JMIR Publications

ABSTRACT

Background:

Machine learning is nowadays being applied to augment or even replace traditional analytic procedures in many domains, including health care.

Objective:

To apply machine learning in an acute mental health setting for suicide risk prediction. This study is novel, adding to existing knowledge by using data collected through a smartphone in place of clinical data which has typically been collected from health care records.

Methods:

We created a smartphone application called Strength Within Me (SWiM) that was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of 66 acute mental health inpatients. In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine best fit.

Results:

Nearest Neighbors (k=2) emerged as the most promising algorithm, with 68% average accuracy (averaged over 25,000 simulations of splitting the training and testing data of 80-20 splits) and average AUC of 0.65. We have therefore taken the first steps in prototyping a system that could continuously and accurately assess risk of suicide via mobile devices.

Conclusions:

This is the first study to suggest it is feasible to utilize smartphone generated user input and passive sensor data to generate a risk algorithm among inpatients at suicide risk. The model reveals fair concordance between phone-derived and research generated clinical data and with iterative development has potential for accurate discriminant risk prediction.