The Intelligent Histories project develops new ways of using commonly available electronic medical information to predict people's future medical risks, helping doctors choose preventive interventions and improve medical care.
Suicide is one of the ten leading causes of death in the United States. Even though the majority of all individuals who die by suicide have contact with a healthcare professional in the month before their death, suicide risk is rarely detected in such cases. PredMed researchers have developed advanced predictive models able to identify between one third and one half of all suicide attempts on average three years before they occur, enabling life-saving interventions and care.
Psychosis often first appears during adolescence or young adulthood, and is difficult to detect. If left undetected and untreated, psychosis can quickly deteriorate to even more severe mental illness. PredMed researchers are developing advanced predictive models to identify cases of first episode psychosis years prior to when they would otherwise be detected by the health system.
Family histories are an essential predictor of disease risk, yet they are often incomplete, inaccurate, and underutilized in today's clinical settings. PredMed researchers are developing improved approaches to providing more complete, accurate and detailed family histories based on electronic health records of patients and their consenting family members. These improved histories enable better clinical risk prediction and decision-making.
Health System Dynamics
Prediction of Patient Placement
Overcrowding in the Emergency Department is reaching alarming rates, with 50% of all EDs operating at or above their space capacity. Overcrowding reduces patient care quality and satisfaction, increases wait times, and limits the capacity of the hospital for disaster response. PredMed researchers have developed real-time models that can accurately predict which patients will be admitted to the hospital from the ED, moments after their arrival at the ED. Proactively and intelligently managing the flow of patients into the Emergency Department provides hospitals with shorter wait times and improved outcomes.
Large hospitals are more stressed than ever by overcrowding and large patient loads. Patients must often wait hours in the hallway for a bed to become available. PredMed researchers are working with clinicians and hospital administrators to develop real-time models that predict which patients will be admitted and discharged when, allowing for more efficient planning and patient flow through the hosptial.
We apply advanced modeling techniques to novel data sources in order to predict and detect outbreaks and other public health trends, especially during times of great uncertainty such as epidemics or large public events.
We develop novel network-based models to predict unknown adverse drug events and drug-drug interactions. Instead of waiting for sufficient post-marketing evidence to accumulate, this predictive approach can identify drug safety issues years in advance.
HealthySocial develops innovative ways for using social networks to promote positive health behaviors and attitudes. HealthySocial apps are used by tens of thousands of people around the world to spread positive health behaviors and attitudes to their friends and loved ones.