LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 57

Search options

  1. Book ; Online: Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia

    Gkoumas, Dimitris / Purver, Matthew / Liakata, Maria

    2023  

    Abstract: Dementia is associated with language disorders which impede communication. Here, we automatically learn linguistic disorder patterns by making use of a moderately-sized pre-trained language model and forcing it to focus on reformulated natural language ... ...

    Abstract Dementia is associated with language disorders which impede communication. Here, we automatically learn linguistic disorder patterns by making use of a moderately-sized pre-trained language model and forcing it to focus on reformulated natural language processing (NLP) tasks and associated linguistic patterns. Our experiments show that NLP tasks that encapsulate contextual information and enhance the gradient signal with linguistic patterns benefit performance. We then use the probability estimates from the best model to construct digital linguistic markers measuring the overall quality in communication and the intensity of a variety of language disorders. We investigate how the digital markers characterize dementia speech from a longitudinal perspective. We find that our proposed communication marker is able to robustly and reliably characterize the language of people with dementia, outperforming existing linguistic approaches; and shows external validity via significant correlation with clinical markers of behaviour. Finally, our proposed linguistic disorder markers provide useful insights into gradual language impairment associated with disease progression.

    Comment: It has been accepted to appear at EMNLP23
    Keywords Computer Science - Computation and Language
    Subject code 410
    Publishing date 2023-10-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  2. Book ; Online: A Digital Language Coherence Marker for Monitoring Dementia

    Gkoumas, Dimitris / Tsakalidis, Adam / Liakata, Maria

    2023  

    Abstract: The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia. Here we propose methods to capture language coherence as a cost-effective, human- ... ...

    Abstract The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia. Here we propose methods to capture language coherence as a cost-effective, human-interpretable digital marker for monitoring cognitive changes in people with dementia. We introduce a novel task to learn the temporal logical consistency of utterances in short transcribed narratives and investigate a range of neural approaches. We compare such language coherence patterns between people with dementia and healthy controls and conduct a longitudinal evaluation against three clinical bio-markers to investigate the reliability of our proposed digital coherence marker. The coherence marker shows a significant difference between people with mild cognitive impairment, those with Alzheimer's Disease and healthy controls. Moreover our analysis shows high association between the coherence marker and the clinical bio-markers as well as generalisability potential to other related conditions.

    Comment: It has been accepted to appear at EMNLP23
    Keywords Computer Science - Computation and Language
    Subject code 160
    Publishing date 2023-10-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  3. Book ; Online: Clinically meaningful timeline summarisation in social media for mental health monitoring

    Song, Jiayu / Chim, Jenny / Tsakalidis, Adam / Ive, Julia / Atzil-Slonim, Dana / Liakata, Maria

    2024  

    Abstract: We introduce the new task of clinically meaningful summarisation of social media user timelines, appropriate for mental health monitoring. We develop a novel approach for unsupervised abstractive summarisation that produces a two-layer summary consisting ...

    Abstract We introduce the new task of clinically meaningful summarisation of social media user timelines, appropriate for mental health monitoring. We develop a novel approach for unsupervised abstractive summarisation that produces a two-layer summary consisting of both high-level information, covering aspects useful to clinical experts, as well as accompanying time sensitive evidence from a user's social media timeline. A key methodological novelty comes from the timeline summarisation component based on a version of hierarchical variational autoencoder (VAE) adapted to represent long texts and guided by LLM-annotated key phrases. The resulting timeline summary is input into a LLM (LLaMA-2) to produce the final summary containing both the high level information, obtained through instruction prompting, as well as corresponding evidence from the user's timeline. We assess the summaries generated by our novel architecture via automatic evaluation against expert written summaries and via human evaluation with clinical experts, showing that timeline summarisation by TH-VAE results in logically coherent summaries rich in clinical utility and superior to LLM-only approaches in capturing changes over time.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 004
    Publishing date 2024-01-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  4. Book ; Online: Creation and evaluation of timelines for longitudinal user posts

    Hills, Anthony / Tsakalidis, Adam / Nanni, Federico / Zachos, Ioannis / Liakata, Maria

    2023  

    Abstract: There is increasing interest to work with user generated content in social media, especially textual posts over time. Currently there is no consistent way of segmenting user posts into timelines in a meaningful way that improves the quality and cost of ... ...

    Abstract There is increasing interest to work with user generated content in social media, especially textual posts over time. Currently there is no consistent way of segmenting user posts into timelines in a meaningful way that improves the quality and cost of manual annotation. Here we propose a set of methods for segmenting longitudinal user posts into timelines likely to contain interesting moments of change in a user's behaviour, based on their online posting activity. We also propose a novel framework for evaluating timelines and show its applicability in the context of two different social media datasets. Finally, we present a discussion of the linguistic content of highly ranked timelines.

    Comment: Accepted at EACL 2023 (main, long); camera-ready version
    Keywords Computer Science - Computation and Language
    Publishing date 2023-03-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  5. Book ; Online: A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering

    Maufe, Matthew / Ravenscroft, James / Procter, Rob / Liakata, Maria

    2022  

    Abstract: Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on ... ...

    Abstract Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on out-of-domain documents without fine-tuning. We demonstrate that synthetic domain-specific datasets can be generated easily using domain-general models, while still providing significant improvements to QA performance. We present two new tools for this task: A flexible pipeline for validating the synthetic QA data and training downstream models on it, and an online interface to facilitate human annotation of this generated data. Using this interface, crowdworkers labelled 1117 synthetic QA pairs, which we then used to fine-tune downstream models and improve domain-specific QA performance by 8.75 F1.

    Comment: To be published in the companion proceedings of EMNLP 2022. 17 pages (11 of which are in the appendix), 7 figures (3 of which are in the appendix)
    Keywords Computer Science - Computation and Language ; Computer Science - Machine Learning ; I.2.7
    Subject code 006
    Publishing date 2022-11-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  6. Book ; Online: Estimating predictive uncertainty for rumour verification models

    Kochkina, Elena / Liakata, Maria

    2020  

    Abstract: The inability to correctly resolve rumours circulating online can have harmful real-world consequences. We present a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. ... ...

    Abstract The inability to correctly resolve rumours circulating online can have harmful real-world consequences. We present a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. We show that these estimates can be used to filter out model predictions likely to be erroneous, so that these difficult instances can be prioritised by a human fact-checker. We propose two methods for uncertainty-based instance rejection, supervised and unsupervised. We also show how uncertainty estimates can be used to interpret model performance as a rumour unfolds.

    Comment: Accepted to the Annual Conference of the Association for Computational Linguistics (ACL) 2020
    Keywords Computer Science - Computation and Language ; Computer Science - Machine Learning
    Publishing date 2020-05-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  7. Book ; Online: Autoencoding Word Representations through Time for Semantic Change Detection

    Tsakalidis, Adam / Liakata, Maria

    2020  

    Abstract: Semantic change detection concerns the task of identifying words whose meaning has changed over time. The current state-of-the-art detects the level of semantic change in a word by comparing its vector representation in two distinct time periods, without ...

    Abstract Semantic change detection concerns the task of identifying words whose meaning has changed over time. The current state-of-the-art detects the level of semantic change in a word by comparing its vector representation in two distinct time periods, without considering its evolution through time. In this work, we propose three variants of sequential models for detecting semantically shifted words, effectively accounting for the changes in the word representations over time, in a temporally sensitive manner. Through extensive experimentation under various settings with both synthetic and real data we showcase the importance of sequential modelling of word vectors through time for detecting the words whose semantics have changed the most. Finally, we take a step towards comparing different approaches in a quantitative manner, demonstrating that the temporal modelling of word representations yields a clear-cut advantage in performance.
    Keywords Computer Science - Computation and Language
    Subject code 006
    Publishing date 2020-04-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  8. Article ; Online: Observational prospective study of social media, smartphone use and self-harm in a clinical sample of young people: study protocol.

    Bye, Amanda / Carter, Ben / Leightley, Daniel / Trevillion, Kylee / Liakata, Maria / Branthonne-Foster, Stella / Williamson, Grace / Zenasni, Zohra / Dutta, Rina

    BMJ open

    2023  Volume 13, Issue 2, Page(s) e069748

    Abstract: Introduction: Young people are the most frequent users of social media and smartphones and there has been an increasing speculation about the potential negative impacts of their use on mental health. This has coincided with a sharp increase in the ... ...

    Abstract Introduction: Young people are the most frequent users of social media and smartphones and there has been an increasing speculation about the potential negative impacts of their use on mental health. This has coincided with a sharp increase in the levels of self-harm in young people. To date, studies researching this potential association are predominantly cross-sectional and reliant on self-report data, which precludes the ability to objectively analyse behaviour over time. This study is one of the first attempts to explore temporal patterns of real-world usage prior to self-harm, to identify whether there are usage patterns associated with an increased risk.
    Methods and analysis: To study the mechanisms by which social media and smartphone use underpin self-harm in a clinical sample of young people, the Social media, Smartphone use and Self-harm in Young People (3S-YP) study uses a prospective, observational study design. Up to 600 young people aged 13-25 years old from secondary mental health services will be recruited and followed for up to 6 months. Primary analysis will compare real-world data in the 7 days leading up to a participant or clinician recorded self-harm episode, to categorise patterns of problematic usage. Secondary analyses will explore potential mediating effects of anxiety, depression, sleep disturbance, loneliness and bullying.
    Ethics and dissemination: This study was approved by the National Research Ethics Service, London - Riverside, as well as by the Joint Research and Development Office of the Institute of Psychiatry, Psychology and Neuroscience and South London and Maudsley NHS Foundation Trust (SLaM), and the SLaM Clinical Research Interactive Search (CRIS) Oversight Committee. The findings from this study will be disseminated through peer-reviewed scientific journals, conferences, websites, social media and stakeholder engagement activities.
    Trial registration number: NCT04601220.
    MeSH term(s) Humans ; Adolescent ; Young Adult ; Adult ; Smartphone ; Prospective Studies ; Social Media ; Cross-Sectional Studies ; Self-Injurious Behavior/epidemiology ; Self-Injurious Behavior/psychology ; Observational Studies as Topic
    Language English
    Publishing date 2023-02-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2599832-8
    ISSN 2044-6055 ; 2044-6055
    ISSN (online) 2044-6055
    ISSN 2044-6055
    DOI 10.1136/bmjopen-2022-069748
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

  9. Book ; Online: Automated clinical coding using off-the-shelf large language models

    Boyle, Joseph S. / Kascenas, Antanas / Lok, Pat / Liakata, Maria / O'Neil, Alison Q.

    2023  

    Abstract: The task of assigning diagnostic ICD codes to patient hospital admissions is typically performed by expert human coders. Efforts towards automated ICD coding are dominated by supervised deep learning models. However, difficulties in learning to predict ... ...

    Abstract The task of assigning diagnostic ICD codes to patient hospital admissions is typically performed by expert human coders. Efforts towards automated ICD coding are dominated by supervised deep learning models. However, difficulties in learning to predict the large number of rare codes remain a barrier to adoption in clinical practice. In this work, we leverage off-the-shelf pre-trained generative large language models (LLMs) to develop a practical solution that is suitable for zero-shot and few-shot code assignment, with no need for further task-specific training. Unsupervised pre-training alone does not guarantee precise knowledge of the ICD ontology and specialist clinical coding task, therefore we frame the task as information extraction, providing a description of each coded concept and asking the model to retrieve related mentions. For efficiency, rather than iterating over all codes, we leverage the hierarchical nature of the ICD ontology to sparsely search for relevant codes.

    Comment: Accepted to the NeurIPS 2023 workshop Deep Generative Models For Health (DGM4H). 9 pages, 3 figures
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; I.2.7 ; I.2.8
    Subject code 004
    Publishing date 2023-10-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    More links

    Kategorien

  10. Article ; Online: Cohort profile: The Social media, smartphone use and Self-harm in Young People (3S-YP) study-A prospective, observational cohort study of young people in contact with mental health services.

    Bye, Amanda / Carter, Ben / Leightley, Daniel / Trevillion, Kylee / Liakata, Maria / Branthonne-Foster, Stella / Cross, Samantha / Zenasni, Zohra / Carr, Ewan / Williamson, Grace / Vega Viyuela, Alba / Dutta, Rina

    PloS one

    2024  Volume 19, Issue 5, Page(s) e0299059

    Abstract: Objectives: The Social media, Smartphone use and Self-Harm (3S-YP) study is a prospective observational cohort study to investigate the mechanisms underpinning associations between social media and smartphone use and self-harm in a clinical youth sample. ...

    Abstract Objectives: The Social media, Smartphone use and Self-Harm (3S-YP) study is a prospective observational cohort study to investigate the mechanisms underpinning associations between social media and smartphone use and self-harm in a clinical youth sample. We present here a comprehensive description of the cohort from baseline data and an overview of data available from baseline and follow-up assessments.
    Methods: Young people aged 13-25 years were recruited from a mental health trust in England and followed up for 6 months. Self-report data was collected at baseline and monthly during follow-up and linked with electronic health records (EHR) and user-generated data.
    Findings: A total of 362 young people enrolled and provided baseline questionnaire data. Most participants had a history of self-harm according to clinical (n = 295, 81.5%) and broader definitions (n = 296, 81.8%). At baseline, there were high levels of current moderate/severe anxiety (n = 244; 67.4%), depression (n = 255; 70.4%) and sleep disturbance (n = 171; 47.2%). Over half used social media and smartphones after midnight on weekdays (n = 197, 54.4%; n = 215, 59.4%) and weekends (n = 241, 66.6%; n = 263, 72.7%), and half met the cut-off for problematic smartphone use (n = 177; 48.9%). Of the cohort, we have questionnaire data at month 6 from 230 (63.5%), EHR data from 345 (95.3%), social media data from 110 (30.4%) and smartphone data from 48 (13.3%).
    Conclusion: The 3S-YP study is the first prospective study with a clinical youth sample, for whom to investigate the impact of digital technology on youth mental health using novel data linkages. Baseline findings indicate self-harm, anxiety, depression, sleep disturbance and digital technology overuse are prevalent among clinical youth. Future analyses will explore associations between outcomes and exposures over time and compare self-report with user-generated data in this cohort.
    MeSH term(s) Humans ; Social Media ; Adolescent ; Self-Injurious Behavior/epidemiology ; Self-Injurious Behavior/psychology ; Smartphone ; Male ; Female ; Prospective Studies ; Young Adult ; Adult ; Mental Health Services ; Anxiety/epidemiology ; Surveys and Questionnaires ; Depression/epidemiology ; Self Report ; England/epidemiology ; Cohort Studies
    Language English
    Publishing date 2024-05-22
    Publishing country United States
    Document type Journal Article ; Observational Study
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0299059
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

To top