Johns Hopkins student teams develop health care solutions using data visualization

When a child is diagnosed with pneumonia—the leading infectious cause of death in children worldwide—the treatment depends on whether the pneumonia is viral or bacterial. Tests to differentiate between the two are both expensive and invasive.

This spring, a team of Johns Hopkins University undergraduate students developed a web-based application that could help simplify the process. Their tool can help doctors predict the cause of pneumonia in an individual patient by comparing characteristics of that child's health with a data set of 1,000 pediatric pneumonia patients.

When the physician enters the child's age, HIV status, and number of viruses and bacteria found in the patient's nasal swab or blood sample, a scatterplot graph appears showing the causes of the disease for similar patients. That allows the doctor to infer the most likely cause of that patient's pneumonia and tailor treatment accordingly.

"It gives doctors quantitative data and evidence to add to their expertise," says Rebecca Yates Coley, instructor for the course in which the students' tool was developed and a postdoctoral research fellow in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.

The course, "Data Analysis and Visualization Practicum for Individualized Health," introduces undergrads to two big ideas, says Coley. One is data visualization, or the art and science of graphically communicating statistics and research findings in meaningful ways. It's an important part of individualized health, allowing doctors not only to tell patients about the specific nature of an illness and/or its expected progression, but to show them.

"This course challenges students to think about health care in a new way."
Risha Zuckerman, program director, Hopkins inHealth

"Data visualization is kind of an afterthought for lots of researchers, but it's actually a skill with best practices," Coley says.

The second idea is the technical component—in this case, the app that serves as the interface for patients and physicians to enter data and get responses. Students devised the app using the open-source statistical software called R, along with app development software Shiny.

"This course challenges students to think about health care in a new way," says Risha Zuckerman, program director for the Johns Hopkins Individualized Health Initiative, which aims to create and disseminate tools that individualize and improve health care and provided the data sets the students used. "Data visualization is a technique that allows physicians to communicate complex information to patients. It has tremendous potential to enhance the doctor-patient relationship and to develop innovative and individualized health care."

Other student projects that came out of the class include a mental health app that predicts symptoms for patients with schizophrenia after several weeks on a particular medication, and a prostate cancer app to predict a patient's outcome using a surveillance approach versus treatment.

"This was the class where I learned how to display data: by using art to graph the trends of data," says Audrey Garman, a public health and history of science double major who just completed her junior year.

Addressing the challenges involved in creating a meaningful display of 1,000 data points gave Garman both marketable skills and experience.

"I think being able to figure out what variables have the greatest impact statistically will be able to shape where policy is headed and how we try to fix these issues," she says.

Zhenke Wu—like Coley, a postdoctoral research fellow in biostatistics and visualization enthusiast—gave the pneumonia team the simulated data set based on his own research and guided them as they analyzed it. Their final product succeeded in highlighting the information important to physicians seeking to individualize pneumonia treatment, he says.

"Diagnosing a disease is not simple; it requires one to integrate multiple specimens of distinct clinical values, and to visualize all this information together in an effective way," Wu says. "The students did a good job of communicating the uncertainty."