Models for retrieval with probabilistic indexing
Information Processing and Management: an International Journal - Modeling data, information and knowledge
Medical students' personal knowledge, searching proficiency, and database use in problem solving
Journal of the American Society for Information Science
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
A task-oriented approach to information retrieval evaluation
Journal of the American Society for Information Science - Special issue: evaluation of information retrieval systems
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The TREC question answering track
Natural Language Engineering
Toward semantics-based answer pinpointing
HLT '01 Proceedings of the first international conference on Human language technology research
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning question classifiers: the role of semantic information
Natural Language Engineering
Probabilistic disambiguation models for wide-coverage HPSG parsing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Computational Linguistics
An automated system for conversion of clinical notes into SNOMED clinical terminology
ACSW '07 Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68
A knowledge based method for the medical question answering problem
Computers in Biology and Medicine
Inter-coder agreement for computational linguistics
Computational Linguistics
A framework of a logic-based question-answering system for the medical domain (LOQAS-Med)
Proceedings of the 2009 ACM symposium on Applied Computing
Question classification using head words and their hypernyms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Investigation of question classifier in question answering
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Healthcom'09 Proceedings of the 11th international conference on e-Health networking, applications and services
Biomedical question answering: A survey
Computer Methods and Programs in Biomedicine
An architecture for complex clinical question answering
Proceedings of the 1st ACM International Health Informatics Symposium
Automatically extracting information needs from complex clinical questions
Journal of Biomedical Informatics
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Analyzing patient records to establish if and when a patient suffered from a medical condition
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
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Objective: Many studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain. As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems. This paper focuses on a new direction, which is to design a novel question processing and classification model for answering clinical questions applied to electronic patient notes. Methods: There are four main steps in the work. Firstly, a relatively large set of clinical questions was collected from staff in an Intensive Care Unit. Then, a clinical question taxonomy was designed for question and answering purposes. Subsequently an annotation guideline was created and used to annotate the question set. Finally, a multilayer classification model was built to classify the clinical questions. Results: Through the initial classification experiments, we realized that the general features cannot contribute to high performance of a minimum classifier (a small data set with multiple classes). Thus, an automatic knowledge discovery and knowledge reuse process was designed to boost the performance by extracting and expanding the specific features of the questions. In the evaluation, the results show around 90% accuracy can be achieved in the answerable subclass classification and generic question templates classification. On the other hand, the machine learning method does not perform well at identifying the category of unanswerable questions, due to the asymmetric distribution. Conclusions: In this paper, a comprehensive study on clinical questions has been completed. A major outcome of this work is the multilayer classification model. It serves as a major component of a patient records based clinical question and answering system as our studies continue. As well, the question collections can be reused by the research community to improve the efficiency of their own question and answering systems.