Machine learning predicts 1-year mortality using EHR data

#artificialintelligence

University of Minnesota researchers have developed a machine learning algorithm using electronic health record data to improve care delivery for seriously ill patients by accurately predicting the risk of 1-year mortality. The random forest (RF) model, which estimates the risk of death within a year of the last day of hospitalization, leverages commonly obtained EHR data such as vital signs, complete blood count, basic and complete metabolic panel, demographic information, as well as ICD codes. Nishant Sahni, MD, adjunct assistant professor in the Department of Medicine at the University of Minnesota Medical School, sees the model as a potential clinical decision support tool for improving end-of-life planning. "Having been a hospitalist for more than 15 years, I find that the end-of-life conversations don't necessarily happen between clinicians and patients," contends Sahni, who notes that seriously ill hospitalized patients are frequently subjected to unnecessary, invasive procedures that do not enhance their quality of life. "Unfortunately, we don't have a lot of prognostic models that just look at a general hospitalized population," adds Sahni.


Machine learning model provides rapid prediction of C. difficile infection risk: Model successfully applied to data from medical centers with different patient populations, electronic health record systems

#artificialintelligence

"Despite substantial efforts to prevent C. difficile infection and to institute early treatment upon diagnosis, rates of infection continue to increase," says Erica Shenoy, MD, PhD, of the MGH Division of Infectious Diseases, co-senior author of the study and assistant professor of Medicine at Harvard Medical School. "We need better tools to identify the highest risk patients so that we can target both prevention and treatment interventions to reduce further transmission and improve patient outcomes." The authors note that most previous models of C. difficile infection risk were designed as "one size fits all" approaches and included only a few risk factors, which limited their usefulness. Co-lead authors Jeeheh Oh, a U-M graduate student in Computer Science and Engineering, and Maggie Makar, MS, of MIT's Computer Science and Artificial Intelligence Laboratory and their colleagues took a "big data" approach that analyzed the whole electronic health record (EHR) to predict a patient's C. difficile risk throughout the course of hospitalization. Their method allows the development of institution-specific models that could accommodate different patient populations, different EHR systems and factors specific to each institution.


Using AI to Amplify Care for Patients With Chronic Disease

#artificialintelligence

Trishan Panch, MD, MPH, is co-founder and chief medical officer for digital health management provider Wellframe (www.wellframe.com). He is an MIT lecturer for Health Sciences and Technology and teaches Masters and PhD students at the Harvard School of Public Health, Harvard Medical School, and MIT. Dr Panch is also on the advisory board of Boston Children's Hospital. He has set up and run primary care organizations in the US, UK, India, and Sri Lanka, providing comprehensive adult, pediatric, obstetric, and mental health care for complex populations in a mixture of urban and rural settings. I vividly remember the scene in the back office of my practice: clinical notes to the left of me, unsigned prescriptions to the right, and I was stuck in the middle looking at new quality metrics for our patient population.


These handy medical devices are inspired by the 'Star Trek' tricorder

Mashable

Two hundred years from now, medical tricorders like the ones depicted in Star Trek will be as common as tongue depressors. They'll be unremarkable, mobile tools on our SpaceX starships shuttling us back and forth to Mars. Diagnosing cancer will only take a single wave over our bodies with a palm-sized sensor. The reality, best embodied in two XPRIZE Tricorder competition finalists, is that the devices are somewhat ungainly and they feature distinctly 21st century diagnostic equipment. They work with mobile phones, but don't try hanging either one of them around your neck.


'Citizen AI': Teaching artificial intelligence to act responsibly

#artificialintelligence

Researchers at Mt. Sinai's Icahn School of Medicine in New York at have a unique collaborator in the hospital: Their in-house artificial intelligence system, known as Deep Patient. The researchers taught Deep Patient to predict risk factors for 78 different diseases by feeding it electronic health records from 700,000 patients. Doctors now turn to the system to aid in diagnoses. While not a person, Deep Patient is more than just a program. Like other advanced AI systems, it learns, makes autonomous decisions, and has grown from a technological tool to a partner, coordinating and collaborating with humans.