7,494 research outputs found
Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment
Mapping and translating professional but arcane clinical jargons to consumer
language is essential to improve the patient-clinician communication.
Researchers have used the existing biomedical ontologies and consumer health
vocabulary dictionary to translate between the languages. However, such
approaches are limited by expert efforts to manually build the dictionary,
which is hard to be generalized and scalable. In this work, we utilized the
embeddings alignment method for the word mapping between unparalleled clinical
professional and consumer language embeddings. To map semantically similar
words in two different word embeddings, we first independently trained word
embeddings on both the corpus with abundant clinical professional terms and the
other with mainly healthcare consumer terms. Then, we aligned the embeddings by
the Procrustes algorithm. We also investigated the approach with the
adversarial training with refinement. We evaluated the quality of the alignment
through the similar words retrieval both by computing the model precision and
as well as judging qualitatively by human. We show that the Procrustes
algorithm can be performant for the professional consumer language embeddings
alignment, whereas adversarial training with refinement may find some relations
between two languages.Comment: Accepted by 2018 KDD Workshop on Machine Learning for Medicine and
Healthcar
Word-Level Loss Extensions for Neural Temporal Relation Classification
Unsupervised pre-trained word embeddings are used effectively for many tasks
in natural language processing to leverage unlabeled textual data. Often these
embeddings are either used as initializations or as fixed word representations
for task-specific classification models. In this work, we extend our
classification model's task loss with an unsupervised auxiliary loss on the
word-embedding level of the model. This is to ensure that the learned word
representations contain both task-specific features, learned from the
supervised loss component, and more general features learned from the
unsupervised loss component. We evaluate our approach on the task of temporal
relation extraction, in particular, narrative containment relation extraction
from clinical records, and show that continued training of the embeddings on
the unsupervised objective together with the task objective gives better
task-specific embeddings, and results in an improvement over the state of the
art on the THYME dataset, using only a general-domain part-of-speech tagger as
linguistic resource.Comment: Accepted at the 27th International Conference on Computational
Linguistics (COLING 2018
Towards Language Agnostic Universal Representations
When a bilingual student learns to solve word problems in math, we expect the
student to be able to solve these problem in both languages the student is
fluent in,even if the math lessons were only taught in one language. However,
current representations in machine learning are language dependent. In this
work, we present a method to decouple the language from the problem by learning
language agnostic representations and therefore allowing training a model in
one language and applying to a different one in a zero shot fashion. We learn
these representations by taking inspiration from linguistics and formalizing
Universal Grammar as an optimization process (Chomsky, 2014; Montague, 1970).
We demonstrate the capabilities of these representations by showing that the
models trained on a single language using language agnostic representations
achieve very similar accuracies in other languages
Clinically Accurate Chest X-Ray Report Generation
The automatic generation of radiology reports given medical radiographs has
significant potential to operationally and improve clinical patient care. A
number of prior works have focused on this problem, employing advanced methods
from computer vision and natural language generation to produce readable
reports. However, these works often fail to account for the particular nuances
of the radiology domain, and, in particular, the critical importance of
clinical accuracy in the resulting generated reports. In this work, we present
a domain-aware automatic chest X-ray radiology report generation system which
first predicts what topics will be discussed in the report, then conditionally
generates sentences corresponding to these topics. The resulting system is
fine-tuned using reinforcement learning, considering both readability and
clinical accuracy, as assessed by the proposed Clinically Coherent Reward. We
verify this system on two datasets, Open-I and MIMIC-CXR, and demonstrate that
our model offers marked improvements on both language generation metrics and
CheXpert assessed accuracy over a variety of competitive baselines
Medical Image Generation using Generative Adversarial Networks
Generative adversarial networks (GANs) are unsupervised Deep Learning
approach in the computer vision community which has gained significant
attention from the last few years in identifying the internal structure of
multimodal medical imaging data. The adversarial network simultaneously
generates realistic medical images and corresponding annotations, which proven
to be useful in many cases such as image augmentation, image registration,
medical image generation, image reconstruction, and image-to-image translation.
These properties bring the attention of the researcher in the field of medical
image analysis and we are witness of rapid adaption in many novel and
traditional applications. This chapter provides state-of-the-art progress in
GANs-based clinical application in medical image generation, and cross-modality
synthesis. The various framework of GANs which gained popularity in the
interpretation of medical images, such as Deep Convolutional GAN (DCGAN),
Laplacian GAN (LAPGAN), pix2pix, CycleGAN, and unsupervised image-to-image
translation model (UNIT), continue to improve their performance by
incorporating additional hybrid architecture, has been discussed. Further, some
of the recent applications of these frameworks for image reconstruction, and
synthesis, and future research directions in the area have been covered.Comment: 19 pages, 3 figures, 5 table
The Use of Autoencoders for Discovering Patient Phenotypes
We use autoencoders to create low-dimensional embeddings of underlying
patient phenotypes that we hypothesize are a governing factor in determining
how different patients will react to different interventions. We compare the
performance of autoencoders that take fixed length sequences of concatenated
timesteps as input with a recurrent sequence-to-sequence autoencoder. We
evaluate our methods on around 35,500 patients from the latest MIMIC III
dataset from Beth Israel Deaconess Hospital
Generative Adversarial Network in Medical Imaging: A Review
Generative adversarial networks have gained a lot of attention in the
computer vision community due to their capability of data generation without
explicitly modelling the probability density function. The adversarial loss
brought by the discriminator provides a clever way of incorporating unlabeled
samples into training and imposing higher order consistency. This has proven to
be useful in many cases, such as domain adaptation, data augmentation, and
image-to-image translation. These properties have attracted researchers in the
medical imaging community, and we have seen rapid adoption in many traditional
and novel applications, such as image reconstruction, segmentation, detection,
classification, and cross-modality synthesis. Based on our observations, this
trend will continue and we therefore conducted a review of recent advances in
medical imaging using the adversarial training scheme with the hope of
benefiting researchers interested in this technique.Comment: 24 pages; v4; added missing references from before Jan 1st 2019;
accepted to MedI
Application of Deep Learning on Predicting Prognosis of Acute Myeloid Leukemia with Cytogenetics, Age, and Mutations
We explore how Deep Learning (DL) can be utilized to predict prognosis of
acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database,
94 AML cases are used in this study. Input data include age, 10 common
cytogenetic and 23 most common mutation results; output is the prognosis
(diagnosis to death, DTD). In our DL network, autoencoders are stacked to form
a hierarchical DL model from which raw data are compressed and organized and
high-level features are extracted. The network is written in R language and is
designed to predict prognosis of AML for a given case (DTD of more than or less
than 730 days). The DL network achieves an excellent accuracy of 83% in
predicting prognosis. As a proof-of-concept study, our preliminary results
demonstrate a practical application of DL in future practice of prognostic
prediction using next-gen sequencing (NGS) data.Comment: 11 pages, 1 table, 1 figure. arXiv admin note: substantial text
overlap with arXiv:1801.0101
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
The rapid growth of Electronic Health Records (EHRs), as well as the
accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting
widespread interests and attentions. Recent progress in the design and
applications of deep learning methods has shown promising results and is
forcing massive changes in healthcare academia and industry, but most of these
methods rely on massive labeled data. In this work, we propose a general deep
learning framework which is able to boost risk prediction performance with
limited EHR data. Our model takes a modified generative adversarial network
namely ehrGAN, which can provide plausible labeled EHR data by mimicking real
patient records, to augment the training dataset in a semi-supervised learning
manner. We use this generative model together with a convolutional neural
network (CNN) based prediction model to improve the onset prediction
performance. Experiments on two real healthcare datasets demonstrate that our
proposed framework produces realistic data samples and achieves significant
improvements on classification tasks with the generated data over several
stat-of-the-art baselines.Comment: To appear in ICDM 2017. This is the full version of paper with 8
page
Large-scale Hierarchical Alignment for Data-driven Text Rewriting
We propose a simple unsupervised method for extracting pseudo-parallel
monolingual sentence pairs from comparable corpora representative of two
different text styles, such as news articles and scientific papers. Our
approach does not require a seed parallel corpus, but instead relies solely on
hierarchical search over pre-trained embeddings of documents and sentences. We
demonstrate the effectiveness of our method through automatic and extrinsic
evaluation on text simplification from the normal to the Simple Wikipedia. We
show that pseudo-parallel sentences extracted with our method not only
supplement existing parallel data, but can even lead to competitive performance
on their own.Comment: RANLP 201
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