A Comparative Analysis of Active Learning for Biomedical Text Mining

Abstract

An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called Active Learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL) based strategies on three unique biomedical datasets. We investigated Random Sampling (RS), Least confidence (LC), Informative diversity and density (IDD), Margin and Maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Additionally, AL-assisted pre-annotations accelerates the de novo annotation process with less annotation time required.

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“…This training process resumes iteratively until a predefined number of queries ("budget") has been addressed to the oracle. We note that AL has already been used in other biomedical text mining applications [6,7], where classic ML classification algorithms, such as support vector machine (SVM) [8] (a well-established classifier based on identifying representative instances that separate the classes of interest in a feature space) and logistic regression [9] (relying on a thresholded probability estimate, mapping the input features of an instance to the probability of the instance belonging to each class) have been examined. In our work, we utilize a common recurrent neural network, "long-short term memory" (LSTM; a neural network embedding sequences to a vector space, making sure that similar sequences are positioned close to each other in the embedding space), as the classification model for the AL setting.…”

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“…This training process resumes iteratively until a predefined number of queries ("budget") has been addressed to the Oracle. We note that AL has already been used in other biomedical text mining applications [6,7], where classic ML classification algorithms, such as Support Vector Machine (SVM) [8] (a well-established classifier based on identifying representative instances that separate the classes of interest in a feature space), and Logistic Regression [9] (relying on a thresholded probability estimate mapping the input features of an instance to the probability of the instance to belong to each class) were examined. In our work, we utilize a common recurrent neural network, the Long-short term memory "LSTM" (a neural network embedding sequences to a vector space, making sure that similar sequences are positioned close to each-other in the embedding space) as the classification model for the AL setting.…”