Publications by authors named "Daniel Schaffert"
Background: The COVID-19 pandemic severely impacted healthcare systems, affecting patient outcomes and resource allocation. This study applied automated machine learning (AutoML) to analyze key health outputs, such as discharge conditions, mortality, and COVID-19 cases, with the goal of improving responses to future crises.
Methods: AutoML was used to train and validate models on an ICD-10 dataset covering the first wave of COVID-19 in Romania (January-September 2020).
Article Synopsis
- Psoriasis vulgaris (PsV) and psoriatic arthritis (PsA) are complex diseases that affect health and quality of life, and predicting treatment responses and disease progression remains a challenge despite its importance.
- This study utilized automated machine learning (AutoML) to develop accurate predictive models to inform clinical decisions for patients with PsV and PsA, focusing on therapy changes and disease progression factors.
- Results showed that the extreme gradient boosted trees classifier effectively predicted therapy changes at the 24-week mark, identifying key factors such as initial treatment type, baseline scores, and quality of life improvements, suggesting some treatments have a lower likelihood of needing adjustments.
Background: Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps.
Objective: We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app.