This work compares supervised machine learning methods using reliable data
from constructive simulations to estimate the most effective moment for
launching missiles during air combat. We employed resampling techniques to
improve the predictive model, analyzing accuracy, precision, recall, and
f1-score. Indeed, we could identify the remarkable performance of the models
based on decision trees and the significant sensitivity of other algorithms to
resampling techniques. The models with the best f1-score brought values of
0.379 and 0.465 without and with the resampling technique, respectively, which
is an increase of 22.69%. Thus, if desirable, resampling techniques can improve
the model's recall and f1-score with a slight decline in accuracy and
precision. Therefore, through data obtained through constructive simulations,
it is possible to develop decision support tools based on machine learning
models, which may improve the flight quality in BVR air combat, increasing the
effectiveness of offensive missions to hit a particular target.

本研究基于可靠的建模模拟数据，比较了监督机器学习方法在估算在空中战斗期间发射导弹的最有效时刻的能力，并采用重新采样技术提高预测模型，发现基于决策树的模型性能显着更优越，而其他算法对重新采样技术具有显著灵敏度。获得最佳 f1-score 的模型在没有和使用重新采样技术时的值分别为 0.379 和 0.465，后者增长了 22.69%。因此，通过构建模拟数据可以开发基于机器学习模型的决策支持工具，从而提高空战中的飞行质量，增加攻击特定目标的有效性。

基于超视距空战模拟的导弹发射有效监督机器学习

Supervised Machine Learning for Effective Missile Launch Based on Beyond Visual Range Air Combat Simulations

Major depressive disorder is a debilitating disease affecting 264 million
people worldwide. While many antidepressant medications are available, few
clinical guidelines support choosing among them. Decision support tools (DSTs)
embodying machine learning models may help improve the treatment selection
process, but often fail in clinical practice due to poor system integration.
We use an iterative, co-design process to investigate clinicians' perceptions
of using DSTs in antidepressant treatment decisions. We identify ways in which
DSTs need to engage with the healthcare sociotechnical system, including
clinical processes, patient preferences, resource constraints, and domain
knowledge. Our results suggest that clinical DSTs should be designed as
multi-user systems that support patient-provider collaboration and offer
on-demand explanations that address discrepancies between predictions and
current standards of care. Through this work, we demonstrate how current trends
in explainable AI may be inappropriate for clinical environments and consider
paths towards designing these tools for real-world medical systems.

研究利用决策辅助工具（DSTs）的机器学习模型来帮助抗抑郁药物的选择，但常常由于系统集成不良而在临床实践中失败。本研究通过迭代的共同设计过程探讨了临床医生使用 DSTs 进行抗抑郁药物治疗决策的感知，指出 DSTs 需要与医疗保健社会技术系统进行有效整合。结果表明，DSTs 应设计为支持患者 - 提供者协作的多用户系统，并提供按需解释，以解决预测和当前护理标准之间的差异的方式。该研究还考虑了如何在临床环境中设计 DSTs，以及设想中的潜在问题和挑战。