Using machine learning to improve surgical outcomes

Sindhura Bonthu, Priscila Rodrigues Armijo, Tiffany Tanner, Qiuming Zhu

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

Abstract

Predicting the severity of patient's condition helps providing accurate clinical care. Mortality prediction is one of the challenges due to distinct characteristics of the patient's data. It is a challenging problem to evaluate the patient's data which is highly sparse, highly biased and imbalanced, and highly mixed. In this paper, we are focusing on processing large volumes of data using neural networks which can be further used for analysis to obtain useful insights, such as identifying the major features contributing to certain outcomes of events or classifying different objects based on the presences of certain attributes and their measurements.

Original languageEnglish (US)
Title of host publicationProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
EditorsM. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1426-1431
Number of pages6
ISBN (Electronic)9781728145495
DOIs
StatePublished - Dec 2019
Event18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 - Boca Raton, United States
Duration: Dec 16 2019Dec 19 2019

Publication series

NameProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

Conference

Conference18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
CountryUnited States
CityBoca Raton
Period12/16/1912/19/19

Keywords

  • Machine-Learning
  • Medical-data-analysis
  • Neural-Networks
  • Resampling
  • Sparse-and-Imbalanced

ASJC Scopus subject areas

  • Strategy and Management
  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Signal Processing
  • Media Technology

Fingerprint Dive into the research topics of 'Using machine learning to improve surgical outcomes'. Together they form a unique fingerprint.

Cite this