Overcoming Uncertain Incompleteness for Robust Multimodal Sequential Diagnosis Prediction via Knowledge Distillation and Random Data Erasing
<|reference_start|>Overcoming Uncertain Incompleteness for Robust Multimodal Sequential Diagnosis Prediction via Knowledge Distillation and Random Data Erasing: In this paper, we present NECHO v2, a novel framework designed to enhance the predictive accuracy of multimodal sequential patient diagnoses under uncertain missing visit sequences, a common challenge in real clinical settings. Firstly, we modify NECHO, designed in a diagnosis code-centric fashion, to handle uncertain modality representation dominance under the imperfect data. Secondly, we develop a systematic knowledge distillation by employing the modified NECHO as both teacher and student. It encompasses a modality-wise contrastive and hierarchical distillation, transformer representation random distillation, along with other distillations to align representations between teacher and student tightly and effectively. We also utilise random erasing on individual data points within sequences during both training and distillation of the teacher to lightly simulate scenario with missing visit information, thereby fostering effective knowledge transfer. As a result, NECHO v2 verifies itself by showing robust superiority in multimodal sequential diagnosis prediction under both balanced and imbalanced incomplete settings on multimodal healthcare data.<|reference_end|>
@article{koo2024overcoming,
    title={Overcoming Uncertain Incompleteness for Robust Multimodal Sequential
  Diagnosis Prediction via Knowledge Distillation and Random Data Erasing},
    author={Heejoon Koo},
    journal={arXiv preprint arXiv:2407.19540},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19540},
    primaryClass={cs.LG cs.AI}
}