Related papers: Child Care Provider Survival Analysis
The ongoing COVID-19 pandemic continues to affect communities around the world. To date, almost 6 million people have died as a consequence of COVID-19, and more than one-quarter of a billion people are estimated to have been infected…
We suggest a mathematical model for computing and regularly updating the next preventive maintenance plan for a wind farm. Our optimization criterium takes into account the current ages of the key components, the major maintenance costs…
By integrating survival analysis, machine learning algorithms, and economic interpretation, this research examines the temporal dynamics associated with attaining a 5 percent rise in purchasing power parity-adjusted GDP per capita over a…
In today's fast-paced tech industry, there is a growing need for tools that evaluate a programmer's job readiness based on their coding performance. This study focuses on predicting the potential of Codeforces users to secure various levels…
In this paper, we propose the use of causal inference techniques for survival function estimation and prediction for subgroups of the data, upto individual units. Tree ensemble methods, specifically random forests were modified for this…
The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically…
Population aging is a global challenge, leading to increased demand for health care and social services for the elderly. Home Health Care (HHC) is a vital solution to serve this segment of the population. Given the increasing demand for…
With the growing competition in banking industry, banks are required to follow customer retention strategies while they are trying to increase their market share by acquiring new customers. This study compares the performance of six…
We investigate the allocation of children to childcare facilities and propose solutions to overcome limitations in the current allocation mechanism. We introduce a natural preference domain and a priority structure that address these…
Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the…
The John H. Chafee Foster Care Program for Successful Transition to Adulthood (CFCIP) allocates funding to provide services to youth who are likely to age out of foster care. These services, covering everything from mentoring to financial…
In optimal policy problems where treatment effects vary at the individual level, optimally allocating treatments to recipients is complex even when potential outcomes are known. We present an algorithm for multi-arm treatment allocation…
The global home healthcare market is growing rapidly due to aging populations, advancements in healthcare technology, and patient preference for home-based care. In this paper, we study the multi-day planning problem of simultaneously…
This paper presents a new approach to Model Predictive Control for environments where essential, discrete variables are partially observed. Under this assumption, the belief state is a probability distribution over a finite number of…
The concept of care pathways is increasingly being used to enhance the quality of care and to optimize the use of resources for health care. Nevertheless, recommendations regarding the sequence of care are mostly based on consensus-based…
Predicting the economy's short-term dynamics -- a vital input to economic agents' decision-making process -- often uses lagged indicators in linear models. This is typically sufficient during normal times but could prove inadequate during…
We present an ensemble learning method that predicts large increases in the hours of home care received by citizens. The method is supervised, and uses different ensembles of either linear (logistic regression) or non-linear (random…
Chronic diseases are long-lasting conditions that require lifelong medical attention. Using big EMR data, we have developed early disease risk prediction models for five common chronic diseases: diabetes, hypertension, CKD, COPD, and…
This paper explores various socioeconomic factors that contribute to individual financial success using machine learning algorithms and approaches. Financial success, a critical aspect of all individual's well-being, is a complex concept…
Pricing actuaries typically operate within the framework of generalized linear models (GLMs). With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency…