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Clinical machine learning models show a significant performance drop when tested in settings not seen during training. Domain generalisation models promise to alleviate this problem, however, there is still scepticism about whether they…
Optical coherence tomography (OCT) microscope (OCM) uses a high-numerical-aperture objective to achieve cellular-level lateral resolution. However, its practical imaging depth range is limited by the depth of focus (DOF). Although…
In this paper, we compare and analyze clustering methods with missing data in health behavior research. In particular, we propose and analyze the use of compressive sensing's matrix completion along with spectral clustering to cluster…
Contactless monitoring using thermal imaging has become increasingly proposed to monitor patient deterioration in hospital, most recently to detect fevers and infections during the COVID-19 pandemic. In this letter, we propose a novel…
Health information technologies are transforming how mental healthcare is paid for through value-based care programs, which tie payment to data quantifying care outcomes. But, it is unclear what outcomes data these technologies should…
Dynamic optical coherence tomography (DOCT) enables label-free functional imaging by capturing temporal OCT signal variations caused by intracellular and intratissue motions. However, the relationship between DOCT signals and the sample…
This study proposes a novel, integrative framework for patient-centered data science in the digital health era. We developed a multidimensional model that combines traditional clinical data with patient-reported outcomes, social…
Objective: Our objective is to explore how public entities in the role of platform providers can address this issue by enabling collaboration within their OGD ecosystems, both in terms of the OGD published on the underpinning platform, as…
Cardiovascular diseases (CVDs) are notably prevalent among patients with obstructive sleep apnea (OSA), posing unique challenges in predicting CVD progression due to the intricate interactions of comorbidities. Traditional models typically…
Physician burnout in the United States has reached critical levels, driven in part by the administrative burden of Electronic Health Record (EHR) documentation and complex diagnostic codes. To relieve this strain and maintain strict patient…
Research on environmental risk modeling relies on numerous indicators to quantify the magnitude and frequency of extreme climate events, their ecological, economic, and social impacts, and the coping mechanisms that can reduce or mitigate…
Missing data is a common problem in real-world settings and particularly relevant in healthcare applications where researchers use Electronic Health Records (EHR) and results of observational studies to apply analytics methods. This issue…
We propose a novel reduced-order methodology to describe complex multi-frequency fluid dynamics from time-resolved snapshot data. Starting point is the Cluster-based Network Model (CNM) thanks to its fully automatable development and human…
Objective: The Learning Health System (LHS) requires integration of research into routine practice. eSource or embedding clinical trial functionalities into routine electronic health record (EHR) systems has long been put forward as a…
Healthcare is becoming a more and more important research topic recently. With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of medical service. However, the complexity of…
Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the most important, outlying data points from thousands of…
Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal.…
This paper describes the technology of data warehouse in healthcare decision-making and tools for support of these technologies, which is used to cancer diseases. The healthcare executive managers and doctors needs information about and…
Often both Aggregate Data (AD) studies and Individual Patient Data (IPD) studies are available for specific treatments. Combining these two sources of data could improve the overall meta-analytic estimates of treatment effects. Moreover,…
The demand of transparency of clinical research results, the need of accelerating the process of transferring innovation in the daily medical practice as well as assuring patient safety and product efficacy make it necessary to extend the…