Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Temporal Point Processes For Modelling Electronic Health Records   The modelling of Electronic Health Records (EHRs) has the potential to drive
more efficient allocation of healthcare resources, enabling early intervention
strategies and advancing personalised healthcare. However, EHRs are challenging
to model due to their realisation as noisy, multi-modal data occurring at
irregular time intervals. To address their temporal nature, we treat EHRs as
samples generated by a Temporal Point Process (TPP), enabling us to model what
happened in an event with when it happened in a principled way. We gather and
propose neural network parameterisations of TPPs, collectively referred to as
Neural TPPs. We perform evaluations on synthetic EHRs as well as on a set of
established benchmarks. We show that TPPs significantly outperform their
non-TPP counterparts on EHRs. We also show that an assumption of many Neural
TPPs, that the class distribution is conditionally independent of time, reduces
performance on EHRs. Finally, our proposed attention-based Neural TPP performs
favourably compared to existing models, whilst aligning with real world
interpretability requirements, an important step towards a component of
clinical decision support systems.

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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg