Identification of Adverse Drug Events in Chinese Clinical Narrative Text

2015 ◽  
pp. 605-612 ◽  
Author(s):  
Caixia Ge ◽  
Yinsheng Zhang ◽  
Huilong Duan ◽  
Haomin Li
Keyword(s):  
Adverse Drug Events ◽  
Narrative Text ◽  
Clinical Narrative

scholarly journals Dictionary construction and identification of possible adverse drug events in Danish clinical narrative text

2013 ◽  
Vol 20 (5) ◽  
pp. 947-953 ◽  
Author(s):  
Robert Eriksson ◽  
Peter Bjødstrup Jensen ◽  
Sune Frankild ◽  
Lars Juhl Jensen ◽  
Søren Brunak
Keyword(s):  
Adverse Drug Events ◽  
Narrative Text ◽  
Clinical Narrative

Understanding clinical narrative text

1995 ◽  
Vol 20 (2) ◽  
pp. 161-173
Author(s):  
S. S. El-Gamal ◽  
M. M. Esmail
Keyword(s):  
Narrative Text ◽  
Clinical Narrative

scholarly journals Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text

2015 ◽  
Vol 22 (5) ◽  
pp. 1009-1019 ◽  
Author(s):  
Yuan Luo ◽  
Yu Xin ◽  
Ephraim Hochberg ◽  
Rohit Joshi ◽  
Ozlem Uzuner ◽  
...  
Keyword(s):  
Medical Knowledge ◽  
Higher Order ◽  
Narrative Text ◽  
Lymphoid Cells ◽  
Training Data ◽  
Graph Representation ◽  
Learning Approaches ◽  
Tensor Factorization ◽  
Clustering Methods ◽  
Clinical Narrative

Abstract Objective Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability. Methods The authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria. Results and Conclusion SANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features.

Communication Key to Cut Adverse Drug Events

2008 ◽  
Vol 42 (4) ◽  
pp. 45
Author(s):  
BRUCE K. DIXON
Keyword(s):  
Adverse Drug Events

Antibiotics Were Leading Cause of Skin Adverse Drug Events

2012 ◽  
Vol 42 (6) ◽  
pp. 16
Author(s):  
DOUG BRUNK
Keyword(s):  
Adverse Drug Events

Preverbation and narrativity in Lithuanian. The distribution of finite simplex and compound verbs in narrative main clauses

2010 ◽  
Vol 1 ◽  
pp. 167-192
Author(s):  
Lea Sawicki
Keyword(s):  
Narrative Text ◽  
Past Tense ◽  
The Past ◽  
Verb Forms ◽  
Compound Verbs ◽  
Finite Verb

The article deals with the use of simplex and compound (prefixed) verbs in narrative text. Main clauses comprising finite verb forms in the past and in the past habitual tense are examined in an attempt to establish to what extent simplex and compound verbs exhibit aspect oppositions, and whether a correlation exists between the occurrence of simplex vs. compound verbs and distinct textual units. The investigation shows that although simple and compound verbs in Lithuanian are not in direct aspect opposition to each other, in the background text portions most of the verbs are prefixless past tense forms or habitual forms, whereas in the plot-advancing text portions, the vast majority of verbs are compound verbs in the simple past tense.  

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