Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories

Sáez, Carlos; Zurriaga, Óscar; Pérez-Panadés, Jordi; Melchor, Inma; Robles, Montserrat; García‐Gómez, Juan Miguel

doi:10.1093/jamia/ocw010

Cited by 46 publications

(43 citation statements)

References 36 publications

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“…Work on identifying issues in sharing data between sites was another trend this year. Saez et al [9], discussed the issue of data quality, but other issues addressed include the difficulty in understanding what's in a large data collection, and issues in maintaining patient privacy when datasets are shared publically. Demner-Fushman's paper on preparing radiology examination documents for distribution, including de-identification and indexing, noted that "an important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections" [10].…”

Section: Discussionmentioning

confidence: 99%

Secondary Use of Patient Data: Review of the Literature Published in 2016

Schlegel¹,

Ficheur²

2017

Yearb Med Inform

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SummaryObjectives: To summarize recent research and emerging trends in the area of secondary use of healthcare data, and to present the best papers published in this field, selected to appear in the 2017 edition of the IMIA Yearbook. Methods: A literature review of articles published in 2016 and related to secondary use of healthcare data was performed using two bibliographic databases. From this search, 941 papers were identified. The section editors independently reviewed the papers for relevancy and impact, resulting in a consensus list of 14 candidate best papers. External reviewers examined each of the candidate best papers and the final selection was made by the editorial board of the Yearbook. Results: From the 941 retrieved papers, the selection process resulted in four best papers. These papers discuss data quality concerns, issues in preserving privacy of patients in shared datasets, and methods of decision support when consuming large amounts of raw electronic health record (EHR) data. Conclusion: In 2016, a significant effort was put into the development of new systems which aim to avoid significant human understanding and pre-processing of healthcare data, though this is still only an emerging area of research. The value of temporal relationships between data received significant study, as did effective information sharing while preserving patient privacy.

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Section: Discussionmentioning

confidence: 99%

Secondary Use of Patient Data: Review of the Literature Published in 2016

Schlegel¹,

Ficheur²

2017

Yearb Med Inform

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“…Firstly, we input missing data using the nearest neighbors (NN) algorithm. Secondly, we assessed the multisource variability . According to the results, we subgrouped the variables of migraine intensity and migraine frequency in order to ensure intergroup differences.…”

Section: Methodsmentioning

confidence: 99%

Subgrouping Factors Influencing Migraine Intensity in Women: A Semi‐automatic Methodology Based on Machine Learning and Information Geometry

et al. 2019

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Background Migraine is a heterogeneous condition with multiple clinical manifestations. Machine learning algorithms permit the identification of population groups, providing analytical advantages over other modeling techniques. Objective The aim of this study was to analyze critical features that permit the differentiation of subgroups of patients with migraine according to the intensity and frequency of attacks by using machine learning algorithms. Methods Sixty‐seven women with migraine participated. Clinical features of migraine, related disability (Migraine Disability Assessment Scale), anxiety/depressive levels (Hospital Anxiety and Depression Scale), anxiety state/trait levels (State‐Trait Anxiety Inventory), and pressure pain thresholds (PPTs) over the temporalis, neck, second metacarpal, and tibialis anterior were collected. Physical examination included the flexion‐rotation test, cervical range of cervical motion, forward head position while sitting and standing, passive accessory intervertebral movements (PAIVMs) with headache reproduction, and joint positioning sense error. Subgrouping was based on machine learning algorithms by using the nearest neighbors algorithm, multisource variability assessment, and random forest model. Results For migraine intensity, group 2 (women with a regular migraine headache intensity score of 7 on an 11‐point Numeric Pain Rating Scale [where 0 = no pain and 10 = maximum pain]) were younger and had lower joint positioning sense error in cervical rotation, greater cervical mobility in rotation and flexion, lower flexion‐rotation test scores, positive PAIVMs reproducing migraine, normal PPTs over the tibialis anterior, shorter migraine history, and lower cranio‐vertebral angles while standing than the remaining migraine intensity subgroups. The most discriminative variable was the flexion‐rotation test score of the symptomatic side. For migraine frequency, no model was able to identify differences between groups (ie, patients with episodic or chronic migraine). Conclusions A subgroup of women with migraine who had common migraine intensity was identified with machine learning algorithms.

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“…Nevertheless, they observed that "a process that standardizes the laboratory results in the CDW will necessitate frequent updates to stay current." The notion of temporal quality was explored by Sáez et al [18] on a Spanish public health mortality registry using methods based on information theory and geometry.…”

Section: Related Workmentioning

confidence: 99%

What can millions of laboratory test results tell us about the temporal aspect of data quality? Study of data spanning 17 years in a clinical data warehouse

Looten

Chang

Neuraz

et al. 2019

Computer Methods and Programs in Biomedicine

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories

Abstract: Multisite and temporal variability in data distributions affects DQ, hindering data reuse, and an assessment of such variability should be a part of systematic DQ procedures.

Cited by 46 publications

References 36 publications

Secondary Use of Patient Data: Review of the Literature Published in 2016

Secondary Use of Patient Data: Review of the Literature Published in 2016

Subgrouping Factors Influencing Migraine Intensity in Women: A Semi‐automatic Methodology Based on Machine Learning and Information Geometry

What can millions of laboratory test results tell us about the temporal aspect of data quality? Study of data spanning 17 years in a clinical data warehouse

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