215 research outputs found
Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
Adverse drug reactions (ADRs) are unwanted or harmful effects experienced
after the administration of a certain drug or a combination of drugs,
presenting a challenge for drug development and drug administration. In this
paper, we present a set of taggers for extracting adverse drug reactions and
related entities, including factors, severity, negations, drug class and
animal. The systems used a mix of rule-based, machine learning (CRF) and deep
learning (BLSTM with word2vec embeddings) methodologies in order to annotate
the data. The systems were submitted to adverse drug reaction shared task,
organised during Text Analytics Conference in 2017 by National Institute for
Standards and Technology, archiving F1-scores of 76.00 and 75.61 respectively.Comment: Paper describing submission for TAC ADR shared tas
Using machine learning and text mining to classify fuzzy social science phenomenon : the case of social innovation
Classifying social science concepts by using machine learning and text-mining is often very challenging, particularly due to the fact that social concepts are often defined in a vague manner. In this paper, we put forward a first conceptual step to overcome this challenge. By using the case of social innovation, which has 252 distinct definitions, we qualitatively demonstrated that these definitions group around four different themes where various definitions utilise one or multiple of these criteria in different combinations to define social innovations. We designed an experiment where a database of social innovation projects annotated i) based on an overall understanding and ii) based on a decomposed definition of four criteria. As a next step, we will test the performance of various model specification on these two approaches
Extracting patient data from tables in clinical literature: Case study on extraction of BMI, weight and number of patients
GNTeam at 2018 n2c2:Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries
Monitoring the administration of drugs and adverse drug reactions are key
parts of pharmacovigilance. In this paper, we explore the extraction of drug
mentions and drug-related information (reason for taking a drug, route,
frequency, dosage, strength, form, duration, and adverse events) from hospital
discharge summaries through deep learning that relies on various
representations for clinical named entity recognition. This work was officially
part of the 2018 n2c2 shared task, and we use the data supplied as part of the
task. We developed two deep learning architecture based on recurrent neural
networks and pre-trained language models. We also explore the effect of
augmenting word representations with semantic features for clinical named
entity recognition. Our feature-augmented BiLSTM-CRF model performed with
F1-score of 92.67% and ranked 4th for entity extraction sub-task among
submitted systems to n2c2 challenge. The recurrent neural networks that use the
pre-trained domain-specific word embeddings and a CRF layer for label
optimization perform drug, adverse event and related entities extraction with
micro-averaged F1-score of over 91%. The augmentation of word vectors with
semantic features extracted using available clinical NLP toolkits can further
improve the performance. Word embeddings that are pre-trained on a large
unannotated corpus of relevant documents and further fine-tuned to the task
perform rather well. However, the augmentation of word embeddings with semantic
features can help improve the performance (primarily by boosting precision) of
drug-related named entity recognition from electronic health records
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