Extract functionality.

(.venv) app_user@hostname:~$python -m ehrqc.extract.Extract -h

or

(.venv) app_user@hostname:~$python -m ehrqc.extract.Extract --help

Output

usage: Extract.py [-h] save_path source_db data_type schema_name

EHRQC

positional arguments:
save_path    Path of the file to store the outputs
source_db    Source name [mimic, omop]
data_type    Data type name [demographics, vitals, lab_measurements]
schema_name  Source schema name

optional arguments:
-h, --help   show this help message and exit

Extract OMOP Demographics¶

To extract the demographics data from omop schema and store it in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.extract.Extract temp/omop_demograpics.csv omop demographics omop_cdm

Extract OMOP Vitals¶

To extract the vitals data from omop schema and store it in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.extract.Extract temp/omop_vitals.csv omop vitals omop_cdm

Extract OMOP Lab measurements¶

To extract the lab measurements data from omop schema and store it in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.extract.Extract temp/omop_lab_measurements.csv omop lab_measurements omop_cdm

Extract MIMIC Demographics¶

To extract the demographics data from mimic schema and store it in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.extract.Extract temp/mimic_demographics.csv mimic demographics mimiciv

Extract MIMIC Vitals¶

To extract the vitals data from mimic schema and store it in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.extract.Extract temp/mimic_vitals.csv mimic vitals mimiciv

Extract MIMIC Lab measurements¶

To extract the lab measurements data from mimic schema and store it in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.extract.Extract temp/mimic_lab_measurements.csv mimic lab_measurements mimiciv

Extract Data¶

A generic function to extract any data from a relational database irrespective of the schema.

Note

This function is available within the EHR-QC-Preprocess module. For installatoin instructions for this module, please visit the following link

Help menu¶

To display the help menu of the Exploration Plot functionality.

(.venv) app_user@hostname:~$python -m ehrqc.extract.ExtractData -h

or

(.venv) app_user@hostname:~$python -m ehrqc.extract.ExtractData --help

Output

usage: ExtractData.py [-h] save_path schema_name sql_file_path

EHR-QC preprocessing utility

positional arguments:
  save_path      Path of the file to store the outputs
  schema_name    Source schema name
  sql_file_path  Path of the file containing SQL query

optional arguments:
  -h, --help     show this help message and exit

Usage¶

To extract the data by executing a query specified in the sql_file_path a relational database schema schema_name and store it in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.extract.ExtractData /save/path.csv name_of_the_db_schema /path/to/query.sql

Exploration Plots¶

Help menu¶

To display the help menu of the Exploration Plot functionality.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Plot -h

or

(.venv) app_user@hostname:~$python -m ehrqc.qc.Plot --help

Output

usage: Plot.py [-h] [-c COLUMN_MAPPING] plot_type source_path save_path

EHRQC

positional arguments:
plot_type             Type of plot to generate [demographics_explore, vitals_explore, lab_measurements_explore]
source_path           Source data path
save_path             Path of the file to store the output

optional arguments:
-h, --help            show this help message and exit
-c COLUMN_MAPPING, --column_mapping COLUMN_MAPPING

The column mapping has to be in json format as shown below;

'{"expected_column_name": "custom_column_name"}'

For instance, if the “Age” attribute in demographics csv file is under the column name “Number of Years” instead of the expected “age” column name as shown below.

Patient ID	Number of Years
00001	57
00002	45
00003	78
00004	35
00005	83

The following mapping can be applied;

'{"age": "Number of Years"}'

Similarly, more than one columns can be mapped in this manner;

For instance, if the demographics csv file contains “Age”, “Sex”, and “Date of Birth” column names inplace of “age”, ‘gender’, and ‘dob’ names that are expected.

Patient ID	Number of Years	Sex	Date of Birth
00001	57	Male	04-02-1966
00002	45	Female	04-02-1975
00003	78	Female	04-02-1945
00004	35	Male	04-02-1988
00005	83	Male	04-02-1940

The following mapping can be applied;

'{"age": "Number of Years", "gender": "Sex", "dob": "Date of Birth"}'

Explore Demographics Plots¶

To generate QC plots from the demograhic data obtained from the source_path and save it in the save_path. If the source csv file is not in a standard format, then a column_mapping needs to be provided.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Plot demographics_explore temp/mimic_demographics.csv temp/mimic_demographics_explore.html -c {<"optional mapping information">}

This function expects the file to contain the information under the following columns;

Expected Column Name	Column Details
age	Age of the person
weight	Weight of the person
height	Height of the person
gender	Gender of the person
ethnicity	Ethnicity of the person

Example Demographics Plots

Explore Vitals Plots¶

To generate QC plots from the vitals data obtained from the source_path and save it in the save_path. If the source csv file is not in a standard format, then a column_mapping needs to be provided.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Plot vitals_explore temp/mimic_vitals.csv temp/mimic_vitals_explore.html -c {<"optional mapping information">}

This function expects the file to contain the information under the following columns;

Expected Column Name	Column Details
heartrate	Heart Rate
sysbp	Systolic Blood Pressure
diabp	Diastolic Blood Pressure
meanbp	Mean Blood Pressure
resprate	Respiratory Rate
tempc	Temperature
spo2	Oxygen Saturation
gcseye	Glasgow Coma Scale - Eye Response
gcsverbal	Glasgow Coma Scale - Verbal Response
gcsmotor	Glasgow Coma Scale - Motor Response

Example Vitals Plots

Explore Lab measurements Plots¶

To generate QC plots from the lab measurements data obtained from the source_path and save it in the save_path. If the source csv file is not in a standard format, then a column_mapping needs to be provided.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Plot lab_measurements_explore temp/mimic_lab_measurements.csv temp/mimic_lab_measurements_explore.html -c {<"optional mapping information">}

This function expects the file to contain the information under the following columns;

Expected Column Name	Column Details
glucose	Glucose
hemoglobin	Hemoglobin
anion_gap	Anion Gap
bicarbonate	Bicarbonate
calcium_total	Calcium Total
chloride	Chloride
creatinine	Creatinine
magnesium	Magnesium
phosphate	Phosphate
potassium	Potassium
sodium	Sodium
urea_nitrogen	Urea Nitrogen
hematocrit	Hematocrit
mch	Mean Cell Hemoglobin
mchc	Mean Corpuscular Hemoglobin Concentration
mcv	Mean Corpuscular Volume
platelet_count	Platelet Count
rdw	Red cell Distribution Width
red_blood_cells	Red Blood Cells
white_blood_cells	White Blood Cells

Example Lab measurements Plots

Explore Data¶

This is a generic utility to plot data exploration graphs.

Note

This utility functions doesn’t expect any particular attributes to be present in the file. Instead, it will plot all the attributes mentioned in the column list parameter making it more flexible.

Note

This function is available within the EHR-QC-Preprocess module. For installatoin instructions for this module, please visit the following link

Help menu¶

To display the help menu of the Exploration Plot functionality.

(.venv) app_user@hostname:~$python -m ehrqc.plot.ExplorationGraphsPdf -h

or

(.venv) app_user@hostname:~$python -m ehrqc.plot.ExplorationGraphsPdf --help

Output

usage: ExplorationGraphsPdf.py [-h] [-c COLUMNS] [-sf [SOURCE_FILE_LIST [SOURCE_FILE_LIST ...]]] [-l [LABELS [LABELS ...]]] save_path

Draw exploration graphs as pdf files

positional arguments:
  save_path             Path of the file to store the output

optional arguments:
  -h, --help            show this help message and exit
  -c COLUMNS, --columns COLUMNS
  -sf [SOURCE_FILE_LIST [SOURCE_FILE_LIST ...]], --source_file_list [SOURCE_FILE_LIST [SOURCE_FILE_LIST ...]]
  -l [LABELS [LABELS ...]], --labels [LABELS [LABELS ...]]

Generate Exploration Plots¶

To generate QC plots using the data obtained from the files specified in -sf parameter and save it in the save_path. Additionally, it also uses the column names provided in the -c parameter and the labels specified as -l.

(.venv) app_user@hostname:~$python -m ehrqc.plot.ExplorationGraphsPdf /path/to/save_file.pdf -c {"Attr 1" : ["Column Name in File 1", "Column Name in File 2"], "Attr 2" : ["Column Name in File 1", "Column Name in File 2"]} -sf /path/to/source_file_1.csv /path/to/source_file_2.csv -l "Label for File 1" "Label for File 2"

Outlier Handling¶

Using IRT ensemble¶

This module allows for plotting the outliers detected in an unsupervised manner using IRT ensemble technique.

Help menu¶

To display the help menu of the Outlier Plot functionality.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Outliers -h

or

(.venv) app_user@hostname:~$python -m ehrqc.qc.Outliers --help

Output

usage: Outliers.py [-h] [-c [COMBINATIONS ...]] source_file save_path

EHRQC

positional arguments:
  source_file           Source data file path
  save_path             Path of the directory to store the output

optional arguments:
  -h, --help            show this help message and exit
  -c [COMBINATIONS ...], --combinations [COMBINATIONS ...]
                        Column combinations to plot (can have multiple column pairs).

Note

Please ensure the csv file does not contain any missing data before using these functions.

Note

There are two ways to call upon this function:

The first approach involves providing the column combinations as command line arguments. Keep in mind that due to computational limitations, a maximum of 10 column pairs can be designated for plotting outliers using this method.
Alternatively, in the second method, you need not specify the column combinations explicitly. In this scenario, the function derives column combinations for all possible pairs. It’s important to note that, due to computational restrictions, the file can contain a maximum of 5 columns. This limitation results in 10 column combination pairs available for outlier plotting using this method.

For more comprehensive information about each of these techniques, please consult the details below:

Plot specifying the combinations¶

To generate outlier plots from the data obtained from the source_path and save it in the save_path with a file named outlier_report.html, you can utilize the optional -c argument to specify column pairs. You have the flexibility to include multiple pairs by reusing this argument multiple times up to a maximum of 10 different column pairs.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Outliers /path/to/source_file.csv /path/to/save/ -c col1 col2 -c col2 col3 -c col3 col1

Plot without specifying the combinations¶

To generate outlier plots from the data obtained from the source_path and save it in the save_path with a file named outlier_report.html. The source file should not contain more than 5 columns.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Outliers /path/to/source_file.csv /path/to/save/

Generated outputs¶

After the function runs successfully, it will generate an HTML file named outlier_report.html in the save_path. This file will contain outlier plots, illustrating the relation between attributes, considering 2 attributes at a time. The points in these plots are color-coded based on their outlier scores.

Example Vitals Outlier Plots

Example Lab measurements Outlier Plots

Using Isolation Forest¶

Note

This function is available within the EHR-QC-Preprocess module. For installatoin instructions for this module, please visit the following link

This module allows for plotting the outliers detected in an unsupervised manner using Isolation Forest technique and also allows to remove the detected outliers.

Help menu¶

To display the help menu of the Outlier Handling functionality.

(.venv) app_user@hostname:~$python -m ehrqc.plot.OutlierIsolationForest -h

or

(.venv) app_user@hostname:~$python -m ehrqc.plot.OutlierIsolationForest --help

Output

usage: OutlierIsolationForest.py [-h] [-col [COLUMNS [COLUMNS ...]]] source_file save_file action

Outlier graphs using IRT ensemble technique

positional arguments:
  source_file           Source data file path
  save_file             Path of the file to store the output
  action                Action to perform [visualise, clean]

optional arguments:
  -h, --help            show this help message and exit
  -col [COLUMNS [COLUMNS ...]], --columns [COLUMNS [COLUMNS ...]]
                        Column names to be used for outlier detection - must have two or more column names (required for both actions i.e. visualise and clean).

Note

Please ensure the csv file does not contain any missing data before using these functions.

Note

The column names containing the actual clinical attributes are to be specified to this function as the data files usually contain id columns that should be excluded.

Note

In the plot functionality, if more than two attributes are present, then the first two principle components are used for plotting.

Plot Outliers¶

To generate outlier plots from the data obtained from the source_file and save it in the save_file. The optional argument -c can be utilised to specify columns to make use while determining outliers.

(.venv) app_user@hostname:~$python -m ehrqc.plot.OutlierIsolationForest /path/to/source_file.csv /path/to/save_file.html visualise -c col1 col2 col3

Remove Outliers¶

To remove outliers from the data obtained from the source_file and save it in the save_file. The optional argument -c can be utilised to specify columns to make use while determining outliers.

(.venv) app_user@hostname:~$python -m ehrqc.plot.OutlierIsolationForest /path/to/source_file.csv /path/to/save_file.html clean -c col1 col2 col3

Impute¶

Help menu¶

To display the help menu;

(.venv) app_user@hostname:~$python -m ehrqc.qc.Impute -h

Output

usage: Impute.py [-h] [-sp SAVE_PATH] [-a ALGORITHM] action source_path

EHRQC

positional arguments:
action                Action to perform [compare, impute]
source_path           Source data path

optional arguments:
-h, --help            show this help message and exit
-sp SAVE_PATH, --save_path SAVE_PATH
                        Path of the file to store the outputs (required only for action=impute)
-a ALGORITHM, --algorithm ALGORITHM
                        Missing data imputation algorithm [mean, median, knn, miss_forest, expectation_maximization, multiple_imputation]

Compare imputation¶

To create a random missingness in the data given by the file at source_path and compare 6 different missing data algorithms [mean, median, knn, miss forest, expectation maximisation, multiple imputation] and report their reconstriction r-squared scores. If the non-numeric feilds from the data obtained from source_path are ignored for imputation. Further, the rows corresponding to the missing values in the data are ignored, instead a random missingness is created of the same proportion as that of original data.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Impute 'compare' temp/mimic_vitals.csv

Imputation¶

To impute missing values in the data obtained from the source_path using the specified algorithm and save it in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Impute impute '/path/to/data.csv' -sp='/path/to/data_imputed.csv' -a=<algorithm name>

This function support the following algorithms

mean
median
knn
miss forest
expectation maximisation
multiple imputation

Anomalies¶

Help menu¶

To display the help menu;

(.venv) app_user@hostname:~$python -m ehrqc.qc.Anomalies -h

or

(.venv) app_user@hostname:~$python -m ehrqc.qc.Anomalies --help

Output

usage: Anomalies.py [-h] [-dm] [-do] [-de] [-di] [-cm] [-co] source_path save_path save_prefix

Detect and Correct Anomalies

positional arguments:
source_path           Source data path
save_path             Path to save the data
save_prefix           Prefix to the saved file

optional arguments:
-h, --help            show this help message and exit
-dm, --detect_missing
                        Detect Missing Values in the dataframe
-do, --detect_outliers
                        Detect Outliers in the dataframe
-de, --detect_errors  Detect Errors in the dataframe
-di, --detect_inconsistencies
                        Detect Inconsistencies in the dataframe
-cm, --correct_missing
                        Correct Missing Values in the dataframe
-co, --correct_outliers
                        Correct Outliers in the dataframe

Detect Anomalies¶

To detect missing data, outliers, errors, and inconsistencies in the data from the source_path and save it as a html file at the save_path with the file prefix save_prefix. To visualise missing data, optional argument -dm needs to be provided. For detecting outliers, optional argument -do needs to be provided.

Example:

(.venv) app_user@hostname:~$python -m ehrqc.qc.Anomalies 'test_data.csv' 'testing' 'test_001' -dm -do

Correct Anomalies¶

To correct missing data and outliers in the data from the source_path and save it as a csv file at the save_path with the file prefix save_prefix. To correct missing data, optional argument -cm needs to be provided. For correcting outliers, optional argument -co needs to be provided.

Example:

(.venv) app_user@hostname:~$python -m ehrqc.qc.Anomalies 'test_data.csv' 'testing' 'test_001' -cm -co

Data using the raw data;

Raw data

After imputing missing values;

Imputed data

After removing outliers;

No outlier data

Rescale¶

Help menu¶

To display the help menu;

(.venv) app_user@hostname:~$python -m ehrqc.qc.Rescale -h

or

(.venv) app_user@hostname:~$python -m ehrqc.qc.Rescale --help

Output

usage: Rescale.py [-h] [-c COLUMNS] [-ssp SCALER_SAVE_PATH] [-mi MIN] [-ma MAX] source_path save_path

EHRQC-Rescale

positional arguments:
source_path           Source data path (csv file)
save_path             Path of a file to store the rescaled output

optional arguments:
-h, --help            show this help message and exit
-c COLUMNS, --columns COLUMNS
                        Names of the columns to be scaled, enclosed in double quotes and seperated by comma
-ssp SCALER_SAVE_PATH, --scaler_save_path SCALER_SAVE_PATH
                        Path of the scaler to save
-mi MIN, --min MIN    Minimum value for the scaler (Default = 0)
-ma MAX, --max MAX    Maximum value for the scaler (Default = 1)

Rescale Data¶

To rescale the data from the source_path and save it as a csv file at the save_path . The optional argument columns can be provided to specify the columns to be rescaled. The optional argument scaler_save_path can be provided to save the scaler in a file. Mininum and Maximum values to the scalers by default is 0 and respectively, but they can be changed by passing --min, and --max arguments.

Example:

(.venv) app_user@hostname:~$python -m ehrqc.qc.Rescale temp/omop_vitals_no_anomalies.csv temp/omop_vitals_rescaled.csv

Before rescaling;

Original scale data

After rescaling;

Rescaled data

Standardise¶

Help menu¶

To display the help menu;

(.venv) app_user@hostname:~$python -m ehrqc.qc.Standardise -h

or

(.venv) app_user@hostname:~$python -m ehrqc.qc.Standardise --help

Output

usage: Standardise.py [-h] [-c COLUMNS] [-ssp SCALER_SAVE_PATH] source_path save_path

EHRQC-Standardise

positional arguments:
source_path           Source data path (csv file)
save_path             Path of a file to store the standardised output

optional arguments:
-h, --help            show this help message and exit
-c COLUMNS, --columns COLUMNS
                        Names of the columns to be scaled, enclosed in double quotes and seperated by comma
-ssp SCALER_SAVE_PATH, --scaler_save_path SCALER_SAVE_PATH
                        Path of the scaler to save

Standardise Data¶

To standardise the data from the source_path and save it as a csv file at the save_path . The optional argument columns can be provided to specify the columns to be standardised. The optional argument scaler_save_path can be provided to save the scaler in a file.

Example:

(.venv) app_user@hostname:~$python -m ehrqc.qc.Standardise temp/omop_vitals_no_anomalies.csv temp/omop_vitals_rescaled.csv

Before standardising;

Original scale data

After standardising;

Rescaled data

Large file handling¶

Frequently, during the initial stages of analyzing Electronic Health Record (EHR) data, we come across files of considerable size. A primary factor contributing to the file’s largeness is the data’s sparseness, where many cells lack values. Typically, this sparseness manifests in certain attributes (columns) within the EHR. For instance, attributes like temperature and heart rate might exhibit substantial coverage in the EHR, while attributes like SPO2 could have only a few recorded values. In such instances, it might be necessary to exclude the sparse attributes from further analysis if they don’t contribute meaningful information for modeling purposes.

This tool provides the capability to manage large files by breaking them down into smaller segments. The initial function generates a report on missing data, indicating the percentage of missing values for all attributes within a specified file. The subsequent function eliminates attributes exceeding the specified missing data threshold and then saves the remaining data to an external file.

Help menu¶

To display the help menu;

(.venv) app_user@hostname:~$python -m ehrqc.qc.Coverage -h

or

(.venv) app_user@hostname:~$python -m ehrqc.qc.Coverage --help

Output

usage: Coverage.py [-h] [-d] [-p PERCENTAGE] [-sp SAVE_PATH] source_file chunksize id_columns [id_columns ...]

Perform Coverage Analysis

positional arguments:
  source_file           Source data file path
  chunksize             Number of chunks the input file should be fragmented into. By default: [chunksize=100]
  id_columns            List of ID columns. They are used to group the other columns to calculate missing percentage.

optional arguments:
  -h, --help            show this help message and exit
  -d, --drop            Drop the columns
  -p PERCENTAGE, --percentage PERCENTAGE
                        Specify the cutoff percentage to drop the columns (required only for drop=True). By default: [-p=50]
  -sp SAVE_PATH, --save_path SAVE_PATH
                        Path of the file to store the outputs (required only for drop=True)

Display Missingness Report¶

To display missing value percentages of all the attributes (columns) within a large csv file, by breaking down it in to number of pieces as indicated by chunksize.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Coverage <Source File> <Chunk Size> <ID Columns>

For Example, if a large csv file is stored at /path/to/large_file.csv containing two id columns id1 and id2, we can use the below command to display the missingness report.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Coverage /path/to/large_file.csv 100 id1 id2

Remove Sparse Attributes¶

To display missing value percentages of all the attributes (columns) within a large csv file, by breaking down it in to number of pieces as indicated by chunksize. Additionally, this function also removes the sparse attributes that are having a high missingness (above the specified threshold -p) and saves the resulting file in save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Coverage <Source File> <Chunk Size> -d -p <Threshold in Percentage> -sp <Save Path>

For Example, if a large csv file is stored at /path/to/large_file.csv containing two id columns id1 and id2, we can use the below command to display the missingness report and remove the columns with coverage below 50 % at the specified save path /path/to/save/.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Coverage /path/to/large_file.csv 100 id1 id2 -d -p 50 -sp /path/to/save/

Pre-processing Pipeline¶

Help menu¶

To display the help menu;

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline -h

or

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline --help

Output

usage: Pipeline.py [-h] [-d] [-i] save_path source_db data_type schema_name

EHRQC

positional arguments:
save_path             Path of the folder to store the outputs
source_db             Source name [mimic, omop]
data_type             Data type name [demographics, vitals, lab_measurements]
schema_name           Source schema name

optional arguments:
-h, --help            show this help message and exit
-d, --draw_graphs     Draw graphs to visualise EHR data quality
-i, --impute_missing  Impute missing values by automatically selecting the best imputation strategy for this data

Extract OMOP Demographics¶

To create a csv file containing the raw data with the name omop_demographics_raw_data.csv in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp omop demographics omop_cdm

Extract OMOP Vitals¶

To create a csv file containing the raw data with the name omop_vitals_raw_data.csv in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp omop vitals omop_cdm

Extract OMOP Lab measurements¶

To create a csv file containing the raw data with the name omop_lab_measurements_raw_data.csv in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp omop lab_measurements omop_cdm

Extract MIMIC Demographics¶

To create a csv file containing the raw data with the name mimic_demographics_raw_data.csv in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp mimic demographics mimiciv

Extract MIMIC Vitals¶

To create a csv file containing the raw data with the name mimic_vitals_raw_data.csv in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp mimic vitals mimiciv

Extract MIMIC Lab measurements¶

To create a csv file containing the raw data with the name mimic_lab_measurements_raw_data.csv, and a in the save_path. ### To extract Lab Measurements data from MIMIC schema

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp mimic lab_measurements mimiciv

MIMIC Demographics Explore Plots¶

To create a csv file containing the raw data with the name mimic_demographics_raw_data.csv, and a html file containing the generated graphs with the name mimic_demographics_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp mimic demographics mimiciv -d

OMOP Demographics Explore Plots¶

To create a csv file containing the raw data with the name omop_demographics_raw_data.csv, and a html file containing the generated graphs with the name omop_demographics_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp omop demographics omop_cdm -d

MIMIC Vitals Explore Plots¶

To create a csv file containing the raw data with the name mimic_vitals_raw_data.csv, and a html file containing the generated graphs with the name mimic_vitals_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp mimic vitals mimiciv -d

OMOP Vitals Explore Plots¶

To create a csv file containing the raw data with the name omop_vitals_raw_data.csv, and a html file containing the generated graphs with the name omop_vitals_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp omop vitals omop_cdm -d

MIMIC Lab measurements Explore Plots¶

To create a csv file containing the raw data with the name mimic_lab_measurements_raw_data.csv, and a html file containing the generated graphs with the name mimic_lab_measurements_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp mimic lab_measurements mimiciv -d

OMOP Lab measurements Explore Plots¶

To create a csv file containing the raw data with the name omop_lab_measurements_raw_data.csv, and a html file containing the generated graphs with the name omop_lab_measurements_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp omop lab_measurements omop_cdm -d

Impute MIMIC Vitals¶

To create a csv file containing the raw data with the name mimic_vitals_raw_data.csv, a csv file containing the imputed data with the name mimic_vitals_imputed_data.csv, and a html file containing the generated graphs with the name mimic_vitals_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp mimic vitals mimiciv -d -i

Impute OMOP Vitals¶

To create a csv file containing the raw data with the name omop_vitals_raw_data.csv, a csv file containing the imputed data with the name omop_vitals_imputed_data.csv, and a html file containing the generated graphs with the name omop_vitals_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp omop vitals omop_cdm -d -i

Impute MIMIC Lab measurements¶

To create a csv file containing the raw data with the name mimic_lab_measurements_raw_data.csv, a csv file containing the imputed data with the name mimic_lab_measurements_imputed_data.csv, and a html file containing the generated graphs with the name mimic_lab_measurements_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp mimic lab_measurements mimiciv -d -i

Impute OMOP Lab measurements¶

To create a csv file containing the raw data with the name omop_lab_measurements_raw_data.csv, a csv file containing the imputed data with the name omop_lab_measurements_imputed_data.csv, and a html file containing the generated graphs with the name omop_lab_measurements_plots.html in the save_path.

(.venv) app_user@hostname:~$python -m ehrqc.qc.Pipeline temp omop lab_measurements omop_cdm -d -i