Natural Language Processing for Forecasting Mortality and Premature Death in Companion Animals

Farrell, Sean; Noble, Peter-John Mäntylä; Al-Moubayed, Noura

doi:10.21203/rs.3.rs-3256060/v1

Cited by 2 publications

(2 citation statements)

References 61 publications

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“…Values were compared using the animals’ true mortality outcome as a baseline result. Following the original PetBERT paper 34 , we report performance using the F1 score, see Table 2 . For added information, we also report accuracy in Table 3 .…”

Section: Methodsmentioning

confidence: 99%

Explainable text-tabular models for predicting mortality risk in companion animals

Burton,

Farrell,

Mäntylä Noble

et al. 2024

Sci Rep

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As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets containing a range of modalities, from the free-text of clinician notes to structured tabular data entries, there is a need for frameworks capable of providing comprehensive explanation values across diverse modalities. Here, we present a multimodal masking framework to extend the reach of SHapley Additive exPlanations (SHAP) to text and tabular datasets to identify risk factors for companion animal mortality in first-opinion veterinary electronic health records (EHRs) from across the United Kingdom. The framework is designed to treat each modality consistently, ensuring uniform and consistent treatment of features and thereby fostering predictability in unimodal and multimodal contexts. We present five multimodality approaches, with the best-performing method utilising PetBERT, a language model pre-trained on a veterinary dataset. Utilising our framework, we shed light for the first time on the reasons each model makes its decision and identify the inclination of PetBERT towards a more pronounced engagement with free-text narratives compared to BERT-base’s predominant emphasis on tabular data. The investigation also explores the important features on a more granular level, identifying distinct words and phrases that substantially influenced an animal’s life status prediction. PetBERT showcased a heightened ability to grasp phrases associated with veterinary clinical nomenclature, signalling the productivity of additional pre-training of language models.

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Section: Methodsmentioning

confidence: 99%

Explainable text-tabular models for predicting mortality risk in companion animals

Burton,

Farrell,

Mäntylä Noble

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Values were compared using the animals' true mortality outcome as a baseline result. Following the original PetBERT paper 35 , we report performance using the F1 score, see Table 1. For added information, we also report accuracy in Table 2.…”

Section: Model Evaluationmentioning

confidence: 99%

Explainable Text-Tabular Models for Predicting Mortality Risk in Companion Animals

Burton,

Farrell,

Moubayed

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets containing a range of modalities, from the free-text of clinician notes to structured tabular data entries, there is a need for frameworks capable of providing comprehensive explanation values across diverse modalities. Here, we present a multimodal masking framework to extend the reach of SHapley Additive exPlanations (SHAP) to text and tabular datasets to identify risk factors for companion animal mortality in first-opinion veterinary electronic health records (EHRs) from across the United Kingdom. The framework is designed to treat each modality consistently, ensuring uniform and consistent treatment of features and thereby fostering predictability in unimodal and multimodal contexts. We present five multimodality approaches, with the best-performing method utilising PetBERT, a language model pre-trained on a veterinary dataset. Utilising our framework, we shed light for the first time on the reasons each model makes its decision and identify the inclination of PetBERT towards a more pronounced engagement with free-text narratives compared to BERT-base's predominant emphasis on tabular data. The investigation also explores the important features on a more granular level, identifying distinct words and phrases that substantially influenced an animal's life status prediction. PetBERT showcased a heightened ability to grasp phrases associated with veterinary clinical nomenclature, signalling the productivity of additional pre-training of language models.

show abstract

Natural Language Processing for Forecasting Mortality and Premature Death in Companion Animals

Cited by 2 publications

References 61 publications

Explainable text-tabular models for predicting mortality risk in companion animals

Explainable text-tabular models for predicting mortality risk in companion animals

Explainable Text-Tabular Models for Predicting Mortality Risk in Companion Animals

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