This chapter focuses on techniques of detecting anomalies, starting with some of the basic statistical techniques and going into data analytics techniques.

Delving into the specifics of spatial and temporal analytics, this chapter explores topics such as spatial neighborhood and temporal evolution of large amounts of network traffic data.

This chapter extends the ideas of complex data by looking into cybersecurity through network and graph data.

This chapter introduces the basic concepts of cybersecurity and the data analytics perspective to cybersecurity. It lays out the areas of study and how data analytics should be a key part of the spectrum of cybersecurity solutions.

Focusing on the big data elements of cybersecurity, this chapter looks at the landscape of the big data technologies and the complexities of the different types of data, including spatial and graph data. It outlines examples in these complex data types and how they can be evaluated using data analytics.

We describe fundamental challenges to estimating heterogeneous treatment effects in the context of the statistical causal inference literature, proposed algorithms for addressing those challenges, and methods to evaluate how well heterogeneous treatment effects have been estimated. We illustrate the proposed algorithms using data from two large randomized trials of blood pressure treatments. We describe directions for future research in medical statistics and machine learning in this domain. The focus will be on how flexible machine learning methods can improve causal estimators, especially in the RCT setting.

To address the challenge of limited training data for machine learning models in the healthcare domain, we advocate for human-in-the-loop machine learning, which involves domain experts in an inter- active process of developing predictive models. Interpretability offers a promising way to facilitate this interaction. We describe an approach that offers a simple decision tree interpretation for any complex blackbox machine learning model. In a case study with physicians, we find that they were able to use the interpretation to discover an unexpected causal issue in a personalized patient risk score trained on electronic medical record data. To account for dynamics in disease progression, we advocate for building decision models that integrate predictions of the disease progression at the individual patient level with system models capturing the dynamic operational environments. We describe a case study on hospital inpatient management, showing how to build a Markov decision framework that leverages predictive analytics on patient readmission risk and prescribes the optimal set of patients to be discharged each day.

In this chapter, we explore how data-driven modeling can improve the understanding of OHCA risk, help identify the limitations of current AED placement strategies, and guide the development of optimal AED networks to increase the chance of AED use and OHCA survival. More specifically, we frame AED network design and related response efforts as a facility location problem, focusing on the maximum coverage location and p-median problems. We also highlight how novel tools that combine techniques from areas including information theory and machine learning with optimization models can shape the future of OHCA response efforts and AED placement strategies.

This chapter provides an overview of how researchers form interdisciplinary partnerships that leverage analytics-driven methods in AI, IE, and OR to tackle the most difficult societal and operational problems in healthcare.

This chapter provides an introduction to analytics-driven hospital capacity management through three projects that employed mathematical programming and discrete event simulation to address common challenges. The first project used mathematical programming to identify the mix of patients at Stanford Hospital that would maximize revenue given the capacity of hospital resources after a planned hospital expansion. The second project used discrete event simulation to plan the physical capacity and operational profile of a new procedural space at a hospital in New England. The third project combined mathematical programming and discrete event simulation to create a tool to schedule surgical procedures at Lucile Packard Children's Hospital Stanford.

This chapter offers lessons from engineering and other industries that promise developments in healthcare, and practical guidance for clinician-engineer partnerships. Section 1 provides guidance on how to establish a shared vocabulary and common understanding between engineers and clinicians of what terms such as AI and ML do and don’t mean. Section 2 identifies challenges clinician-engineering partnerships must overcome to deliver sustained value and ways to avoid common causes of failure. Section 3 provides specific advice on how to design projects to produce value at a series of stages rather than rely on the success of one, ambitious final model. Section 4 concludes by drawing on cautionary lessons from healthcare and other industries.

Designing AI-assisted technology to better understand major depressive disorder and further develop appropriate strategies for monitoring and treatment of major depression under resource constraints is an important and challenging task. In this chapter, we present seven studies that developed methods for AI-assisted, data-driven decision support systems to aid healthcare professionals. These methods focus on modeling chronic depression’s complex disease trajectories, identifying patients at high risk of progression, and recommending adaptive and cost-effective follow-up care. Long-term goals of this research include improving patient health outcomes and facilitating efficient allocation of healthcare providers’ limited resource through the use of novel technology.