Robert Stewart

Also published as: Rob Stewart


2020

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Development of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records
Jaya Chaturvedi | Natalia Viani | Jyoti Sanyal | Chloe Tytherleigh | Idil Hasan | Kate Baird | Sumithra Velupillai | Robert Stewart | Angus Roberts
Proceedings of the Twelfth Language Resources and Evaluation Conference

Free text fields within electronic health records (EHRs) contain valuable clinical information which is often missed when conducting research using EHR databases. One such type of information is medications which are not always available in structured fields, especially in mental health records. Most use cases that require medication information also generally require the associated temporal information (e.g. current or past) and attributes (e.g. dose, route, frequency). The purpose of this study is to develop a corpus of medication annotations in mental health records. The aim is to provide a more complete picture behind the mention of medications in the health records, by including additional contextual information around them, and to create a resource for use when developing and evaluating applications for the extraction of medications from EHR text. Thus far, an analysis of temporal information related to medications mentioned in a sample of mental health records has been conducted. The purpose of this analysis was to understand the complexity of medication mentions and their associated temporal information in the free text of EHRs, with a specific focus on the mental health domain.

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Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer’s Disease (AD) using Mental Health Records: a Classification Approach
Zixu Wang | Julia Ive | Sinead Moylett | Christoph Mueller | Rudolf Cardinal | Sumithra Velupillai | John O’Brien | Robert Stewart
Proceedings of the 3rd Clinical Natural Language Processing Workshop

While Dementia with Lewy Bodies (DLB) is the second most common type of neurodegenerative dementia following Alzheimer’s Disease (AD), it is difficult to distinguish from AD. We propose a method for DLB detection by using mental health record (MHR) documents from a (3-month) period before a patient has been diagnosed with DLB or AD. Our objective is to develop a model that could be clinically useful to differentiate between DLB and AD across datasets from different healthcare institutions. We cast this as a classification task using Convolutional Neural Network (CNN), an efficient neural model for text classification. We experiment with different representation models, and explore the features that contribute to model performances. In addition, we apply temperature scaling, a simple but efficient model calibration method, to produce more reliable predictions. We believe the proposed method has important potential for clinical applications using routine healthcare records, and for generalising to other relevant clinical record datasets. To the best of our knowledge, this is the first attempt to distinguish DLB from AD using mental health records, and to improve the reliability of DLB predictions.

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Comparative Analysis of Text Classification Approaches in Electronic Health Records
Aurelie Mascio | Zeljko Kraljevic | Daniel Bean | Richard Dobson | Robert Stewart | Rebecca Bendayan | Angus Roberts
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing

Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research. However these present specific challenges compared to other classification tasks, notably due to the particular nature of the medical lexicon and language used in clinical records. Recent advances in embedding methods have shown promising results for several clinical tasks, yet there is no exhaustive comparison of such approaches with other commonly used word representations and classification models. In this work, we analyse the impact of various word representations, text pre-processing and classification algorithms on the performance of four different text classification tasks. The results show that traditional approaches, when tailored to the specific language and structure of the text inherent to the classification task, can achieve or exceed the performance of more recent ones based on contextual embeddings such as BERT.

2018

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Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health
Julia Ive | George Gkotsis | Rina Dutta | Robert Stewart | Sumithra Velupillai
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Mental health problems represent a major public health challenge. Automated analysis of text related to mental health is aimed to help medical decision-making, public health policies and to improve health care. Such analysis may involve text classification. Traditionally, automated classification has been performed mainly using machine learning methods involving costly feature engineering. Recently, the performance of those methods has been dramatically improved by neural methods. However, mainly Convolutional neural networks (CNNs) have been explored. In this paper, we apply a hierarchical Recurrent neural network (RNN) architecture with an attention mechanism on social media data related to mental health. We show that this architecture improves overall classification results as compared to previously reported results on the same data. Benefitting from the attention mechanism, it can also efficiently select text elements crucial for classification decisions, which can also be used for in-depth analysis.

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Time Expressions in Mental Health Records for Symptom Onset Extraction
Natalia Viani | Lucia Yin | Joyce Kam | Ayunni Alawi | André Bittar | Rina Dutta | Rashmi Patel | Robert Stewart | Sumithra Velupillai
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development. In this study, we focus on the first step, identifying time expressions in EHRs for patients with schizophrenia. We developed a gold standard corpus, compared this corpus to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions. To the best of our knowledge, this is the first resource annotated for temporal entities in the mental health domain.

2016

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Identifying First Episodes of Psychosis in Psychiatric Patient Records using Machine Learning
Genevieve Gorrell | Sherifat Oduola | Angus Roberts | Tom Craig | Craig Morgan | Rob Stewart
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

2013

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Finding Negative Symptoms of Schizophrenia in Patient Records
Genevieve Gorrell | Angus Roberts | Richard Jackson | Robert Stewart
Proceedings of the Workshop on NLP for Medicine and Biology associated with RANLP 2013