Interpretable machine learning models for COPD ease of breathing estimation.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitoring has been shown to improve quality of life and reduce exacerbations, but remains an ongoing area of research. We introduce a novel method for estimating changes in ease of breathing for COPD patients, using obstructed breathing data collected via wearables.

Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches.

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts.

Optimized continuous homecare provisioning through distributed data-driven semantic services and cross-organizational workflows.

Background: In healthcare, an increasing collaboration can be noticed between different caregivers, especially considering the shift to homecare. To provide optimal patient care, efficient coordination of data and workflows between these different stakeholders is required. To achieve this, data should be exposed in a machine-interpretable, reusable manner.

Patients with chronic cluster headache may show reduced activity energy expenditure on ambulatory wrist actigraphy recordings during daytime attacks.

Objective: To investigate the changes in activity energy expenditure (AEE) throughout daytime cluster headache (CH) attacks in patients with chronic CH and to evaluate the usefulness of actigraphy as a digital biomarker of CH attacks.

Background: CH is a primary headache disorder characterized by attacks of severe to very severe unilateral pain (orbital, supraorbital, temporal, or in any combination of these sites), with ipsilateral cranial autonomic symptoms and/or a sense of restlessness or agitation. We hypothesized increased AEE from hyperactivity during attacks measured by actigraphy.

From Lab to Real World: Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization.

Human activity recognition (HAR) algorithms today are designed and evaluated on data collected in controlled settings, providing limited insights into their performance in real-world situations with noisy and missing sensor data and natural human activities. We present a real-world HAR open dataset compiled from a wristband equipped with a triaxial accelerometer. During data collection, participants had autonomy in their daily life activities, and the process remained unobserved and uncontrolled.

Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.

Background: Atrial Fibrillation (AF) is the most common arrhythmia in the intensive care unit (ICU) and is associated with increased morbidity and mortality. Identification of patients at risk for AF is not routinely performed as AF prediction models are almost solely developed for the general population or for particular ICU populations. However, early AF risk identification could help to take targeted preemptive actions and possibly reduce morbidity and mortality.

Pathogen-based target attainment of optimized continuous infusion dosing regimens of piperacillin-tazobactam and meropenem in surgical ICU patients: a prospective single center observational study.

Background: Several studies have indicated that commonly used piperacillin-tazobactam (TZP) and meropenem (MEM) dosing regimens lead to suboptimal plasma concentrations for a range of pharmacokinetic/pharmacodynamic (PK/PD) targets in intensive care unit (ICU) patients. These targets are often based on a hypothetical worst-case scenario, possibly overestimating the percentage of suboptimal concentrations. We aimed to evaluate the pathogen-based clinically relevant target attainment (CRTA) and therapeutic range attainment (TRA) of optimized continuous infusion dosing regimens of TZP and MEM in surgical ICU patients.

Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings.

A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate sensor data, such as carbon dioxide (CO) and temperature, which is fed into Streaming MASSIF, a semantic stream processing platform, to perform the computations.

Assessing the added value of context during stress detection from wearable data.

Background: Insomnia, eating disorders, heart problems and even strokes are just some of the illnesses that reveal the negative impact of stress overload on health and well-being. Early detection of stress is therefore of utmost importance. Whereas the gold-standard for detecting stress is by means of questionnaires, more recent work uses wearable sensors to find continuous and qualitative physical markers of stress.

An adaptation algorithm for personalised virtual reality exposure therapy.

Background: Anxiety disorders are highly prevalent in mental health problems. The lives of people suffering from an anxiety disorder can be severely impaired. Virtual Reality Exposure Therapy (VRET) is an effective treatment, which immerses patients in a controlled Virtual Environment (VE).

Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients.

Background: Beta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling is the golden standard to predict drug concentrations. However, currently available PopPK models often lack predictive accuracy, making them less suited to guide dosing regimen adaptations.

Bridging the gap between expressivity and efficiency in stream reasoning: a structural caching approach for IoT streams.

In today's data landscape, data streams are well represented. This is mainly due to the rise of data-intensive domains such as the Internet of Things (IoT), Smart Industries, Pervasive Health, and Social Media. To extract meaningful insights from these streams, they should be processed in real time, while solving an integration problem as these streams need to be combined with more static data and their domain knowledge.

mBrain: towards the continuous follow-up and headache classification of primary headache disorder patients.

Background: The diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation.

Parameter Efficient Neural Networks With Singular Value Decomposed Kernels.

Traditionally, neural networks are viewed from the perspective of connected neuron layers represented as matrix multiplications. We propose to compose these weight matrices from a set of orthogonal basis matrices by approaching them as elements of the real matrices vector space under addition and multiplication. Making use of the Kronecker product for vectors, this composition is unified with the singular value decomposition (SVD) of the weight matrix.

GENDIS: Genetic Discovery of Shapelets.

In the time series classification domain, shapelets are subsequences that are discriminative of a certain class. It has been shown that classifiers are able to achieve state-of-the-art results by taking the distances from the input time series to different discriminative shapelets as the input. Additionally, these shapelets can be visualized and thus possess an interpretable characteristic, making them appealing in critical domains, where longitudinal data are ubiquitous.

Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling.

Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or preterm using a public resource, called the Term/Preterm Electrohysterogram database. However, we argue that these results are overly optimistic due to a methodological flaw being made.

MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph.

Background: Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relational, directed graph representations of domain knowledge. Recently, deep learning-based techniques have been gaining a lot of popularity.

Clinical information extraction for preterm birth risk prediction.

This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed.

A Dynamic Dashboarding Application for Fleet Monitoring Using Semantic Web of Things Technologies.

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications.

Empowering Children with ASD and Their Parents: Design of a Serious Game for Anxiety and Stress Reduction.

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.

Data Mining in the Development of Mobile Health Apps: Assessing In-App Navigation Through Markov Chain Analysis.

Background: Mobile apps generate vast amounts of user data. In the mobile health (mHealth) domain, researchers are increasingly discovering the opportunities of log data to assess the usage of their mobile apps. To date, however, the analysis of these data are often limited to descriptive statistics.

Modeling the Prediction of the Session Rating of Perceived Exertion in Soccer: Unraveling the Puzzle of Predictive Indicators.

Purpose: To predict the session rating of perceived exertion (sRPE) in soccer and determine its main predictive indicators.

Methods: A total of 70 external-load indicators (ELIs), internal-load indicators, individual characteristics, and supplementary variables were used to build a predictive model.

Results: The analysis using gradient-boosting machines showed a mean absolute error of 0.

A decision support system to follow up and diagnose primary headache patients using semantically enriched data.

Background: Headache disorders are an important health burden, having a large health-economic impact worldwide. Current treatment & follow-up processes are often archaic, creating opportunities for computer-aided and decision support systems to increase their efficiency. Existing systems are mostly completely data-driven, and the underlying models are a black-box, deteriorating interpretability and transparency, which are key factors in order to be deployed in a clinical setting.

Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks.

Introduction: Blood cultures are often performed in the intensive care unit (ICU) to detect bloodstream infections and identify pathogen type, further guiding treatment. Early detection is essential, as a bloodstream infection can give cause to sepsis, a severe immune response associated with an increased risk of organ failure and death.

Problem Statement: The early clinical detection of a bloodstream infection is challenging but rapid targeted treatment, within the first place antimicrobials, substantially increases survival chances.