Cardiovascular diseases (CVDs), the leading cause of death worldwide, can be
prevented in most cases through behavioral interventions. Therefore, effective
communication of CVD risk and projected risk reduction by risk factor
modification plays a crucial role in reducing CVD risk at the individual level.
However, despite interest in refining risk estimation with improved prediction
models such as SCORE2, the guidelines for presenting these risk estimations in
clinical practice remained essentially unchanged in the last few years, with
graphical score charts (GSCs) continuing to be one of the prevalent systems.
This work describes the design and implementation of Petal-X, a novel tool to
support clinician-patient shared decision-making by explaining the CVD risk
contributions of different factors and facilitating what-if analysis. Petal-X
relies on a novel visualization, Petal Product Plots, and a tailor-made global
surrogate model of SCORE2, whose fidelity is comparable to that of the GSCs
used in clinical practice. We evaluated Petal-X compared to GSCs in a
controlled experiment with 88 healthcare students, all but one with experience
with chronic patients. The results show that Petal-X outperforms GSC in
critical tasks, such as comparing the contribution to the patient's 10-year CVD
risk of each modifiable risk factor, without a significant loss of perceived
transparency, trust, or intent to use. Our study provides an innovative
approach to the visualization and explanation of risk in clinical practice
that, due to its model-agnostic nature, could continue to support
next-generation artificial intelligence risk assessment models.

通过行为干预可以预防心血管疾病（CVDs）作为全球死亡的主要原因，因此，在个体层面上减少 CVD 风险的有效沟通和风险因素改善预计风险的能力起着至关重要的作用。本研究描述了 Petla-X 的设计和实施，这是一个支持临床医生 - 患者共享决策的新工具，用于解释不同因素对 CVD 风险的贡献并促进假设分析。通过比较 GSCs，我们的实验结果表明，Petal-X 在关键任务中表现出色，且在透明度、信任度或使用意图方面没有明显损失。本研究提供了一种创新的临床实践风险可视化和解释方法，将支持下一代人工智能风险评估模型，其模型无关性质为其持续发展提供了可能性。

Petal-X：改进心血管风险沟通的以人为中心的视觉解释

Petal-X: Human-Centered Visual Explanations to Improve Cardiovascular  Risk Communication

We consider the problem of aligning a large language model (LLM) to model the
preferences of a human population. Modeling the beliefs, preferences, and
behaviors of a specific population can be useful for a variety of different
applications, such as conducting simulated focus groups for new products,
conducting virtual surveys, and testing behavioral interventions, especially
for interventions that are expensive, impractical, or unethical. Existing work
has had mixed success using LLMs to accurately model human behavior in
different contexts. We benchmark and evaluate two well-known fine-tuning
approaches and evaluate the resulting populations on their ability to match the
preferences of real human respondents on a survey of preferences for battery
electric vehicles (BEVs). We evaluate our models against their ability to match
population-wide statistics as well as their ability to match individual
responses, and we investigate the role of temperature in controlling the
trade-offs between these two. Additionally, we propose and evaluate a novel
loss term to improve model performance on responses that require a numeric
response.

对使用大型语言模型 (LLM) 来模拟人群的偏好进行了研究和评估，包括模拟焦点小组、虚拟调查和测试行为干预的应用，以及使用两种知名精调方法对电池电动汽车 (BEVs) 偏好调查的人群进行评估。同时，还提出并评估了一种新的损失项以改善对需要数字回答的问题的模型性能。

使用 LLMs 模拟目标人群的信念和偏好

Using LLMs to Model the Beliefs and Preferences of Targeted Populations

Obesity is a critical healthcare issue affecting the United States. The least
risky treatments available for obesity are behavioral interventions meant to
promote diet and exercise. Often these interventions contain a mobile component
that allows interventionists to collect participants level data and provide
participants with incentives and goals to promote long term behavioral change.
Recently, there has been interest in using direct financial incentives to
promote behavior change. However, adherence is challenging in these
interventions, as each participant will react differently to different
incentive structure and amounts, leading researchers to consider personalized
interventions. The key challenge for personalization, is that the clinicians do
not know a priori how best to administer incentives to participants, and given
finite intervention budgets how to disburse costly resources efficiently. In
this paper, we consider this challenge of designing personalized weight loss
interventions that use direct financial incentives to motivate weight loss
while remaining within a budget. We create a machine learning approach that is
able to predict how individuals may react to different incentive schedules
within the context of a behavioral intervention. We use this predictive model
in an adaptive framework that over the course of the intervention computes what
incentives to disburse to participants and remain within the study budget. We
provide both theoretical guarantees for our modeling and optimization
approaches as well as demonstrate their performance in a simulated weight loss
study. Our results highlight the cost efficiency and effectiveness of our
personalized intervention design for weight loss.

本篇研究论文考虑如何设计个性化的减肥干预措施，使用直接财务激励措施促进体重减轻但保持预算不超支，针对这一挑战，我们创建了一种机器学习方法，能够预测个体对不同激励计划的反应，并将该预测模型用于自适应框架中，以计算在干预期间向参与者提供哪些激励，并保持研究预算。