Evidence Based Medicine (EBM) is an approach to clinical practice whereby medical decisions are informed by primary evidence, such as the results of randomized control trials (RCTs). Evidence-based practice requires efficient information access to such evidence, and also retrieval and analysis of documents relevant to a specified clinical topic. Evidence-based practitioners use specific criteria when judging whether an RCT is relevant to a given question. They generally follow the PICO criterion [1]: Population (P) (i.e., participants in a study); Intervention (I); Comparison (C) (if appropriate); and Outcome (O) (of an Intervention). Variations and extensions of this classification have been proposed, such as the PECODR tagset [2]. To better serve the information needs of the EBM community, we explore the use of classification techniques to identify relevant key sentences in a given document, and classify these against specified medical criteria. Such information could be leveraged in various ways: e.g., to improve search performance; to enable structured querying with specific categories; and to aid users in more quickly making judgements against specified PICO criteria.

In this paper, we build a classifier that performs two tasks. First, it identifies the key sentences in an abstract, filtering out those that do not provide the most relevant information. Second, it classifies sentences according to medical tags (based on the PICO criteria) used by our medical research partners. We project these two tasks into an (N+1)-way classification task, with N semantic labels for key sentences and 1 label (i.e. Other) for labeling non-key sentences. For this purpose, we have built a corpus of 1,000 medical abstracts, hand-annotated at the sentence level by domain experts, which we use to develop and evaluate our system.

A major difference of our approach from previous work is the combination of key-sentence identification and classification, whereas others (e.g., [3, 4]) have separated these tasks and assumed that all sentences are relevant at the classification step. Many sentences in abstracts do not fall into any of the pre-defined categories (due to vagueness, diversion from the central topic, etc.), and the identification of such extraneous material is useful.

Our classification techniques use an extensive set of features, derived from context, semantic relations, structure and sequencing of the text. In particular, for sequence information we use features from previous sentences in the given abstract, and use predicted labels as features in a novel way. We employ Conditional Random Fields (CRF) [5], which are well suited for learning over sequential data (such as cohesive, structured text). In the following sections, we first describe related work in Section . In Section , we provide details of our experimental setup including the construction of the corpus, the learners, and features. We present our results and error analysis in Sections and respectively. We conclude and discuss future directions in Section .

The generalised use of PICO and similar schemas by clinicians when performing search, and their improvement on performance in user studies [6], has fueled interest in the development of automatic aids for this task.

Demner-Fushman and Lin [7] were the first to present automatic classifiers for PICO-elements. In their work, they used the MetaMap parser [8], hand-built rules, and statistical classifiers to identify sentences or phrases in abstracts relevant to each PICO element. Only for the element Outcome did they use a supervised classifier (Naive Bayes) with a large set of features, including n-grams, position, and semantic information from MetaMap. They trained this classifier over 275 hand-annotated abstracts, and reported accuracies in the range of 74%-93% depending on the type of abstract and the evaluation threshold. It is important to note that this is the only previous work in the literature that uses the Other tag as we do. Demner-Fushman and Lin [7] also applied their final PICO classifiers to a novel weighting formula for medical information retrieval (IR), significantly improving the baseline for the task. In a related paper [9], the same authors applied PICO classification to the task of clustering medical results, showing that it improved information delivery. The main limitations of their classifiers were the small size of the annotated data, and the reliance on hand-crafted rules for some of the PICO classes. One drawback of their IR system was the use of parameters that were hand-assigned or estimated over a small dataset. More recently, Chung [4] performed PICO classification by combining rhetorical roles with PICO elements, in order to achieve higher performance and alleviate the hand-annotation cost. Chung uses four rhetorical roles, namely Aim, Method, Results, and Conclusions; she requires that each sentence in an abstract fall into one of these roles. Chung then focuses on categorising one PICO class at a time, for a more fine-grained analysis. There are two limitations to this approach: (i) the overall classification performance across medical tags is not known; and (ii) sentences are forced to always have one semantic tag. We address these limitations by focusing directly on labels of interest for EBM, allowing sentences to be labeled as Other, and by allowing multiple labels per sentence when required. We believe that this is a more realistic setting than the one presented in previous work, and will provide better insight on the performance we should expect for this kind of task. This makes our approach not directly comparable to [4], but we are able to apply her technique and features and evaluate the performance of her system over medical labels only. We also extend her approach by adding new types of features.

Other work on sentence classification has focused on rhetorical role classification, which aims at identifying the roles of sentences in text (e.g. Motivation, Result, etc.). Training and test data for this task is easy to obtain from structured scientific abstracts, which provide section headings. This approach has been used in many supervised systems [3, 10–13]. With respect to feature representations, previous work has relied mostly on contextual features, such as n-grams and words in specific locations. Heuristics derived from sequential features of abstracts, such as relative location of sentences and section headings have recently been explored [3, 4]. In terms of finding suitable machine learners, well-known machine learning techniques have been applied to the tasks, including Naïve Bayes (NB) [7], Support Vector Machines (SVM) [4], Hidden Markov Models (HMM) [14], and Conditional Random Fields (CRF) [4]. Also, [12] proposed a probability-based learner inspired by the sequence of abstracts.

Recent work by [15] has shown the difficulty of identifying PICO elements in text, and has proposed a location-based information retrieval weighting strategy, motivated by the distribution of PICO elements. The authors also applied a weighting model based on the PICO information from the query, obtaining significant improvements from both approaches. However, their annotation of PICO tags was based on open text, disregarding sentence boundaries, which led to agreement problems between the annotators. Further, their classifier was built using the section headings of structured abstracts (e.g. Patients, Outcome’, etc.) without human supervision, which could introduce noise.

In this section we describe the construction of the corpus, the classifiers and features, and the experimental setting.

Data collection

We extracted 1,000 abstracts from MEDLINE for annotation. Our focus was on medical research, and in order to extract relevant abstracts we used queries from two institutions that develop systematic reviews of the literature: The Global Evidence Mapping Initiative (GEM) [16], and The Agency for Healthcare Research and Quality (AHRQ) [17]. GEM focuses on traumatic brain injury and spinal cord injury, and they provided the results of hand-constructed queries targeting diverse aspects of this subdomain. We randomly extracted 500 abstracts from a list of 74,000 query results for our annotation.

In order to diversify the contents of the corpus, the remaining 500 abstracts were randomly sampled from a set of AHRQ queries covering different medical issues (e.g. “Systematic Review of the Literature Regarding the Diagnosis of Sleep Apnoea”).

Some of the abstracts used in our experiments (376 out of 1,000) are structured, which means that they contain section headings (e.g. Aim, Method, etc.). These headings are helpful in capturing the rhetorical structure of the text, and we use them as features (when available). See the abstract of this paper you are reading for an example of a structured abstract, and Figure 1 for an example of an unstructured abstract.

In this section, we first evaluate the performance of a benchmark system, which uses features that have been previously explored in the literature. We then analyse the different feature sets described earlier (lexical, semantic, structural, and sequential) in turn. Finally, we evaluate our system over an external dataset.

The error analysis of the predictions over the external dataset illustrates that the annotators seem to have followed different guidelines. There is a large set of Other sentences in the external dataset that are misclassified, and this could be due to the tendency in the external data to annotate only the most salient sentences as evidence for a particular label, ignoring supporting sentences.

We have explored classification of sentences in abstracts with medical tags. Unlike previous work, we identify both irrelevant sentences and the semantic tags of relevant sentences using supervised techniques. We evaluated the performance of a variety of feature configurations over different sets of data, including an external corpus.

Our results for 5-way classification (which excludes the Other label) compare to the state of the art. The numbers are high for structured abstracts (89% f-score), but significantly lower for unstructured abstracts (74% f-score). However, for the latter we improve on the results of the benchmark system by 3.2% . The results for unstructured abstracts also demonstrate the difficulty of dealing with this kind of data, which has not been previously evaluated for this task. In the breakdown of the results per class, we see large differences in performance depending on the category, with Outcome showing strong performance, and Intervention and Study Design the weakest performance.

The 6-way classification task has not been previously explored using supervised approaches, and most work disregards irrelevant sentences. An exception is [7], which uses a small dataset for training an Outcome classifier, and utilises rule-based classifiers for the rest. In our experiments we can see that this is a very challenging task, particularly for unstructured abstracts, for which the f-score drops to 66.9%. Again, the application of our feature set is able to improve over the benchmark system, but the performance is much lower than for the 5-way task. We observed that sentences labelled as Other may have a large overlap with other labels, and further analysis of the annotation would be important in future work. Classification over the external dataset shows a drop in performance, and only for the category Outcome do we achieve good results. The cross-annotation of the other categories has proved problematic for the dataset from [18], and we need to further explore whether this is due to discrepancies in the annotations or the different domains of the training and test data.

With respect to the feature analysis, overall the best-performing features we used for our task were those based on unigrams, section headings, and sequential information from preceding sentences. Use of these features led to clear improvement over the simple BOW approach, and outperform feature sets used in previous work.

For future work, our aim is to improve our performance over unstructured abstracts with the aid of information from structured abstracts. We also plan to further analyse the annotated corpus, and in particular the sentences annotated as Other, to develop a more robust system. Finally, we plan to apply our classifier to an external application, such as improving performance of information retrieval against PICO criteria.