Semi-structured interviews (SSIs) are a commonly employed data-collection
method in healthcare research, offering in-depth qualitative insights into
subject experiences. Despite their value, the manual analysis of SSIs is
notoriously time-consuming and labor-intensive, in part due to the difficulty
of extracting and categorizing emotional responses, and challenges in scaling
human evaluation for large populations. In this study, we develop RACER, a
Large Language Model (LLM) based expert-guided automated pipeline that
efficiently converts raw interview transcripts into insightful domain-relevant
themes and sub-themes. We used RACER to analyze SSIs conducted with 93
healthcare professionals and trainees to assess the broad personal and
professional mental health impacts of the COVID-19 crisis. RACER achieves
moderately high agreement with two human evaluators (72%), which approaches the
human inter-rater agreement (77%). Interestingly, LLMs and humans struggle with
similar content involving nuanced emotional, ambivalent/dialectical, and
psychological statements. Our study highlights the opportunities and challenges
in using LLMs to improve research efficiency and opens new avenues for scalable
analysis of SSIs in healthcare research.

本研究通过使用 RACER，一种基于大型语言模型的专业指导自动化流程，有效地将原始访谈记录转化为有洞察力的领域相关主题和子主题。这项研究强调了使用大型语言模型提高研究效率的机遇和挑战，为医疗保健研究中可扩展的 SSIs 分析开辟了新的途径。

RACER：一种基于 LLM 的可扩展半结构化心理健康访谈分析方法

RACER: An LLM-powered Methodology for Scalable Analysis of  Semi-structured Mental Health Interviews

This paper introduces RACER, the Rational Artificial Intelligence
Car-following model Enhanced by Reality, a cutting-edge deep learning
car-following model, that satisfies partial derivative constraints, designed to
predict Adaptive Cruise Control (ACC) driving behavior while staying
theoretically feasible. Unlike conventional models, RACER effectively
integrates Rational Driving Constraints (RDCs), crucial tenets of actual
driving, resulting in strikingly accurate and realistic predictions. Against
established models like the Optimal Velocity Relative Velocity (OVRV), a
car-following Neural Network (NN), and a car-following Physics-Informed Neural
Network (PINN), RACER excels across key metrics, such as acceleration,
velocity, and spacing. Notably, it displays a perfect adherence to the RDCs,
registering zero violations, in stark contrast to other models. This study
highlights the immense value of incorporating physical constraints within AI
models, especially for augmenting safety measures in transportation. It also
paves the way for future research to test these models against human driving
data, with the potential to guide safer and more rational driving behavior. The
versatility of the proposed model, including its potential to incorporate
additional derivative constraints and broader architectural applications,
enhances its appeal and broadens its impact within the scientific community.

RACER 是一种基于深度学习的创新型汽车跟驰模型，它满足导数约束条件，旨在预测自适应巡航控制（ACC）的驾驶行为，同时保持理论可行性。与传统模型不同，RACER 有效地整合了实际驾驶的理性驾驶约束，从而实现了惊人准确和逼真的预测。与 Optimal Velocity Relative Velocity（OVRV）、汽车跟驰神经网络（NN）和物理约束沙龙网络（PINN）等已建立的模型相比，RACER 在加速度、速度和间距等关键指标上表现出色。值得注意的是，RACER 完全遵循理性驾驶约束，零违规，与其他模型形成鲜明对比。本研究强调了将物理约束纳入人工智能模型中的巨大价值，特别是为增强交通安全措施而助力。它也为未来的研究提供了可能，以测试这些模型与人类驾驶数据的匹配度，有望引导更安全和更理性的驾驶行为。所提出模型的通用性，以及它吸引力的提高和在科学界的影响的拓宽，还包括了其潜在的能够整合额外导数约束和更广泛的体系结构应用。