In the field of health-care and bio-medical research, understanding the
relationship between the symptoms of diseases is crucial for early diagnosis
and determining hidden relationships between diseases. The study aimed to
understand the extent of symptom types in disease prediction tasks. In this
research, we analyze a pre-generated symptom-based human disease dataset and
demonstrate the degree of predictability for each disease based on the
Convolutional Neural Network and the Support Vector Machine. Ambiguity of
disease is studied using the K-Means and the Principal Component Analysis. Our
results indicate that machine learning can potentially diagnose diseases with
the 98-100% accuracy in the early stage, taking the characteristics of symptoms
into account. Our result highlights that types of unusual symptoms are a good
proxy for disease early identification accurately. We also highlight that
unusual symptoms increase the accuracy of the disease prediction task.

本研究分析通过卷积神经网络和支持向量机预测疾病，并使用 K-Means 和主成分分析研究了疾病的歧义性，结果表明机器学习可以在疾病早期阶段对疾病进行 98-100% 准确的诊断。异常症状是诊断疾病的良好代理，同时也提高了疾病预测任务的准确性。

基于卷积神经网络的人类疾病症状关系探索性分析

Explorative analysis of human disease-symptoms relations using the Convolutional Neural Network

Artificial intelligence (AI) has huge potential to improve the health and
well-being of people, but adoption in clinical practice is still limited. Lack
of transparency is identified as one of the main barriers to implementation, as
clinicians should be confident the AI system can be trusted. Explainable AI has
the potential to overcome this issue and can be a step towards trustworthy AI.
In this paper we review the recent literature to provide guidance to
researchers and practitioners on the design of explainable AI systems for the
health-care domain and contribute to formalization of the field of explainable
AI. We argue the reason to demand explainability determines what should be
explained as this determines the relative importance of the properties of
explainability (i.e. interpretability and fidelity). Based on this, we propose
a framework to guide the choice between classes of explainable AI methods
(explainable modelling versus post-hoc explanation; model-based,
attribution-based, or example-based explanations; global and local
explanations). Furthermore, we find that quantitative evaluation metrics, which
are important for objective standardized evaluation, are still lacking for some
properties (e.g. clarity) and types of explanations (e.g. example-based
methods). We conclude that explainable modelling can contribute to trustworthy
AI, but the benefits of explainability still need to be proven in practice and
complementary measures might be needed to create trustworthy AI in health care
(e.g. reporting data quality, performing extensive (external) validation, and
regulation).

本文回顾了近期文献，提出了可解释人工智能系统的设计指南以及可解释人工智能领域的规范化贡献。该文章提出了一个框架，以指导选择可解释人工智能方法的类别，进而解释重要属性的相对重要性。同时也发现，尽管定量评价指标对于客观标准化评估很重要，但对于某些属性（如 “清晰度”）和解释类型（如 “基于实例的方法”）仍然缺乏。最终的结论是，可解释建模可以为可信的人工智能做出贡献，但还需要在实践中证明可解释性的好处，并可能需要采取补充措施，例如报告数据质量，进行广泛（外部）验证和监管等措施。

可解释性在构建值得信赖的医疗人工智能中的作用：术语、设计选择和评估策略的综合调查

The role of explainability in creating trustworthy artificial  intelligence for health care: a comprehensive survey of the terminology,  design choices, and evaluation strategies

Emotion is intrinsic to humans and consequently emotion understanding is a
key part of human-like artificial intelligence (AI). Emotion recognition in
conversation (ERC) is becoming increasingly popular as a new research frontier
in natural language processing (NLP) due to its ability to mine opinions from
the plethora of publicly available conversational data in platforms such as
Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential
applications in health-care systems (as a tool for psychological analysis),
education (understanding student frustration) and more. Additionally, ERC is
also extremely important for generating emotion-aware dialogues that require an
understanding of the user's emotions. Catering to these needs calls for
effective and scalable conversational emotion-recognition algorithms. However,
it is a strenuous problem to solve because of several research challenges. In
this paper, we discuss these challenges and shed light on the recent research
in this field. We also describe the drawbacks of these approaches and discuss
the reasons why they fail to successfully overcome the research challenges in
ERC.

情感识别技术在人工智能领域中有着广泛的应用，并且在自然语言处理中被广泛研究。本文讨论了情感识别技术在对话中所面临的挑战和最近的研究进展，同时讨论了现有方法所存在的不足和失败原因。