More than 5 million children under five years die from largely preventable or
treatable medical conditions every year, with an overwhelmingly large
proportion of deaths occurring in underdeveloped countries with low vaccination
uptake. One of the United Nations' sustainable development goals (SDG 3) aims
to end preventable deaths of newborns and children under five years of age. We
focus on Nigeria, where the rate of infant mortality is appalling. In
particular, low vaccination uptake in Nigeria is a major driver of more than
2,000 daily deaths of children under the age of five years. In this paper, we
describe our collaboration with government partners in Nigeria to deploy
ADVISER: AI-Driven Vaccination Intervention Optimiser. The framework, based on
an integer linear program that seeks to maximize the cumulative probability of
successful vaccination, is the first successful deployment of an AI-enabled
toolchain for optimizing the allocation of health interventions in Nigeria. In
this paper, we provide a background of the ADVISER framework and present
results, lessons, and success stories of deploying ADVISER to more than 13,000
families in the state of Oyo, Nigeria.

在尼日利亚部署 ADVISER 框架以优化卫生干预方法，通过首次成功使用 AI 技术的工具链来最大程度地提高成功接种的累积概率，向尼日利亚奥约州的超过 13,000 个家庭提供服务。

在尼日利亚使用人工智能提高儿童疫苗接种率：部署 ADVISER 的影响和经验教训

Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence  for Child Vaccination Uptake in Nigeria

We describe an AI-powered chatbot to aid with health improvement by
generating Episodic Future Thinking (EFT) cue texts that should reduce delay
discounting. In prior studies, EFT has been shown to address maladaptive health
behaviors. Those studies involved participants, working with researchers,
vividly imagining future events, and writing a description that they
subsequently will frequently review, to ensure a shift from an inclination
towards immediate rewards. That should promote behavior change, aiding in
health tasks such as treatment adherence and lifestyle modifications. The AI
chatbot is designed to guide users in generating personalized EFTs, automating
the current labor-intensive interview-based process. This can enhance the
efficiency of EFT interventions and make them more accessible, targeting
specifically those with limited educational backgrounds or communication
challenges. By leveraging AI for EFT intervention, we anticipate broadened
access and improved health outcomes across diverse populations

通过 AI 聊天机器人生成未来情景思考（Episodic Future Thinking）提醒文本来降低延迟贴现，在健康改善方面进行协助，促进行为改变，提高健康干预效率并扩大受众群体。

为健康生成情节未来思考（EFT）提示文本的 AI 聊天机器人

AI Chatbot for Generating Episodic Future Thinking (EFT) Cue Texts for  Health

More than 5 million children under five years die from largely preventable or
treatable medical conditions every year, with an overwhelmingly large
proportion of deaths occurring in under-developed countries with low
vaccination uptake. One of the United Nations' sustainable development goals
(SDG 3) aims to end preventable deaths of newborns and children under five
years of age. We focus on Nigeria, where the rate of infant mortality is
appalling. We collaborate with HelpMum, a large non-profit organization in
Nigeria to design and optimize the allocation of heterogeneous health
interventions under uncertainty to increase vaccination uptake, the first such
collaboration in Nigeria. Our framework, ADVISER: AI-Driven Vaccination
Intervention Optimiser, is based on an integer linear program that seeks to
maximize the cumulative probability of successful vaccination. Our optimization
formulation is intractable in practice. We present a heuristic approach that
enables us to solve the problem for real-world use-cases. We also present
theoretical bounds for the heuristic method. Finally, we show that the proposed
approach outperforms baseline methods in terms of vaccination uptake through
experimental evaluation. HelpMum is currently planning a pilot program based on
our approach to be deployed in the largest city of Nigeria, which would be the
first deployment of an AI-driven vaccination uptake program in the country and
hopefully, pave the way for other data-driven programs to improve health
outcomes in Nigeria.

我们与尼日利亚的 HelpMum 合作，研发和优化各种卫生干预措施的分配，以增加疫苗接种率，并将其用于一个基于整数线性规划的框架 ADVISER 中，它旨在最大化成功接种的累积概率。我们提出的启发式方法在实验评估中优于基准方法，为 Nigeria 提高健康状况的数据驱动计划开辟了道路。

ADVISER: 基于人工智能的疫苗接种干预优化器，旨在提高尼日利亚的疫苗接种率

ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria

Storytelling is fundamental to language, including culture, conversation and
communication in their broadest senses. It thus emerges as an essential
component of intelligent systems, including systems where natural language is
not a primary focus or where we do not usually think of a story being involved.
In this paper we explore the emergence of storytelling as a requirement in
embodied conversational agents, including its role in educational and health
interventions, as well as in a general-purpose computer interface for people
with disabilities or other constraints that prevent the use of traditional
keyboard and speech interfaces. We further present a characterization of
storytelling as an inventive fleshing out of detail according to a particular
personal perspective, and propose the DREAMT model to focus attention on the
different layers that need to be present in a character-driven storytelling
system. Most if not all aspects of the DREAMT model have arisen from or been
explored in some aspect of our implemented research systems, but currently only
at a primitive and relatively unintegrated level. However, this experience
leads us to formalize and elaborate the DREAMT model mnemonically as follows: -
Description/Dialogue/Definition/Denotation - Realization/Representation/Role -
Explanation/Education/Entertainment - Actualization/Activation -
Motivation/Modelling - Topicalization/Transformation

本文探讨了故事讲述在具有身体体验的对话系统、教育和健康干预以及计算机用户接口中的作用，提出了 DREAMT 模型以便更好地实现个性化的故事讲述。