Predicting long-term neurocognitive outcome after pediatric intensive care unit admission for bronchiolitis-preliminary exploration of the potential of machine learning.

PURPOSE: For successful prevention and intervention, it is important to unravel the complex constellation of factors that affect neurocognitive functioning after pediatric intensive care unit (PICU) admission. This study aims (1) to elucidate the potential relevance of patient and PICU-related characteristics for long-term adverse neurocognitive outcome after PICU admission for bronchiolitis, and (2) to perform a preliminary exploration of the potential of machine learning as compared to linear regression to improve neurocognitive outcome prediction in a relatively small sample of children after PICU admission. METHODS: This cross-sectional observational study investigated 65 children aged 6-12 years with previous PICU admission for bronchiolitis (age ≤ 1 year). They were compared to demographically comparable healthy peers (n = 76) on neurocognitive functioning. Patient and PICU-related characteristics used for the prediction models were as follows: demographic characteristics, perinatal and disease parameters, laboratory results, and intervention characteristics, including hourly validated mechanical ventilation parameters. Neurocognitive outcome was measured by intelligence and computerized neurocognitive testing. Prediction models were developed for each of the neurocognitive outcomes using Regression Trees, k-Nearest Neighbors, and conventional linear regression analysis. RESULTS: The patient group had lower intelligence than the control group (p < .001, d = -0.59) and poorer performance in neurocognitive functions, i.e., speed and attention (p = .03, d = -0.41) and verbal memory (p < .001, d = -0.60). Lower intelligence was predicted by lower birth weight and lower socioeconomic status (R2 = 25.9%). Poorer performance on the speed and attention domain was predicted by younger age at follow-up (R2 = 53.5%). Poorer verbal memory was predicted by lower birth weight, younger age at follow-up, and greater exposure to acidotic events (R2 = 50.6%). The machine learning models did not reveal added value in terms of model performance as compared to linear regression. CONCLUSION: The findings of this study suggest that in children with previous PICU admission for bronchiolitis, (1) lower birth weight, younger age at follow-up, and lower socioeconomic status are associated with poorer neurocognitive outcome; and (2) greater exposure to acidotic events during PICU admission is associated with poorer verbal memory outcome. The findings of this study provide no evidence for the added value of machine learning models as compared to linear regression analysis in the prediction of long-term neurocognitive outcome in a relatively small sample of children. WHAT IS KNOWN: • Adverse neurocognitive outcomes are described in PICU survivors, which are known to interfere with development in other major domains of functioning, such as mental health, academic achievement, and socioeconomic success, highlighting neurocognition as an important outcome after PICU admission. • Machine learning is a rapidly growing field of artificial intelligence that is increasingly applied in health care settings, with great potential to capture the complexity of outcome prediction. WHAT IS NEW: • This study shows that lower birth weight, lower socioeconomic status, and greater exposure to acidotic events during PICU admission for bronchiolitis are associated with poorer long-term neurocognitive outcome after PICU admission. Results provide no evidence for the added value of machine learning models in a relatively small sample of children. • As bronchiolitis seldom manifests neurologically, the relation between acidotic events and neurocognitive outcome may reflect either potentially harmful effects of acidosis itself or related processes such as hypercapnia or hypoxic and/or ischemic events during PICU admission. This study further highlights the importance of structured follow-up to monitor long-term outcome of children after PICU admission.

Social Robots for Elderly Care: An Inventory of Promising Use Cases and Business Models.

This paper discusses a study that aimed to elicit promising application areas and potential business models for social robotics in healthcare. For this goal, we conducted focus groups with care professionals and the management of elderly care organizations in the Netherlands and Germany. Three use cases were mentioned as the most promising: the robot as a ubiquitous aid, the robot as a helper in the room, and the robot as a guide. Finally, we discuss the implications of the medical device and privacy legislation for these three use cases.

Fit by Bits: An Explorative Study of Sports Physiotherapists' Perception of Quantified Self Technologies.

Our aim was to determine sport physiotherapists' attitudes towards Quantified Self technology usage and adoption and to analyze factors that may influence this attitude. A survey was used to study a sample in the Netherlands. Assessment was made of attitudes of towards Quantified Self technology usage by clients of therapists, by therapists themselves and intention to adopt Quantified Self technology. Results show that the uptake of Quantified Self technology by sports physiotherapists is rather low but that the intention to adopt Quantified Self technology by sports physiotherapists is quite high. These results can provide a foundation to provide an infrastructure for sports physiotherapists to fulfill their wishes with regard to Quantified Self technology.