Job request: 6160
- Organisation:
- University of Manchester
- Workspace:
- hosp_pred
- ID:
- rlcfkfrnzbvviv5f
This page shows the technical details of what happened when the authorised researcher ali requested one or more actions to be run against real patient data in the project, within a secure environment.
By cross-referencing the list of jobs with the
pipeline section below, you can infer what
security level
various outputs were written to. Researchers can never directly
view outputs marked as
highly_sensitive
;
they can only request that code runs against them. Outputs
marked as
moderately_sensitive
can be viewed by an approved researcher by logging into a highly
secure environment. Only outputs marked as
moderately_sensitive
can be requested for release to the public, via a controlled
output review service.
Jobs
-
- Job identifier:
-
wvioaui4r4gqzbto
Pipeline
Show project.yaml
version: '3.0'
expectations:
population_size: 10000
actions:
# generate_study_population:
# run: cohortextractor:latest generate_cohort --study-definition study_definition --index-date-range "2019-01-01 to today by month" --skip-existing --output-dir=output/measures --output-format=csv.gz
# outputs:
# highly_sensitive:
# cohort: output/measures/input_*.csv.gz
generate_study_population_hospitalisation:
run: cohortextractor:latest generate_cohort --study-definition study_definition_hospitalisation --index-date-range "2020-10-01 to 2020-12-01 by month" --skip-existing --output-dir=output/hospitalisation_data --output-format=csv.gz
outputs:
highly_sensitive:
cohort: output/hospitalisation_data/input_hospitalisation_*.csv.gz
# generate_study_population_elderly:
# run: cohortextractor:latest generate_cohort --study-definition study_definition_elderly
# --output-format=csv.gz
# outputs:
# highly_sensitive:
# cohort: output/input_elderly.csv.gz
# generate_measures:
# run: cohortextractor:latest generate_measures --study-definition study_definition --skip-existing --output-dir=output/measures
# needs: [generate_study_population]
# outputs:
# moderately_sensitive:
# measure_csv: output/measures/measure_*.csv
# describe_elderly_agedis:
# run: r:latest analysis/tables/gen_csv_age_check.R
# needs: [generate_study_population_elderly]
# outputs:
# moderately_sensitive:
# agetable: output/age_quant.csv
# describe:
# run: r:latest analysis/plot/overall_ab_prescribing.R
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# cohort: output/overall.png
# boxplot: output/overallbox.png
# describe_percentile:
# run: r:latest analysis/plot/overall_ab_prescribing_2575percentile.R
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# percentile: output/overall_25th_75th_percentile.png
# describe_starpu:
# run: r:latest analysis/plot/starpu_ab_prescribing.R
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# cohort: output/starpuline.png
# boxplot: output/starpubox.png
#generate_notebook_starpu:
# run: jupyter:latest jupyter nbconvert /workspace/analysis/starpu.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_risk --ExecutePreprocessor.timeout=86400
# needs: [generate_measures]
# outputs:
# moderately_sensitive:
# notebook: output/hospitalisation_risk/starpu.html
# figures: output/hospitalisation_risk/*
#tables: output/tables/*
#csvs: output/*/* # two possible subfolders
#text: output/text/*
# generate_notebook_hospitalisation_analysis:
# run: jupyter:latest jupyter nbconvert /workspace/analysis/hospitalisation_analysis.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_risk --ExecutePreprocessor.timeout=86400
# needs: [generate_study_population_hospitalisation]
# outputs:
# moderately_sensitive:
# notebook: output/hospitalisation_risk/hospitalisation_analysis.html
# figures: output/hospitalisation_risk/*
generate_notebook_hospitalisation_prediction_uti:
run: jupyter:latest jupyter nbconvert /workspace/analysis/hospitalisation_prediction_uti.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_prediction_uti --ExecutePreprocessor.timeout=86400
needs: [generate_study_population_hospitalisation]
outputs:
moderately_sensitive:
notebook: output/hospitalisation_prediction_uti/hospitalisation_prediction_uti.html
figures: output/hospitalisation_prediction_uti/*
generate_notebook_hospitalisation_prediction_urti:
run: jupyter:latest jupyter nbconvert /workspace/analysis/hospitalisation_prediction_urti.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_prediction_urti --ExecutePreprocessor.timeout=86400
# needs: [generate_study_population_hospitalisation]
outputs:
moderately_sensitive:
notebook: output/hospitalisation_prediction_urti/hospitalisation_prediction_urti.html
figures: output/hospitalisation_prediction_urti/*
# generate_notebook_hospitalisation_prediction:
# run: jupyter:latest jupyter nbconvert /workspace/analysis/hospitalisation_prediction.ipynb --execute --to html --output-dir=/workspace/output/hospitalisation_prediction --ExecutePreprocessor.timeout=86400
# needs: [generate_study_population_hospitalisation]
# outputs:
# moderately_sensitive:
# notebook: output/hospitalisation_prediction/hospitalisation_prediction.html
# figures: output/hospitalisation_prediction/*
# describe_infection_ab_UTI:
# run: r:latest analysis/plot/infection_ab_UTI.R
# needs: [generate_study_population, generate_measures]
# outputs:
# moderately_sensitive:
# plot: output/UTI.png
Timeline
-
Created:
-
Started:
-
Finished:
-
Runtime: 00:05:44
These timestamps are generated and stored using the UTC timezone on the TPP backend.
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