An artificial intelligence model appeared to predict the likelihood of unplanned hospitalizations during chemoradiation therapy among a cohort of patients with various cancer types. The results, presented at American Society for Radiation Oncology Annual Meeting, showed the model, which used daily step counts measured through wearable devices as a proxy to monitor patients' health, provided physicians with a real-time method to provide personalized care. About 10% to 20% of patients who undergo outpatient radiation or chemoradiation require acute care via an ED visit or hospital admission during their course of treatment. These unplanned hospitalizations can cause treatment delays and stress that may affect clinical outcomes, according to a press release. "Wearable devices allow for continuous, objective capture of patient-generated health data outside of the clinical setting, which minimizes travel and has the potential to have a more realistic and equitable assessment of a person's health status," Isabel Friesner, clinical data researcher at University of California, San Francisco, said during the presentation.

An artificial intelligence (AI) model developed by researchers can predict the likelihood that a patient may have an unplanned hospitalization during their radiation treatments for cancer. The machine-learning model uses daily step counts as a proxy to monitor patients' health as they go through cancer therapy, offering clinicians a real-time method to provide personalized care. Findings will be presented today at the American Society for Radiation Oncology (ASTRO) Annual Meeting. An estimated 10-20% of patients who receive outpatient radiation or chemoradiation therapy will need acute care in the form of an emergency department (ED) visit or hospital admission during their cancer treatment. These unplanned hospitalizations can be a major challenge for people undergoing cancer treatment, causing treatment interruptions and stress that may impact clinical outcomes.

CHICAGO--An artificial intelligence machine-learning program has demonstrated the ability to accurately forecast which head and neck cancer patients are likely to experience severe weight loss, necessitating the use of a feeding tube, researchers at MD Anderson Cancer Center in Houston told attendees at the 2019 ASTRO Annual Meeting (Abstract 141). It marks the first time that such a sophisticated "self-teaching" computer algorithm has accurately identified patients likely to develop problems eating, said Jay Reddy, MD, PhD, Assistant Professor of Radiation Oncology and lead author of the study. "With head and neck radiation, a lot of toxicity occurs; however it's not always clear which patients will experience serious side effects," he told a press conference. Reddy and his colleagues used machine learning models to analyze large datasets from three sources--electronic health records, an internal web-based patient charting tool, and the hospital's records and verification system--in an effort to discern and eventually predict patients with weight loss exceeding 10 percent of total body weight, the need for a feeding tube, and/or any unplanned hospitalization within 3 months of radiation. Machine learning is a relatively powerful application of artificial intelligence (AI)–think facial recognition software--by which a computer program can automatically learn and improve itself by analyzing large quantities of data.