While machine learning (ML) includes a valuable array of tools for analyzing
biomedical data, significant time and expertise is required to assemble
effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek
to facilitate ML application by automating a subset of analysis pipeline
elements. In this study we develop and validate a Simple, Transparent,
End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to
investigate the added utility of photography-based phenotypes for predicting
obstructive sleep apnea (OSA); a common and underdiagnosed condition associated
with a variety of health, economic, and safety consequences. STREAMLINE is
designed to tackle biomedical binary classification tasks while adhering to
best practices and accommodating complexity, scalability, reproducibility,
customization, and model interpretation. Benchmarking analyses validated the
efficacy of STREAMLINE across data simulations with increasingly complex
patterns of association. Then we applied STREAMLINE to evaluate the utility of
demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and
photography-based craniofacial (CF) and intraoral (IO) anatomy measures in
predicting any OSA or moderate/severe OSA using 3,111 participants from Sleep
Apnea Global Interdisciplinary Consortium (SAGIC). OSA analyses identified a
significant increase in ROC-AUC when adding CF to DEM+DX+SYM to predict
moderate/severe OSA. A consistent but non-significant increase in PRC-AUC was
observed with the addition of each subsequent feature set to predict any OSA,
with CF and IO yielding minimal improvements. Application of STREAMLINE to OSA
data suggests that CF features provide additional value in predicting
moderate/severe OSA, but neither CF nor IO features meaningfully improved the
prediction of any OSA beyond established demographics, comorbidity and symptom
characteristics.

使用 STREAMLINE 方法，这项研究探索了基于摄影的表型特征在预测阻塞性睡眠呼吸暂停（OSA）中的应用价值，结果发现摄影特征在预测中重度 / 严重 OSA 方面提供了额外的价值，但没有超越传统人口统计学、并发症和症状特征在预测 OSA 中的效果。

STREAMLINE：一个面向生物医学的自动机器学习流水线，应用于检验基于摄影特征对 OSA 预测在国际睡眠中心的实用性

STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine  Applied to Examine the Utility of Photography-Based Phenotypes for OSA  Prediction Across International Sleep Centers

The Zero-touch network and Service Management (ZSM) framework represents an
emerging paradigm in the management of the fifth-generation (5G) and Beyond
(5G+) networks, offering automated self-management and self-healing
capabilities to address the escalating complexity and the growing data volume
of modern networks. ZSM frameworks leverage advanced technologies such as
Machine Learning (ML) to enable intelligent decision-making and reduce human
intervention. This paper presents a comprehensive survey of Zero-Touch Networks
(ZTNs) within the ZSM framework, covering network optimization, traffic
monitoring, energy efficiency, and security aspects of next-generational
networks. The paper explores the challenges associated with ZSM, particularly
those related to ML, which necessitate the need to explore diverse network
automation solutions. In this context, the study investigates the application
of Automated ML (AutoML) in ZTNs, to reduce network management costs and
enhance performance. AutoML automates the selection and tuning process of a ML
model for a given task. Specifically, the focus is on AutoML's ability to
predict application throughput and autonomously adapt to data drift.
Experimental results demonstrate the superiority of the proposed AutoML
pipeline over traditional ML in terms of prediction accuracy. Integrating
AutoML and ZSM concepts significantly reduces network configuration and
management efforts, allowing operators to allocate more time and resources to
other important tasks. The paper also provides a high-level 5G system
architecture incorporating AutoML and ZSM concepts. This research highlights
the potential of ZTNs and AutoML to revolutionize the management of 5G+
networks, enabling automated decision-making and empowering network operators
to achieve higher efficiency, improved performance, and enhanced user
experience.

零触式网络和服务管理（ZSM）框架是第五代（5G）及其之后（5G+）网络管理的新兴范式，通过自动化的自管理和自愈能力应对现代网络的日益复杂和不断增长的数据量。该研究通过综述零触式网络（ZTNs）的领域，探讨了 ZSM 框架中的网络优化、流量监控、能源效率和安全性等方面。研究发现，将自动化机器学习（AutoML）应用于 ZTNs 中，可以降低网络管理成本并提高性能。实验结果表明，AutoML 对于应用吞吐量的预测具有卓越的准确性。将 AutoML 与 ZSM 概念集成，显著降低了网络配置和管理工作量，使运营商能够更多地投入时间和资源到其他重要任务。该研究还提供了一个集成 AutoML 和 ZSM 概念的高层 5G 系统架构。研究突显了 ZTNs 和 AutoML 在改进 5G + 网络管理方面的潜力，实现自动化决策，并使网络运营商能够实现更高的效率、更好的性能和更优化的用户体验。

零触碰网络：迈向下一代网络自动化

Zero-Touch Networks: Towards Next-Generation Network Automation

Developing smart software services requires both Software Engineering and
Artificial Intelligence (AI) skills. AI practitioners, such as data scientists
often focus on the AI side, for example, creating and training Machine Learning
(ML) models given a specific use case and data. They are typically not
concerned with the entire software development life-cycle, architectural
decisions for the system and performance issues beyond the predictive ML models
(e.g., regarding the security, privacy, throughput, scalability, availability,
as well as ethical, legal and regulatory compliance). In this manuscript, we
propose a novel approach to enable Model-Driven Software Engineering and
Model-Driven AI Engineering. In particular, we support Automated ML, thus
assisting software engineers without deep AI knowledge in developing
AI-intensive systems by choosing the most appropriate ML model, algorithm and
techniques with suitable hyper-parameters for the task at hand. To validate our
work, we carry out a case study in the smart energy domain.

本文提出了一种新方法，支持自动化机器学习，帮助软件工程师在不需要深入了解人工智能的情况下，选择适当的机器学习模型、算法和技术以及适当的超参数来开发人工智能密集型系统，并进行智能能源领域的案例研究。