Clinical decision support for stroke using multi–view learning based models for NIHSS scores

Vaibhav Rajan, Sakyajit Bhattacharya, Ranjan Shetty, Amith Sitaram, G. Vivek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cerebral stroke is a leading cause of physical disability and death in the world. The severity of a stroke is assessed by a neurological examination using a scale known as the NIH stroke scale (NIHSS). As a measure of stroke severity, the NIHSS score is widely adopted and has been found to also be useful in outcome prediction, rehabilitation planning and treatment planning. In many applications, such as in patient triage in under–resourced primary health care centres and in automated clinical decision support tools, it would be valuable to obtain the severity of stroke with minimal human intervention using simple parameters like age, past conditions and blood investigations. In this paper we propose a new model for predicting NIHSS scores which, to our knowledge, is the first statistical model for stroke severity. Our multi–view learning approach can handle data from heterogeneous sources with mixed data distributions (binary, categorical and numerical) and is robust against missing values – strengths that many other modeling techniques lack. In our experiments we achieve better predictive accuracy than other commonly used methods.

Original languageEnglish
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Revised Selected Papers
PublisherSpringer Verlag
Pages190-199
Number of pages10
ISBN (Print)9783319429953
DOIs
Publication statusPublished - 01-01-2016
Externally publishedYes
Event20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2016 and Workshop on Biologically Inspired Data Mining Techniques, BDM 2016, Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2016, Workshop on Predictive Analytics for Critical Care, PACC 2016 and Workshop on Data Mining in Business and Finance, WDMBF 2016 - Auckland, New Zealand
Duration: 19-04-201619-04-2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9794
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2016 and Workshop on Biologically Inspired Data Mining Techniques, BDM 2016, Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2016, Workshop on Predictive Analytics for Critical Care, PACC 2016 and Workshop on Data Mining in Business and Finance, WDMBF 2016
CountryNew Zealand
CityAuckland
Period19-04-1619-04-16

Fingerprint

Decision Support
Stroke
Planning
Health care
Patient rehabilitation
Blood
Model
Experiments
Mixed Data
Learning
Missing Values
Rehabilitation
Disability
Data Distribution
Categorical
Healthcare
Statistical Model
Binary
Statistical Models
Prediction

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Rajan, V., Bhattacharya, S., Shetty, R., Sitaram, A., & Vivek, G. (2016). Clinical decision support for stroke using multi–view learning based models for NIHSS scores. In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Revised Selected Papers (pp. 190-199). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9794). Springer Verlag. https://doi.org/10.1007/978-3-319-42996-0_16
Rajan, Vaibhav ; Bhattacharya, Sakyajit ; Shetty, Ranjan ; Sitaram, Amith ; Vivek, G. / Clinical decision support for stroke using multi–view learning based models for NIHSS scores. Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Revised Selected Papers. Springer Verlag, 2016. pp. 190-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Rajan, V, Bhattacharya, S, Shetty, R, Sitaram, A & Vivek, G 2016, Clinical decision support for stroke using multi–view learning based models for NIHSS scores. in Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9794, Springer Verlag, pp. 190-199, 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2016 and Workshop on Biologically Inspired Data Mining Techniques, BDM 2016, Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2016, Workshop on Predictive Analytics for Critical Care, PACC 2016 and Workshop on Data Mining in Business and Finance, WDMBF 2016, Auckland, New Zealand, 19-04-16. https://doi.org/10.1007/978-3-319-42996-0_16

Clinical decision support for stroke using multi–view learning based models for NIHSS scores. / Rajan, Vaibhav; Bhattacharya, Sakyajit; Shetty, Ranjan; Sitaram, Amith; Vivek, G.

Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Revised Selected Papers. Springer Verlag, 2016. p. 190-199 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9794).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Rajan V, Bhattacharya S, Shetty R, Sitaram A, Vivek G. Clinical decision support for stroke using multi–view learning based models for NIHSS scores. In Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Revised Selected Papers. Springer Verlag. 2016. p. 190-199. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-42996-0_16