Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics

Peek, Niels; Holmes, John; Sun, Jimeng

doi:10.15265/iy-2014-0018

Cited by 59 publications

(24 citation statements)

References 31 publications

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“…Despite these benefits, concerns regarding social media data remain [15, 17, 19, 20]. Internet usage is increasing in broad segments of the population [21], but generalizability is hampered by the fact that users still tend to be younger and better educated than non-users [22, 23].…”

Section: Introductionmentioning

confidence: 99%

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Symptom clusters in women with breast cancer: an analysis of data from social media and a research study

et al. 2015

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Purpose User-generated content on social media sites, such as health-related online forums, offers researchers a tantalizing amount of information, but concerns regarding scientific application of such data remain. This paper compares and contrasts symptom cluster patterns derived from messages on a breast cancer forum with those from a symptom checklist completed by breast cancer survivors participating in a research study. Methods Over 50,000 messages generated by 12,991 users of the breast cancer forum on MedHelp.org were transformed into a standard form and examined for the co-occurrence of 25 symptoms. The k-medoid clustering method was used to determine appropriate placement of symptoms within clusters. Findings were compared with a similar analysis of a symptom checklist administered to 653 breast cancer survivors participating in a research study. Results The following clusters were identified using forum data: menopausal/psychological, pain/fatigue, gastrointestinal, and miscellaneous. Study data generated the clusters: menopausal, pain, fatigue/sleep/gastrointestinal, psychological, and increased weight/appetite. Although the clusters are somewhat different, many symptoms that clustered together in the social media analysis remained together in the analysis of the study participants. Density of connections between symptoms, as reflected by rates of co-occurrence and similarity, was higher in the study data. Conclusions The copious amount of data generated by social media outlets can augment findings from traditional data sources. When different sources of information are combined, areas of overlap and discrepancy can be detected, perhaps giving researchers a more accurate picture of reality. However, data derived from social media must be used carefully and with understanding of its limitations.

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Section: Introductionmentioning

confidence: 99%

“…Information presented online may not comport to the quality or format desired by researchers. Use of abbreviations and typographical errors appears to be common [20]. Information may be inaccurate and impossible to verify, and important details may be missing.…”

Section: Introductionmentioning

confidence: 99%

Symptom clusters in women with breast cancer: an analysis of data from social media and a research study

et al. 2015

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“…[15][16][17][18] Technical complications encompass information technology infrastructure, domain expertise, and implementation of the ETL processes. 19 While nontechnical considerations are pertinent and are often the driving force in shaping data network engagement, this paper focuses exclusively on identifying and addressing technical challenges.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Classification of Electronic Health Data Extraction-Transformation-Loading Challenges in Data Network Participation

Ong¹,

Pradhananga²,

Holve³

et al. 2017

eGEMs

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“…Big data utilization requires tools and techniques for the proper organization, efficient access, and smart analysis of huge volumes of digital data [5, 6]. Artifact in the data presents both technical and methodological challenges to meaningful data use [7]. Human experts can curate datasets by annotating events as real or artifact, but such expert annotation can be costly in terms of time, effort and economic constraints [8, 9].…”

Section: Introductionmentioning

confidence: 99%

Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data

Hravnak

Chen

Dubrawski

et al. 2015

J Clin Monit Comput

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PURPOSE Huge hospital information system databases can be mined for knowledge discovery and decision support, but artifact in stored non-invasive vital sign (VS) high-frequency data streams limits its use. We used machine-learning (ML) algorithms trained on expert-labeled VS data streams to automatically classify VS alerts as real or artifact, thereby “cleaning” such data for future modeling. METHODS 634 admissions to a step-down unit had recorded continuous noninvasive VS monitoring data (heart rate [HR], respiratory rate [RR], peripheral arterial oxygen saturation [SpO2] at 1/20Hz., and noninvasive oscillometric blood pressure [BP]) Time data were across stability thresholds defined VS event epochs. Data were divided Block 1 as the ML training/cross-validation set and Block 2 the test set. Expert clinicians annotated Block 1 events as perceived real or artifact. After feature extraction, ML algorithms were trained to create and validate models automatically classifying events as real or artifact. The models were then tested on Block 2. RESULTS Block 1 yielded 812 VS events, with 214 (26%) judged by experts as artifact (RR 43%, SpO2 40%, BP 15%, HR 2%). ML algorithms applied to the Block 1 training/cross-validation set (10-fold cross-validation) gave area under the curve (AUC) scores of 0.97 RR, 0.91 BP and 0.76 SpO2. Performance when applied to Block 2 test data was AUC 0.94 RR, 0.84 BP and 0.72 SpO2). CONCLUSIONS ML-defined algorithms applied to archived multi-signal continuous VS monitoring data allowed accurate automated classification of VS alerts as real or artifact, and could support data mining for future model building.

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Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics

Cited by 59 publications

References 31 publications

Symptom clusters in women with breast cancer: an analysis of data from social media and a research study

Symptom clusters in women with breast cancer: an analysis of data from social media and a research study

A Framework for Classification of Electronic Health Data Extraction-Transformation-Loading Challenges in Data Network Participation

Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data

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