The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction

This study investigates the use of unsupervised word embeddings and sequence features for sample representation in an active learning framework built to extract clinical concepts from clinical free text. The objective is to further reduce the manual annotation effort while achieving higher effectiveness compared to a set of baseline features. Unsupervised features are derived from skip-gram word embeddings and a sequence representation approach. The comparative performance of unsupervised features and baseline hand-crafted features in an active learning framework are investigated using a wide range of selection criteria including least confidence, information diversity, information density and diversity, and domain knowledge informativeness. Two clinical datasets are used for evaluation: the i2b2/VA 2010 NLP challenge and the ShARe/CLEF 2013 eHealth Evaluation Lab. Our results demonstrate significant improvements in terms of effectiveness as well as annotation effort savings across both datasets. Using unsupervised features along with baseline features for sample representation lead to further savings of up to 9% and 10% of the token and concept annotation rates, respectively

Disease Name Extraction from Clinical Text Using Conditional Random Fields

The aim of the research done in this thesis was to extract disease and disorder names from clinical texts. We utilized Conditional Random Fields (CRF) as the main method to label diseases and disorders in clinical sentences. We used some other tools such as MetaMap and Stanford Core NLP tool to extract some crucial features. MetaMap tool was used to identify names of diseases/disorders that are already in UMLS Metathesaurus. Some other important features such as lemmatized versions of words, and POS tags were extracted using the Stanford Core NLP tool. Some more features were extracted directly from UMLS Metathesaurus, including semantic types of words. We participated in the SemEval 2014 competition\u27s Task 7 and used its provided data to train and evaluate our system. Training data contained 199 clinical texts, development data contained 99 clinical texts, and the test data contained 133 clinical texts, these included discharge summaries, echocardiogram, radiology, and ECG reports. We obtained competitive results on the disease/disorder name extraction task. We found through ablation study that while all features contributed, MetaMap matches, POS tags, and previous and next words were the most effective features

Normalization of Disease Mentions with Convolutional Neural Networks

Normalization of disease mentions has an important role in biomedical natural language processing (BioNLP) applications, such as the construction of biomedical databases. Various disease mention normalization systems have been developed, though state-of-the-art systems either rely on candidate concept generation, or do not generalize to new concepts not seen during training. This thesis explores the possibility of building a disease mention normalization system that both generalizes to unseen concepts and does not rely on candidate generation. To this end, it is hypothesized that modern neural networks are sophisticated enough to solve this problem. This hypothesis is tested by building a normalization system using deep learning approaches, and evaluating the accuracy of this system on the NCBI disease corpus. The system leverages semantic information in the biomedical literature by using continuous vector space representations for strings of disease mentions and concepts. A neural encoder is trained to encode vector representations of strings of disease mentions and concepts. This encoder theoretically enables the model to generalize to unseen concepts during training. The encoded strings are used to compare the similarity between concepts and a given mention. Viewing normalization as a ranking problem, the concept with the highest similarity estimated is selected as the predicted concept for the mention. For the development of the system, synthetic data is used for pre-training to facilitate the learning of the model. In addition, various architectures are explored. While the model succeeds in prediction without candidate concept generation, its performance is not comparable to those of the state-of-the-art systems. Normalization of disease mentions without candidate generation while including the possibility for the system to generalize to unseen concepts is not trivial. Further efforts can be focused on, for example, testing more neural architectures, and the use of more sophisticated word representations

Computer-based identification of relationships between medical concepts and cluster analysis in clinical notes

Clinical notes contain information about medical concepts or entities (such as diseases, treatments and drugs) that provide a comprehensive and overall impression of the patient’s health. The automatic extraction of these entities is relevant for health experts and researchers as they identify associations between the latter. However, automatically extracting information from clinical notes is challenging, due to their narrative format. This research describes a process to automatically extract and aggregate medical entities from clinical notes, as well as the process to identify clusters of patients and disease-treatment relationships. The i2b2 2008 Obesity dataset was used, and consists of 1237 discharge summaries of overweight and diabetic patients. Therefore, this thesis is focused on obesity diseases. For the automatic extraction of medical entities, MetaMap and cTAKES were used, and the automatic extraction capacity of both tools compared. Also, UMLS enabled the aggregation of the extracted entities. Two approaches were applied for cluster analysis. Firstly, a sparse K-means algorithm was used over a patient-disease matrix with 14 comorbidities related to obesity. Secondly, to visualize and analyze other diseases present in the clinical notes, 86 diseases were used to identify clusters of patients with a network-based approach. Furthermore, bipartite graphs were used to explore disease-treatment relationships among some of the clusters obtained. The result of the experiments we conducted show cTAKES slightly outperforming MetaMap, but this situation can change, considering other configuration options in the respective tools, including an abbreviation list. Moreover, concept aggregation (with similar and different semantic types) was shown to be a good strategy for improving medical entity extraction. The sparse K-means enabled identification of three types of clusters (high, medium and low), based on the number of comorbidities and the percentage of patients suffering from them. These results show that diabetes, hypercholesterolemia, atherosclerotic cardiovascular diseases, congestive heart failure, obstructive sleep apnea, and depression were the most prevalent diseases. With the network approach, it was possible to visualize and analyze patient information. In it, three sub-graphs or clusters were identified: obese patients with metabolic problems, obese patients with infection problems, and obese patients with a mechanical problem. Bipartite graphs for a disease-treatment relationship showed treatments for different types of diseases, which means that obese patients are suffering from multiple diseases. This work shows that clinical notes are a rich source of information, and they can be used to explore, visualize, and analyze patient’s information by applying different approaches. More work is needed to explore the relationship between the different medical entities from clinical notes and from different disease datasets. Also, considering that some medical documents express events in time, this characteristic should be considered in future works to form a personalized portrait of clusters, diseases and patients

Recognising Biomedical Names: Challenges and Solutions

The growth rate in the amount of biomedical documents is staggering. Unlocking information trapped in these documents can enable researchers and practitioners to operate confidently in the information world. Biomedical Named Entity Recognition (NER), the task of recognising biomedical names, is usually employed as the first step of the NLP pipeline. Standard NER models, based on sequence tagging technique, are good at recognising short entity mentions in the generic domain. However, there are several open challenges of applying these models to recognise biomedical names: ● Biomedical names may contain complex inner structure (discontinuity and overlapping) which cannot be recognised using standard sequence tagging technique; ● The training of NER models usually requires large amount of labelled data, which are difficult to obtain in the biomedical domain; and, ● Commonly used language representation models are pre-trained on generic data; a domain shift therefore exists between these models and target biomedical data. To deal with these challenges, we explore several research directions and make the following contributions: (1) we propose a transition-based NER model which can recognise discontinuous mentions; (2) We develop a cost-effective approach that nominates the suitable pre-training data; and, (3) We design several data augmentation methods for NER. Our contributions have obvious practical implications, especially when new biomedical applications are needed. Our proposed data augmentation methods can help the NER model achieve decent performance, requiring only a small amount of labelled data. Our investigation regarding selecting pre-training data can improve the model by incorporating language representation models, which are pre-trained using in-domain data. Finally, our proposed transition-based NER model can further improve the performance by recognising discontinuous mentions