Automatic classification of sentences to support Evidence Based Medicine

By Su Nam Kim, David Martínez, Lawrence J. Cavedon, Lars Yencken. 2011

208 citations recorded by the source corpus.

AimGiven a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels.MethodWe constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome). We explored the use of various features based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF) for classification.ResultsFor the classification tasks over all labels, our systems achieved micro-averaged f-scores of 80.9% and 66.9% over datasets of structured and unstructured abstracts respectively, using sequential features. In labeling only the key sentences, our systems produced f-scores of 89.3% and 74.0% over structured and unstructured abstracts respectively, using the same sequential features. The results over an external dataset were lower (f-scores of 63.1% for all labels, and 83.8% for key sentences).ConclusionsOf the features we used, the best for classifying any given sentence in an abstract were based on unigrams, section headings, and sequential information from preceding sentences. These features resulted in improved performance over a simple bag-of-words approach, and outperformed feature sets used in previous work.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Iz Beltagy
2. Kyle Lo
3. Arman Cohan
4. Noah A. Smith
5. Graham Neubig
6. Jimmy J. Lin
7. Percy Liang
8. Hannaneh Hajishirzi

In Molecular Biology

1. Kyle Lo
2. Arman Cohan
3. Iz Beltagy
4. M. V. Koroteev
5. Daniel S. Weld
6. Heike Adel
7. Jimmy J. Lin
8. Graham Neubig

In Natural Language Processing

1. Kyle Lo
2. Iz Beltagy
3. Arman Cohan
4. Jacob Eisenstein
5. Xiaochuang Han
6. Jimmy J. Lin
7. M. V. Koroteev
8. Samuel Broscheit

Citations in the corpus

• SciBERT: A Pretrained Language Model for Scientific Text (2019, methodology): “Dependency Parsing (DEP) PICO, like NER, is a sequence labeling task where the model extracts spans describing the Participants, Interventions, Comparisons, and Outcomes in a clinical trial paper (Kim et al., 2011).”
• Systematic review automation technologies (2014, methodology): “Often, information highlighting algorithms focus on PICO elements reported in the trial [95-97] but may also focus on the size of the trial [97,98], and its randomization procedure [95,97].”