Ophir Frieder – Georgetown University
10:00 – 10:15
Keynote: Information for Malaria Eradication
David Smith – University of Washington
10:15 – 11:00

Global malaria eradication is the consensus long-term goal for malaria, which will be achieved by eliminating malaria from countries and regions. Strategic planning for malaria eradication and successful implementation of elimination campaigns can be improved by gathering, integrating and utilizing big and small data.

David L. Smith is Professor of Global Health at the Institute for Health Metrics and Evaluation, University of Washington.
Personal Big Data: A 360-degree view of health and wellbeing
Ramon Dempers – Umbra Health Corporation
11:00 – 11:30

What is Personal Big Data? How is it captured? How is it measured? What does it mean? What's possible now? What's possible in the future? Why should DARPA care? By harnessing the power of Personal Big Data, we unlock the ability to:

Ramon J. Dempers is Founder, Chief Executive Officer & Chief Technical Officer of Umbra Health Corporation. He is a serial entrepreneur with 30 years experience in the development and implementation of innovative, proprietary solutions encompassing learning systems, system interoperability & integration, knowledge management, clinical trials, FDA compliance and industry standards. Previously, he held leadership positions with IBM Global Services and Sun Microsystems.
Quantifying the White Space
Glen Coppersmith – Qntfy
11:30 – 12:00

Behavioral assessment and measurement today are typically invasive and human intensive (for both patient and clinician). Moreover, by their nature, they focus on retrospective analysis by the patient (or the patient’s loved ones) about emotionally charged situations — a process rife with biases, not repeatable, and expensive. We examine all the data in the &lquot;white space&rquot; between interactions with the healthcare system (social media data, wearables, activities, nutrition, mood, etc.), and have shown quantified signals relevant to mental health that can be extracted from them. These methods to gather and analyze disparate data unobtrusively and in real time enable a range of new scientific questions, diagnostic capabilities, assessment of novel treatments, and quantified key performance measures for behavioral health. These techniques hold special promise for suicide risk, given the dearth of unbiased accounts of a person’s behavior leading up to a suicide attempt. We are beginning to see the promise of using these disparate data for revolution in mental health.

Glen Coppersmith is Chief Executive Officer at Qntfy, a company scaling therapeutic impact by empowering mental health clinicians and patients with data science and technology. He is also a Research Scientist at the Human Language Technology Center of Excellence at Johns Hopkins University.
Twitter and Influenza-Like Illness at a Children’s Hospital
David Hartley – Cincinnati Children’s Hospital Medical Center
12:00 – 12:30

Forecasting the onset of epidemics is important for hospital emergency departments and related services. We are exploring the use of social media for providing early warning of respiratory disease epidemics by monitoring tweets originating in our hospital’s catchment community. By applying machine learning and time series analysis to georeferenced tweets, it may be possible to provide lead-time sufficient for hospitals to prepare for seasonal events such as influenza and surprise events such as enterovirus. We describe recent and ongoing work in Cincinnati.

David Hartley is an Associate Professor of Pediatrics at Cincinnati Children's Hospital Medical Center and the University of Cincinnati.
Special pre-lunch session: An Ounce of Prevention is Worth a Pound of Cure: A Prescription for Producing Health Cyber Security and Privacy Experts to Assure Safe and Secure Medical "Things"
Lance J. Hoffman – George Washington University
12:30 – 12:40

In the future, intelligent systems in homes, offices, public spaces, and embedded on and in humans will generate too much health-related data to be processed by humans. Some of this data will be used by individuals (patients), but much of it will end up in analytical systems and then be used by advertisers, health care providers, the government, and even data criminals. Since cutting edge applications typically pay attention to utility before they address privacy and security, data breaches will likely take place on a massive scale, sometimes negatively affecting physical and mental health. Many of these problems can be addressed up front by specialists in medical cyber security and privacy. We here present initial thoughts on developing these experts, based on our experience since 2002 leading a successful cyber security education program.

Lance J. Hoffman is co-Director of the Cyber Security and Privacy Institute at The George Washington University. He is also Principal Investigator for its CyberCorps project which has produced dozens of experts in cyber security and privacy to work for government agencies and thinks that lessons learned there can be applied to niche fields like health information security and privacy.
Lunch (12:45 – 14:00)
Machine Learning to Enable Precision Medicine
Mohammed A. Eslami – Netrias
14:00 – 14:30

Biomedical researchers cannot adequately leverage large-scale genomic, transcriptomic and proteomic data that is now collected. Current informatics tools do not capitalize on the great advancements made in Machine Learning (ML) that can enable them to generate more, and more rapid, breakthroughs. Big Data technologies can facilitate the complete integration of heterogeneous sets of experimental data to identify key metabolic pathways and drug targets to enable precision medicine.

Mohammed A. Eslami is the Chief Data Scientist at Netrias, and is a Research Associate with the University of Maryland, College Park.
Correlating Genotype to Behavioral Phenotype
Ryan Leary – Qntfy
14:30 – 15:00

An open hypothesis remains regarding the potential correlation between genotype and social/behavioral interactions. The latter is an important element in a clinician’s diagnosis of a patient’s mental health, while traditionally being difficult to quantify with any reliability. We are at an opportune time to bridge the traditional genotypic/phenotypic gap with social media’s ubiquity, the surge in wearables, and gene sequencing becoming more mainstream and affordable. This wide swath of data will allow for the emergence of quantified behavioral profiles spanning genotypes and phenotypes. This might provide new fundamental classes for scientific examination, with the hope of explaining some of the widely observed variance and accuracy of traditional modeling approaches that use only one facet. The integrated analysis of these sets of heterogeneous data covering the spectrum of human experience will lead to new insight, better predictive models, more accurate clinical diagnoses, and providing the foundation for pragmatic precision medicine — in totality, there is great potential for revolution in both behavioral genetics and mental healthcare.

Ryan Leary is Chief Technical Officer at Qntfy, a company scaling therapeutic impact by empowering mental health clinicians and patients with data science and technology. Recently on the DARPA Quantitative Crisis Response (QCR) program, Ryan designed, oversaw, and contributed to development of the highly-scalable analytic pipeline responsible for the coordination and execution of analytics for the entire program.
Natural Language Processing to Understand a User's State
Andrew Yates – Max Planck Institute & Georgetown University
15:00 – 15:30

Understanding users’ self-reported symptoms and descriptions of their state is an important task with many applications including pharmacovigilence and health detection. We will describe methods for doing so in the context of a larger system for monitoring a user’s “state of being.”

Andrew Yates is a Postdoctoral Researcher at the Max Planck Institute for Informatics and a Research Scientist in the Information Retrieval Laboratory at Georgetown University.
Break (15:30 – 16:00)
Detecting Rationales for Vaccine Refusal
David A. Broniatowski – The George Washington University
16:00 – 16:30

Vaccine refusal is an increasingly important public health concern. Public health agencies must understand how rationales for this dangerous behavior vary by group to target their messages accordingly. We propose to detect and categorize these rationales using social media data.

David A. Broniatowski is Assistant Professor of Engineering Management and Systems Engineering at The George Washington University.
Fusing the Clinical and the Social for Mental Healthcare
Kristy Hollingshead – Florida Institute for Human and Machine Cognition
16:30 – 17:00

Electronic health records have begun to emerge as a new and valuable data source for mental health research, but clinical records can be a challenge to obtain, and provide only a narrow window into a patient's condition. Social media provides a complementary resource: now used regularly by over a billion people worldwide, social media offers insight into the thoughts, feelings, and behaviors of patients in their daily lives, and a finer-grained longitudinal view of their mental health. In this talk, we discuss our recent efforts working toward the combination of clinical records and social media posts, arguing that this combination provides a heretofore untapped potential for data-driven discovery, predictive modeling, and the development of personalized interventions in mental health.

Kristy Hollingshead is a Research Scientist at the Florida Institute for Human and Machine Cognition (IHMC).