def list_notes(output, data_node_host, dataset_id, fhir_store_id):
"""Gets clinical notes of a NLP data node FHIR store."""
clinical_notes = client.list_notes(host=data_node_host,
dataset_id=dataset_id,
fhir_store_id=fhir_store_id)
# Stdout or store to json
utils.stdout_or_json(list(clinical_notes), output)
def list_datasets(data_node_host, output):
"""List annotations of a NLP data node annotation store."""
# Create annotation store object
datasets = client.list_datasets(
host=data_node_host
)
utils.stdout_or_json(list(datasets), output)
def list_annotations(data_node_host, dataset_id, annotation_store_id, output):
"""List annotations of a NLP data node annotation store."""
# Create annotation store object
annotations = client.list_annotations(
host=data_node_host, dataset_id=dataset_id,
annotation_store_id=annotation_store_id
)
utils.stdout_or_json(list(annotations), output)
def get_annotation(data_node_host, dataset_id, annotation_store_id,
annotation_id, output):
"""Get annotation for a NLP data node dataset."""
annotation = client.get_annotation(
host=data_node_host, dataset_id=dataset_id,
annotation_store_id=annotation_store_id,
annotation_id=annotation_id
)
utils.stdout_or_json(annotation.to_dict(), output)
def annotate_note(annotator_host, note_json, output, tool_type):
"""Annotate a note with specified annotator"""
with open(note_json, "r") as note_f:
notes = json.load(note_f)
all_annotations = []
for note in notes:
note_name = note.pop("note_name")
annotations = client.annotate_note(host=annotator_host,
note=note,
tool_type=tool_type)
annotations['annotationSource'] = {
"resourceSource": {
"name": note_name
}
}
all_annotations.append(annotations)
utils.stdout_or_json(all_annotations, output)
def evaluate_prediction(pred_filepath, gold_filepath, output, tool_type):
"""Evaluate the performance of a prediction file. Example prediction and
goldstandard files are found in test/data/new_prediction.json and
test/data/new_goldstandard.json respectively.
"""
eval_mapping = {
"nlpsandbox:date-annotator":
evaluation.DateEvaluation,
"nlpsandbox:person-name-annotator":
evaluation.PersonNameEvaluation,
"nlpsandbox:physical-address-annotator":
evaluation.PhysicalAddressEvaluation
}
evaluator = eval_mapping[tool_type]()
evaluator.convert_dict(pred_filepath, gold_filepath)
results = evaluator.eval()
utils.stdout_or_json(results, output)
def evaluate_prediction(pred_filepath, gold_filepath, output, tool_type):
"""Evaluate the performance of a prediction file. Example prediction and
goldstandard files are found in test/data/new_prediction.json and
test/data/new_goldstandard.json respectively.
"""
eval_mapping = {
"nlpsandbox:date-annotator": evaluation.DateEvaluation,
"nlpsandbox:person-name-annotator": evaluation.PersonNameEvaluation,
"nlpsandbox:location-annotator": evaluation.LocationEvaluation,
'nlpsandbox:id-annotator': evaluation.IdEvaluation,
'nlpsandbox:contact-annotator': evaluation.ContactEvaluation,
'nlpsandbox:covid-symptom-annotator': evaluation.CovidSymptomEvaluation
}
print(f"eval_mapping: {eval_mapping}")
evaluator = eval_mapping[tool_type]()
evaluator.convert_dict(sys_file=pred_filepath, gs_file=gold_filepath)
results = evaluator.eval()
utils.stdout_or_json(results, output)
def get_tool(annotator_host, output):
"""Get tool information"""
tool = client.get_tool(host=annotator_host)
utils.stdout_or_json(tool.to_dict(), output)
def store_dataset(data_node_host, dataset_id, output):
"""Create a dataset in the data node"""
# Create dataset
dataset = client.store_dataset(host=data_node_host,
dataset_id=dataset_id)
utils.stdout_or_json(dataset.to_dict(), output)