Existing studies on using social media for deriving mental health status of
users focus on the depression detection task. However, for case management and
referral to psychiatrists, healthcare workers require practical and scalable
depressive disorder screening and triage system. This study aims to design and
evaluate a decision support system (DSS) to reliably determine the depressive
triage level by capturing fine-grained depressive symptoms expressed in user
tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is
routinely used in clinical practice. The reliable detection of depressive
symptoms from tweets is challenging because the 280-character limit on tweets
incentivizes the use of creative artifacts in the utterances and figurative
usage contributes to effective expression. We propose a novel BERT based robust
multi-task learning framework to accurately identify the depressive symptoms
using the auxiliary task of figurative usage detection. Specifically, our
proposed novel task sharing mechanism, co-task aware attention, enables
automatic selection of optimal information across the BERT layers and tasks by
soft-sharing of parameters. Our results show that modeling figurative usage can
demonstrably improve the model's robustness and reliability for distinguishing
the depression symptoms.

本研究旨在设计和评估一种决策支持系统（DSS），通过模拟在临床实践中常用的患者健康问卷 - 9（PHQ-9）来捕获用户推文中表达的细粒度抑郁症状，从而可靠地确定抑郁症状的分级。研究结果表明，建模比喻用法可以显著提高模型的鲁棒性和可靠性，以区分抑郁症状。

基于比喻语言的多任务学习框架识别推文中的抑郁症状

Identifying Depressive Symptoms from Tweets: Figurative Language Enabled  Multitask Learning Framework

We address a challenging and practical task of labeling questions in speech
in real time during telephone calls to emergency medical services in English,
which embeds within a broader decision support system for emergency
call-takers. We propose a novel multimodal approach to real-time sequence
labeling in speech. Our model treats speech and its own textual representation
as two separate modalities or views, as it jointly learns from streamed audio
and its noisy transcription into text via automatic speech recognition. Our
results show significant gains of jointly learning from the two modalities when
compared to text or audio only, under adverse noise and limited volume of
training data. The results generalize to medical symptoms detection where we
observe a similar pattern of improvements with multimodal learning.

提出了一种新的多模态方法，将语音和其文本表示作为两个视图进行联合学习，从而实现语音实时序列标记。该模型应用于电话紧急医疗服务，作为紧急呼叫中心决策支持系统中的一部分，取得了显著的改进。

MultiQT: 实时问句跟踪的多模态学习技术

MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech

Associative classification is a recent and rewarding technique which
integrates association rule mining and classification to a model for prediction
and achieves maximum accuracy. Associative classifiers are especially fit to
applications where maximum accuracy is desired to a model for prediction. There
are many domains such as medical where the maximum accuracy of the model is
desired. Heart disease is a single largest cause of death in developed
countries and one of the main contributors to disease burden in developing
countries. Mortality data from the registrar general of India shows that heart
disease are a major cause of death in India, and in Andhra Pradesh coronary
heart disease cause about 30%of deaths in rural areas. Hence there is a need to
develop a decision support system for predicting heart disease of a patient. In
this paper we propose efficient associative classification algorithm using
genetic approach for heart disease prediction. The main motivation for using
genetic algorithm in the discovery of high level prediction rules is that the
discovered rules are highly comprehensible, having high predictive accuracy and
of high interestingness values. Experimental Results show that most of the
classifier rules help in the best prediction of heart disease which even helps
doctors in their diagnosis decisions.

本文提出一种使用遗传算法的高效关联分类算法，用于预测心脏病，实验结果表明所发现的分类器规则有助于预测心脏病，甚至帮助医生做出诊断决策。