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Warfarin Dose Estimation on High-dimensional and Incomplete Data

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Title:Warfarin Dose Estimation on High-dimensional and Incomplete Data
Authors:Wang, Zeyuan
Poon, Josiah
Yang, Jie
Poon, Simon
Keywords:Big Data on Healthcare Application
deep learning
dose prediction
high-dimensional features
incomplete data
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Date Issued:05 Jan 2021
Abstract:Warfarin is a widely used oral anticoagulant worldwide. However, due to the complex relationship between individual factors, it is challenging to estimate the optimal warfarin dose to give full play to its ideal efficacy. Currently, there are plenty of studies using machine learning or deep learning techniques to help with the optimal warfarin dose selection. But few of them can resolve missing values and high-dimensional data naturally, that are two main concerns when analyzing clinical real world data. In this work, we propose to regard each patient’s record as a set of observed individual factors, and represent them in an embedding space, that enables our method can learn from the incomplete date directly and avoid the negative impact from the high-dimensional feature set. Then, a novel neural network is proposed to combine the set of embedded vectors non-linearly, that are capable of capturing their correlations and locating the informative ones for prediction. After comparing with the baseline models on the open source data from International Warfarin Pharmacogenetics Consortium, the experimental results demonstrate that our proposed method outperform others by a significant margin. After further analyzing the model performance in different dosing subgroups, we can conclude that the proposed method has the high application value in clinical, especially for the patients in high-dose and medium-dose subgroups.
Pages/Duration:9 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Big Data on Healthcare Application

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