We work with all areas of the hospital, using data to discover solutions to complex problems.

The predictive analytics team at Johns Hopkins All Children’s Hospital helps to find optimal solutions to complex problems by analyzing the immense amount of information compiled every day in 21st century health care and hospital records.

Predictive analytics incorporates a variety of quantitative methods and techniques—including statistics, mathematics and computer science along with technology (both hardware and software)—to develop algorithms designed to discover new patterns and make predictions about what may happen in the future, based on the trends we can observe in current or historical data.

The predictive analytics team at Johns Hopkins All Children’s

We deliver predictive analytics solutions in domains that include clinical decision support, chronic disease management, readmission prevention and adverse event avoidance.

Luis Ahumada, Ph.D., predictive analytics director, with Ali Jalali, Ph.D., predictive analytics data scientist, leads a team of doctorate-level data scientists. The team has a wealth of experience in managing large datasets, working with advanced data analysis frameworks and formulating novel data management and modeling methodologies.

Improving the future of health care now

Predictive analytics uses advanced mathematical methods, powerful statistical models and machine learning algorithms to represent, interrogate, and interpret data that is frequently too complex for human intelligence alone. There is great interest in the health care field in using predictive analytics techniques and artificial intelligence methods such as machine learning and natural language processing to teach computers to learn for themselves how to discover and capture hidden relationships in data.

These data come from many sources and may include the electronic health records, intelligent medical devices, laboratory tests and monitoring devices. Using all the elements of predictive analytics, we can forecast trends, predict patterns or unknown outcomes, produce estimations of likelihoods or parameters, generate classification labels, and contribute many other aggregate or individualized forecasts.

Often the unknown event of interest is in the future, but we can apply predictive analytics to any type of unknown whether it be in the past, present or future. The core function of predictive analytics relies on discovering unknown relationships between explanatory variables and predicted variables by analyzing past events to predict the unknown outcome. The accuracy and usability of results depend greatly on the level of data analysis and the quality of the input data. In health care, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities.