Active learning: a step towards automating medical concept extraction
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This article is published in Journal of the American Medical Informatics Association.The article was published on 2016-03-01 and is currently open access. It has received 47 citations till now. The article focuses on the topics: Supervised learning & Robustness (computer science).read more
Citations
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Journal ArticleDOI
Clinical Text Data in Machine Learning: Systematic Review.
Irena Spasic,Goran Nenadic +1 more
TL;DR: The data annotation bottleneck is identified as one of the key obstacles to machine learning approaches in clinical NLP, and future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which do not require data annotation.
Journal ArticleDOI
Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey
TL;DR: A survey of machine intelligence algorithms within the context of healthcare applications is provided and includes a comprehensive list of the most commonly used computational models and algorithms.
Journal ArticleDOI
Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records
Alexander W. Forsyth,Regina Barzilay,Kevin S. Hughes,Dickson Lui,Karl A. Lorenz,Karl A. Lorenz,Andrea C. Enzinger,Andrea C. Enzinger,James A. Tulsky,James A. Tulsky,Charlotta Lindvall,Charlotta Lindvall +11 more
TL;DR: The potential of machine learning to gather, track, and analyze symptoms experienced by cancer patients during chemotherapy is demonstrated and may yield machine learning methods suitable to be deployed in routine clinical care, quality improvement, and research applications.
Posted Content
Learning for Biomedical Information Extraction: Methodological Review of Recent Advances.
TL;DR: This review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological development, including open information extraction and deep learning.
Journal ArticleDOI
Doccurate : A Curation-Based Approach for Clinical Text Visualization
TL;DR: Doccurate is presented, a novel system embodying a curation-based approach for the visualization of large clinical text datasets that offers automation auditing and customizability to physicians while also preserving and extensively linking to the original text.
References
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Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
Journal ArticleDOI
Natural language processing: an introduction.
TL;DR: The historical evolution of NLP is described, and common NLP sub-problems in this extensive field are summarized, and possible future directions for NLP are considered.
Book ChapterDOI
Heterogenous uncertainty sampling for supervised learning
David D. Lewis,Jason A. Catlett +1 more
TL;DR: This work test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program) and finds that the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.
Journal ArticleDOI
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.
TL;DR: The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks, which showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations.
Related Papers (5)
Applying active learning to assertion classification of concepts in clinical text
Yukun Chen,Subramani Mani,Hua Xu +2 more