Publications by authors named "Athanasios Anastasiou"

12 Publications

  • Page 1 of 1

VIVID: Independent Living of Aging Adults Suffered a Stroke.

Stud Health Technol Inform 2020 Jun;270:509-513

Biomedical Engineering Laboratory, National Technical University of Athens, Athens, Greece.

Based on the recent statistics published by the Stroke Association (UK), first-time incidence of stroke occurs almost 17 million times a year worldwide (one every two seconds), making Stroke as the second cause of death in the world. By the age of 75, 1 in 5 women and 1 in 6 men will have a stroke, which is one of the largest causes of disability, as half of all stroke survivors have a disability, making those persons dependent on others (1 in 5 are cared for by family and/or friends). People living longer is a cause for celebration, but older people are more vulnerable to mental health, cognition and physical problems, especially if they have already experienced a stroke (minor or mild). Depression is a main condition after a stroke and may be experienced in the form of sadness, unexplained pains, loss of interest in socializing, weight loss etc. The abovementioned conditions reduce the person's ability to remain active and independent, affecting their well-being and quality of living. Independent living of aging adults that have suffered a stroke is the key motivation for the VIVID project.
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http://dx.doi.org/10.3233/SHTI200212DOI Listing
June 2020

Data Quality Challenges in a Learning Health System.

Stud Health Technol Inform 2020 Jun;270:143-147

Institute of Communication and Computer Systems (ICCS), Athens, Greece.

This paper discusses the topic of data quality, which concerns the global research and business community and constitutes a challenging task. The data quality prerequisite becomes even more critical when it pertains to critical and sensitive data, such as the healthcare domain data. To begin with, the paper outlines the basic definitions and concepts of data quality and its dimensions. The related research work on data quality assessment is presented and our approach for data quality assurance is introduced. This approach is implemented in our designed cloud platform, called MODELHealth, which is intended for supporting clinical work and administrative decision-making process.
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http://dx.doi.org/10.3233/SHTI200139DOI Listing
June 2020

Utilizing Key Item Method to Manage Musculoskeletal Disorders in a Hospital Workplace.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:3420-3423

Management of musculoskeletal disorders (MSDs) is a necessity for the modern work environment. In hospitals, these disorders have a particularly high frequency among health care workers whose work entails lifting and transporting patients as well as washing, dressing and feeding them. This paper, presents an electronic application which is based on the method of basic items (KIM - Key Item Method) in order to reduce incidents of MSDs resulting from manual transport of loads in healthcare facilities. The sample consisted of 15 female hospital meal servers from Metaxa Hospital (Piraeus, Greece) in order to assess the activities of lifting, carrying, transporting, pushing and pulling of loads which are part of their daily work duties. The key requirement for the application was not only helping the risk assessment but also leading to targeted, easily applicable and low cost corrective measures. The results of this electronic tool application showed increased usability and benefits which were associated with the used database and the detailed information relatively to the corrective measures, such as training of the employees to change body posture, replacement of wheels on trolleys and redesigning of serving aisles which proved beneficial.
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http://dx.doi.org/10.1109/EMBC.2019.8857649DOI Listing
July 2019

MODELHealth: Facilitating Machine Learning on Big Health Data Networks.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:2174-2177

MODELHealth is a platform that aims to facilitate the implementation of Machine Learning (ML) techniques on medical data in order to upgrade the delivery of healthcare services. MODELHealth platform is a "holistic" approach to the implementation of processes for the development and utilization of ML algorithms in many forms, including Neural Networks, and can be used to assist clinical work and administrative decision-making. It covers the entire lifecycle of these processes, from pumping, homogenization, anonymization, and enrichment of the initial data, to the final disposal of efficient algorithms through Application Program Interfaces for consumption by any authorized Information System.
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http://dx.doi.org/10.1109/EMBC.2019.8857394DOI Listing
July 2019

Leaf age effects on the spectral predictability of leaf traits in Amazonian canopy trees.

Sci Total Environ 2019 May 16;666:1301-1315. Epub 2019 Feb 16.

Centre for Ecology and Hydrology, Wallingford, Oxfordshire OX10 8BB, UK.

Recent work has shown that leaf traits and spectral properties change through time and/or seasonally as leaves age. Current field and hyperspectral methods used to estimate canopy leaf traits could, therefore, be significantly biased by variation in leaf age. To explore the magnitude of this effect, we used a phenological dataset comprised of leaves of different leaf age groups -developmental, mature, senescent and mixed-age- from canopy and emergent tropical trees in southern Peru. We tested the performance of partial least squares regression models developed from these different age groups when predicting traits for leaves of different ages on both a mass and area basis. Overall, area-based models outperformed mass-based models with a striking improvement in prediction observed for area-based leaf carbon (C) estimates. We observed trait-specific age effects in all mass-based models while area-based models displayed age effects in mixed-age leaf groups for P and N. Spectral coefficients and variable importance in projection (VIPs) also reflected age effects. Both mass- and area-based models for all five leaf traits displayed age/temporal sensitivity when we tested their ability to predict the traits of leaves of other age groups. Importantly, mass-based mature models displayed the worst overall performance when predicting the traits of leaves from other age groups. These results indicate that the widely adopted approach of using fully expanded mature leaves to calibrate models that estimate remotely-sensed tree canopy traits introduces error that can bias results depending on the phenological stage of canopy leaves. To achieve temporally stable models, spectroscopic studies should consider producing area-based estimates as well as calibrating models with leaves of different age groups as they present themselves through the growing season. We discuss the implications of this for surveys of canopies with synchronised and unsynchronised leaf phenology.
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http://dx.doi.org/10.1016/j.scitotenv.2019.01.379DOI Listing
May 2019

Automated classification of primary care patient safety incident report content and severity using supervised machine learning (ML) approaches.

Health Informatics J 2020 12 7;26(4):3123-3139. Epub 2019 Mar 7.

Cardiff University, UK; Macquarie University, Australia.

Learning from patient safety incident reports is a vital part of improving healthcare. However, the volume of reports and their largely free-text nature poses a major analytic challenge. The objective of this study was to test the capability of autonomous classifying of free text within patient safety incident reports to determine incident type and the severity of harm outcome. Primary care patient safety incident reports (n=31333) previously expert-categorised by clinicians (training data) were processed using J48, SVM and Naïve Bayes.The SVM classifier was the highest scoring classifier for incident type (AUROC, 0.891) and severity of harm (AUROC, 0.708). Incident reports containing deaths were most easily classified, correctly identifying 72.82% of reports. In conclusion, supervised ML can be used to classify patient safety incident report categories. The severity classifier, whilst not accurate enough to replace manual processing, could provide a valuable screening tool for this critical aspect of patient safety.
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http://dx.doi.org/10.1177/1460458219833102DOI Listing
December 2020

Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study.

BJGP Open 2018 Jul 13;2(2):bjgpopen18X101589. Epub 2018 Jun 13.

Research Professor, School of Computing, Electronics and Mathematics, Faculty of Science and Engineering, Plymouth University, Plymouth, UK.

Background: Up to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice.

Aim: The aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed.

Design & Setting: The study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data.

Method: Routinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia.

Results: The model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.

Conclusion: The results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.
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http://dx.doi.org/10.3399/bjgpopen18X101589DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6184101PMC
July 2018

An Internet of Things platform architecture for supporting ambient assisted living environments.

Technol Health Care 2017 ;25(3):391-401

Internet of Things (IoT) is the logical further development of today's Internet, enabling a huge amount of devices to communicate, compute, sense and act. IoT sensors placed in Ambient Assisted Living (AAL) environments, enable the context awareness and allow the support of the elderly in their daily routines, ultimately allowing an independent and safe lifestyle. The vast amount of data that are generated and exchanged between the IoT nodes require innovative context modeling approaches that go beyond currently used models. Current paper presents and evaluates an open interoperable platform architecture in order to utilize the technical characteristics of IoT and handle the large amount of generated data, as a solution to the technical requirements of AAL applications.
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http://dx.doi.org/10.3233/THC-161270DOI Listing
April 2018

Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements.

New Phytol 2017 May 15;214(3):1049-1063. Epub 2016 Feb 15.

Centre for Ecology and Hydrology, Wallingford, OX10 8BB, UK.

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (P ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (N ) and carbon (C ) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R  = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R  = 0.07-0.73; %RMSE = 7-38) and multiple (R  = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.
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http://dx.doi.org/10.1111/nph.13853DOI Listing
May 2017

Post-processing for spectral coherence of magnetoencephalogram background activity: application to Alzheimer's disease.

Annu Int Conf IEEE Eng Med Biol Soc 2014 ;2014:6345-8

Estimating the connectivity between magnetoencephalogram (MEG) signals provides an excellent opportunity to analyze whole brain functional integration across a spectrum of conditions from health to disease. For this purpose, spectral coherence has been used widely as an easy-to-interpret metric of signal coupling. However, a number of systematic effects may influence the estimations of spectral coherence and subsequent inferences about brain activity. In this pilot study, we focus on the potentially confounding effects of the field spread and the on-going dynamic temporal variability inherent in the signals. We propose two simple post-processing approaches to account for these: 1) a jack-knife procedure to account for the variance in the estimation of spectral coherence; and 2) a detrending technique to reduce its dependence on sensor proximity. We illustrate the effect of these techniques in the estimation of MEG spectral coherence in the α band for 36 patients with Alzheimer's disease and 26 control subjects.
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http://dx.doi.org/10.1109/EMBC.2014.6945079DOI Listing
October 2015

Smart adaptable system for older adults' Daily Life Activities Management - The ABLE platform.

Annu Int Conf IEEE Eng Med Biol Soc 2014 ;2014:5816-9

In this paper we propose a system (ABLE) that will act as the main platform for a number of low-cost, mature technologies that will be integrated in order to create a dynamically adaptive Daily Life Activities Management environment in order to facilitate the everyday life of senior (but not exclusively) citizens at home. While the main target group of ABLE's users is the ageing population its use can be extended to all people that are vulnerable or atypical in body, intellect or emotions and are categorized by society as disabled. The classes of assistive products that are well defined in the international standard, ISO9999 such as assistive products for personal medical treatment, personal care and protection, communication, information and reaction and for personal mobility, will be easily incorporated in our proposed platform. Furthermore, our platform could integrate and implement the above classes under several service models that will be analyzed further.
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http://dx.doi.org/10.1109/EMBC.2014.6944950DOI Listing
December 2015

Connectivity for healthcare and well-being management: examples from six European projects.

Int J Environ Res Public Health 2009 07 6;6(7):1947-71. Epub 2009 Jul 6.

Faculty of Health and Social Work, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, Devon, UK.

Technological advances and societal changes in recent years have contributed to a shift in traditional care models and in the relationship between patients and their doctors/carers, with (in general) an increase in the patient-carer physical distance and corresponding changes in the modes of access to relevant care information by all groups. The objective of this paper is to showcase the research efforts of six projects (that the authors are currently, or have recently been, involved in), CAALYX, eCAALYX, COGKNOW, EasyLine+, I2HOME, and SHARE-it, all funded by the European Commission towards a future where citizens can take an active role into managing their own healthcare. Most importantly, sensitive groups of citizens, such as the elderly, chronically ill and those suffering from various physical and cognitive disabilities, will be able to maintain vital and feature-rich connections with their families, friends and healthcare providers, who can then respond to, and prevent, the development of adverse health conditions in those they care for in a timely manner, wherever the carers and the people cared for happen to be.
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http://dx.doi.org/10.3390/ijerph6071947DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2738891PMC
July 2009
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