相关论文: Hospital Case Cost Estimates Modelling - Algorithm…

There has been much research activity in recent times about providing the data infrastructures needed for the provision of personalised healthcare. In particular the requirement of integrating multiple, potentially distributed,…

Developing new drugs for target diseases is a time-consuming and expensive task, drug repurposing has become a popular topic in the drug development field. As much health claim data become available, many studies have been conducted on the…

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase…

For decades, hospital services have been faced with the challenge of ensuring quality care for patients despite the pressures on staff due to workload overload. Nursing staff are particularly affected by this reality, with a patient/nursing…

This paper explores different methods to estimate prices paid per efficiency unit of labor in panel data. We study the sensitivity of skill price estimates to different assumptions regarding workers' choice problem, identification…

The burden of diseases is rising worldwide, with unequal treatment efficacy for patient populations that are underrepresented in clinical trials. Healthcare, however, is driven by the average population effect of medical treatments and,…

Routinely collected clinical patient data posits a valuable resource for data-driven medical innovation. Such secondary data use for medical research purposes is dependent on the patient's consent. To gain an understanding of the patients'…

A series of examples of computational models is provided, where the model aim is to interpret numerical results in terms of internal states of agents minds. Two opposite strategies or research can be distinguished in the literature. First…

Ambulance demand estimation at fine time and location scales is critical for fleet management and dynamic deployment. We are motivated by the problem of estimating the spatial distribution of ambulance demand in Toronto, Canada, as it…

In a data-scarce field such as healthcare, where models often deliver predictions on patients with rare conditions, the ability to measure the uncertainty of a model's prediction could potentially lead to improved effectiveness of decision…

Personalised medicine strives to identify the right treatment for the right patient at the right time, integrating different types of biological and environmental information. Such information come from a variety of sources: omics data…

Hospitals face challenges in long-term intensive care unit (ICU) capacity planning under uncertain demand. Admission rates fluctuate over time, and LOS distributions vary with patient heterogeneity, hospital location, case mix, and clinical…

We derive a general multiple state model for critical illness insurances. In contrast to the classical model, we take into account that the probability of death for a dread disease sufferer may depend on the duration of the disease, and the…

Data is a precious resource in today's society, and is generated at an unprecedented and constantly growing pace. The need to store, analyze, and make data promptly available to a multitude of users introduces formidable challenges in…

The complexity of database systems has increased significantly along with the continuous growth of data, resulting in NoSQL systems and forcing Information Systems (IS) architects to constantly adapt their data models (i.e., the data…

Healthcare cost prediction is a challenging task due to the high-dimensionality and high correlation among covariates. Additionally, the skewed, heavy-tailed, and often multi-modal nature of cost data can complicate matters further due to…

The State of Washington sells patient-level health data for $50. This publicly available dataset has virtually all hospitalizations occurring in the State in a given year, including patient demographics, diagnoses, procedures, attending…

Bayesian models are increasing fit to large administrative data sets and then used to make individualized recommendations. For instance, Medicare's Hospital Compare webpage provides information to patients about specific hospital mortality…

We consider high-dimensional regression over subgroups of observations. Our work is motivated by biomedical problems, where disease subtypes, for example, may differ with respect to underlying regression models, but sample sizes at the…

Accurately estimating personalized treatment effects within a study site (e.g., a hospital) has been challenging due to limited sample size. Furthermore, privacy considerations and lack of resources prevent a site from leveraging…