Factors influencing the occurrence and costs of pinhole leak corrosion in suburban Washington, D.C., were analyzed. A mail survey of households was used to elicit experiences and repair costs associated with pinhole leaks. Regression analyses, with correction for sample selection bias, were used to analyze survey responses regarding leak occurrences and costs. Pinhole leak occurrences were found to be associated with plumbing type, property age, pipe failure history, and dwelling distance from a water treatment plant. The number and location of pinhole leaks in the dwelling and the pipe type are associated with the financial costs of pinhole leaks. For example, repair costs of leaks occurring in the basement are much lower compared with repair costs on the second floor. Faster responses to pinhole leak outbreaks by utility managers and policymakers would reduce costs of pinhole leak repairs. Expanding state‐funded property insurance to cover damage from leaks when private insurance coverage is inadequate would also be beneficial.
Lee et al | http://dx.
AHP-analytical hierarchical process, CA-conjoint analysis, PEX-cross-linked polyethylene
Copper
PEX
Epoxy coatingLee et al | http://dx.
Coronavirus disease (COVID-19) caused an overwhelming healthcare, economic, social, and psychological impact on the world during 2020 and first part of 2021. Certain populations, especially those with Substance Use Disorders (SUD), were particularly vulnerable to contract the virus and also likely to suffer from a greater psychosocial and psychological burden. COVID-19 and addiction are two conditions on the verge of a collision, potentially causing a major public health threat. There is surge of addictive behaviors (both new and relapse), including use of alcohol, nicotine, and recreational drugs. This book chapter analyzed the bi-directional relationship between COVID-19 and SUD by leveraging descriptive summaries, advanced analytics, and machine learning approaches. The data sources included healthcare claims dataset as well as state level alcohol consumption to help in investigating the bi-directional relationship between the two conditions. Results suggest that alcohol and nicotine use increased during the pandemic and that the profile of SUD patients included diagnoses and symptoms of COVID-19, depression and anxiety, as well as hypertensive conditions.
Data scientists and statisticians are often at odds when determining the best approaches and choosing between machine learning and statistical modeling to solve their analytical challenges and problem statements across industries. However, machine learning and statistical modeling are actually more closely related to each other rather than being on different sides of an analysis battleground. The decision on which approach to choose is often based on the problem at hand, expected outcome(s), real world application of the results and insights, as well as the availability and granularity of data for the analysis. Overall machine learning and statistical modeling are complementary techniques that are guided on similar mathematical principles, but leverage different tools to arrive at insights. Determining the best approach should consider the problem to be solved, empirical evidence and resulting hypothesis, data sources and their completeness, number of variables/data elements, assumptions, and expected outcomes such as the need for predictions or causality and reasoning. Experienced analysts and data scientists are often well versed in both types of approaches and their applications, hence use best suited tools for their analytical challenges. Due to the importance and relevance of the subject in the current analytics environment, this chapter will present an overview of each approach as well as outline their similarities and differences to provide the needed understanding when selecting the proper technique for problems at hand. Furthermore, the chapter will also provide examples of applications in the healthcare industry and outline how to decide which approach is best when analyzing healthcare data. Understanding of the best suited methodologies can help the healthcare industry to develop and apply advanced analytical tools to speed up the diagnostic and treatment processes as well as improve the quality of life for their patients.
Background
Endometriosis is a common progressive female health disorder in which tissues similar to the lining of the uterus grow on other parts of the body like ovaries, fallopian tubes, bowel, and other parts of reproductive organs. In women, it is one of the most common causes of pelvic pain and infertility. In the US, one in every ten women of reproductive age group has endometriosis. The actual cause of endometriosis is still unknown, and it is quite difficult to diagnose. There are several theories regarding the cause; however, not a single theory has been scientifically proven.
Methods
In this paper, we try to identify the drivers of endometriosis’ diagnoses via leveraging advanced Machine Learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a great extent, if likelihood of endometriosis can be predicted well in advance. As a result, the proper medical care and treatment can be given to the impacted patients. To demonstrate the feasibility, Logistic Regression (LR) and eXtreme Gradient Boosting (XGB) algorithms were trained on 36 months of medical history data.
Results
The machine learning models were used to predict the likelihood of disease on qualified patients from the healthcare claims patient level database. Several directly and indirectly features were identified as important in accurate prediction of the condition onset, including selected diagnosis and procedure codes.
Conclusions
Leveraging the machine learning approaches can aid early prediction of the disease and offer an opportunity for patients to receive the needed medical treatment earlier in the patient journey. Creating a typing tool that can be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers could further aid the objective of improving the diagnosis activities and inform the diagnostic processes that would result in timely and precise diagnosis, ultimately increasing patient care and quality of life.
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