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  • 1. AAl Abdulsalam, Abdulrahman
    et al.
    Velupillai, Sumithra
    Meystre, Stephane
    UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text2016In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), Association for Computational Linguistics , 2016, p. 1256-1262Conference paper (Refereed)
    Abstract [en]

    The 2016 Clinical TempEval continued the 2015 shared task on temporal information extraction with a new evaluation test set. Our team, UtahBMI, participated in all subtasks using machine learning approaches with ClearTK (LIBLINEAR), CRF++ and CRFsuite packages. Our experiments show that CRF-based classifiers yield, in general, higher recall for multi-word spans, while SVM-based classifiers are better at predicting correct attributes of TIMEX3. In addition, we show that an ensemble-based approach for TIMEX3 could yield improved results. Our team achieved competitive results in each subtask with an F1 75.4% for TIMEX3, F1 89.2% for EVENT, F1 84.4% for event relations with document time (DocTimeRel), and F1 51.1% for narrative container (CONTAINS) relations.

  • 2. Bittar, A.
    et al.
    Velupillai, Sumithra
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS. Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
    Roberts, A.
    Dutta, R.
    Text classification to inform suicide risk assessment in electronic health records2019In: 17th World Congress on Medical and Health Informatics, MEDINFO 2019, IOS Press, 2019, Vol. 264, p. 40-44Conference paper (Refereed)
    Abstract [en]

    Assessing a patient's risk of an impending suicide attempt has been hampered by limited information about dynamic factors that change rapidly in the days leading up to an attempt. The storage of patient data in electronic health records (EHRs) has facilitated population-level risk assessment studies using machine learning techniques. Until recently, most such work has used only structured EHR data and excluded the unstructured text of clinical notes. In this article, we describe our experiments on suicide risk assessment, modelling the problem as a classification task. Given the wealth of text data in mental health EHRs, we aimed to assess the impact of using this data in distinguishing periods prior to a suicide attempt from those not preceding such an attempt. We compare three different feature sets, one structured and two text-based, and show that inclusion of text features significantly improves classification accuracy in suicide risk assessment. © 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).

  • 3.
    Dalianis, Hercules
    et al.
    KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
    Nilsson, Gunnar
    Velupillai, Sumithra
    KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
    Is de-identification of electronic health records possible?: Or can we use health record corpora for research?2009In: Virtual healthcare interaction: Papers from AAAI fall symposium ; [November 5 - 7, 2009, at the Westin Arlington Gateway in Arlington, Virginia USA], AAAI Press, 2009, p. 2-3Conference paper (Refereed)
  • 4. Downs, J.
    et al.
    Velupillai, Sumithra
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.
    George, G.
    Holden, R.
    Kikoler, M.
    Dean, H.
    Fernandes, A.
    Dutta, R.
    Detection of Suicidality in Adolescents with Autism Spectrum Disorders: Developing a Natural Language Processing Approach for Use in Electronic Health Records2017In: Advances in Printing and Media Technology, ISSN 0892-2284, E-ISSN 1942-597X, Vol. 2017, p. 641-649Article in journal (Refereed)
    Abstract [en]

    Over 15% of young people with autism spectrum disorders (ASD) will contemplate or attempt suicide during adolescence. Yet, there is limited evidence concerning risk factors for suicidality in childhood ASD. Electronic health records (EHRs) can be used to create retrospective clinical cohort data for large samples of children with ASD. However systems to accurately extract suicidality-related concepts need to be developed so that putative models of suicide risk in ASD can be explored. We present a systematic approach to 1) adapt Natural Language Processing (NLP) solutions to screen with high sensitivity for reference to suicidal constructs in a large clinical ASD EHR corpus (230,465 documents), and 2) evaluate within a screened subset of 500 patients, the performance of an NLP classification tool for positive and negated suicidal mentions within clinical text. When evaluated, the NLP classification tool showed high system performance for positive suicidality with precision, recall, and F1 scores all > 0.85 at a document and patient level. The application therefore provides accurate output for epidemiological research into the factors contributing to the onset and recurrence of suicidality, and potential utility within clinical settings as an automated surveillance or risk prediction tool for specialist ASD services.

  • 5. Gkotsis, George
    et al.
    Oellrich, Anika
    Hubbard, Tim
    Dobson, Richard
    Liakata, Maria
    Velupillai, Sumithra
    KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
    Dutta, Rina
    The language of mental health problems in social media2016In: Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology, Association for Computational Linguistics , 2016, p. 63-73Conference paper (Refereed)
    Abstract [en]

    Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.

  • 6. Gkotsis, George
    et al.
    Oellrich, Anika
    Velupillai, Sumithra
    KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
    Liakata, Maria
    Hubbard, Tim J. P.
    Dobson, Richard J. B.
    Dutta, Rina
    Characterisation of mental health conditions in social media using Informed Deep Learning2017In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7Article in journal (Refereed)
    Abstract [en]

    The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients' own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of 'in the moment' daily exchange, with topics including well- being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.

  • 7. Gkotsis, George
    et al.
    Velupillai, Sumithra
    KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
    Oellrich, Anika
    Dean, Harry
    Liakata, Maria
    Dutta, Rina
    Don’t Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records2016In: Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology, Association for Computational Linguistics , 2016, p. 95-105Conference paper (Refereed)
    Abstract [en]

    Mental Health Records (MHRs) contain freetext documentation about patients’ suicide and suicidality. In this paper, we address the problem of determining whether grammatic variants (inflections) of the word “suicide” are af- firmed or negated. To achieve this, we populate and annotate a dataset with over 6,000 sentences originating from a large repository of MHRs. The resulting dataset has high InterAnnotator Agreement (κ 0.93). Furthermore, we develop and propose a negation detection method that leverages syntactic features of text1 . Using parse trees, we build a set of basic rules that rely on minimum domain knowledge and render the problem as binary classification (affirmed vs. negated). Since the overall goal is to identify patients who are expected to be at high risk of suicide, we focus on the evaluation of positive (affirmed) cases as determined by our classifier. Our negation detection approach yields a recall (sensitivity) value of 94.6% for the positive cases and an overall accuracy value of 91.9%. We believe that our approach can be integrated with other clinical Natural Language Processing tools in order to further advance information extraction capabilities.

  • 8. Ive, J.
    et al.
    Viani, N.
    Chandran, D.
    Bittar, A.
    Velupillai, Sumithra
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Theoretical Computer Science, TCS. King's College London, IoPPN, London, SE5 8AF, United Kingdom.
    KCL-Health-NLP@CLEF eHealth 2018 Task 1: ICD-10 coding of French and Italian death certificates with character-level convolutional neural networks2018In: CEUR Workshop Proceedings, CEUR-WS , 2018, Vol. 2125Conference paper (Refereed)
    Abstract [en]

    In this paper we describe the participation of the KCL-Health-NLP team in the CLEF eHealth 2018 lab, specifically Task 1: Multilingual Information Extraction-ICD10 coding. The task involves the automatic coding of causes of death in death certificates in French, Italian and Hungarian according to the ICD-10 taxonomy. Choosing to work on the two Romance languages, we treated the task as a sequence-to-sequence prediction problem. Our system has an encoder-decoder architecture, with convolutional neural networks based on character em-beddings as encoders and recurrent neural network decoders. Our hypothesis was that a character-level representation would allow our model to generalise across two genealogically related languages. Results obtained by pre-training our Italian model on the French data set confirmed this intuition. We also explored the impact of character-level features extracted from dictionary-matched ICD codes. We obtained F-measures of 0.72/0.64 and 0.78 on the French aligned/raw and Italian raw internal test data, respectively. On the blind test set released by the task organisers, our top results were 0.65/0.52 and 0.69 F-measure, respectively.

  • 9. Kalyanam, Janani
    et al.
    Velupillai, Sumithra
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA. KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
    Conway, Mike
    Lanckriet, Gert
    From Event Detection to Storytelling on Microblogs2016In: PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, IEEE, 2016, p. 437-442Conference paper (Refereed)
    Abstract [en]

    The problem of detecting events from content published on microblogs has garnered much interest in recent times. In this paper, we address the questions of what happens after the outbreak of an event in terms of how the event gradually progresses and attains each of its milestones, and how it eventually dissipates. We propose a model based approach to capture the gradual unfolding of an event over time. This enables the model to automatically produce entire timeline trajectories of events from the time of their outbreak to their disappearance. We apply our model on the Twitter messages collected about Ebola during the 2014 outbreak and obtain the progression timelines of several events that occurred during the outbreak. We also compare our model to several existing topic modeling and event detection baselines in literature to demonstrate its efficiency.

  • 10.
    Neveol, Aurelie
    et al.
    Univ Paris Saclay, CNRS, LIMSI, Rue John von Neumann, F-91405 Orsay, France..
    Dalianis, Hercules
    Stockholm Univ, DSV, Kista, Sweden..
    Velupillai, Sumithra
    KTH, School of Computer Science and Communication (CSC). Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England..
    Savova, Guergana
    Childrens Hosp Boston, Boston, MA USA.;Harvard Med Sch, Boston, MA USA..
    Zweigenbaum, Pierre
    Univ Paris Saclay, CNRS, LIMSI, Rue John von Neumann, F-91405 Orsay, France..
    Clinical Natural Language Processing in languages other than English: opportunities and challenges2018In: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 9, article id 12Article, review/survey (Refereed)
    Abstract [en]

    Background: Natural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area. Main Body: We envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application. Conclusion: We show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages.

  • 11.
    Rosell, Magnus
    et al.
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Velupillai, Sumithra
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Revealing Relations between Open and Closed Answers in Questionnaires through Text Clustering Evaluation2008In: Proceedings of the Sixth International Language Resources and Evaluation (LREC'08), 2008, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Open answers in questionnaires contain valuable information that is very time-consuming to analyze manually. We present a method forhypothesis generation from questionnaires based on text clustering. Text clustering is used interactively on the open answers, and the usercan explore the cluster contents. The exploration is guided by automatic evaluation of the clusters against a closed answer regarded as acategorization. This simplifies the process of selecting interesting clusters. The user formulates a hypothesis from the relation betweenthe cluster content and the closed answer categorization. We have applied our method on an open answer regarding occupation comparedto a closed answer on smoking habits. With no prior knowledge of smoking habits in different occupation groups we have generated thehypothesis that farmers smoke less than the average. The hypothesis is supported by several separate surveys. Closed answers are easyto analyze automatically but are restricted and may miss valuable aspects. Open answers, on the other hand, fully capture the dynamicsand diversity of possible outcomes. With our method the process of analyzing open answers becomes feasible.

  • 12.
    Rosell, Magnus
    et al.
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Velupillai, Sumithra
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    The Impact of Phrases in Document Clustering for Swedish2005In: Proceedings of the 15th NODALIDA conference, Joensuu 2005 / [ed] Werner, S., 2005, p. 173-179Conference paper (Refereed)
    Abstract [en]

    We have investigated the impact of using phrases in the vector spacemodel for clustering documents in Swedish in different ways. The investigation is carried out on two textsets from different domains: one set of newspaper articles and one set of medical papers.The use of phrases do not improveresults relative the ordinary use ofwords. The results differ significantly between the text types. Thisindicates that one could benefit from different text representations for different domains although a fundamentally different approach probably would be needed.

  • 13. Samuelsson, Y.
    et al.
    Täckström, O.
    Velupillai, Sumithra
    KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
    Eklund, J.
    Fišel, M.
    Saers, M.
    Mixing and blending syntactic and semantic dependencies2008In: CoNLL - Proc. Twelfth Conf. Comput. Nat. Lang. Learn., 2008, p. 248-252Conference paper (Refereed)
    Abstract [en]

    Our system for the CoNLL 2008 shared task uses a set of individual parsers, a set of stand-alone semantic role labellers, and a joint system for parsing and semantic role labelling, all blended together. The system achieved a macro averaged labelled F 1- score of 79.79 (WSJ 80.92, Brown 70.49) for the overall task. The labelled attachment score for syntactic dependencies was 86.63 (WSJ 87.36, Brown 80.77) and the labelled F 1-score for semantic dependencies was 72.94 (WSJ 74.47, Brown 60.18).

  • 14.
    Stewart, R.
    et al.
    United Kingdom.
    Jackson, R.
    United Kingdom.
    Patel, R.
    united Kingdom.
    Velupillai, Sumithra
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.
    Gkotsis, G.
    United Kingdom.
    Hoyle, D.
    United Kingdom.
    Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records2018In: F1000 Research, E-ISSN 2046-1402, Vol. 7, article id 210Article in journal (Refereed)
    Abstract [en]

    Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions.

  • 15. Velupillai, Sumithra
    et al.
    Dalianis, Hercules
    Hassel, Martin
    Nilsson, Gunnar H.
    Developing a standard for de-identifying electronic patient records written in Swedish: Precision, recall and F-measure in a manual and computerized annotation trial2009In: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 78, no 12, p. E19-E26Article in journal (Refereed)
    Abstract [en]

    Background: Electronic patient records (EPRs) contain a large amount of information written in free text. This information is considered very valuable for research but is also very sensitive since the free text parts may contain information that could reveal the identity of a patient. Therefore, methods for de-identifying EPRs are needed. The work presented here aims to perform a manual and automatic Protected Health Information (PHI)-annotation trial for EPRs written in Swedish. Methods: This study consists of two main parts: the initial creation of a manually PHI-annotated gold standard, and the porting and evaluation of an existing de-identification software written for American English to Swedish in a preliminary automatic deidentification trial. Results are measured with precision, recall and F-measure. Results: This study reports fairly high Inter-Annotator Agreement (IAA) results on the manually created gold standard, especially for specific tags such as names. The average IAA over all tags was 0.65 F-measure (0.84 F-measure highest pairwise agreement). For name tags the average IAA was 0.80 F-measure (0.91 F-measure highest pairwise agreement). Porting a de-identification software written for American English to Swedish directly was unfortunately non-trivial, yielding poor results. Conclusion: Developing gold standard sets as well as automatic systems for de-identification tasks in Swedish is feasible. However, discussions and definitions on identifiable information is needed, as well as further developments both on the tag sets and the annotation guidelines, in order to get a reliable gold standard. A completely new de-identification software needs to be developed.

  • 16.
    Velupillai, Sumithra
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS. King's College London, London, United Kingdom.
    Epstein, S.
    Bittar, A.
    Stephenson, T.
    Dutta, R.
    Downs, J.
    Identifying suicidal adolescents from mental health records using natural language processing2019In: 17th World Congress on Medical and Health Informatics, MEDINFO 2019, IOS Press, 2019, Vol. 264, p. 413-417Conference paper (Refereed)
    Abstract [en]

    Suicidal ideation is a risk factor for self-harm, completed suicide and can be indicative of mental health issues. Adolescents are a particularly vulnerable group, but few studies have examined suicidal behaviour prevalence in large cohorts. Electronic Health Records (EHRs) are a rich source of secondary health care data that could be used to estimate prevalence. Most EHR documentation related to suicide risk is written in free text, thus requiring Natural Language Processing (NLP) approaches. We adapted and evaluated a simple lexicon- and rule-based NLP approach to identify suicidal adolescents from a large EHR database. We developed a comprehensive manually annotated EHR reference standard and assessed NLP performance at both document and patient level on data from 200 patients (~5000 documents). We achieved promising results (>80% f1 score at both document and patient level). Simple NLP approaches can be successfully used to identify patients who exhibit suicidal risk behaviour, and our proposed approach could be useful for other populations and settings.

  • 17.
    Velupillai, Sumithra
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.
    Hadlaczky, Gergo
    Karolinska Inst, Natl Ctr Suicide Res & Prevent NASP, Dept Learning Informat Management & Eth LIME, Stockholm, Sweden.;Stockholm Hlth Care Serv SLSO, Natl Ctr Suicide Res & Prevent NASP, Ctr Hlth Econ Informat & Hlth Serv Res CHIS, Stockholm, Sweden..
    Baca-Garcia, Enrique
    IIS Jimenez Diaz Fdn, Dept Psychiat, Madrid, Spain.;Univ Autonoma Madrid, Dept Psychiat, Madrid, Spain.;Gen Hosp Villalba, Dept Psychiat, Madrid, Spain.;Carlos III Inst Hlth, CIBERSAM, Madrid, Spain.;Univ Hosp Rey Juan Carlos, Dept Psychiat, Mostoles, Spain.;Univ Hosp Infanta Elena, Dept Psychiat, Valdemoro, Spain.;Univ Catolica Maule, Dept Psychiat, Talca, Chile..
    Gorrell, Genevieve M.
    Univ Sheffield, Dept Comp Sci, Sheffield, S Yorkshire, England..
    Werbeloff, Nomi
    UCL, Div Psychiat, London, England..
    Nguyen, Dong
    Alan Turing Inst, London, England.;Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland..
    Patel, Rashmi
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England.;South London & Maudsley NHS Fdn Trust, London, England..
    Leightley, Daniel
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England..
    Downs, Johnny
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England.;South London & Maudsley NHS Fdn Trust, London, England..
    Hotopf, Matthew
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England.;South London & Maudsley NHS Fdn Trust, London, England..
    Dutta, Rina
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England.;South London & Maudsley NHS Fdn Trust, London, England..
    Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior2019In: Frontiers in Psychiatry, ISSN 1664-0640, E-ISSN 1664-0640, Vol. 10, article id 36Article in journal (Refereed)
    Abstract [en]

    Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.

  • 18.
    Velupillai, Sumithra
    et al.
    KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
    Mowery, Danielle
    Conway, Mike
    Hurdle, John
    Kious, Brent
    Vocabulary Development To Support Information Extraction of Substance Abuse from Psychiatry Notest2016In: Proceedings of BioNLP 2016, Association for Computational Linguistics , 2016, p. 92-101Conference paper (Refereed)
    Abstract [en]

    Extracting information from mental health records can be useful for large-scale clinical studies (e.g., to predict medication adherence or to understand medication effects) in this clinical specialty largely underserved by the Natural Language Processing (NLP) community. Vocabularies that contain medical terms for specific clinical use-cases, such as signs, symptoms, histories, social risk factors, are valuable resources for the development of NLP systems that aid clinicians in extracting information from text. Substance abuse is an important variable for many clinical use-cases, but, to our knowledge, there are no publicly available vocabularies that cover these types of terms. In this study, we apply and combine three methods for generating vocabularies related to substance abuse. We propose a simple and systematic method to generate highly relevant vocabularies and evaluate these vocabularies with respect to size and content, as well as coverage and relevance when applied to authentic psychiatric notes.

  • 19.
    Velupillai, Sumithra
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS. Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England..
    Suominen, Hanna
    Australian Natl Univ, CSIRO Data61, Univ Canberra, Coll Engn & Comp Sci, Canberra, ACT, Australia.;Univ Turku, Turku, Finland..
    Liakata, Maria
    Univ Warwick, Alan Turing Inst, Dept Comp Sci, Coventry, W Midlands, England..
    Roberts, Angus
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England..
    Shah, Anoop D.
    UCL, Inst Hlth Informat, London, England.;Univ Coll London NHS Fdn Trust, London, England..
    Morley, Katherine
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England.;Univ Melbourne, Melbourne Sch Populat & Global Hlth, Melbourne, Vic, Australia..
    Osborn, David
    UCL, Div Psychiat, London, England.;Camden & Islington NHS Fdn Trust, London, England..
    Hayes, Joseph
    UCL, Div Psychiat, London, England.;Camden & Islington NHS Fdn Trust, London, England..
    Stewart, Robert
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England.;South London & Maudsley NHS Fdn Trust, London, England..
    Downs, Johnny
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England.;South London & Maudsley NHS Fdn Trust, London, England..
    Chapman, Wendy
    Univ Utah, Dept Biomed Informat, Salt Lake City, UT 84112 USA..
    Dutta, Rina
    Kings Coll London, Inst Psychiat Psychol & Neurosci, London, England.;South London & Maudsley NHS Fdn Trust, London, England..
    Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances2018In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 88, p. 11-19Article in journal (Refereed)
    Abstract [en]

    The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient-or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.

  • 20. Viani, N.
    et al.
    Kam, J.
    Yin, L.
    Verma, S.
    Stewart, R.
    Patel, R.
    Velupillai, Sumithra
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS. King's College London, London, United Kingdom.
    Annotating temporal relations to determine the onset of psychosis symptoms2019In: 17th World Congress on Medical and Health Informatics, MEDINFO 2019, IOS Press, 2019, p. 418-422Conference paper (Refereed)
    Abstract [en]

    For patients with a diagnosis of schizophrenia, determining symptom onset is crucial for timely and successful intervention. In mental health records, information about early symptoms is often documented only in free text, and thus needs to be extracted to support clinical research. To achieve this, natural language processing (NLP) methods can be used. Development and evaluation of NLP systems requires manually annotated corpora. We present a corpus of mental health records annotated with temporal relations for psychosis symptoms. We propose a methodology for document selection and manual annotation to detect symptom onset information, and develop an annotated corpus. To assess the utility of the created corpus, we propose a pilot NLP system. To the best of our knowledge, this is the first temporally-annotated corpus tailored to a specific clinical use-case.

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