Simple evolutionary algorithm for quantifying how medical history factors predict disease outcomes

10 pages•Published: March 22, 2022

Abstract

The medical history information contained in electronic health records (EHR) is a valuable and largely untapped data mining source for predicting patient outcomes and thereby improving treatment. This paper presents a simple but novel evolutionary algorithm (EA) for identifying how various medical history and demographic factors predict clinical outcomes. For this initial study, our EA was tested using synthetic data concerning COVID-19 hospitalization rates and we show that the EA results are more informative than logistic regression, neural network, or decision tree results.

Keyphrases: disease outcomes, electronic health records, evolutionary algorithm, genetic algorithm, medical history factors

In: Hisham Al-Mubaid, Tamer Aldwairi and Oliver Eulenstein (editors). Proceedings of 14th International Conference on Bioinformatics and Computational Biology, vol 83, pages 43-52.

Links:	https://easychair.org/publications/paper/wpBv
	https://doi.org/10.29007/7pd1

BibTeX entry

@inproceedings{BICOB2022:Simple_evolutionary_algorithm_quantifying,
  author    = {James Camp and Hisham Al-Mubaid},
  title     = {Simple evolutionary algorithm for quantifying how medical history factors predict disease outcomes},
  booktitle = {Proceedings of 14th International Conference on Bioinformatics and Computational Biology},
  editor    = {Hisham Al-Mubaid and Tamer Aldwairi and Oliver Eulenstein},
  series    = {EPiC Series in Computing},
  volume    = {83},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/wpBv},
  doi       = {10.29007/7pd1},
  pages     = {43-52},
  year      = {2022}}

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