Understanding health management and safety decisions using signal processing and machine learning

Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

A healthcare resource allocation generally plays a vital role in the number of patients treated (p_nt) and the patient waiting time (w_t) in healthcare institutions. This study aimed to estimate p_nt and w_t as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δ_i) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δ_i of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for p_nt; 0.9514, 0.9517, 0.9514, and 0.9514 for w_t, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ_0.2 (AB had a better accuracy at 0.8709 based on the value of δ_0.2 for p_nt) in the test stage. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for p_nt; 0.8820, 0.8821, 0.8819, and 0.8818 for w_t in the training phase, respectively. All scenarios created by the δ_i coefficient should be preferred for ED since the income provided by the p_nt value to the hospital was more than the cost of healthcare resources. On the contrary, the w_t estimation results of ML algorithms based on the δ_i coefficient differed. Although w_t values in all ML algorithms with δ_0.0 and δ_0.1 coefficients reduced the cost of the hospital, w_t values based on δ_0.2 and δ_0.3 increased the cost of the hospital.

Experiences of Siblings of Children With Neurodevelopmental Disorders: Comparing Qualitative Analysis and Machine Learning to Study Narratives

INTRODUCTION

Relatively few studies have focused on the wellbeing, experiences and needs of the siblings of children with a psychiatric diagnosis. However, the studies that have been conducted suggest that the impact of such circumstances on these siblings is significant. Studying narratives of diagnosed children or relatives has proven to be a successful approach to gain insights that could help improve care. Only a few attempts have been made to study narratives in psychiatry utilizing a machine learning approach.

METHOD

In this current study, 13 narratives of the experiences of siblings of children with a neurodevelopmental disorders were collected through largely unstructured interviews. The interviews were analyzed using the traditional qualitative, hermeneutic phenomenology method as well as latent Dirichlet allocation (LDA), an unsupervised machine learning method clustering words from documents into topics. One aim of this study was to evaluate the experiences of the siblings in order to find leads to improve care and support for these siblings. Furthermore, the outcomes of both analyses were compared to evaluate the role of machine learning in analyzing narratives.

RESULTS

Qualitative analysis of the interviews led to the formulation of nine main themes: confrontation with conflicts, coping strategies siblings, need for rest and time for myself, need for support and attention from personal circle, wish for normality, influence on personal choices and possibilities for development, doing things together, recommendations and advices, ambivalence and loyalty. Using unsupervised machine learning (LDA) 24 topics were formed that mostly overlapped with the qualitative themes found. Both the qualitative analysis and the LDA analysis detected themes that were unique to the respective analysis.

CONCLUSION

The present study found that studying narratives of siblings of children with a neurodevelopmental disorder contributes to a better understanding of the subjects' experiences. Siblings cope with ambivalent feelings toward their brother or sister and this emotional conflict often leads to adapted behavior. Several coping strategies are developed to deal with the behavior of their brother or sister like seeking support or ignoring. Devoted support, time and attention from close relatives, especially parents, is needed. The LDA analysis didn't appear useful to distract meaning and context from the narratives, but it was proposed that machine learning could be a valuable and quick addition to the traditional qualitative methods by finding overlooked topics and giving a rudimental overview of topics in narratives.

The School Attachment Monitor-A novel computational tool for assessment of attachment in middle childhood

Background

Attachment research has been limited by the lack of quick and easy measures. We report development and validation of the School Attachment Monitor (SAM), a novel measure for largescale assessment of attachment in children aged 5–9, in the general population. SAM offers automatic presentation, on computer, of story-stems based on the Manchester Child Attachment Story Task (MCAST), without the need for trained administrators. SAM is delivered by novel software which interacts with child participants, starting with warm-up activities to familiarise them with the task. Children’s story completion is video recorded and augmented by ‘smart dolls’ that the child can hold and manipulate, with movement sensors for data collection. The design of SAM was informed by children of users’ age range to establish their task understanding and incorporate their innovative ideas for improving SAM software.

Methods

130 5–9 year old children were recruited from mainstream primary schools. In Phase 1, sixty-one children completed both SAM and MCAST. Inter-rater reliability and rating concordance was compared between SAM and MCAST. In Phase 2, a further 44 children completed SAM complete and, including those children completing SAM in Phase 1 (total n = 105), a machine learning algorithm was developed using a “majority vote” procedure where, for each child, 500 non-overlapping video frames contribute to the decision.

Results

Using manual rating, SAM-MCAST concordance was excellent (89% secure versus insecure; 97% organised versus disorganised; 86% four-way). Comparison of human ratings of SAM versus the machine learning algorithm showed over 80% concordance.

Conclusions

We have developed a new tool for measuring attachment at the population level, which has good reliability compared to a validated attachment measure and has the potential for automatic rating–opening the door to measurement of attachment in large populations.