@inproceedings{bohr-etal-2025-perspective,
title = "Perspective: Leveraging Domain Knowledge for Tabular Machine Learning in the Medical Domain",
author = "Bohr, Arijana and
Altstidl, Thomas and
Eskofier, Bjoern and
Salin, Emmanuelle",
editor = "Chang, Shuaichen and
Hulsebos, Madelon and
Liu, Qian and
Chen, Wenhu and
Sun, Huan",
booktitle = "Proceedings of the 4th Table Representation Learning Workshop",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.trl-1.11/",
doi = "10.18653/v1/2025.trl-1.11",
pages = "143--155",
ISBN = "979-8-89176-268-8",
abstract = "There has been limited exploration of how to effectively integrate domain knowledge into machine learning for medical tabular data. Traditional approaches often rely on non-generalizable processes tailored to specific datasets. In contrast, recent advances in deep learning for language and tabular data are leading the way toward more generalizable and scalable methods of domain knowledge inclusion. In this paper, we first explore the need for domain knowledge in medical tabular data, categorize types of medical domain knowledge, and discuss how each can be leveraged in tabular machine learning. We then outline strategies for integrating this knowledge at various stages of the machine learning pipeline. Finally, building on recent advances in tabular deep learning, we propose future research directions to support the integration of domain knowledge."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bohr-etal-2025-perspective">
<titleInfo>
<title>Perspective: Leveraging Domain Knowledge for Tabular Machine Learning in the Medical Domain</title>
</titleInfo>
<name type="personal">
<namePart type="given">Arijana</namePart>
<namePart type="family">Bohr</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Thomas</namePart>
<namePart type="family">Altstidl</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bjoern</namePart>
<namePart type="family">Eskofier</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emmanuelle</namePart>
<namePart type="family">Salin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 4th Table Representation Learning Workshop</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shuaichen</namePart>
<namePart type="family">Chang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Madelon</namePart>
<namePart type="family">Hulsebos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qian</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wenhu</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Huan</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-268-8</identifier>
</relatedItem>
<abstract>There has been limited exploration of how to effectively integrate domain knowledge into machine learning for medical tabular data. Traditional approaches often rely on non-generalizable processes tailored to specific datasets. In contrast, recent advances in deep learning for language and tabular data are leading the way toward more generalizable and scalable methods of domain knowledge inclusion. In this paper, we first explore the need for domain knowledge in medical tabular data, categorize types of medical domain knowledge, and discuss how each can be leveraged in tabular machine learning. We then outline strategies for integrating this knowledge at various stages of the machine learning pipeline. Finally, building on recent advances in tabular deep learning, we propose future research directions to support the integration of domain knowledge.</abstract>
<identifier type="citekey">bohr-etal-2025-perspective</identifier>
<identifier type="doi">10.18653/v1/2025.trl-1.11</identifier>
<location>
<url>https://aclanthology.org/2025.trl-1.11/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>143</start>
<end>155</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Perspective: Leveraging Domain Knowledge for Tabular Machine Learning in the Medical Domain
%A Bohr, Arijana
%A Altstidl, Thomas
%A Eskofier, Bjoern
%A Salin, Emmanuelle
%Y Chang, Shuaichen
%Y Hulsebos, Madelon
%Y Liu, Qian
%Y Chen, Wenhu
%Y Sun, Huan
%S Proceedings of the 4th Table Representation Learning Workshop
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-268-8
%F bohr-etal-2025-perspective
%X There has been limited exploration of how to effectively integrate domain knowledge into machine learning for medical tabular data. Traditional approaches often rely on non-generalizable processes tailored to specific datasets. In contrast, recent advances in deep learning for language and tabular data are leading the way toward more generalizable and scalable methods of domain knowledge inclusion. In this paper, we first explore the need for domain knowledge in medical tabular data, categorize types of medical domain knowledge, and discuss how each can be leveraged in tabular machine learning. We then outline strategies for integrating this knowledge at various stages of the machine learning pipeline. Finally, building on recent advances in tabular deep learning, we propose future research directions to support the integration of domain knowledge.
%R 10.18653/v1/2025.trl-1.11
%U https://aclanthology.org/2025.trl-1.11/
%U https://doi.org/10.18653/v1/2025.trl-1.11
%P 143-155
Markdown (Informal)
[Perspective: Leveraging Domain Knowledge for Tabular Machine Learning in the Medical Domain](https://aclanthology.org/2025.trl-1.11/) (Bohr et al., TRL 2025)
ACL