Dynamic Local Attention with Hierarchical Patching for Irregular Clinical Time Series

Irregular multivariate time series data is prevalent in the clinical and healthcare domains. It is characterized by time-wise and feature-wise irregularities, making it challenging for machine learning methods to work with. To solve this, we introduce a new model architecture composed of two modules: (1) DLA, a Dynamic Local Attention mechanism that uses learnable queries and feature-specific local windows when computing the self-attention operation. This results in aggregating irregular time steps raw input within each window to a harmonized regular latent space representation while taking into account the different features' sampling rates. (2) A hierarchical MLP mixer that processes the output of DLA through multi-scale patching to leverage information at various scales for the downstream tasks. Our approach outperforms state-of-the-art methods on three real-world datasets, including the latest clinical MIMIC IV dataset.Comment: Findings of Machine Learning for Health (ML4H) 202

SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting

Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these methods are less effective for time series forecasting, as optimization of instance discrimination is not directly applicable to predicting the future state from the history context. Moreover, the construction of positive and negative pairs in current technologies strongly relies on specific time series characteristics, restricting their generalization across diverse types of time series data. To address these limitations, we propose SimTS, a simple representation learning approach for improving time series forecasting by learning to predict the future from the past in the latent space. SimTS does not rely on negative pairs or specific assumptions about the characteristics of the particular time series. Our extensive experiments on several benchmark time series forecasting datasets show that SimTS achieves competitive performance compared to existing contrastive learning methods. Furthermore, we show the shortcomings of the current contrastive learning framework used for time series forecasting through a detailed ablation study. Overall, our work suggests that SimTS is a promising alternative to other contrastive learning approaches for time series forecasting.Comment: 13 pages, 6 figure

Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models

We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose predictionComment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 17 page

Sensing river and floodplain biodiversity : developing a prototype

Freshwaters, such as rivers and floodplains, are among the world’s most diverse ecosystems, but they are losing biodiversity faster than any other ecosystem, mainly due to human activities. A major problem is the low awareness of biodiversity loss. Triggering emotions and amazement may increase people’s biodiversity perception in a more holistic way. Therefore, with an immersive audio visual VR-simulation prototype based on 3D point clouds and sound recordings above and below water developed in the Unity game engine, we want to allow for sensing river biodiversity. Feedback from a user study demonstrates that the prototype can promote laypersons’ awareness of biodiversity loss and provides insights for its further enhancement

Comparison of an exergame and a moderate-intensity endurance training intervention on physiological parameters

Background Exergames are interactive video games that stimulate an active, whole-body gaming experience (Best, 2013). By combining electronic entertainment with physical exercise, exergames offer novel opportunities to expand physical activity in different age groups and settings. Even though studies have found a significant increase in energy expenditure when playing exergames compared to normal video games, most games only induce low to moderate-intensity activity which is too low to result in relevant physical adjustments (Biddiss & Irwin, 2010). This study assessed the effects of an 8-week exergame-training (EXT) in an innovative exergame called the ExerCube and compared it with a typical moderate-intensity endurance training (ET) intervention. Methods In total, 19 individuals (10 female; age 26.9 ±8.7 years; body mass index (BMI) 23.6 ±3.1 kg/m2) participated and were block randomized into an EXT group (n = 9) and an ET group (n = 10). Throughout the 8-week intervention period, the EXT group attended 20-30-minutes of EXT three times a week while the ET group completed 15-45-minutes of ET (jogging/cycling at 65-75% of maximal heart rate) three times a week. Before and after the intervention BMI, systolic and diastolic blood pressure, and VO2max (spiroergometry; start: 50 or 75 W; increment: 25 W/min) were assessed and compared (paired-samples t-test, ANOVA). Results Significant time × group interaction effects were found for VO2max (F(1,17) = 11.345; p = .004, ηp2 = .400). The EXT group revealed significant within-group effects in VO2max from pre (43.2 ±10.6 ml/kg*min) to post (46.9 ±10.9 ml/kg*min; p = .004, d = 1.308) while the ET group revealed no significant changes (pre: 39.4 ±5.4 ml/kg*min; post: 39.7 ±4.9 ml/kg*min; p = .466, d = .241). No significant time × group interaction effects were detected in systolic blood pressure (F(1,17) = .050; p = .825, ηp2 = .003) or diastolic blood pressure (F(1,17) = .005; p = .943, ηp2 = .000). However, there was a significant decrease in the peripheral systolic blood pressure from pre (122 ±10 mmHg) to post (117 ±12 mmHg; p = .034, d = .792) in the ET group but not in the EXT group (pre: 118 ±8; post: 114 ±7; p = .156, d = .523). Concerning BMI, no significant interaction effects (F(1,17) = 2.818; p = .111, ηp2 = .142) were detected. Conclusions The EXT seems to be more effective as conventional ET exercise approach to improve endurance performance. This is promising as exergame may develop intrinsic motivation/enjoyment for physical activity. Further studies confirming these findings and extending to psychological variables are needed. References Best, J. R. (2013). Exergaming in youth: Effects on physical and cognitive health. Zeitschrift für Psychologie, 221(2), 72-78. https://doi.org/10.1027/2151-2604/a000137 Biddiss, E., & Irwin, J. (2010). Active video games to promote physical activity in children and youth: A systematic review. Archives of Pediatrics & Adolescent Medicine, 164(7), 664-672. https://doi.org/10.1001/archpediatrics.2010.10