Predicting Post Myocardial Infarction Complication: A Study Using Dual-Modality and Imbalanced Flow Cytometry Data

Nada ALdausari, Frans Coenen, Anh Nguyen, Eduard Shantsila

2024

Abstract

Previous research indicated that white blood cell counts and phenotypes can predict complications after Myocardial Infarction (MI). However, progress is hindered by the need to consider complex interactions among different cell types and their characteristics and manual adjustments of flow cytometry data. This study aims to improve MI complication prediction by applying deep learning techniques to white blood cell test data ob-tained via flow cytometry. Using data from a cohort study of 246 patients with acute MI, we focused on Major Adverse Cardiovascular Events as the primary outcome. Flow cytometry data, available in tabular and image formats, underwent data normalisation and class imbalance adjustments. We built two classification models: a neural network for tabular data and a convolutional neural network for image data. Combining outputs from these models using a voting mechanism enhanced the detection of post-MI complications, improving the average F1 score to 51 compared to individual models. These findings demonstrate the potential of integrating diverse data handling and analytical methods to advance medical diagnostics and patient care.

Paper Citation

in Harvard Style

ALdausari N., Coenen F., Nguyen A. and Shantsila E. (2024). Predicting Post Myocardial Infarction Complication: A Study Using Dual-Modality and Imbalanced Flow Cytometry Data. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 81-90. DOI: 10.5220/0012998300003838

in Bibtex Style

@conference{kdir24,
author={Nada ALdausari and Frans Coenen and Anh Nguyen and Eduard Shantsila},
title={Predicting Post Myocardial Infarction Complication: A Study Using Dual-Modality and Imbalanced Flow Cytometry Data},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={81-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012998300003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Predicting Post Myocardial Infarction Complication: A Study Using Dual-Modality and Imbalanced Flow Cytometry Data
SN - 978-989-758-716-0
AU - ALdausari N.
AU - Coenen F.
AU - Nguyen A.
AU - Shantsila E.
PY - 2024
SP - 81
EP - 90
DO - 10.5220/0012998300003838
PB - SciTePress