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Article ; Online: TransforMED: End-to-Εnd Transformers for Evidence-Based Medicine and Argument Mining in medical literature.

Stylianou, Nikolaos / Vlahavas, Ioannis

2021 Volume 117, Page(s) 103767

Abstract: Argument Mining (AM) refers to the task of automatically identifying arguments in a text and finding their relations. In medical literature this is done by identifying Claims and Premises and classifying their relations as either Support or Attack. ... ...

Abstract	Argument Mining (AM) refers to the task of automatically identifying arguments in a text and finding their relations. In medical literature this is done by identifying Claims and Premises and classifying their relations as either Support or Attack. Evidence-Based Medicine (EBM) refers to the task of identifying all related evidence in medical literature to allow medical practitioners to make informed choices and form accurate treatment plans. This is achieved through the automatic identification of Population, Intervention, Comparator and Outcome entities (PICO) in the literature to limit the collection to only the most relevant documents. In this work, we combine EBM with AM in medical literature to increase the performance of the individual models and create high quality argument graphs, annotated with PICO entities. To that end, we introduce a state-of-the-art EBM model, used to predict the PICO entities and two novel Argument Identification and Argument Relation classification models that utilize the PICO entities to enhance their performance. Our final system works in a pipeline and is able to identify all PICO entities in a medical publication, the arguments presented in them and their relations.
Language	English
Publishing date	2021-03-31
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2021.103767
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Developing and validating a predictive model for future emergency hospital admissions.

Stylianou, Neophytos / Young, Jason / Peden, Carol J / Vasilakis, Christos

Health informatics journal

2022 Volume 28, Issue 2, Page(s) 14604582221101538

Abstract: Although many emergency hospital admissions may be unavoidable, a proportion of these admissions represent a failure of the care system. The adverse consequences of avoidable emergency hospital admissions affect patients, carers, care systems and ... ...

Abstract	Although many emergency hospital admissions may be unavoidable, a proportion of these admissions represent a failure of the care system. The adverse consequences of avoidable emergency hospital admissions affect patients, carers, care systems and substantially increase care costs. The aim of this study was to develop and validate a risk prediction model to estimate the individual probability of emergency admission in the next 12 months within a regional population. We deterministically linked routinely collected data from secondary care with population level data, resulting in a comprehensive research dataset of 190,466 individuals. The resulting risk prediction tool is based on a logistic regression model with five independent variables. The model indicated a discrimination of area under the receiver operating characteristic curve of 0.9384 (95% CI 0.9325-0.9443). We also experimented with different probability cut-off points for identifying high risk patients and found the model's overall prediction accuracy to be over 95% throughout. In summary, the internally validated model we developed can predict with high accuracy the individual risk of emergency admission to hospital within the next year. Its relative simplicity makes it easily implementable within a decision support tool to assist with the management of individual patients in the community.
MeSH term(s)	Emergency Service, Hospital ; Hospitalization ; Hospitals ; Humans ; Logistic Models ; ROC Curve ; Retrospective Studies
Language	English
Publishing date	2022-05-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2213115-2
ISSN	1741-2811 ; 1460-4582
ISSN (online)	1741-2811
ISSN	1460-4582
DOI	10.1177/14604582221101538
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: CoreLM

Stylianou, Nikolaos / Vlahavas, Ioannis

Coreference-aware Language Model Fine-Tuning

2021

Abstract: Language Models are the underpin of all modern Natural Language Processing (NLP) tasks. The introduction of the Transformers architecture has contributed significantly into making Language Modeling very effective across many NLP task, leading to ... ...

Abstract	Language Models are the underpin of all modern Natural Language Processing (NLP) tasks. The introduction of the Transformers architecture has contributed significantly into making Language Modeling very effective across many NLP task, leading to significant advancements in the field. However, Transformers come with a big computational cost, which grows quadratically with respect to the input length. This presents a challenge as to understand long texts requires a lot of context. In this paper, we propose a Fine-Tuning framework, named CoreLM, that extends the architecture of current Pretrained Language Models so that they incorporate explicit entity information. By introducing entity representations, we make available information outside the contextual space of the model, which results in a better Language Model for a fraction of the computational cost. We implement our approach using GPT2 and compare the fine-tuned model to the original. Our proposed model achieves a lower Perplexity in GUMBY and LAMBDADA datasets when compared to GPT2 and a fine-tuned version of GPT2 without any changes. We also compare the models' performance in terms of Accuracy in LAMBADA and Children's Book Test, with and without the use of model-created coreference annotations. Comment: 12 pages, 2 figures, Accepted at Fourth Workshop on Computational Models of Reference, Anaphora and Coreference
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	121
Publishing date	2021-11-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mental Health Disorders During the COVID-19 Outbreak in Cyprus.

Stylianou, Neophytos / Samouti, Gregoria / Samoutis, George

Journal of medicine and life

2020 Volume 13, Issue 3, Page(s) 300–305

Abstract: Cyprus has been affected by COVID-19 since March 2019. With a case fatality rate of 2.6% (until June 2020) and the social isolation measures enforced on the population, the population's mental health has been affected. We aimed to assess the mental ... ...

Abstract	Cyprus has been affected by COVID-19 since March 2019. With a case fatality rate of 2.6% (until June 2020) and the social isolation measures enforced on the population, the population's mental health has been affected. We aimed to assess the mental health burden of the Cypriot population during the outbreak and to explore the potential influence factors. Using a web-based cross-sectional survey, we collected data from 216 volunteers regarding demographic data, COVID-19-related knowledge, generalized anxiety disorder (GAD), and major depressive symptoms. The overall prevalence of GAD and major depressive symptoms of the public were 13.89% and 8.33%, respectively. No demographic had shown any statistical significance with GAD. The younger age group of the study showed a statistically significant association with major depressive symptoms when compared to the adult population in both univariate and multivariable analyses. Our study identified a mental health burden of the Cypriot population, especially the younger age groups. As part of the preparedness for situations as the one we are experiencing and the future impact the pandemic may have on society, interventions should be focused on vulnerable groups of the population to alleviate the psychosocial effects.
MeSH term(s)	Adult ; Aged ; Anxiety Disorders/epidemiology ; Betacoronavirus/physiology ; COVID-19 ; Consumer Health Information ; Coronavirus Infections/epidemiology ; Coronavirus Infections/psychology ; Cross-Sectional Studies ; Cyprus/epidemiology ; Depressive Disorder, Major/epidemiology ; Female ; Humans ; Internet ; Male ; Mental Health ; Middle Aged ; Pandemics ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/psychology ; Prevalence ; SARS-CoV-2 ; Surveys and Questionnaires
Keywords	covid19
Language	English
Publishing date	2020-10-13
Publishing country	Romania
Document type	Journal Article
ZDB-ID	2559353-5
ISSN	1844-3117 ; 1844-3109 ; 1844-122X
ISSN (online)	1844-3117 ; 1844-3109
ISSN	1844-122X
DOI	10.25122/jml-2020-0114
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: COVID-19: Forecasting confirmed cases and deaths with a simple time series model.

Petropoulos, Fotios / Makridakis, Spyros / Stylianou, Neophytos

International journal of forecasting

2020 Volume 38, Issue 2, Page(s) 439–452

Abstract: Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the ... ...

Abstract	Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision-making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics, making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time series approach to modelling and predicting the short-term behavior of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant.
Language	English
Publishing date	2020-12-04
Publishing country	Netherlands
Document type	Journal Article
ISSN	0169-2070
ISSN	0169-2070
DOI	10.1016/j.ijforecast.2020.11.010
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: E.T.

Stylianou, Nikolaos / Vlahavas, Ioannis

Entity-Transformers. Coreference augmented Neural Language Model for richer mention representations via Entity-Transformer blocks

2020

Abstract: In the last decade, the field of Neural Language Modelling has witnessed enormous changes, with the development of novel models through the use of Transformer architectures. However, even these models struggle to model long sequences due to memory ... ...

Abstract	In the last decade, the field of Neural Language Modelling has witnessed enormous changes, with the development of novel models through the use of Transformer architectures. However, even these models struggle to model long sequences due to memory constraints and increasing computational complexity. Coreference annotations over the training data can provide context far beyond the modelling limitations of such language models. In this paper we present an extension over the Transformer-block architecture used in neural language models, specifically in GPT2, in order to incorporate entity annotations during training. Our model, GPT2E, extends the Transformer layers architecture of GPT2 to Entity-Transformers, an architecture designed to handle coreference information when present. To that end, we achieve richer representations for entity mentions, with insignificant training cost. We show the comparative model performance between GPT2 and GPT2E in terms of Perplexity on the CoNLL 2012 and LAMBADA datasets as well as the key differences in the entity representations and their effects in downstream tasks such as Named Entity Recognition. Furthermore, our approach can be adopted by the majority of Transformer-based language models. Comment: 10 pages, 4 figures, 5 tables, accepted at CRAC2020
Keywords	Computer Science - Computation and Language ; Computer Science - Machine Learning
Subject code	121
Publishing date	2020-11-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Characteristics of primary care practices associated with patient education during COVID-19: results of the cross-sectional PRICOV-19 study in 38 countries.

Kirkove, Delphine / Willems, Sara / Van Poel, Esther / Dardenne, Nadia / Donneau, Anne-Françoise / Perrin, Elodie / Ponsar, Cécile / Mallen, Christian / Stylianou, Neophytos / Collins, Claire / Gagnayre, Rémi / Pétré, Benoit

BMC primary care

2024 Volume 24, Issue Suppl 1, Page(s) 285

Abstract: Background: In response to the COVID-19 pandemic, the World Health Organization established a number of key recommendations such as educational activities especially within primary care practices (PCPs) which are a key component of this strategy. This ... ...

Abstract	Background: In response to the COVID-19 pandemic, the World Health Organization established a number of key recommendations such as educational activities especially within primary care practices (PCPs) which are a key component of this strategy. This paper aims to examine the educational activities of PCPs during COVID-19 pandemic and to identify the factors associated with these practices across 38 countries. Methods: A "Patient Education (PE)" score was created based on responses to six items from the self-reported questionnaire among PCPs (n = 3638) compiled by the PRICOV-19 study. Statistical analyses were performed on 3638 cases, with PCPs with missing PE score values were excluded. Results: The PE score features a mean of 2.55 (SD = 0.68) and a median of 2.50 (2.16 - 3.00), with a maximum of 4.00, and varies quite widely between countries. Among all PCPs characteristics, these factors significantly increase the PE score: the payment system type (with a capitation payment system or another system compared to the fee for service), the perception of average PCP with patients with chronic conditions and the perception of adequate governmental support. Conclusion: The model presented in this article is still incomplete and requires further investigation to identify other configuration elements favorable to educational activities. However, the results already highlight certain levers that will enable the development of this educational approach appropriate to primary care.
MeSH term(s)	Humans ; Cross-Sectional Studies ; Pandemics ; COVID-19 ; Patient Education as Topic ; Primary Health Care
Language	English
Publishing date	2024-04-18
Publishing country	England
Document type	Journal Article
ISSN	2731-4553
ISSN (online)	2731-4553
DOI	10.1186/s12875-024-02348-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: A Neural Entity Coreference Resolution Review

Stylianou, Nikolaos / Vlahavas, Ioannis

2019

Abstract: Entity Coreference Resolution is the task of resolving all mentions in a document that refer to the same real world entity and is considered as one of the most difficult tasks in natural language understanding. It is of great importance for downstream ... ...

Abstract	Entity Coreference Resolution is the task of resolving all mentions in a document that refer to the same real world entity and is considered as one of the most difficult tasks in natural language understanding. It is of great importance for downstream natural language processing tasks such as entity linking, machine translation, summarization, chatbots, etc. This work aims to give a detailed review of current progress on solving Coreference Resolution using neural-based approaches. It also provides a detailed appraisal of the datasets and evaluation metrics in the field, as well as the subtask of Pronoun Resolution that has seen various improvements in the recent years. We highlight the advantages and disadvantages of the approaches, the challenges of the task, the lack of agreed-upon standards in the task and propose a way to further expand the boundaries of the field. Comment: 52 pages, 8 figures, 4 tables, Published in Expert Systems with Applications
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
Subject code	410
Publishing date	2019-10-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: EBM+: Advancing Evidence-Based Medicine via two level automatic identification of Populations, Interventions, Outcomes in medical literature.

Stylianou, Nikolaos / Razis, Gerasimos / Goulis, Dimitrios G / Vlahavas, Ioannis

Artificial intelligence in medicine

2020 Volume 108, Page(s) 101949

Abstract: Evidence-Based Medicine (EBM) has been an important practice for medical practitioners. However, as the number of medical publications increases dramatically, it is becoming extremely difficult for medical experts to review all the contents available and ...

Abstract	Evidence-Based Medicine (EBM) has been an important practice for medical practitioners. However, as the number of medical publications increases dramatically, it is becoming extremely difficult for medical experts to review all the contents available and make an informative treatment plan for their patients. A variety of frameworks, including the PICO framework which is named after its elements (Population, Intervention, Comparison, Outcome), have been developed to enable fine-grained searches, as the first step to faster decision making. In this work, we propose a novel entity recognition system that identifies PICO entities within medical publications and achieves state-of-the-art performance in the task. This is achieved by the combination of four 2D Convolutional Neural Networks (CNNs) for character feature extraction, and a Highway Residual connection to facilitate deep Neural Network architectures. We further introduce a PICO Statement classifier, that identifies sentences that not only contain all PICO entities but also answer questions stated in PICO. To facilitate this task we also introduce a high quality dataset, manually annotated by medical practitioners. With the combination of our proposed PICO Entity Recognizer and PICO Statement classifier we aim to advance EBM and enable its faster and more accurate practice.
MeSH term(s)	Evidence-Based Medicine ; Humans ; Language ; Neural Networks, Computer
Language	English
Publishing date	2020-08-13
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	645179-2
ISSN	1873-2860 ; 0933-3657
ISSN (online)	1873-2860
ISSN	0933-3657
DOI	10.1016/j.artmed.2020.101949
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Mental Health Disorders During the COVID-19 Outbreak in Cyprus

Stylianou, Neophytos / Samouti, Gregoria / Samoutis, George

J Med Life

Abstract	Cyprus has been affected by COVID-19 since March 2019. With a case fatality rate of 2.6% (until June 2020) and the social isolation measures enforced on the population, the population's mental health has been affected. We aimed to assess the mental health burden of the Cypriot population during the outbreak and to explore the potential influence factors. Using a web-based cross-sectional survey, we collected data from 216 volunteers regarding demographic data, COVID-19-related knowledge, generalized anxiety disorder (GAD), and major depressive symptoms. The overall prevalence of GAD and major depressive symptoms of the public were 13.89% and 8.33%, respectively. No demographic had shown any statistical significance with GAD. The younger age group of the study showed a statistically significant association with major depressive symptoms when compared to the adult population in both univariate and multivariable analyses. Our study identified a mental health burden of the Cypriot population, especially the younger age groups. As part of the preparedness for situations as the one we are experiencing and the future impact the pandemic may have on society, interventions should be focused on vulnerable groups of the population to alleviate the psychosocial effects.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #875108
Database	COVID19

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