Accounting for diverse feature-types improves patient stratification on tabular clinical datasets

Saptarshi Bej; Chaithra Umesh; Manjunath Mahendra; Kristian Schultz; Jit Sarkar; Olaf Wolkenhauer

doi:10.1016/j.mlwa.2023.100490

Accounting for diverse feature-types improves patient stratification on tabular clinical datasets

Saptarshi Bej^* (First Author), Chaithra Umesh^* (Co-Author), Manjunath Mahendra (Co-Author), Kristian Schultz^* (Co-Author), Jit Sarkar^* (Co-Author), Olaf Wolkenhauer^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	100490
Number of pages	1
Journal	Machine Learning with Applications
Volume	14
DOIs	https://doi.org/10.1016/j.mlwa.2023.100490
State	Published - 2023

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10.1016/j.mlwa.2023.100490

Cite this

@article{bdfb6527e7a4470b8b9adaa873529f69,

title = "Accounting for diverse feature-types improves patient stratification on tabular clinical datasets",

author = "Saptarshi Bej and Chaithra Umesh and Manjunath Mahendra and Kristian Schultz and Jit Sarkar and Olaf Wolkenhauer",

year = "2023",

doi = "10.1016/j.mlwa.2023.100490",

language = "English",

volume = "14",

pages = "100490",

journal = "Machine Learning with Applications",

publisher = "Elsevier",

}

TY - JOUR

T1 - Accounting for diverse feature-types improves patient stratification on tabular clinical datasets

AU - Bej, Saptarshi

AU - Umesh, Chaithra

AU - Mahendra, Manjunath

AU - Schultz, Kristian

AU - Sarkar, Jit

AU - Wolkenhauer, Olaf

PY - 2023

Y1 - 2023

U2 - 10.1016/j.mlwa.2023.100490

DO - 10.1016/j.mlwa.2023.100490

M3 - Article

VL - 14

SP - 100490

JO - Machine Learning with Applications

JF - Machine Learning with Applications

ER -