ML Basis for Intelligence Augmentation:Toward Personalized Modeling, Reasoning under Data-Knowledge Symbiosis, and Interpretable Interaction for AI-assisted Human Decision-making

今天人们所做的许多工作--在医疗保健、商业、科学企业和军事行动中--都是在团队中完成的。团队中的协作决策是一个复杂而具有挑战性的过程，需要整合、理解和处理不同类型的信息。该项目旨在推进人工智能和机器学习作为智能增强（IA）工具的使用，以促进和改善临床团队的协作决策，重点是AI辅助诊断和治疗。研究人员的关注点反映了IA在医疗保健中的实际重要性和影响，特别是在与大流行的持续斗争中，医疗决策的效率，有效性和成本效益至关重要。然而，所提出的方法将适用于IA的其他形式和用例，例如政策制定，公共卫生响应，情报和商业运营，最终促进国家健康，繁荣，虽然现代机器学习研究已经广泛参与解决基于广泛数据的各种模式发现和识别任务-无论是以完全自主的方式还是以基本的人类-人工智能协作环境，如众包，有效地增强和协助诊断，治疗，规划，物流领域的复杂协作人类决策工作仍然是一个开放的挑战。在临床决策中，理解和治疗疾病必须依赖于大量的知识和专业知识，并基于来自不同信息来源的证据，从文本（病史）到图像（放射照片），再到时间序列数据（生命体征）。理解这种多模态信息需要在临床团队中进行有效的沟通和协作。研究人员建议研究IA机器学习中的一些关键技术挑战：（1）对人类决策过程进行建模;（2）将背景知识纳入数据驱动系统;（3）为团队间和团队内的高效协作构建人机界面。为此，研究人员将：（1）开发一个基于对个体决策者建模的机器学习框架，该框架能够准确检测医疗诊断中的错误，并可用作协作决策环境中的推荐引擎;（2）开发整合客观医学知识的原则性策略（例如，从快速增长的医学文献中自动提取），具有医疗专业人员团队的临床经验和专业知识;（3）设计人类可理解的界面，使团队内部和团队之间的决策能够有效沟通，包括解释模型如何得出每个建议决策的新工具，以及可以促进人类-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Much of the work people do today—in healthcare, business, scientific enterprises, and military operations—is performed in teams. Collaborative decision-making effort within a team is a complex and challenging process of integrating, understanding, and acting upon different types of information. This project aims to advance the use of artificial intelligence and machine learning as intelligence augmentation (IA) tools for facilitating and improving collaborative decision making in clinical teams, focusing on AI-assisted diagnosis and treatment. The focus of the investigators reflects the practical importance and impact of IA in healthcare, especially in the on-going fight with the pandemic where efficiency, validity, and cost-effectiveness of medical decision-making is critical. However, the proposed methods will apply to other forms and use-cases of IA, such as policy making, public health responses, intelligence and business operations, ultimately advancing national health, prosperity, and welfare.Although modern machine learning research has been widely involved in solving various pattern discovery and recognition tasks based on a wide spectrum of data—either in a fully autonomous fashion or in rudimentary human-AI collaborative settings such as crowdsourcing—effectively augmenting and assisting complex collaborative human decision-making efforts in the space of diagnosis, treatment, planning, logistics remains to be an open challenge. In clinical decision- making, understanding and treating the disease must rely on the vast knowledge and expertise and be based on evidence coming from heterogeneous sources of information, ranging from text (medical history), to imagery (radiograms), to time series data (vitals). Making sense of such multimodal information requires effective communication and collaboration within clinical teams. The investigators propose to study some of the key technical challenges in machine learning for IA: (1) modeling human decision-making processes; (2) incorporating background knowledge into data-driven systems; and (3) building human-AI interface for productive inter- and intra-team collaboration. To that end, the investigators will: (1) develop a machine learning framework based on modeling individual decision-makers that enables accurate detection of errors in medical diagnosis and can be used as a recommendation engine in collaborative decision-making settings; (2) develop principled strategies for integrating objective medical knowledge (e.g., automatically extracted from rapidly growing medical literature) with the clinical experience and expertise of a team of health professionals; (3) design human-interpretable interfaces that enable efficient communication in decision making within and across teams, including new tools for interpreting how the models arrived at each recommended decision and natural language interfaces that can facilitate human-AI collaboration.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.