TY - GEN
T1 - Information extraction to improve standard compliance
T2 - 28th Australasian Joint Conference on Artificial Intelligence, AI 2015
AU - Zhou, Liyuan
AU - Suominen, Hanna
N1 - Funding Information:
NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the Information and Communications Technology (ICT) Centre of Excellence Program. We thank Maricel Angel, RN at NICTA, for helping HS to create the dataset. LZ conducted all experiments under HS’s supervision.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Clinical handover refers to healthcare workers transferring responsibility and accountability for patient care, e.g., between shifts or wards. Safety and quality health standards call for this process to be systematically structured across the organisation and synchronous with its documentation. This paper evaluates information extraction as a way to help comply with these standards. It implements the handover process of first specifying a structured handover form, whose hierarchy of headings guides the handover narrative, followed by the technology filling it out objectively and almost instantly for proofing and sign-off. We trained a conditional random field with 8 feature types on 101 expert-annotated documents to 36-class classify. This resulted in good generalization to an independent set of 50 validation and 50 test documents that we now release: 77.9% F1 in filtering out irrelevant information, up to 98.4% F1 for the 35 classes for relevant information, and 52.9% F1 after macro-averaging over these 35 classes, whilst these percentages were 86.2, 100.0, and 70.2 for the leave-one-document-out cross-validation across the first set of 101 documents. Also as a result of this study, the validation and test data were released to support further research.
AB - Clinical handover refers to healthcare workers transferring responsibility and accountability for patient care, e.g., between shifts or wards. Safety and quality health standards call for this process to be systematically structured across the organisation and synchronous with its documentation. This paper evaluates information extraction as a way to help comply with these standards. It implements the handover process of first specifying a structured handover form, whose hierarchy of headings guides the handover narrative, followed by the technology filling it out objectively and almost instantly for proofing and sign-off. We trained a conditional random field with 8 feature types on 101 expert-annotated documents to 36-class classify. This resulted in good generalization to an independent set of 50 validation and 50 test documents that we now release: 77.9% F1 in filtering out irrelevant information, up to 98.4% F1 for the 35 classes for relevant information, and 52.9% F1 after macro-averaging over these 35 classes, whilst these percentages were 86.2, 100.0, and 70.2 for the leave-one-document-out cross-validation across the first set of 101 documents. Also as a result of this study, the validation and test data were released to support further research.
KW - Artificial intelligence applications
KW - Clinical handover
KW - Computer systems evaluation
KW - Information extraction
KW - Test-set generation
UR - http://www.scopus.com/inward/record.url?scp=84952653252&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/information-extraction-improve-standard-compliance-case-clinical-handover
U2 - 10.1007/978-3-319-26350-2_57
DO - 10.1007/978-3-319-26350-2_57
M3 - Conference contribution
AN - SCOPUS:84952653252
SN - 9783319263496
VL - 9457
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 644
EP - 649
BT - AI 2015, Advances in Artificial Intelligence
A2 - Pfahringer, Bernhard
A2 - Renz, Jochen
PB - Springer
CY - Cham, Switzerland
Y2 - 30 November 2015 through 4 December 2015
ER -