Medical data quality assessment: On the development of an automated framework for medical data curation

Pezoulas, Vasileios C.; Κούρου, Κωνσταντίνα; Kalatzis, Fanis G.; Exarchos, Themis P.; Venetsanopoulou, Aliki; Zampeli, Evangelia; Gandolfo, Saviana; Skopouli, Fotini N.; Vita, Salvatore De; Tzioufas, Athanasios G.; Fotiadis, Dimitrios I.

doi:10.1016/j.compbiomed.2019.03.001

Cited by 77 publications

(57 citation statements)

References 18 publications

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“…The development of a novel composite index for lymphoma prediction in pSS, based on harmonized data in the largest expected cohort of cases and controls, is one of the key clinical unmet needs in HarmonicSS. Both traditional statistical methods and novel engineerbased methodologies [42,96] will be used to achieve the best result. Two previous scores for lymphoma prediction, i.e.…”

Section: Current Novel Initiativesmentioning

confidence: 99%

Predicting lymphoma development in patients with Sjögren’s syndrome

Vita

Gandolfo

2019

Expert Review of Clinical Immunology

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Introduction: The issue of predicting lymphoma in primary Sjögren's syndrome (pSS) starts from its clinical and biologic essence, i.e., an autoimmune exocrinopathy with sicca syndrome, inflammation and lymphoproliferation of MALT (mucosa-associated lymphoid tissue) in exocrine glands. Areas covered: The two major predictors to be firstly focused are persistent salivary gland (SG) swelling and cryoglobulinemic vasculitis with related features as purpura and low C4, or the sole serum cryoglobulinemia repeatedly detected. They are pathogenetically linked and reflect a heavier MALT involvement by histopathology, with the expansion of peculiar rheumatoid factor (RF)-positive clones/ idiotypes. Other predictors include lymphadenopathy, splenomegaly, neutropenia, lymphopenia, serum beta2-microglobulin, monoclonal immunoglobulins, light chains, and RF. Composite indexes/scores may also predict lymphoma. Expert opinion: Prediction at baseline needs amelioration, and must be repeated in the follow-up. Careful clinical characterization, with harmonization and stratification of large cohorts, is a relevant preliminary step. Validated and new biomarkers are needed in biologic fluids and tissues. SG echography with automatic scoring could represent a future imaging biomarker, still lacking. Scoring MALT involvement in pSS, as an additional tool to evaluate disease activity and possibly to predict lymphoma, is welcomed. All these efforts are now ongoing within the HarmonicSS project and in other research initiatives in pSS.

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Section: Current Novel Initiativesmentioning

confidence: 99%

Predicting lymphoma development in patients with Sjögren’s syndrome

Vita

Gandolfo

2019

Expert Review of Clinical Immunology

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“…Untuk melihat teknik dan pendekatan yang digunakan dalam masing-masing penelitian, dibuatlah klasterisasi berdasarkan bidang yang dapat dilihat pada tabel 4 dibawah. [33] Outlier Load modelling [38] Prediction Local search algorithm [25] Economy Classification Genetic algorithms [27] Prediction Machine Learning [42] Education Association Relationships in learning systems [20] Health Classification Machine learning; Neural Network [39] Medical Outlier Cluster analysis [31] Outlier Data quality assessment [39] Regression Logistic regression; Machine learning; Neural network; Support vector machine [26] Regression Multi-criteria decision analysis; Spatial analysis [45] Social Clustering Hierarchical clustering, K-Medoids, fuzzy clustering, and Self-Organising Maps (SOM) [31] Teknik yang paling banyak digunakan pada penelitian kualitatif adalah clustering dan outlier dapat dilihat pada gambar 4 dibawah. Clustering sendiri merupakan salah satu metode data mining yang bersifat tanpa arahan (unsupervised), maksudnya metode ini diterapkan tanpa adanya latihan (training) dan tanpa ada guru (teacher) serta tidak memerlukan target output.…”

Section: Gambar 3 Sebaran Bidang Penggunaan Data Kualitatifunclassified

Analisis Data Artikel Sistem Pakar Menggunakan Metode Systematic Review

Sastypratiwi

Nyoto

2020

JEPIN

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Sistem pakar memiliki berbagai keunggulan dibandingkan kepakaran manusia karena sistem pakar terjangkau, permanen, konsisten, proses yang cepat, dan dapat digandakan. Hal ini menyebabkan sistem pakar berkembang diberbagai bidang. Perlu adanya kajian tentang data implementasi maupun metode yang digunakan dalam sistem pakar. Penelitian ini bertujuan untuk menganalisis data pada artikel sistem pakar dengan mengguankan metode systematic review serta menentukan string yang cocok dalam pengumpulan data. Langkah penelitian menggunakan flowchart PRISMA dengan penentuan string yang sesuai berdasarkan lingkup sistem pakar dan teknik dalam data mining. Pencarian dilakukan pada database online. Metode systematic review ini, dapat menjadi salah satu alternatif metode dalam penulisan karya ilmiah berdasarkan artikel sebelumnya. Dimana hasil dalam artikel sistem pakar sebagai studi dalam penelitian bahwa bidang yang masih unggul adalah computer science serta prediction adalah teknik yang paling banyak digunakan.

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“…This issue is particularly important in the healthcare sector, as important decisions are based on data analytic. Pezoulas et al (2019), which proposes a data curation framework consisting of three layers; data evaluation module, data quality control module and the data standardization module, ensures a curated dataset of high quality as an output. The introduced framework is particularly useful in the medical industry and can be easily integrated with big data platforms deployed by health care institutions, to ensure the continual assessment of the quality of electronic medical and health records.…”

Section: Health and Medicinementioning

confidence: 99%

“…Combine tourism and culture from different sources (Cui et al, 2013) Health and Medicine 1. Ensure the continual assessment of the quality of electronic medical and health records Pezoulas et al (2019;(Pezoulas et al, 2019). Landis et al, 2015;2.…”

Section: Comparative Studymentioning

confidence: 99%

Data Streams Curation for Better Machine Learning Functionality and Result to Serve IoT and other Applications: A Survey

Salah¹,

Al-Omari²,

Alwidian³

et al. 2019

Journal of Computer Science

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Data Curation on data streams is effective in operating and reducing costs of BIG DATA analytic. Basically, analytic preparation requires data curation of available heterogeneous data sets available in big data clusters and such analytic process becomes harder when it comes to the concept of conducting the curation process on Data-on-Motion, in order to come at actionable insights and valuable analytic on a real-time basis including the Machine Learning further analytic and processing. In our paper, we identified and surveyed the different issues and challenges among different areas that are related to the big data. In addition to investigate, the most common techniques and methods followed through the implementations including Streams Curation, the Machine Learning Different Algorithms used in such implementations and the Feature Engineering different techniques that can be considered as curation pre-processing paradigm for data streams analytic. Furthermore, our paper shows the different application areas were data curation concept plays a critical role. Finally, we draw the map between the techniques and methods that are related to the data curation field to emphasize on its main critical role among Business, Retails, Culture, Arts, Health, Medicine, Social Media, Wireless Sensor Networks, Natural Language Processing (NLP) and Automated Feature Engineering (FE). On other hand, we identified the different issues and challenges among different areas including the IoT and Media Streams Curation to help the scholars in this region accordingly.

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Medical data quality assessment: On the development of an automated framework for medical data curation

Cited by 77 publications

References 18 publications

Predicting lymphoma development in patients with Sjögren’s syndrome

Predicting lymphoma development in patients with Sjögren’s syndrome

Analisis Data Artikel Sistem Pakar Menggunakan Metode Systematic Review

Data Streams Curation for Better Machine Learning Functionality and Result to Serve IoT and other Applications: A Survey

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