Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network

Recent advancements in medical entity linking have been applied in the area of scientific literature and social media data. However, with the adoption of telemedicine and conversational agents such as Alexa in healthcare settings, medical name inference has become an important task. Medication name inference is the task of mapping user friendly medication names from a free-form text to a concept in a normalized medication list. This is challenging due to the differences in the use of medical terminology from health care professionals and user conversations coming from the lay public. We begin with mapping descriptive medication phrases (DMP) to standard medication names (SMN). Given the prescriptions of each patient, we want to provide them with the flexibility of referring to the medication in their preferred ways. We approach this as a ranking problem which maps SMN to DMP by ordering the list of medications in the patient's prescription list obtained from pharmacies. Furthermore, we leveraged the output of intermediate layers and performed medication clustering. We present the Medication Inference Model (MIM) achieving state-of-the-art results. By incorporating medical entities based attention, we have obtained further improvement for ranking models

Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion

With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. Often, different modalities are complementary to each other. Such fact motivated a lot of research attention on fusing the multi-modal feature spaces to comprehensively characterize the data objects. Most of the existing state-of-the-art focused on how to fuse the energy or information from multi-modal spaces to deliver a superior performance over their counterparts with single modal. Recently, deep neural networks have exhibited as a powerful architecture to well capture the nonlinear distribution of high-dimensional multimedia data, so naturally does for multi-modal data. Substantial empirical studies are carried out to demonstrate its advantages that are benefited from deep multi-modal methods, which can essentially deepen the fusion from multi-modal deep feature spaces. In this paper, we provide a substantial overview of the existing state-of-the-arts on the filed of multi-modal data analytics from shallow to deep spaces. Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces. Finally, we share our viewpoints regarding some future directions on this field.Comment: Appearing at ACM TOMM, 26 page

SECNLP: A Survey of Embeddings in Clinical Natural Language Processing

Traditional representations like Bag of words are high dimensional, sparse and ignore the order as well as syntactic and semantic information. Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well as capture the prior knowledge which can transferred to downstream tasks. Even though embedding has become de facto standard for representations in deep learning based NLP tasks in both general and clinical domains, there is no survey paper which presents a detailed review of embeddings in Clinical Natural Language Processing. In this survey paper, we discuss various medical corpora and their characteristics, medical codes and present a brief overview as well as comparison of popular embeddings models. We classify clinical embeddings into nine types and discuss each embedding type in detail. We discuss various evaluation methods followed by possible solutions to various challenges in clinical embeddings. Finally, we conclude with some of the future directions which will advance the research in clinical embeddings.Comment: Published in Journal of Biomedical Informatics (For updated version, refer 10.1016/j.jbi.2019.103323