Perspective: Leveraging Domain Knowledge for Tabular Machine Learning in the Medical Domain (2025)

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.

## introduction tabular data plays a fundamental role in the medical field, capturing patient-specific details such as demographics, medical history, biomarkers, and diagnostic codes @xcite . many clinical machine learning models rely on this data for tasks such as disease diagnosis @xcite and adverse events prediction @xcite . however, developing these models poses unique challenges. for instance, models can often learn shortcuts when modeling the data, leading to potentially harmful decisions. @xcite , for example, show that a model trained to predict pneumonia risk can incorrectly identify asthma as a protective factor. this error can occur because asthmatic patients generally receive more aggressive treatment, leading to better outcomes. in contrast to clinicians who draw on prior training and domain expertise, models are typically developed with limited prior knowledge @xcite . they rely on statistical associations between input features and targets and do not understand the underlying physiology @xcite . learning these associations can be further complicated by the heterogeneous features and complex interactions present in medical datasets @xcite . the lack of knowledge can also hinder the development of models for specialized medical tasks @xcite , as it can limit their ability to perform reliably in various clinical settings. in addition, inconsistencies in data standardization of medical datasets @xcite can be a barrier to the generalizability of models across medical environments. this paper explores how the integration of domain knowledge into machine learning for medical tabular data can help address these challenges. in particular, it can guide variable selection @xcite , mitigate data quality issues @xcite and help establish consistent standardization @xcite . it can also help ensure that models meet natural laws and regulatory requirements, which data-driven approaches may ignore @xcite . ultimately, this could support the translation of machine learning into clinical practice, a hurdle many existing models have yet to overcome @xcite . despite the widespread use of tabular data in healthcare, to our knowledge, there has been no comprehensive investigation of domain knowledge integration for medical tabular data. in this paper, we first detail the types of medical domain knowledge and their potential uses. we then provide an overview of strategies for incorporating medical domain knowledge into tabular machine learning at all pipeline stages. in particular, we investigate how recent methods in table representation learning, such as foundation models @xcite or llm-based table representation @xcite , can be adapted for this purpose. finally, we suggest promising research directions for automated knowledge integration in clinical machine learning for medical tabular data. in the medical domain, the importance of integrating domain knowledge has been increasingly recognized @xcite @xcite , especially in areas such as medical imaging @xcite . while previous work has shown that domain knowledge can benefit tabular clinical decision systems @xcite , it is often poorly integrated into clinical machine learning pipelines and requires custom algorithms @xcite . @xcite identify three challenges hindering the adoption of domain knowledge in medical computer vision models, which are also relevant to tabular data: identifying relevant sources, selecting appropriate representations, and integrating them into deep learning models. ## medical domain knowledge in this section, we build on prior work in machine learning and domain-informed models @xcite to propose a categorization of medical domain knowledge. ## discussion as medical machine learning becomes increasingly prominent, incorporating domain knowledge is vital. some approaches emphasize the scalability and diversity of large datasets, relying, for instance, on pre-trained models @xcite . others prioritize the structured integration of domain knowledge using ontologies or graphs @xcite . this becomes especially important when dealing with heterogeneous, highdimensional, or noisy data. however, access to expert input and curated databases can be limited, and integrating this knowledge effectively is often complex. in addition, clinical practices and medical understanding evolve, and relying on outdated ontologies or prior assumptions may introduce biases. moreover, models trained on historical data may learn and reinforce prior clinical behaviors, leading to the risk of self-fulfilling prophecies in real-world decision support systems (de-arteaga and elmer, 2023). furthermore, relying too heavily on domain constraints can unintentionally limit the discovery of novel patterns or rare cases. thus, further empirical evaluations should assess the benefits of knowledge integration methods across medical datasets of different types and quality. in general, we first recommend early discussions with medical partners to determine potential biases and confounders. while confounders can be unavoidable for retrospective studies, they should be recognized as limitations. domain knowledge should also be included during data preprocessing to harmonize values following ontologies and guidelines or to assess the reasons for missing data and impute them accordingly. domain knowledge can also engineer medically relevant features or integrate information from knowledge bases for feature selection. moreover, model training can leverage pre-trained models or mathematical relationships. finally, validation should be based on clinical expertise, and potential generalizability should be assessed for other patient populations or hospital settings. while this process can be time-consuming, recent studies suggest that domain knowledge integration can be automated by leveraging foundation models for knowledge extraction @xcite and its integration in the pipeline @xcite . this paves the way toward scalable medical deep-learning models. yet, medical foundation models also need to be evaluated in terms of privacy preservation, bias propagation, and generalizability. recently, studies have led benchmarking efforts for scientific foundation models. @xcite show that while expert knowledge did not always improve code validity, it consistently increased success rates-supporting the idea that domain expertise can improve model outcomes, and its inclusion should be further studied for foundation models. however, medical machine learning on complex tabular datasets cannot rely yet on endto-end llms. closer collaboration between the fields of healthcare and tabular machine learning could leverage deep learning advances to design models that integrate domain knowledge more efficiently. promising research directions include adapting and validating automated approaches for domain knowledge integration and transfer learning for tabular data @xcite . ## limitations the current study presents several limitations that should be acknowledged. the presented work is not a systematic review and does not aim to cover all relevant literature comprehensively. thus, it has been influenced by the authors' experiences within the field of medical machine learning. in addition, while we propose an overview and diverse examples for integrating domain knowledge into the medical machine learning pipeline, we do not offer concrete recommendations that are applicable to all use cases. indeed, the appropriate approach may vary depending on the medical context and application. therefore, we encourage interdisciplinary discussions between medical experts and machine learning practitioners to define a concrete guide collaboratively. moreover, the efficacy of the discussed methods of domain knowledge integration may vary according to data quality. we do not offer a systematic assessment of these integration methods on various data types, which would be valuable in gaining a deeper understanding of the impact of domain knowledge. finally, our focus was limited to tabular data. integrating domain knowledge into multimodal machine learning models, which utilize data such as text, images, or time series, represents an important direction for future research, but was beyond the scope of this work.