Automatic ICD code assignment utilizing textual descriptions and hierarchical structure of ICD code International Classification of Diseases (ICD) codes provides a hierarchy of diagnostic codes for classifying diseases, which plays an important role in healthcare. Medical coding which assigns a subset of ICD codes to a patient visit is a mandatory process that is crucial for patient care and billing. Manual coding is timeconsuming, expensive, and errorprone. In this paper, we present a deep learning based network that utilizes the information of textual descriptions and hierarchical structure of ICD codes to make a code assignment automatically. On one hand, we use the machine comprehension framework to model the relationship between the diagnostic description and the textual descriptions, enabling the model put more attention on most relevant segments of diagnostic description for each possible codes. On the other hand, we exploit treeLSTM architecture to model hierarchical structure, capturing the hierarchical relationship among codes and the semantics of each code. We demonstrate the effectiveness of the proposed methods on a Chinese dataset and an English dataset.

Hierarchical Adaptive Multitask Learning Framework for Patient Diagnoses and Diagnostic Category Classification 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 lowlevel 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 MIMICIII data and ICD9 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.