GISTelemed: An online-based GIS approach to epidemiological analysis in telemedicine systems

Inácio, Andrei de Souza; Savaris, Alexandre; Alves, João Marcus; Wangenheim, Aldo von

doi:10.1109/embc.2016.7591942

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Extensive work has been done on standardizing ontologies and data structures to make EHR data, such as Informatics for Integrating Biology & the Bedside (i2b2) 6,7 or Health Level 7, 8 available for research and other secondary usage. Although several systems have been created to serve distinct research settings, [9][10][11] these systems are often either too specialized or heavy-duty systems requiring laborious setup and administration tasks. Thus, they do not allow rapid iteration and testing of methods and results.…”

Section: Introductionmentioning

confidence: 99%

Open Source Infrastructure for Health Care Data Integration and Machine Learning Analyses

Isoviita

Salminen

Azar

et al. 2019

JCO Clinical Cancer Informatics

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PURPOSE We have created a cloud-based machine learning system (CLOBNET) that is an open-source, lean infrastructure for electronic health record (EHR) data integration and is capable of extract, transform, and load (ETL) processing. CLOBNET enables comprehensive analysis and visualization of structured EHR data. We demonstrate the utility of CLOBNET by predicting primary therapy outcomes of patients with high-grade serous ovarian cancer (HGSOC) on the basis of EHR data. MATERIALS AND METHODS CLOBNET is built using open-source software to make data preprocessing, analysis, and model training user friendly. The source code of CLOBNET is available in GitHub. The HGSOC data set was based on a prospective cohort of 208 patients with HGSOC who were treated at Turku University Hospital, Finland, from 2009 to 2019 for whom comprehensive clinical and EHR data were available. RESULTS We trained machine learning (ML) models using clinical data, including a herein developed dissemination score that quantifies the disease burden at the time of diagnosis, to identify patients with progressive disease (PD) or a complete response (CR) on the basis of RECIST (version 1.1). The best performance was achieved with a logistic regression model, which resulted in an area under receiver operating characteristic curve (AUROC) of 0.86, with a specificity of 73% and a sensitivity of 89%, when it classified between patients who experienced PD and CR. CONCLUSION We have developed an open-source computational infrastructure, CLOBNET, that enables effective and rapid analysis of EHR and other clinical data. Our results demonstrate that CLOBNET allows predictions to be made on the basis of EHR data to address clinically relevant questions.

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