ir_datasets: Clinical TrialsClinical trial information from ClinicalTrials.gov. Used for the Clinical Trials subtasks in TREC Precision Medicine.
A snapshot of ClinicalTrials.gov from April 2017 for use with the clinicaltrials/2017/trec-pm-2017 and clinicaltrials/2017/trec-pm-2018 Clinical Trials subtasks.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2017 docs
[doc_id] [title] [condition] [summary] [detailed_description] [eligibility]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017')
# Index clinicaltrials/2017
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2017')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])
You can find more details about PyTerrier indexing here.
The TREC 2017 Precision Medicine clinical trials subtask.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2017")
for query in dataset.queries_iter():
query # namedtuple<query_id, disease, gene, demographic, other>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2017/trec-pm-2017 queries
[query_id] [disease] [gene] [demographic] [other]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2017')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('disease'))
You can find more details about PyTerrier retrieval here.
Language: en
Note: Uses docs from clinicaltrials/2017
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2017")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2017/trec-pm-2017 docs
[doc_id] [title] [condition] [summary] [detailed_description] [eligibility]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2017')
# Index clinicaltrials/2017
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2017')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])
You can find more details about PyTerrier indexing here.
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | not relevant |
| 1 | possibly relevant |
| 2 | definitely relevant |
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2017")
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2017/trec-pm-2017 qrels --format tsv
[query_id] [doc_id] [relevance] [iteration]
...
You can find more details about the CLI here.
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2017')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
[pipeline],
dataset.get_topics('disease'),
dataset.get_qrels(),
[MAP, nDCG@20]
)
You can find more details about PyTerrier experiments here.
Bibtex:
@inproceedings{Roberts2017TrecPm, title={Overview of the TREC 2017 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen M. Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar and Shubham Pant}, booktitle={TREC}, year={2017} }The TREC 2018 Precision Medicine clinical trials subtask.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2018")
for query in dataset.queries_iter():
query # namedtuple<query_id, disease, gene, demographic>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2017/trec-pm-2018 queries
[query_id] [disease] [gene] [demographic]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2018')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('disease'))
You can find more details about PyTerrier retrieval here.
Language: en
Note: Uses docs from clinicaltrials/2017
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2018")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2017/trec-pm-2018 docs
[doc_id] [title] [condition] [summary] [detailed_description] [eligibility]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2018')
# Index clinicaltrials/2017
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2017')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])
You can find more details about PyTerrier indexing here.
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | not relevant |
| 1 | possibly relevant |
| 2 | definitely relevant |
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2017/trec-pm-2018")
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2017/trec-pm-2018 qrels --format tsv
[query_id] [doc_id] [relevance] [iteration]
...
You can find more details about the CLI here.
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2017/trec-pm-2018')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2017') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
[pipeline],
dataset.get_topics('disease'),
dataset.get_qrels(),
[MAP, nDCG@20]
)
You can find more details about PyTerrier experiments here.
Bibtex:
@inproceedings{Roberts2018TrecPm, title={Overview of the TREC 2018 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen M. Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar}, booktitle={TREC}, year={2018} }A snapshot of ClinicalTrials.gov from May 2019 for use with the clinicaltrials/2019/trec-pm-2019 Clinical Trials subtask.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2019")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2019 docs
[doc_id] [title] [condition] [summary] [detailed_description] [eligibility]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2019')
# Index clinicaltrials/2019
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2019')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])
You can find more details about PyTerrier indexing here.
The TREC 2019 Precision Medicine clinical trials subtask.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2019/trec-pm-2019")
for query in dataset.queries_iter():
query # namedtuple<query_id, disease, gene, demographic>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2019/trec-pm-2019 queries
[query_id] [disease] [gene] [demographic]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2019/trec-pm-2019')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2019') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pipeline(dataset.get_topics('disease'))
You can find more details about PyTerrier retrieval here.
Language: en
Note: Uses docs from clinicaltrials/2019
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2019/trec-pm-2019")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2019/trec-pm-2019 docs
[doc_id] [title] [condition] [summary] [detailed_description] [eligibility]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2019/trec-pm-2019')
# Index clinicaltrials/2019
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2019')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])
You can find more details about PyTerrier indexing here.
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | not relevant |
| 1 | possibly relevant |
| 2 | definitely relevant |
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2019/trec-pm-2019")
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2019/trec-pm-2019 qrels --format tsv
[query_id] [doc_id] [relevance] [iteration]
...
You can find more details about the CLI here.
import pyterrier as pt
from pyterrier.measures import *
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2019/trec-pm-2019')
index_ref = pt.IndexRef.of('./indices/clinicaltrials_2019') # assumes you have already built an index
pipeline = pt.BatchRetrieve(index_ref, wmodel='BM25')
# (optionally other pipeline components)
pt.Experiment(
[pipeline],
dataset.get_topics('disease'),
dataset.get_qrels(),
[MAP, nDCG@20]
)
You can find more details about PyTerrier experiments here.
Bibtex:
@inproceedings{Roberts2019TrecPm, title={Overview of the TREC 2019 Precision Medicine Track}, author={Kirk Roberts and Dina Demner-Fushman and Ellen Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar and Shubham Pant and Funda Meric-Bernstam}, booktitle={TREC}, year={2019} }A snapshot of ClinicalTrials.gov from April 2021 for use with the TREC Clinical Trials 2021 Track.
Queries for the TREC Clinical Trials 2021 Track will be released later.
Language: en
Examples:
import ir_datasets
dataset = ir_datasets.load("clinicaltrials/2021")
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, title, condition, summary, detailed_description, eligibility>
You can find more details about the Python API here.
ir_datasets export clinicaltrials/2021 docs
[doc_id] [title] [condition] [summary] [detailed_description] [eligibility]
...
You can find more details about the CLI here.
import pyterrier as pt
pt.init()
dataset = pt.get_dataset('irds:clinicaltrials/2021')
# Index clinicaltrials/2021
indexer = pt.IterDictIndexer('./indices/clinicaltrials_2021')
index_ref = indexer.index(dataset.get_corpus_iter(), fields=['title', 'condition', 'summary', 'detailed_description', 'eligibility'])
You can find more details about PyTerrier indexing here.