A5009732553- Machine Learning in Healthcare
- Health disparities and outcomes
- Explainable Artificial Intelligence (XAI)
- Global Health Care Issues
- Climate Change and Health Impacts
- demographic modeling and climate adaptation
- Medical Coding and Health Information
- Artificial Intelligence in Healthcare
- COVID-19 and healthcare impacts
- Privacy-Preserving Technologies in Data
- Adversarial Robustness in Machine Learning
- Sepsis Diagnosis and Treatment
- Intelligent Tutoring Systems and Adaptive Learning
- Healthcare cost, quality, practices
- Bacterial Identification and Susceptibility Testing
- Streptococcal Infections and Treatments
- Urban Transport and Accessibility
Tilburg University
2024
University Medical Center Utrecht
2024
Utrecht University
2024
Hanze University of Applied Sciences
2023
National Institute for Public Health and the Environment
2022
Abstract Background Local policymakers require information about public health, housing and well-being at small geographical areas. A municipality can for example use this to organize targeted activities with the aim of improving their residents. Surveys are often used gather data, but many neighborhoods have only few or even zero respondents. In that case, estimating status local population directly from survey responses is prone be unreliable. Methods Small Area Estimation (SAE) a...
During the COVID-19 pandemic, provision of non-COVID healthcare was recurrently severely disrupted. The objective to determine whether disruption hospital use, either due cancelled, postponed, or forgone care, during first pandemic year impacted socioeconomic groups differently compared with pre-pandemic use.
To develop predictive models for blood culture (BC) outcomes in an emergency department (ED) setting.Retrospective observational study.ED of a large teaching hospital the Netherlands between 1 September 2018 and 24 June 2020.Adult patients from whom BCs were collected ED. Data demographic information, vital signs, administered medications ED laboratory radiology results extracted electronic health record, if available at end visits.The primary outcome was performance two (logistic regression...
When deploying machine learning models in high-stakes real-world environments such as health care, it is crucial to accurately assess the uncertainty concerning a model's prediction on abnormal inputs. However, there scarcity of literature analyzing this problem medical data, especially mixed-type tabular data Electronic Health Records. We close gap by presenting series tests including large variety contemporary estimation techniques, order determine whether they are able identify...
Decentralizations of governmental tasks in the field public health and well being, make analysis Life Expectancy (LE) data at municipality level more important for obtaining insight into local trends. On basis 4-year moving average Chiang II type LE determinations from 355 Dutch municipalities over period 1996 - 2019, characteristics their growth trajectories were investigated by a mixed four parameter logistic regression model with random parameters municipalities. For almost all it was...
In a data-scarce field such as healthcare, where models often deliver predictions on patients with rare conditions, the ability to measure uncertainty of model's prediction could potentially lead improved effectiveness decision support tools and increased user trust. This work advances understanding estimation for classification risk medical tabular data, in two-fold way. First, we expand refine set heuristics select an technique, introducing tests clinically-relevant scenarios...
<title>Abstract</title> Decentralizations of governmental tasks in the field public health and well being, make analysis Life Expectancy (LE) data at municipality levelmore important for obtaining insight into local trends.On basis 4-year moving average Chiang II type LE determinations from 355 Dutch municipalities over period 1996 - 2019,the characteristics their growth trajectories were investigatedby a mixed four parameter logistic regression model with random parameters...