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Introduction: The length of stay of critically ill patients in the intensive care unit (ICU) is an indication of patient ICU resource usage and varies considerably. Planning of postoperative ICU admissions is important as ICUs often have no nonoccupied beds available. Problem statement: Estimation of the ICU bed availability for the next coming days is entirely based on clinical judgement by intensivists and therefore too inaccurate. For this reason, predictive models have much potential for improving planning for ICU patient admission. Objective: Our goal is to develop and optimize models for patient survival and ICU length of stay (LOS) based on monitored ICU patient data. Furthermore, these models are compared on their use of sequential organ failure (SOFA) scores as well as underlying raw data as input features. Methodology: Different machine learning techniques are trained, using a 14,480 patient dataset, both on SOFA scores as well as their underlying raw data values from the first five days after admission, in order to predict i) the patient LOS, and ii) the patient mortality. Furthermore, to help physicians in assessing the prediction credibility, a probabilistic model is tailored to the output of our best-performing model, assigning a belief to each patient status prediction. A two-by-two grid is built, using the classification outputs of the mortality and prolonged stay predictors to improve the patient LOS regression models. Results: For predicting patient mortality and a prolonged stay, the best performing model is a support vector machine (SVM) with G A,D = 65.9% (area under the curve (AUC) of 0.77) and G S ,L = 73.2% (AUC of 0.82). In terms of LOS regression, the best performing model is support vector regression, achieving a mean absolute error of 1.79 days and a median absolute error of 1.22 days for those patients surviving a nonprolonged stay. Conclusion: Using a classification grid based on the predicted patient mortality and prolonged stay, allows more accurate modeling of the patient LOS. The detailed models allow to support the decisions made by physicians in an ICU setting.

Autism Spectrum Disorder (ASD) is characterized by social interaction difficulties and communication difficulties. Moreover, children with ASD often suffer from other co-morbidities, such as anxiety and depression. Finding appropriate treatment can be difficult as symptoms of ASD and co-morbidities often overlap. Due to these challenges, parents of children with ASD often suffer from higher levels of stress. This research aims to investigate the feasibility of empowering children with ASD and their parents through the use of a serious game to reduce stress and anxiety and a supporting parent application. The New Horizon game and the SpaceControl application were developed together with therapists and according to guidelines for e-health patient empowerment. The game incorporates two mini-games with relaxation techniques. The performance of the game was analyzed and usability studies with three families were conducted. Parents and children were asked to fill in the Spence's Children Anxiety Scale (SCAS) and Spence Children Anxiety Scale-Parents (SCAS-P) anxiety scale. The game shows potential for stress and anxiety reduction in children with ASD.Sensors 2020, 20, 966 2 of 41 language. ASD is a heterogeneous condition: the profile of each child or adult with ASD is unique [4]. An estimated 1 in 132 individuals suffers from ASD [5]. As a result of these challenges, related to social functioning and communication, people with ASD often suffer from other conditions, such as mood swings and anxiety. It is estimated that up to 72% of children with ASD suffer from co-morbidities [6], however, co-morbidity rates remain largely unknown for ASD, as co-morbid conditions can be difficult to diagnose [7][8][9][10]. Depression and anxiety are two of the most common ASD co-morbidities with rates around 40 to 50% [11].Due to communication difficulties, children with ASD are often unable to express themselves when stressed or frustrated. Combined with unique symptoms in every child and overlap in symptoms of co-morbidities, it is challenging to find the appropriate response. As a result, temper tantrums are common, which can be an important cause of parental stress [1]. Determining the correct diagnosis or finding appropriate treatment is difficult due to the challenges in communication and uniqueness in symptoms [7].Empowering both the children and their parents by raising awareness of the child's anxiety and stress can result in better management of the child's temper tantrums, which can result in a decrease in stress and anxiety. The term "patient empowerment" is used in many different settings and domains, but no general definition exists. The consensus is that patient empowerment is used to describe situations where patients and users are encouraged to take control of their health management [12]. In other words, patient empowerment is enabling patients to actively understand their health [13], but also the support to restore a sense of hope, respect, self-efficacy and the drive to face seemingly insurmountable challenges [14...

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