Mahnoosh Kholghi
Mahnoosh Kholghi
Australian e-Health Research Centre, CSIRO
Bestätigte E-Mail-Adresse bei
Zitiert von
Zitiert von
Active learning: a step towards automating medical concept extraction
M Kholghi, L Sitbon, G Zuccon, A Nguyen
Journal of the American Medical Informatics Association 23 (2), 289-296, 2016
An analytical framework for data stream mining techniques based on challenges and requirements
M Kholghi, M Keyvanpour
arXiv preprint arXiv:1105.1950, 2011
Classification and evaluation of data mining techniques for data stream requirements
M Kholghi, H Hassanzadeh, MR Keyvanpour
2010 International Symposium on Computer, Communication, Control and …, 2010
Active Learning Reduces Annotation Time for Clinical Concept Extraction
M Kholghi, L Sitbon, G Zuccon, A Nguyen
International Journal of Medical Informatics 106, 25-31, 2017
Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction
L De Vine, M Kholghi, G Zuccon, L Sitbon, A Nguyen
The 13th Annual Workshop of the Australasian Language Technology Association …, 2015
External Knowledge and Query Strategies in Active Learning: a Study in Clinical Information Extraction
M Kholghi, L Sitbon, G Zuccon, A Nguyen
Proceeding CIKM '15 Proceedings of the 24th ACM International on Conference …, 2015
Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals
H Hassanzadeh, M Kholghi, A Nguyen, K Chu
AMIA Annual Symposium 2018, 2018
Clinical Information Extraction Using Small Data: an Active Learning Approach Based on Sequence Representations and Word Embeddings
M Kholghi, L De Vine, L Sitbon, G Zuccon, A Nguyen
Journal of the Association for Information Science and Technology 68 (11 …, 2017
The benefits of word embeddings features for active learning in clinical information extraction
M Kholghi, L De Vine, L Sitbon, G Zuccon, A Nguyen
The 14th Annual Workshop of the Australasian Language Technology Association …, 2016
Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks
M Kholghi, L Sitbon, Z Guido, A Nguyen
AusDM 2014 : The Twelfth Australasian Data Mining Conference 158, 69-78, 2014
Active learning framework combining semi-supervised approach for data stream mining
M Kholghi, MR Keyvanpour
International Conference on Intelligent Computing and Information Science …, 2011
Active learning for classifying long‐duration audio recordings of the environment (Shortlisted for Robert May Prize 2018)
M Kholghi, Y Phillips, M Towsey, L Sitbon, P Roe
Methods in Ecology and Evolution 9 (9), 1948-1958, 2018
A prospective cohort study of prodromal Alzheimer′s disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA)
MK Lupton, GA Robinson, RJ Adam, S Rose, GJ Byrne, O Salvado, ...
NeuroImage: Clinical 29, 2020
Active learning for concept extraction from clinical free text
M Kholghi
Queensland University of Technology, 2017
Comparative evaluation of data stream indexing models
M Kholghi, MR Keyvanpour
arXiv preprint arXiv:1208.0684, 2012
Sleep as a risk factor of Alzheirmer's disease
M Kholghi, M Lupton, A Fazlollahi, Q Zhang, M Breakspear, J Fripp, ...
AAIC Satellite Symposium 2019, 2019
Semi-supervised Feature Learning vs. unsupervised feature learning for classifying environmental audio recordings
M Kholghi, 2019
Semi-supervised feature learning vs. unsupervised feature learning for classifying
M Kholghi
QUT Ecoacoustics Research Group| www. ecosounds. org Queensland University …, 2019
A Human-in-the-loop Framework for Content Description of Long-duration Environmental Audio Recordings
M Kholghi, M Towsey, P Roe
Australasian Computer Science Week (ASCW) 2018, 2018
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