Using machine learning to detect risky behavior in psychiatric clinics
Part of Mental health clinical trials.
This trial uses a computer system to watch for risky behaviors in adult psychiatric patients while they are in their hospital rooms. It may help doctors and nurses keep patients safer.
Summary written for real people, not researchers, by Clin2.
Who can take part
- You are 18 years old or older.
- You are currently staying in a psychiatric hospital or clinic as an inpatient.
- This study is for the room where you sleep while you're there.
Quick eligibility check
Answer a few plain-language questions, based on this study's own requirements, to get a preliminary sense of fit.
Similar studies
Other trials that look related to this one.
This trial tests whether a machine learning model can help doctors decide when to order blood cultures for patients in the emergency department. It aims to reduce unnecessary tests while still catching infections.
This study looks at how patients and families can be more involved in care for children admitted to the cardiology unit at SickKids. It may help improve how the hospital works with families.
This trial is testing a way to predict delirium early in ICU patients. If it works, doctors could help prevent confusion and agitation, which are common and scary for patients.
This study looks at the Behavioral Emergency Response Team, a special team that helps hospital patients who are having a mental health or behavioral crisis. It aims to improve how this team is used and might help patients get better care faster.
This study uses computer analysis and AI to predict how people with severe mental disorders might recover and to suggest better support. It may help improve rehabilitation plans tailored to each person.
This study looks at better ways to predict and prevent suicide risk in people who go to behavioral health clinics. It aims to improve care and help save lives.
Hear when a new Machine Learning trial opens
We’ll email you when one opens — at most once a week, no account needed, unsubscribe anytime.