Background Digital phenotyping and machine learning are currently being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance mental health globally. Objective This study aimed to apply machine learning in an acute mental health setting for suicide risk prediction. This study uses a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data, which have typically been collected from health care records. Methods We created a smartphone app called Strength Within Me, which 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 inpatients with acute mental health (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine the best fit. Results K-nearest neighbors (KNN; k=2) with uniform weighting and the Euclidean distance metric emerged as the most promising algorithm, with 68% mean accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross-validation) and an average area under the curve of 0.65. We applied a combined 5×2 F test to test the model performance of KNN against the baseline classifier that guesses training majority, random forest, support vector machine and logistic regression, and achieved F statistics of 10.7 (P=.009) and 17.6 (P=.003) for training majority and random forest, respectively, rejecting the null of performance being the same. Therefore, we have taken the first steps in prototyping a system that could continuously and accurately assess the risk of suicide via mobile devices. Conclusions Predicting for suicidality is an underaddressed area of research to which this paper makes a useful contribution. This is part of the first generation of studies to suggest that 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, it has the potential for accurate discriminant risk prediction. However, although full automation and independence of clinical judgment or input would be a worthy development for those individuals who are less likely to access specialist mental health services, and for providing a timely response in a crisis situation, the ethical and legal implications of such advances in the field of psychiatry need to be acknowledged.
We report a case of a 64-year-old lady who developed clinical features of Wernicke-Korsakoff syndrome following a laparotomy for small bowel obstruction. Following the operation she developed paralytic ileus and required total parenteral nutrition for one month. A suspected history of average 40 units of weekly alcohol consumption prior to the operation could not be confirmed and the patient did not show any sign of alcohol dependence. Within a few months of treatment with a daily oral dose of thiamine 200 mgs supplemented by multivitamins the patient showed subjective evidence of improvement in confusion, confabulation, and anterograde amnesia, although objective tests showed residual deficits in many areas of cognitive functioning, including immediate and delayed recall of verbal and non-verbal materials, planning and switching of attention.
Epilepsy is often a familial disorder, although it is not known if relatives of an epileptic proband suffer from any psychological disorders, even without a clinical manifestation of epilepsy. The present study considers this concept in the setting of the largest male prison in Europe, HMP Liverpool. The study formed part of the National Prison Needs Assessment Survey, and consisted of a cross-sectional, questionnaire study. There was a high response rate, and the results indicate that prisoners have a high prevalence of a family history of epilepsy. The study also showed that prisoners who report such a history have significantly more psychological symptoms than those prisoners without such a family history. The conclusion is that the psychological health of relatives of those with epilepsy requires further investigation because the morbidity is higher than expected and may be associated with offending behaviour.
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