Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Machine Learning
Machine Learning
Medical Language Processing: Computer Management of Narrative Data
Medical Language Processing: Computer Management of Narrative Data
MPLUS: a probabilistic medical language understanding system
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Computational Linguistics
Distinguishing historical from current problems in clinical reports: which textual features help?
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
ConText: an algorithm for identifying contextual features from clinical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Methodological Review: What can natural language processing do for clinical decision support?
Journal of Biomedical Informatics
Journal of Biomedical Informatics
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Summarization of clinical information: A conceptual model
Journal of Biomedical Informatics
An ontology for clinical questions about the contents of patient notes
Journal of Biomedical Informatics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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The growth of digital clinical data has raised questions as to how best to leverage this data to aid the world of healthcare. Promising application areas include Information Retrieval and Question-Answering systems. Such systems require an in-depth understanding of the texts that are processed. One aspect of this understanding is knowing if a medical condition outlined in a patient record is recent, or if it occurred in the past. As well as this, patient records often discuss other individuals such as family members. This presents a second problem - determining if a medical condition is experienced by the patient described in the report or some other individual. In this paper, we investigate the suitability of a machine learning (ML) based system for resolving these tasks on a previously unexplored collection of Patient History and Physical Examination reports. Our results show that our novel Score-based feature approach outperforms the standard Linguistic and Contextual features described in the related literature. Specifically, near-perfect performance is achieved in resolving if a patient experienced a condition. While for the task of establishing when a patient experienced a condition, our ML system significantly outperforms the ConText system (87% versus 69% f-score, respectively).