TY  - JOUR
PY  - 2021//
TI  - Machine learning and suicide prevention: is an algorithm the solution?
JO  - Nederlands Tijdschrift voor Geneeskunde
A1  - De Beurs, Derek
A1  - Mens, Kasper
SP  - D5800
EP  - D5800
VL  - 165
IS  - 
N2  - Suicide is inherently difficult to predict. Epidemiological research identified many general risk factors such as a depression, but these predictors have limited predictive power. Machine learning offers a set of tools that can combine hundreds of predictors resulting in the most optimal prediction. It might therefore offer a powerful way to predict inherently complex behaviour such as suicide. In a recent study, state of the art ML algorithms where applied to a large Swedish dataset of 126.205 patients treated in psychiatry containing over 400 potential risk factors. Although the presented results such as an area under the curve if 88% sounds promising, many questions on for example the cost of a false negative remain unanswered. In our comment, we critically discuss the presented findings, and bring up some unanswered questions.<p />  <p>Language: nl</p>
LA  - nl
SN  - 0028-2162
UR  - http://dx.doi.org/
ID  - ref1
ER  -