Biomedical Information Extraction Pipelines for Public Health in the Age of Deep Learning

abstract: Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Towards the semantic interpretation of personal health messages from social media

Recent attempts have been made to utilise social media platforms, such as Twitter, to provide early warning and monitoring of health threats in populations (i.e. Internet biosurveillance). It has been shown in the literature that a system based on keyword matching that exploits social media messages could report flu surveillance well ahead of the Centers of Disease Control and Prevention (CDC). However, we argue that a simple keyword matching may not capture semantic interpretation of social media messages that would enable healthcare experts or machines to extract and leverage medical knowledge from social media messages. In this paper, we motivate and describe a new task that aims to tackle this technology gap by extracting semantic interpretation of medical terms mentioned in social media messages, which are typically written in layman’s language. Achieving such a task would enable an automatic integration between the data about direct patient experiences extracted from social media and existing knowledge from clinical databases, which leads to advances in the use of community health experiences in healthcare services.The authors gratefully acknowledge funding from the EPSRC (grant number EP/M005089/1)This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2811271.281127

Detecting drugs and adverse events from Spanish health social media streams

Adverse Drug Reactions (ADRs) are the 4th cause of death in hospitalized patients. Despite the importance of clinical trials, they have many limitations mainly based on time and population. Therefore, other ways of spotting ADRs had to be created, as for instance, the healthcare professionals reporting systems and the spontaneous patients reporting systems created by the FDA or the EMA. Nevertheless, it has been proven that the results obtained are not yet as satisfactory as expected. Health-related social media can be used along with these reporting systems in order to obtain possible information from a source where patients feel more comfortable sharing their experiences by exchanging information. Therefore, the creation of the first corpus annotated with drugs and adverse events from social media in Spanish in order to train and evaluate machine-learning techniques is one of the main goals throughout this project. Furthermore, the implementation of a dictionary-based approach to detect mentions and to be tested with this gold-standard is the other main goal in this investigation.Ingeniería de Telecomunicacione

Identificação e análise de estados de saúde em mensagens do twitter

Social media has become very widely used all over the world for its ability to connect people from different countries and create global communities. One of the most prominent social media platforms is Twitter. Twitter is a platform where users can share text segments with a maximum length of 280 characters. Due to the nature of the platform, it generates very large amounts of text data about its users’ lives. This data can be used to extract health information about a segment of the population for the purpose of public health surveillance. Social Media Mining for Health Shared Task is a challenge that encompasses many Natural Language Processing tasks related to the use of social media data for health research purposes. This dissertation describes the approach I used in my participation in the Social Media Mining for Health Shared Task. I participated in task 1 of the Shared Task. This task was divided into three subtasks. Subtask 1a consisted of the classification of Tweets regarding the presence of Adverse Drug Events. Subtask 1b was a Named Entity Recognition task that aimed at detecting Adverse Drug Effect spans in tweets. Subtask 1c was a normalization task that sought to match an Adverse Drug Event mention to a Medical Dictionary for Regulatory Activities preferred term ID. Toward discovering the best approach for each of the subtasks I made many experiments with different models and techniques to distinguish the ones that were more suited for each subtask. To solve these subtasks, I used transformer-based models as well as other techniques that aim at solving the challenges present in each of the subtasks. The best-performing approach for subtask 1a was a BERTweet large model trained with an augmented training set. As for subtask 1b, the best results were obtained through a RoBERTa large model with oversampled training data. Regarding subtask 1c, I used a RoBERTa base model trained with data from an additional dataset beyond the one made available by the shared task organizers. The systems used for subtasks 1a and 1b both achieved state-of-the-art performance, however, the approach for the third subtask was not able to achieve favorable results. The system used in subtask 1a achieved an F1 score of 0.698, the one used in subtask 1b achieved a relaxed F1 score of 0.661, and the one used in the final subtask achieved a relaxed F1 score of 0.116.As redes sociais tornaram-se muito utilizadas por todo o mundo, permitindo ligar pessoas de diferentes países e criar comunidades globais. O Twitter, uma das redes sociais mais populares, permite que os seus utilizadores partilhem segmentos curtos de texto com um máximo de 280 caracteres. Esta partilha na rede gera uma enorme quantidade de dados sobre os seus utilizadores, podendo ser analisados sobre múltiplas perspetivas. Por exemplo, podem ser utilizados para extrair informação sobre a saúde de um segmento da população tendo em vista a vigilância de saúde pública. O objetivo deste trabalho foi a investigação e o desenvolvimento de soluções técnicas para participar no “Social Media Mining for Health Shared Task” (#SMM4H), um desafio constituído por diversas tarefas de processamento de linguagem natural relacionadas com o uso de dados provenientes de redes sociais para o propósito de investigação na área da saúde. O trabalho envolveu o desenvolvimento de modelos baseados em transformadores e outras técnicas relacionadas, para participação na tarefa 1 deste desafio, que por sua vez está dividida em 3 subtarefas: 1a) classificação de tweets relativamente à presença ou não de eventos adversos de medicamentos (ADE); 1b) reconhecimento de entidades com o objetivo de detetar menções de ADE; 1c) tarefa de normalização com o objetivo de associar as menções de ADE ao termo MedDRA correspondente (“Medical Dictionary for Regulatory Activities”). A abordagem com melhor desempenho na tarefa 1a foi um modelo BERTweet large treinado com dados gerados através de um processo de data augmentation. Relativamente à tarefa 1b, os melhores resultados foram obtidos usando um modelo RoBERTa large com dados de treino sobreamostrados. Na tarefa 1c utilizou-se um modelo RoBERTa base treinado com dados adicionais provenientes de um conjunto de dados externo. A abordagem utilizada na terceira tarefa não conseguiu alcançar resultados relevantes (F1 de 0.12), enquanto que os sistemas desenvolvidos para as duas primeiras alcançaram resultados ao nível dos melhores do desafio (F1 de 0.69 e 0.66 respetivamente).Mestrado em Engenharia Informátic

Learning structured medical information from social media

Our goal is to summarise and aggregate information from social media regarding the symptoms of a disease, the drugs used and the treatment effects both positive and negative. To achieve this we first apply a supervised machine learning method to automatically extract medical concepts from natural language text. In an environment such as social media, where new data is continuously streamed, we need a methodology that will allow us to continuously train with the new data. To attain such incremental re-training, a semi-supervised methodology is developed, which is capable of learning new concepts from a small set of labelled data together with the much larger set of unlabelled data. The semi-supervised methodology deploys a conditional random field (CRF) as the base-line training algorithm for extracting medical concepts. The methodology iteratively augments to the training set sentences having high confidence, and adds terms to existing dictionaries to be used as features with the base-line model for further classification. Our empirical results show that the base-line CRF performs strongly across a range of different dictionary and training sizes; when the base-line is built with the full training data the $F_1$ score reaches the range 84\%--90\%. Moreover, we show that the semi-supervised method produces a mild but significant improvement over the base-line. We also discuss the significance of the potential improvement of the semi-supervised methodology and found that it is significantly more accurate in most cases than the underlying base-line model

Text Mining Methods for Analyzing Online Health Information and Communication

The Internet provides an alternative way to share health information. Specifically, social network systems such as Twitter, Facebook, Reddit, and disease specific online support forums are increasingly being used to share information on health related topics. This could be in the form of personal health information disclosure to seek suggestions or answering other patients\u27 questions based on their history. This social media uptake gives a new angle to improve the current health communication landscape with consumer generated content from social platforms. With these online modes of communication, health providers can offer more immediate support to the people seeking advice. Non-profit organizations and federal agencies can also diffuse preventative information in such networks for better outcomes. Researchers in health communication can mine user generated content on social networks to understand themes and derive insights into patient experiences that may be impractical to glean through traditional surveys. The main difficulty in mining social health data is in separating the signal from the noise. Social data is characterized by informal nature of content, typos, emoticons, tonal variations (e.g. sarcasm), and ambiguities arising from polysemous words, all of which make it difficult in building automated systems for deriving insights from such sources. In this dissertation, we present four efforts to mine health related insights from user generated social data. In the first effort, we build a model to identify marketing tweets on electronic cigarettes (e-cigs) and assess different topics in marketing and non-marketing messages on e-cigs on Twitter. In our next effort, we build ensemble models to classify messages on a mental health forum for triaging posts whose authors need immediate attention from trained moderators to prevent self-harm. The third effort deals with models from our participation in a shared task on identifying tweets that discuss adverse drug reactions and those that mention medication intake. In the final task, we build a classifier that identifies whether a particular tweet about the popular Juul e-cig indicates the tweeter actually using the product. Our methods range from linear classifiers (e.g., logistic regression), classical nonlinear models (e.g., nearest neighbors), recent deep neural networks (e.g., convolutional neural networks), and ensembles of all these models in using different supervised training regimens (e.g., co-training). The focus is more on task specific system building than on building specific individual models. Overall, we demonstrate that it is possible to glean insights from social data on health related topics through natural language processing and machine learning with use-cases from substance use and mental health