Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons

The convergence of smart technologies and predictive modelling in prisons presents an exciting opportunity to revolutionize the monitoring of inmate behaviour, allowing for the early detection of signs of distress and the effective mitigation of suicide risks. While machine learning algorithms have been extensively employed in predicting suicidal behaviour, a critical aspect that has often been overlooked is the interoperability of these models. Most of the work done on model interpretations for suicide predictions often limits itself to feature reduction and highlighting important contributing features only. To address this research gap, we used Anchor explanations for creating human-readable statements based on simple rules, which, to our knowledge, have never been used before for suicide prediction models. We also overcome the limitation of anchor explanations, which create weak rules on high-dimensionality datasets, by first reducing data features with the help of SHapley Additive exPlanations (SHAP). We further reduce data features through anchor interpretations for the final ensemble model of XGBoost and random forest. Our results indicate significant improvement when compared with state-of-the-art models, having an accuracy and precision of 98.6% and 98.9%, respectively. The F1-score for the best suicide ideation model appeared to be 96.7%

Developing counseling skills through pre-recorded videos and role play: a pre- and post-intervention study in a Pakistani medical school

<p>Abstract</p> <p>Background</p> <p>Interactive methods like role play, recorded video scenarios and objective structured clinical exam (OSCE) are being regularly used to teach and assess communication skills of medical students in the western world. In developing countries however, they are still in the preliminary phases of execution in most institutes. Our study was conducted in a naïve under resourced setup to assess the impact of such teaching methodologies on the counseling skills of medical students.</p> <p>Methods</p> <p>Fifty four 4^thyear MBBS students were identified to be evaluated for communication skills by trained facilitators in a pre-intervention OSCE. The same group of students was given a demonstration of ideal skill level by means of videos and role playing sessions in addition to real life interaction with patients during hospital and community rotations. A post-intervention evaluation was carried out six months later through OSCE and direct observation through structured checklist (DOS) in hospital and community settings. The combined and individual performance levels of these students were analyzed.</p> <p>Results</p> <p>There was a statistically significant difference in the communication skills of students when assessed in the post-intervention OSCE (p = 0.000). Individual post-intervention percentages of study participants displayed improvement as well (n = 45, p = 0.02). No difference was observed between the scores of male and female students when assessed for two specific competencies of antenatal care and breast feeding counseling (p = 0.11). The mean DOS (%) score of 12 randomly selected students was much lower as compared to the post-intervention (%) score but the difference between them was statistically non significant, a result that may have been affected by the small sample size as well as other factors that may come into play in real clinical settings and were not explored in this study (59.41 ± 7.8 against 82.43 ± 22.08, p = 0.88).</p> <p>Conclusions</p> <p>Videos and role play in combination with community and clinical exposure are effective modes of teaching counseling skills to medical students. They can be successfully utilized even in a limited resource setup, as demonstrated by our trial.</p