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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Hierarchical Adaptive Multi-task Learning Framework for Patient Diagnoses and Diagnostic Category Classification
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Hierarchical Adaptive Multi-task Learning Framework for Patient Diagnoses and Diagnostic Category Classification

机译：用于患者诊断和诊断类别分类的分层自适应多任务学习框架

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摘要

The problems a patient suffers from can be summarized in terms of a list of patient diagnoses. The diagnoses are typically organized in a hierarchy (or a lattice structure) in which many different low-level diagnoses are covered by one or more diagnostic categories. An interesting machine learning problem is related to learning of a wide range of diagnostic models (at different levels of abstraction) that can automatically assign a diagnosis or a diagnostic category to a specific patient. While one can always approach this problem by learning models for each diagnostic task independently, an interesting open question is how one can leverage the knowledge of a diagnostic hierarchy to improve the classification and outperform independent diagnostic models. In this work, we study this problem by designing a new hierarchical classification learning framework in which multiple diagnostic classification targets are explicitly related via diagnostic hierarchy relations. By conducting experiments on MIMIC-III data and ICD-9 diagnosis hierarchy, we demonstrate that our framework leads to improved classification performance on individual diagnostic tasks when compared to independently learned diagnostic models. This improvement is stronger for diagnoses with a low prior and smaller number of positive training examples.
机译：可以从患者诊断列表中总结患者所遭受的问题。诊断通常按层次结构（或网格结构）进行组织，其中一个或多个诊断类别涵盖许多不同的低层诊断。一个有趣的机器学习问题与学习各种诊断模型（处于不同的抽象级别）有关，这些模型可以自动将诊断或诊断类别分配给特定患者。虽然总可以通过独立地为每个诊断任务学习模型来解决该问题，但一个有趣的开放问题是如何利用诊断层次结构的知识来改进分类并胜过独立的诊断模型。在这项工作中，我们通过设计一个新的层次分类学习框架来研究此问题，在该框架中，通过诊断层次关系明确地关联了多个诊断分类目标。通过对MIMIC-III数据和ICD-9诊断层次进行实验，我们证明了与独立学习的诊断模型相比，我们的框架可提高单个诊断任务的分类性能。对于具有较低先验经验和较少数量的积极训练实例的诊断，此改进会更强。

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