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Drug Addiction and Rehabilitation Therapy
2021-11-12 - 2021-11-13    
All Day
Conference Series LLC Ltd is delighted to invite the Scientists, Physiotherapists, neurologists, Doctors, researchers & experts from the arena of Drug Addiction and Rehabilitation therapy, [...]
Drug Addiction and Rehabilitation Therapy
2021-11-12 - 2021-11-13    
All Day
This Rehabilitation 2021 Conference is based on the theme “Exploring latest Innovations in Drug Addiction and Rehabilitation”. Rehabilitation 2021, Singapore welcomes proposals and ideas from [...]
3D Printing and Additive Manufacturing
2021-11-15 - 2021-11-16    
All Day
DLP (Digital Light Processing) is a similar process to stereolithography in that it is a 3D printing process that works with photopolymers. The major difference [...]
Microfluidics and Bio-MEMS 2021
2021-11-16 - 2021-11-17    
All Day
Lab-on-a-chip (LOC) devices integrate and scale down laboratory functions and processes to a miniaturized chip format. Many LOC devices are used in a wide array [...]
Food Technology & Processing
2021-12-01 - 2021-12-02    
All Day
Food Technology 2021 scientific committee feels esteemed delight to invite participants from around the world to join us at 25th International Conference on Food Technology [...]
Events on 2021-11-15
Events on 2021-11-16
Events on 2021-12-01
Articles News

A study shows that AI can detect suicide risk early.

EMR Industry

As artificial intelligence helps doctors discover diseases like cancer at an early stage, it is also demonstrating its potential in tackling mental health crises. According to one study, artificial intelligence can detect patients who are at danger of suicide, providing a tool for prevention in everyday medical settings.

The study, published in the JAMA Network Open Journal, examined two approaches of notifying doctors about suicide risk: an active “pop-up” alarm demanding immediate attention and a passive system (less urgent) that displays risk information in a patient’s electronic chart.

The study discovered that active warnings beat the passive strategy, encouraging doctors to assess suicide risk in 42% of cases, against only 4% with the passive system. Furthermore, it emphasized the importance of using certain techniques to initiate a discourse about suicide risks.

This breakthrough, which combines automated risk identification with deliberately tailored alarms, provides hope for identifying and supporting more people in need of suicide prevention services.

Colin Walsh, an Associate Professor of Biomedical Informatics, Medicine, and Psychiatry at Vanderbilt University Medical Center, emphasized the importance of this breakthrough. “Most people who die by suicide have seen a healthcare provider in the year before their death, often for reasons unrelated to mental health,” according to Walsh.

Previous research indicates that 77% of people who commit suicide had contact with primary care doctors in the year before their death. These findings highlight the essential role AI can play in bridging the gap between conventional medical treatment and mental health interventions.

The Suicide Attempt and Ideation Likelihood model (VSAIL), an AI-driven system developed at Vanderbilt, was tested in three neurology clinics. The method uses normal data from electronic health records to calculate a patient’s 30-day probability of attempting suicide. When high-risk patients were identified, practitioners were encouraged to start focused conversations about mental health.

Walsh clarified: “Universal screening isn’t practical everywhere, but VSAIL helps us focus on high-risk patients and spark meaningful screening conversations.”

While the findings were promising, the researchers emphasized the importance of striking a balance between the benefits of active alerts and their possible drawbacks, such as workflow disruption. The authors proposed that comparable methods may be implemented for other medical specialties in order to broaden their reach and impact.

Cambridge University published a research earlier in 2022 that used PRISMA criteria to assess individuals who were at risk of attempting suicide.