Evidence suggests that different prompts lead large language models (LLMs) to
generate responses with varying quality. Yet, little is known about prompts'
effects on response quality in healthcare domains. In this exploratory study,
we address this gap, focusing on a specific healthcare domain: dementia
caregiving. We first developed an innovative prompt template with three
components: (1) system prompts (SPs) featuring 4 different roles; (2) an
initialization prompt; and (3) task prompts (TPs) specifying different levels
of details, totaling 12 prompt combinations. Next, we selected 3 social media
posts containing complicated, real-world questions about dementia caregivers'
challenges in 3 areas: memory loss and confusion, aggression, and driving. We
then entered these posts into GPT-4, with our 12 prompts, to generate 12
responses per post, totaling 36 responses. We compared the word count of the 36
responses to explore potential differences in response length. Two experienced
dementia care clinicians on our team assessed the response quality using a
rating scale with 5 quality indicators: factual, interpretation, application,
synthesis, and comprehensiveness (scoring range: 0-5; higher scores indicate
higher quality).

通过使用 12 个提示组合在 GPT-4 中生成 36 个响应来比较 36 个响应的字数，本研究旨在探索不同提示对医疗保健领域中响应质量的影响，关注特定的医疗保健领域：痴呆症护理。

不同提示对 GPT-4 回答痴呆照料问题质量的影响

Effects of Different Prompts on the Quality of GPT-4 Responses to  Dementia Care Questions

We propose and study Collpasing Bandits, a new restless multi-armed bandit
(RMAB) setting in which each arm follows a binary-state Markovian process with
a special structure: when an arm is played, the state is fully observed, thus
"collapsing" any uncertainty, but when an arm is passive, no observation is
made, thus allowing uncertainty to evolve. The goal is to keep as many arms in
the "good" state as possible by planning a limited budget of actions per round.
Such Collapsing Bandits are natural models for many healthcare domains in which
workers must simultaneously monitor patients and deliver interventions in a way
that maximizes the health of their patient cohort. Our main contributions are
as follows: (i) Building on the Whittle index technique for RMABs, we derive
conditions under which the Collapsing Bandits problem is indexable. Our
derivation hinges on novel conditions that characterize when the optimal
policies may take the form of either "forward" or "reverse" threshold policies.
(ii) We exploit the optimality of threshold policies to build fast algorithms
for computing the Whittle index, including a closed-form. (iii) We evaluate our
algorithm on several data distributions including data from a real-world
healthcare task in which a worker must monitor and deliver interventions to
maximize their patients' adherence to tuberculosis medication. Our algorithm
achieves a 3-order-of-magnitude speedup compared to state-of-the-art RMAB
techniques while achieving similar performance.

提出了一种新的无休止多臂赌博机设置 ——Collapsing Bandits，并在医疗保健领域中实现了应用，该算法使用何维特指数技术和阈值策略来优化可观察状态数量较少、不可观察状态数量较多的多臂赌博机问题，评估了算法在多种数据分布上的性能。

折叠赌博算法及其在公共卫生干预中的应用

Collapsing Bandits and Their Application to Public Health Interventions

As a subfield of machine learning, reinforcement learning (RL) aims at
empowering one's capabilities in behavioural decision making by using
interaction experience with the world and an evaluative feedback. Unlike
traditional supervised learning methods that usually rely on one-shot,
exhaustive and supervised reward signals, RL tackles with sequential decision
making problems with sampled, evaluative and delayed feedback simultaneously.
Such distinctive features make RL technique a suitable candidate for developing
powerful solutions in a variety of healthcare domains, where diagnosing
decisions or treatment regimes are usually characterized by a prolonged and
sequential procedure. This survey discusses the broad applications of RL
techniques in healthcare domains, in order to provide the research community
with systematic understanding of theoretical foundations, enabling methods and
techniques, existing challenges, and new insights of this emerging paradigm. By
first briefly examining theoretical foundations and key techniques in RL
research from efficient and representational directions, we then provide an
overview of RL applications in healthcare domains ranging from dynamic
treatment regimes in chronic diseases and critical care, automated medical
diagnosis from both unstructured and structured clinical data, as well as many
other control or scheduling domains that have infiltrated many aspects of a
healthcare system. Finally, we summarize the challenges and open issues in
current research, and point out some potential solutions and directions for
future research.

本文综述了加强学习在医疗领域的广泛应用，包括慢性病和危重护理中的动态治疗方案、基于结构化和非结构化临床数据的自动医学诊断以及许多其他控制或调度领域。其中讨论了当前研究中面临的挑战和开放问题，并指出了一些潜在的解决方案和未来研究方向。