iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine

Krithara, Anastasia; Aisopos, Fotis; Rentoumi, Vassiliki; Nentidis, Anastasios; Bougatiotis, Konstantinos; Vidal, María-Esther; Menasalvas, Ernestina; Rodríguez‐González, Alejandro; Samaras, Eleftherios; Garrard, Peter; Torrente, María; Pulla, M. Provencio; Dimakopoulos, Nikos; Mauricio, Rui; Rambla, Jordi; Tartaglia, Gian Gaetano; Paliouras, George

doi:10.1109/cbms.2019.00032

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Finally, the increasing availability of large datasets in the form of both open data resources (such as DPUK (https://www.dementiasplatform.uk/ (accessed on 28 July 2021)) or the AD & FTD Mutation Database (https://uantwerpen.vib.be/ADMutations (accessed on 28 July 2021))) and more recently described methods for integrating these different sources of information [46][47][48] is set to further improve the analytical and predictive capabilities of data science in complex domains. These include the interplay of factors that under the development of common but aetiologically heterogeneous medical conditions, including dementia [49]. While medical datasets that incorporate sufficient structured information are currently few and far between, such resources are increasing in number and availability due to the growing recognition of the potential of data science and data mining and the increasing number of relevant data portals.…”

Section: Discussionmentioning

confidence: 99%

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

et al. 2021

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Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients.

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Section: Discussionmentioning

confidence: 99%

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

et al. 2021

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“…Recent collaborative projects are enabling access to thousands of medical records, with overarching goals that include harnessing this complex data, along with other sources, to aid early diagnosis or increase its accuracy, such as identifying features of misdiagnosis. Dementias Platform UK (DPUK; https://www.dementiasplatform.uk/) enables access to an online portal of rich cohort data, while project iASiS (http://project-iasis.eu/) has a particular focus on NLP techniques, mining EHRs and other records for information that will lead to better decision making at individual and policy levels (Krithara et al, 2019). This review outlines the contributions of machine learning and NLP to the problem of dementia detection, and is structured according to discourse properties of potential importance.…”

Section: Introductionmentioning

confidence: 99%

How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease

Clarke

Foltz

Garrard

2020

Cortex

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Natural Language Processing (NLP) is an ever-growing field of computational science that aims to model natural human language. Combined with advances in machine learning, which learns patterns in data, it offers practical capabilities including automated language analysis. These approaches have garnered interest from clinical researchers seeking to understand the breakdown of language due to pathological changes in the brain, offering fast, replicable and objective methods. The study of Alzheimer's disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease. There is currently no disease-modifying treatment for AD, the leading cause of dementia in people over the age of 65, but detection of those at risk of developing the disease could help with the identification and testing of medications which can take effect before the underlying pathology has irreversibly spread. We outline important components of natural language, as well as NLP tools and approaches with which they can be extracted, analysed and used for disease identification and risk prediction. We review literature using these tools to model discourse across the spectrum of AD, including the contribution of machine learning approaches and Automatic Speech Recognition (ASR). We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches. Challenges discussed include the availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability.

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“…They include treatments for COVID-19, Alzheimer, and Hypertension collected from scientific literature [2,24,30]; also, drugs for frequent comorbidities are part of these treatments. Four drug-drug interaction checker tools are used to assess the quality of deduced DDIs: COVID-19, 31 WebMD, 32 Medscape, 33 and DrugBank. 34 The goal of the study is to validate if the drugs in a treatment that participate in more DDIs increase the number of DDIs in the treatment.…”

Section: Dq In Scientific Open Data Ecosystemmentioning

confidence: 99%

“…An initial version of the aforementioned dashboard [33] has been provided to a group of relevant stakeholders to assess its quality and characteristics. Specifically, 44 evaluators have participated in dedicated training and evaluation sessions, running different scenarios, testing the dashboard functionalities, and querying to retrieve data and information of interest.…”

Section: User Acceptancementioning

confidence: 99%

Knowledge graphs for enhancing transparency in health data ecosystems1

Aisopos

Jozashoori

Emetis

et al. 2023

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Tailoring personalized treatments demands the analysis of a patient’s characteristics, which may be scattered over a wide variety of sources. These features include family history, life habits, comorbidities, and potential treatment side effects. Moreover, the analysis of the services visited the most by a patient before a new diagnosis, as well as the type of requested tests, may uncover patterns that contribute to earlier disease detection and treatment effectiveness. Built on knowledge-driven ecosystems, we devise DE4LungCancer, a health data ecosystem of data sources for lung cancer. In this data ecosystem, knowledge extracted from heterogeneous sources, e.g., clinical records, scientific publications, and pharmacological data, is integrated into knowledge graphs. Ontologies describe the meaning of the combined data, and mapping rules enable the declarative definition of the transformation and integration processes. DE4LungCancer is assessed regarding the methods followed for data quality assessment and curation. Lastly, the role of controlled vocabularies and ontologies in health data management is discussed, as well as their impact on transparent knowledge extraction and analytics. This paper presents the lessons learned in the DE4LungCancer development. It demonstrates the transparency level supported by the proposed knowledge-driven ecosystem, in the context of the lung cancer pilots of the EU H2020-funded project BigMedilytic, the ERA PerMed funded project P4-LUCAT, and the EU H2020 projects CLARIFY and iASiS.

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iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine

Cited by 10 publications

References 15 publications

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients

How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease

Knowledge graphs for enhancing transparency in health data ecosystems1

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