With the wide application of electronic health records (EHR) in healthcare
facilities, health event prediction with deep learning has gained more and more
attention. A common feature of EHR data used for deep-learning-based
predictions is historical diagnoses. Existing work mainly regards a diagnosis
as an independent disease and does not consider clinical relations among
diseases in a visit. Many machine learning approaches assume disease
representations are static in different visits of a patient. However, in real
practice, multiple diseases that are frequently diagnosed at the same time
reflect hidden patterns that are conducive to prognosis. Moreover, the
development of a disease is not static since some diseases can emerge or
disappear and show various symptoms in different visits of a patient. To
effectively utilize this combinational disease information and explore the
dynamics of diseases, we propose a novel context-aware learning framework using
transition functions on dynamic disease graphs. Specifically, we construct a
global disease co-occurrence graph with multiple node properties for disease
combinations. We design dynamic subgraphs for each patient's visit to leverage
global and local contexts. We further define three diagnosis roles in each
visit based on the variation of node properties to model disease transition
processes. Experimental results on two real-world EHR datasets show that the
proposed model outperforms state of the art in predicting health events.

使用动态疾病图中的转移函数构建全局疾病共现图，为多疾病组合提供上下文敏感的学习框架，从而在电子健康记录数据上实现有效的健康事件预测，取得了比现有技术更好的实验结果。

基于动态疾病图上的转移函数的上下文感知健康事件预测

Context-aware Health Event Prediction via Transition Functions on  Dynamic Disease Graphs

Visual grounding, which aims to build a correspondence between visual objects
and their language entities, plays a key role in cross-modal scene
understanding. One promising and scalable strategy for learning visual
grounding is to utilize weak supervision from only image-caption pairs.
Previous methods typically rely on matching query phrases directly to a
precomputed, fixed object candidate pool, which leads to inaccurate
localization and ambiguous matching due to lack of semantic relation
constraints.
In our paper, we propose a novel context-aware weakly-supervised learning
method that incorporates coarse-to-fine object refinement and entity relation
modeling into a two-stage deep network, capable of producing more accurate
object representation and matching. To effectively train our network, we
introduce a self-taught regression loss for the proposal locations and a
classification loss based on parsed entity relations.
Extensive experiments on two public benchmarks Flickr30K Entities and
ReferItGame demonstrate the efficacy of our weakly grounding framework. The
results show that we outperform the previous methods by a considerable margin,
achieving 59.27\% top-1 accuracy in Flickr30K Entities and 37.68\% in the
ReferItGame dataset respectively (Code is available at
this https URL).

本研究提出了一种新的上下文感知弱监督学习方法，它将粗到细的物体细化和实体关系建模结合到一个两阶段深度网络中，以更准确地表述和匹配物体，并通过自我学习回归和关系分析来有效训练必要的分类方法。在 Flickr30K 和 ReferItGame 数据集上的广泛实验表明，本文所提出的弱强化框架比以前的方法具有更好的算法性能，Flickr30K 实体和 ReferItGame 数据集上的 Top-1 准确度分别达到 59.27％和 37.68％。