A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research

Alex S. Felmeister; Angela J. Waanders; Jennifer L. Mason; Jeff Stevens; L. Charles Bailey; Shiva Ganesan; Ingo Helbig

doi:10.1109/BIBM49941.2020.9313388

A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research

Alex S. Felmeister, Angela J. Waanders, Jennifer L. Mason, Jeff Stevens, L. Charles Bailey, Shiva Ganesan, Ingo Helbig

Pediatrics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

We provide a generalizable framework for the systematic analysis of complicated, longitudinal clinical features in pediatric cancer. We use a threefold pipeline of exploratory data analysis, ontological categorization through a multi-modal data transformation process towards predictive analytics. We derive a data-driven phenotype from a subset of a sample of over 1900 brain tumor cases focused specifically on High-Grade Gliomas. We implement an analyst-friendly process to make machine learning-ready data sets based on domain ontologies ready for enumeration and vectorization. The results are clinical domain expert readable data points from 4.3 million observational events across 16,000 patient days. In this research, we address the gap in phenotypic data features by utilizing extensive harmonized observational clinical data and identify resources and specific processes for their use in rare tumor research.

Original language	English (US)
Title of host publication	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Editors	Taesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1943-1950
Number of pages	8
ISBN (Electronic)	9781728162157
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313388
State	Published - Dec 16 2020
Event	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of Duration: Dec 16 2020 → Dec 19 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/Territory	Korea, Republic of
City	Virtual, Seoul
Period	12/16/20 → 12/19/20

Keywords

biomedical research
biospecimen banking
data-driven phenotype
electronic medical records
rare disease

ASJC Scopus subject areas

Computer Science Applications
Information Systems and Management
Medicine (miscellaneous)
Health Informatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BIBM49941.2020.9313388

Cite this

Felmeister, A. S., Waanders, A. J., Mason, J. L., Stevens, J., Bailey, L. C., Ganesan, S., & Helbig, I. (2020). A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research. In T. Park, Y.-R. Cho, X. T. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 (pp. 1943-1950). Article 9313388 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM49941.2020.9313388

Felmeister, Alex S. ; Waanders, Angela J. ; Mason, Jennifer L. et al. / A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research. Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. editor / Taesung Park ; Young-Rae Cho ; Xiaohua Tony Hu ; Illhoi Yoo ; Hyun Goo Woo ; Jianxin Wang ; Julio Facelli ; Seungyoon Nam ; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1943-1950 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

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title = "A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research",

abstract = "We provide a generalizable framework for the systematic analysis of complicated, longitudinal clinical features in pediatric cancer. We use a threefold pipeline of exploratory data analysis, ontological categorization through a multi-modal data transformation process towards predictive analytics. We derive a data-driven phenotype from a subset of a sample of over 1900 brain tumor cases focused specifically on High-Grade Gliomas. We implement an analyst-friendly process to make machine learning-ready data sets based on domain ontologies ready for enumeration and vectorization. The results are clinical domain expert readable data points from 4.3 million observational events across 16,000 patient days. In this research, we address the gap in phenotypic data features by utilizing extensive harmonized observational clinical data and identify resources and specific processes for their use in rare tumor research.",

keywords = "biomedical research, biospecimen banking, data-driven phenotype, electronic medical records, rare disease",

author = "Felmeister, {Alex S.} and Waanders, {Angela J.} and Mason, {Jennifer L.} and Jeff Stevens and Bailey, {L. Charles} and Shiva Ganesan and Ingo Helbig",

note = "Funding Information: This research is supported by the iFellowship Program administered by the University of Pittsburgh, and funded by The Andrew W. Mellon Foundation. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",

year = "2020",

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doi = "10.1109/BIBM49941.2020.9313388",

language = "English (US)",

series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1943--1950",

editor = "Taesung Park and Young-Rae Cho and Hu, {Xiaohua Tony} and Illhoi Yoo and Woo, {Hyun Goo} and Jianxin Wang and Julio Facelli and Seungyoon Nam and Mingon Kang",

booktitle = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

address = "United States",

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Felmeister, AS, Waanders, AJ, Mason, JL, Stevens, J, Bailey, LC, Ganesan, S & Helbig, I 2020, A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research. in T Park, Y-R Cho, XT Hu, I Yoo, HG Woo, J Wang, J Facelli, S Nam & M Kang (eds), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020., 9313388, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1943-1950, 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual, Seoul, Korea, Republic of, 12/16/20. https://doi.org/10.1109/BIBM49941.2020.9313388

A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research. / Felmeister, Alex S.; Waanders, Angela J.; Mason, Jennifer L. et al.
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. ed. / Taesung Park; Young-Rae Cho; Xiaohua Tony Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1943-1950 9313388 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research

AU - Felmeister, Alex S.

AU - Waanders, Angela J.

AU - Mason, Jennifer L.

AU - Stevens, Jeff

AU - Bailey, L. Charles

AU - Ganesan, Shiva

AU - Helbig, Ingo

N1 - Funding Information: This research is supported by the iFellowship Program administered by the University of Pittsburgh, and funded by The Andrew W. Mellon Foundation. Publisher Copyright: © 2020 IEEE.

PY - 2020/12/16

Y1 - 2020/12/16

N2 - We provide a generalizable framework for the systematic analysis of complicated, longitudinal clinical features in pediatric cancer. We use a threefold pipeline of exploratory data analysis, ontological categorization through a multi-modal data transformation process towards predictive analytics. We derive a data-driven phenotype from a subset of a sample of over 1900 brain tumor cases focused specifically on High-Grade Gliomas. We implement an analyst-friendly process to make machine learning-ready data sets based on domain ontologies ready for enumeration and vectorization. The results are clinical domain expert readable data points from 4.3 million observational events across 16,000 patient days. In this research, we address the gap in phenotypic data features by utilizing extensive harmonized observational clinical data and identify resources and specific processes for their use in rare tumor research.

AB - We provide a generalizable framework for the systematic analysis of complicated, longitudinal clinical features in pediatric cancer. We use a threefold pipeline of exploratory data analysis, ontological categorization through a multi-modal data transformation process towards predictive analytics. We derive a data-driven phenotype from a subset of a sample of over 1900 brain tumor cases focused specifically on High-Grade Gliomas. We implement an analyst-friendly process to make machine learning-ready data sets based on domain ontologies ready for enumeration and vectorization. The results are clinical domain expert readable data points from 4.3 million observational events across 16,000 patient days. In this research, we address the gap in phenotypic data features by utilizing extensive harmonized observational clinical data and identify resources and specific processes for their use in rare tumor research.

KW - biomedical research

KW - biospecimen banking

KW - data-driven phenotype

KW - electronic medical records

KW - rare disease

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U2 - 10.1109/BIBM49941.2020.9313388

DO - 10.1109/BIBM49941.2020.9313388

M3 - Conference contribution

AN - SCOPUS:85100329435

T3 - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

SP - 1943

EP - 1950

BT - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

A2 - Park, Taesung

A2 - Cho, Young-Rae

A2 - Hu, Xiaohua Tony

A2 - Yoo, Illhoi

A2 - Woo, Hyun Goo

A2 - Wang, Jianxin

A2 - Facelli, Julio

A2 - Nam, Seungyoon

A2 - Kang, Mingon

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Y2 - 16 December 2020 through 19 December 2020

ER -

Felmeister AS, Waanders AJ, Mason JL, Stevens J, Bailey LC, Ganesan S et al. A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research. In Park T, Cho YR, Hu XT, Yoo I, Woo HG, Wang J, Facelli J, Nam S, Kang M, editors, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1943-1950. 9313388. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313388

A Framework for Analysis, Ontological Evaluation, and Visualization in Preparation to Predictive Analytics in Pediatric Brain Tumor Research

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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