Research

Research projects at iiD focus on building connections. We encourage crosspollination of ideas across disciplines, and to develop new forms of collaboration that will advance research and education across the full spectrum of disciplines at Duke. The topics below show areas of research focus at iiD. See all of our research.

A team of students led by Janet Bettger and an interdisciplinary team with the 6th Vital Sign Study will use Census and other public data to examine the representativeness of people who participated in this smartphone based population health study. Students will design an online interactive map and other web-based tools that can be easily updated with new study participants illustrating key relationships such as health status with rurality, medical service availability, and sociodemographics. The online tools will be used to direct education efforts on the importance of walking speed as a marker of health and as the sixth vital sign. Findings from the data analysis will be used by GANDHI to direct scale-up of smartphone based research in target geographic areas and with specific population subgroups such as older adults and those with chronic illness.

A team of students led by faculty and researchers at the Social Science Research Institute will bring together data that will facilitate research using social determinants of health (SDH) to examine, understand, and ameliorate health disparities. This project will identify SDH variables that have the potential to be linked to data from the MURDOCK Study, a longitudinal health study based in Cabbarus County, NC. Much of this data – information relevant to understanding socioeconomic status, education, the physical and social environment, employment, and social support networks – is publicly available or easily obtained and its aggregation and analysis offer opportunities to significantly improve predictions of health risks and improve personalized care. Students will evaluate potential data sources, develop ethical policies to protect respondent privacy, clean and merge data, create documentation for data sharing and reuse, and use statistical tools and neighborhood mapping software to examine patterns of disparity.

Despite overwhelming scientific evidence on the benefits of vaccinations, pregnant women and parents of young children often refuse to accept, or choose to space-out, vaccinations for themselves or their children. This phenomenon, termed vaccine hesitancy, has been blamed for several vaccine-preventable outbreaks in the U.S. As part of larger study to understand vaccine hesitancy locally, students will conduct secondary data analysis of the coverage and timeliness of maternal and pediatric vaccines in Durham, and identify determinants of timely vaccination uptake. Results may inform the development of interventions to reduce hesitancy and improve the coverage and timeliness of maternal and pediatric vaccine uptake in Durham.

A team of students will contribute to an effort to operationalize the application of distributed computing methodologies in the analysis of electronic medical records (EMR) at Duke.  Specifically, the team will compare and contrast conventional (Oracle Exadata) and distributed (Apache SPARK) systems in the analysis of EMR data, and create recommendations for implementation.  Students will then use these systems to execute natural language processing (NLP) on clinical narratives and radiology notes with existing, ongoing analyses of Duke data.  This Data+ team will work with the Duke Forge, an interdepartmental collaboration focused on data science research and innovation in health and biomedical sciences.

A team of students lead by Rachel Richesson (Duke University School of Nursing) will explore patterns of health care treatment and utilization for several rare metabolic disorders treated at Duke University Health System (DUHS).  Students will gain an understanding of medical data, the use of reference terminologies to generate new relationships and inferences, and various data analysis and visualization techniques to describe and compare the clinical profiles of patients with different conditions. Students will interact with faculty experts from multiple disciplines (statistics, network analysis, medicine, genetics, and population health) to demonstrate how data-driven clinical profiles can inform our understanding of patients’ health care experience and support clinical care and research.

Would you like to know what influences patients’ medical decisions when outcomes are uncertain? Using a big data approach, we will explore a large number of physician-patient conversations and disentangle the complex decision-making process.  Students will be introduced not only to data science but also to behavioral research and aspects of communication in healthcare. This work will inform physicians on how to reduce overutilization of unnecessary interventions and ensure the well-being of patients.

A team of students led by researchers in the Center for Health Policy and Inequalities Research will develop a platform that visualizes significant life events across time for more than 3,000 orphaned and separated children in Cambodia, Ethiopia, India, Kenya, and Tanzania from the Positive Outcomes for Orphans (POFO) study. The types of life events visualized on the timeline will include: the death of a parent, changes in living locations, school levels achieved, special events, traumatic events, and reported wellbeing at different ages. This data will be displayed via mobile devices and will serve to allow the participant to visualize and verify the information provided about their lives. Ultimately, the platform will allow researchers to ensure accuracy of the data provided and also allow greater audiences to visualize the individuality of the study's aggregate data.

A team of students led by clinical and non-clinical global reproductive health researchers at the Duke Global Health Institute will develop an interactive, web-based platform that curates raw data on contraceptive discontinuation from the Demographic and Health Surveys (DHS) into a tool to help researchers and family planning advocates develop fresh insights around contraceptive discontinuation. Students will develop and refine the prototype, debut it with experts in online data visualization platforms at RTI and prepare a dissemination plan for the tool. Students will have an opportunity to pilot creative ways to incorporate social media data into the tool and ways to validate this data against ground-truth data from population representative surveys.

A team of students led by a computational biologist and a cell biologist will develop methods to identify cell subsets and their developmental, maturation and activation lineage relationships using deep learning approaches. Students will learn to process single cell RNA sequencing data and use the Python programming language and TensorFlow to characterize lung stem cells involved in wound healing. This work will help Duke researchers establish a deep learning pipeline for single cell analysis with applications in immunology, cell biology and cancer.

 A team of students lead by Dr. Nicole Schramm-Sapyta of the Duke Institute for Brain Sciences will provide analytical consulting support to the Durham Crisis Intervention Team (CIT) Collaborative, a county-wide effort to provide law enforcement and first responders with specialized training in mental illness and crisis intervention techniques.  The team will build on last summer’s descriptive analysis of 9-1-1 call data by incorporating data from partner agencies to assess whether CIT training reduces recidivism, increases utilization of mental health services, and generally improves the lives of Durham citizens with mental illness. 

The aim of this Data Expedition was for students to learn hands-on data visualization techniques using a variety of data types. Students first discussed how data visualization is useful, and tips to make graphs both visually appealing and easy to understand. 

Anna Vivian (Physics, Art History) and Vinai Oddiraju (Stats) spent ten weeks working closely with the director of the Durham Neighborhood Compass. Their goal was to produce metrics for things like ambient stress and neighborhood change, to visualize these metrics within the Compass system, and to interface with a variety of community stakeholders in their work.

Maddie Katz (Global Health and Evolutionary Anthropology Major), Parker Foe (Math/Spanish, Smith College), and Tony Li (Math, Cornell) spent ten weeks analyzing data from the National Transgender Discrimination Survey. Their goal was to understand how the discrimination faced by the trans community is realized on a state, regional, and national level, and to partner with advocacy organizations around their analysis.

Computer Science major Yumin Zhang and IIT student Akhil Kumar Pabbathi spent ten weeks working closely with Dr. Joe McClernon from Psychiatry and Behavioral Sciences to understand smoking and tobacco purchase behavior through activity space analysis.

Biomedical Engineering and Electrical and Computer Engineering major David Brenes, and Electrical and Computer Engineering/Computer Science majors Xingyu Chen and David Yang spent ten weeks working with mobile eye tracker data to optimize data processing and feature extraction. They generated their own video data with SMI Eye Tracking Glasses, and created computer vision algorithms to categorize subject gazing behavior in a grocery purchase decision-making environment.

Biomedical Engineering major Chi Kim Trinh, and Biostatistics MS student Can Cui spent ten weeks constructing a computational and statistical framework to evaluate the effects of health coaching on Type II Diabetes patients’ quality metrics, including Hemoglobin A1c, blood pressure, eye exam consistency, tobacco use, and prescription adherence to statins, aspirin, and angiotensin converter enzyme (ACE)/ angiotensin receptor blocker (ARB).

Ana Galvez (Cultural and Evolutionary Anthropology), Xinyu Li (Biology), and Jonathan Rub (Math, Computer Science) spent ten weeks studying the impact of diet on organ and bone growth in developing laboratory rats. The goal was to provide insight into the growth dynamics of these model organisms that could eventually be generalized to inform research on human development.

Robbie Ha (Computer Science, Statistics), Peilin Lai  (Computer Science, Mathematics), and Alejandro Ortega (Mathematics) spent ten weeks analyzing the content and dissemination of images of the Syrian refugee crisis, as part of a general data-driven investigation of Western photojournalism and how it has contributed to our understanding of this crisis.

A team of students led by Duke mathematician Marc Ryser and University of Southern California Pathology professor Darryl Shibata will characterize phenotypic evolution during the growth of human colorectal tumors. 

Over ten weeks, Computer Science Majors Amber Strange and Jackson Dellinger joined forces with Psychology major Rachel Buchanan to perform a data-driven analysis of mental health intervention practices by Durham Police Department. They worked closely with leadership from the Durham Crisis Intervention Team (CIT) Collaborative, made up of officers who have completed 40 hours of specialized training in mental illness and crisis intervention techniques.

Over ten weeks, Computer Science majors Daniel Bass-Blue and Susie Choi joined forces with Biomedical Engineering major Ellie Wood to prototype interactive interfaces from Type II diabetics' mobile health data. Their specific goals were to encourage patient self-management and to effectively inform clinicians about patient behavior between visits.

A team of students led by Dr. Shanna Sprinkle of Duke Surgery will combine success metrics of Duke Surgery residents from a set of databases and create a user interface for residency program directors and possibly residents themselves to view and better understand residency program performance.

Lauren Fox (Cultural Anthropology) and Elizabeth Ratliff (Statistics, Global Health) spent ten weeks analyzing and mapping pedestrian, bicycle, and motor vehicle data provided by Durham's Department of Transportation. This project was a continuation of a seminar on "ghost bikes" taught by Prof. Harris Solomon.

Felicia Chen (Computer Science, Statistics), Nikkhil Pulimood (Computer Science, Mathematics), and James Wang (Statistics, Public Policy) spent ten weeks working with Counter Tools, a local nonprofit that provides support to over a dozen state health departments. The project goal was to understand how open source data can lead to the creation of a national database of tobacco retailers.

Over ten weeks, BME and ECE majors Serge Assaad and Mark Chen joined forces with Mechanical Engineering Masters student Guangshen Ma to automate the diagnosis of vascular anomalies from Doppler Ultrasound data, with goals of improving diagnostic accuracy and reducing physician time spent on simple diagnoses. They worked closely with Duke Surgeon Dr. Leila Mureebe and Civil and Environmental Engineering Professor Wilkins Aquino.

Angelo Bonomi (Chemistry), Remy Kassem (ECE, Math), and Han (Alessandra) Zhang (Biology, CompSci) spent ten weeks analyzing data from social networks for communities of people facing chronic conditions. The social network data, provided by MyHealth Teams, contained information shared by community members about their diagnoses, symptoms, co-morbidities, treatments, and details about each treatment.

Furthering the work of a 2016 Data+ team in predictive modeling of pancreatic cancer from electronic medical record (EMR) data, students Siwei Zhang (Masters Biostatistics) and Jake Ukleja (Computer Science) spent ten weeks building a model to predict pancreatic cancer from Electronic Medical Records (EMR) data. They worked with nine years worth of EMR data, including ICD9 diagnostic codes, that contained records from over 200,000 patients.

Gary Koplik (Masters in Economics and Computation) and Matt Tribby (CompSci, Statistics) spent ten weeks investigating the burden of rare diseases on the Duke University Health System (DUHS). They worked with a massive set of ICD diagnosis codes and visit data provided by DUHS.

Over ten weeks, Biology major Jacob Sumner and Neuroscience major Julianna Zhang joined forces with Biostatistics Masters student Jing Lyu to analyze potential drug diversion in the Duke Medical Center. Early detection of drug diversion assists health care providers in helping patients recover from their condition, as well as mitigate the effects on any patients under their care.

Albert Antar(Biology), and Zidi Xiu (Biostatistics) spent ten weeks leveraging Duke Electronic Medical Record (EMR) data to build predictive models of Pancreatic ductal adenocarcinoma (PDAC). PDAC is the 4th leading cause of cancer deaths in the US, and is most often is diagnosed in stage IV, with a survival rate of only 1% and life expectancy measured in months. Diagnosis of PDAC is very challenging due of deep anatomical placement, and significant risk imposed by traditional biopsy. The goal of this project is to utilize EMR data to identify potential avenues for diagnosing PDAC in the early treatable stages of disease.

Priya Sarkar (Computer Science), Lily Zerihun (Biology and Global Health), and Anqi Zhang (Biostatistics) spent ten weeks utilizing Duke Electronic Medical Record (EMR) data to identify subgroups of diabetic patients, and predict future complications associated with Type II Diabetes.

Vivek Sriram (Computer Science and Math), Lina Yang (Biostatistics), and Pablo Ortiz (BME) spent ten weeks working in close collaboration with the Department of Biostatistics and Bioinformatics implementing an image analysis pipeline for immunofluorescence microscopy images of developing mouse lungs.

Computer Science and Psychology major Molly Chen, and Neuroscience major Emily Wu spent ten weeks working with patient diagnosis co-occurence data derived from Duke Electronic Medical Records to develop network visualizations of co-occurring disorders within demographic groups. Their goal was to make healthcare more holistic, and reduce healthcare disparities by improving patient and provider awareness of co-occurring disorders for patients within similar demographic groups.

Statistical Science majors Nathaniel Brown and Corey Vernot, and Economics student Guan-Wun Hao spent ten weeks exploring changes in food purchase behavior and nutritional intake following the event of a new Metformin prescription for Type II Diabetes. They worked closely with Matthew Harding and researchers in the BECR Center, as well as Dr. Susan Spratt, an endocrinologist in Duke Medicine.

Anne Driscoll (Economics, Statistical Science), and Austin Ferguson (Math, Physics) spent ten weeks examining metrics for inter-departmental cooperativity and productivity, and developing a collaboration network of Duke faculty. This project was sponsored by the Duke Clinical and Translational Science Award, with the larger goal of promoting collaborative success in the School of Medicine and School of Nursing.

Joel Tewksbury (BME) and Miriam Goldman (Math and Statistics, Arizona State University) spent ten weeks analyzing time-series darkness visual adaptation scores from over 1200 study participants to identify trends in night vision, and ultimately genetic markers that might confer a visual advantage.

Lindsay Hirschhorn (Mechanical Engineering) and Kelsey Sumner (Global Health and Evolutionary Anthropology) spent ten weeks determining optimal vaccination clinic locations in Durham County for a simulated Zika virus outbreak. They worked closely with researchers at RTI International to construct models of disease spread and health impact, and developed an interactive visualization tool.

With the significant international consequences of recent outbreaks, the ITP Lab conducted extensive stakeholder interviews and macro-level health policy analysis to expose gaps in pandemic preparedness and develop legal frameworks for future threats. 

This project summarizes the existing sample agreements from different institutions, analyzes the key contractual issues in the formation of alliances, and develops master charts of legal provisions to compare different approaches, to provide a reference for the formation of new alliances in the era of epidemic disease outbreaks. 

Paclitaxel (Taxol) is a small molecule drug belonging to the taxane family. It is one of the most commonly used chemotherapeutics, used for treatment of many cancers, as a monotherapy or in combination with other drugs to treat breast, lung and ovarian cancer as well as Kaposi’s sarcoma. Taxol is on the World Health Organization’s (WHO) List of Essential Medicines, a list that includes most the important medications for basic health. The worldwide demand for paclitaxel is exceeding the current supply. 

With the significant international consequences of recent outbreaks, the ITP Lab conducted extensive stakeholder interviews and macro-level health policy analysis to expose gaps in pandemic preparedness and develop legal frameworks for future threats. 

Two to three undergraduates joined a research group led by Douglas Boyer and Ingrid Daubechies, with the goal of testing and developing mathematical and statistical methodology for measuring similarities between bones and teeth.

Kelsey SumnerEvAnth and Global Health major and Christopher Hong, CompSci/ECE major, spent ten weeks analyzing high-dimensional microRNA data taken from patients with viral and/or bacterial conditions. They worked closely with the medical faculty and practitioners who generated the data.