"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction

With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology (DAO)) to jointly extract entities and their relationship using gated fusion sharing mechanism. Our model is further tailored to provide more focus to the entities mention in the sentence through entity-position aware attention layer, where ontology is used to locate the target entities position. Experimental results show that inclusion of the knowledge-aware attentive representation in association with BERT can extract the cannabis-depression relationship with better coverage in comparison to the state-of-the-art relation extractor

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

Measuring the Severity of Depression from Text using Graph Representation Learning

The common practice of psychology in measuring the severity of a patient's depressive symptoms is based on an interactive conversation between a clinician and the patient. In this dissertation, we focus on predicting a score representing the severity of depression from such a text. We first present a generic graph neural network (GNN) to automatically rate severity using patient transcripts. We also test a few sequence-based deep models in the same task. We then propose a novel form for node attributes within a GNN-based model that captures node-specific embedding for every word in the vocabulary. This provides a global representation of each node, coupled with node-level updates according to associations between words in a transcript. Furthermore, we evaluate the performance of our GNN-based model on a Twitter sentiment dataset to classify three different sentiments and on Alzheimer's data to differentiate Alzheimer’s disease from healthy individuals respectively. In addition to applying the GNN model to learn a prediction model from the text, we provide post-hoc explanations of the model's decisions for all three tasks using the model's gradients

Social analytics for health integration, intelligence, and monitoring

Nowadays, patient-generated social health data are abundant and Healthcare is changing from the authoritative provider-centric model to collaborative and patient-oriented care. The aim of this dissertation is to provide a Social Health Analytics framework to utilize social data to solve the interdisciplinary research challenges of Big Data Science and Health Informatics. Specific research issues and objectives are described below. The first objective is semantic integration of heterogeneous health data sources, which can vary from structured to unstructured and include patient-generated social data as well as authoritative data. An information seeker has to spend time selecting information from many websites and integrating it into a coherent mental model. An integrated health data model is designed to allow accommodating data features from different sources. The model utilizes semantic linked data for lightweight integration and allows a set of analytics and inferences over data sources. A prototype analytical and reasoning tool called “Social InfoButtons” that can be linked from existing EHR systems is developed to allow doctors to understand and take into consideration the behaviors, patterns or trends of patients’ healthcare practices during a patient’s care. The tool can also shed insights for public health officials to make better-informed policy decisions. The second objective is near-real time monitoring of disease outbreaks using social media. The research for epidemics detection based on search query terms entered by millions of users is limited by the fact that query terms are not easily accessible by non-affiliated researchers. Publically available Twitter data is exploited to develop the Epidemics Outbreak and Spread Detection System (EOSDS). EOSDS provides four visual analytics tools for monitoring epidemics, i.e., Instance Map, Distribution Map, Filter Map, and Sentiment Trend to investigate public health threats in space and time. The third objective is to capture, analyze and quantify public health concerns through sentiment classifications on Twitter data. For traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues, due to their expenses and significant time delays. A two-step sentiment classification model is built to measure the concern. In the first step, Personal tweets are distinguished from Non-Personal tweets. In the second step, Personal Negative tweets are further separated from Personal Non-Negative tweets. In the proposed classification, training data is labeled by an emotion-oriented, clue-based method, and three Machine Learning models are trained and tested. Measure of Concern (MOC) is computed based on the number of Personal Negative sentiment tweets. A timeline trend of the MOC is also generated to monitor public concern levels, which is important for health emergency resource allocations and policy making. The fourth objective is predicting medical condition incidence and progression trajectories by using patients’ self-reported data on PatientsLikeMe. Some medical conditions are correlated with each other to a measureable degree (“comorbidities”). A prediction model is provided to predict the comorbidities and rank future conditions by their likelihood and to predict the possible progression trajectories given an observed medical condition. The novel models for trajectory prediction of medical conditions are validated to cover the comorbidities reported in the medical literature

Analysis of user-generated content from online social communities to characterize and predict depression degree

The identification of a mental disorder at its early stages is a challenging task because it requires clinical interventions that may not be feasible in many cases. Social media such as online communities and blog posts have shown some promising features to help detect and characterise mental disorder at an early stage. In this work, we make use of user-generated content to identify depression and further characterise its degree of severity. We used the user-generated post contents and its associated mood tag to understand and differentiate the linguistic style and sentiments of the user content. We applied machine learning and statistical analysis methods to discriminate the depressive posts and communities from non-depressive ones. The depression degree of a depressed post is identified using variations of valence values based on the mood tag. The proposed methodology achieved 90%, 95% and 92% accuracy for the classification of depressive posts, depressive communities and depression degree, respectively. </jats:p

Detecting Depression in Social Media : An Emotional Analysis Approach

Depression has been an ongoing mental health issue that has been affecting a wide range of humanity, particularly the young adults. To address and observe the more general public in a natural habitat, social media is examined for constructing a system to accurately detect depression. Despite the assiduous effort to construct a novel mechanism to detect depression from social media, behavioral approaches had underlying problems for users with a short activity span. To address this problem, emotion analysis was used as a tool to extract the emotion(s) of a user’s post to identify those with depression. Via machine learning techniques to construct an emotion classifier which in turn creates emotion embeddings for a binary classifier, this study proposes a pipeline structure to identify reddit posts from the depression subreddit. The model yielded promising results, introducing emotional analysis as a novel methodology in assessing mental health within social media

Ascertaining Pain in Mental Health Records:Combining Empirical and Knowledge-Based Methods for Clinical Modelling of Electronic Health Record Text

In recent years, state-of-the-art clinical Natural Language Processing (NLP), as in other domains, has been dominated by neural networks and other statistical models. In contrast to the unstructured nature of Electronic Health Record (EHR) text, biomedical knowledge is increasingly available in structured and codified forms, underpinned by curated databases, machine-readable clinical guidelines, and logically defined terminologies. This thesis examines the incorporation of external medical knowledge into clinical NLP and tests these methods on a use case of ascertaining physical pain in clinical notes of mental health records.Pain is a common reason for accessing healthcare resources and has been a growing area of research, especially its impact on mental health. Pain also presents a unique NLP problem due to its ambiguous nature and the varying circumstances in which it can be used. For these reasons, pain has been chosen as a use case, making it a good case study for the application of the methods explored in this thesis. Models are built by assimilating both structured medical knowledge and clinical NLP and leveraging the inherent relations that exist within medical ontologies. The data source used in this project is a mental health EHR database called CRIS, which contains de-identified patient records from the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in Western Europe.A lexicon of pain terms was developed to identify documents within CRIS mentioning painrelated terms. Gold standard annotations were created by conducting manual annotations on these documents. These gold standard annotations were used to build models for a binary classification task, with the objective of classifying sentences from the clinical text as “relevant”, which indicates the sentence contains relevant mentions of pain, i.e., physical pain affecting the patient, or “not relevant”, which indicates the sentence does not contain mentions of physical pain, or the mention does not relate to the patient (ex: someone else in physical pain). Two models incorporating structured medical knowledge were built:1. a transformer-based model, SapBERT, that utilises a knowledge graph of the UMLS ontology, and2. a knowledge graph embedding model that utilises embeddings from SNOMED CT, which was then used to build a random forest classifier. This was achieved by modelling the clinical pain terms and their relations from SNOMED CT into knowledge graph embeddings, thus combining the data-driven view of clinical language, with the logical view of medical knowledge.These models have been compared with NLP models (binary classifiers) that do not incorporate such structured medical knowledge:1. a transformer-based model, BERT_base, and2. a random forest classifier model.Amongst the two transformer-based models, SapBERT performed better at the classification task (F1-score: 0.98), and amongst the random forest models, the one incorporating knowledge graph embeddings performed better (F1-score: 0.94). The SapBERT model was run on sentences from a cohort of patients within CRIS, with the objective of conducting a prevalence study to understand the distribution of pain based on sociodemographic and diagnostic factors.The contribution of this research is both methodological and practical, showing the difference between a conventional NLP approach of binary classification and one that incorporates external knowledge, and further utilising the models obtained from both these approaches ina prevalence study which was designed based on inputs from clinicians and a patient and public involvement group. The results emphasise the significance of going beyond the conventional approach to NLP when addressing complex issues such as pain.<br/

Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media

We introduce a hybrid abstractive summarisation approach combining hierarchical VAE with LLMs (LlaMA-2) to produce clinically meaningful summaries from social media user timelines, appropriate for mental health monitoring. The summaries combine two different narrative points of view: clinical insights in third person useful for a clinician are generated by feeding into an LLM specialised clinical prompts, and importantly, a temporally sensitive abstractive summary of the user's timeline in first person, generated by a novel hierarchical variational autoencoder, TH-VAE. We assess the generated summaries via automatic evaluation against expert summaries and via human evaluation with clinical experts, showing that timeline summarisation by TH-VAE results in more factual and logically coherent summaries rich in clinical utility and superior to LLM-only approaches in capturing changes over time