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Thomas Brox Røst

Publications and source records attributed to Thomas Brox Røst.

2 recordsLinked to original sources

Visualizing Patient Trajectories and Disorder Co-occurrences in Child and Adolescent Mental Health

Understanding patient trajectories and identifying patterns in episodes of care is critical for effective healthcare decision-making. We present a patient timeline visualization using clustered episodes of care derived from over 35 years of Child and Adolescent Mental Health Services (CAMHS) data. Patients were categorized into 12 groups based on three features: age group (preschoolers, middle childhood, teenagers) at the start of the first episode, gender, and presence or absence of Attention-Deficit Hyperactivity Disorder (ADHD), in order to group similar patients. The patients, timeline with demographics, and episode of care information are displayed in the trajectory to facilitate understanding of the patient and associated events, allowing observation of temporal patterns and variations. These plots reveal similarities and differences in care needs and patterns across groups. Females without ADHD have a steady increase in the number of episodes of care with age. Females with ADHD and all males experienced a peak in the number of episodes during middle childhood, followed by a decline in the teenage years. To compare and understand the intensity and co-occurring disorders with ADHD across different groups, we plotted an ADHD co-occurrence graph, and Tourette's syndrome was co-occurring predominantly in all age groups. We evaluated and refined our visualizations with the involvement of clinicians, who found them useful for understanding the context of CAMHS care. These visual tools make the population data in the Electronic Health Records (EHR) available for decision-making and enhancing the understanding of care and disorder patterns across groups.

cs.HC↗

DeepLearningKit - an GPU Optimized Deep Learning Framework for Apple's iOS, OS X and tvOS developed in Metal and Swift

In this paper we present DeepLearningKit - an open source framework that supports using pretrained deep learning models (convolutional neural networks) for iOS, OS X and tvOS. DeepLearningKit is developed in Metal in order to utilize the GPU efficiently and Swift for integration with applications, e.g. iOS-based mobile apps on iPhone/iPad, tvOS-based apps for the big screen, or OS X desktop applications. The goal is to support using deep learning models trained with popular frameworks such as Caffe, Torch, TensorFlow, Theano, Pylearn, Deeplearning4J and Mocha. Given the massive GPU resources and time required to train Deep Learning models we suggest an App Store like model to distribute and download pretrained and reusable Deep Learning models.

cs.LG↗