Ewa Kleczyk

By Ewa J. Kleczyk, Jill Bana and Rishabh Arora

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.

Part of the book: Addictions

By Ewa J. Kleczyk, Tarachand Yadav and Stalin Amirtharaj

Endometriosis is a commonly occurring progressive gynecological disorder, in which tissues similar to the lining of the uterus grow on other parts of the female body, including ovaries, fallopian tubes, and bowel. It is one of the primary causes of pelvic discomfort and fertility challenges in women. The actual cause of the endometriosis is still undetermined. As a result, the objective of the chapter is to identify the drivers of endometriosis’ diagnoses via leveraging selected advanced machine learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a greater extent if a likelihood of endometriosis could be predicted well in advance. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) algorithms leveraged 36 months of medical history data to demonstrate the feasibility. Several direct and indirect features were identified as important to an accurate prediction of the condition onset, including selected diagnosis and procedure codes. Creating analytical tools based on the model results that could be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers might aid the objective of improving the diagnostic processes and result in a timely and precise diagnosis, ultimately increasing patient care and quality of life.

Part of the book: Endometriosis

By Michele Bennett, Ewa J. Kleczyk, Karin Hayes and Rajesh Mehta

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.

Part of the book: Artificial Intelligence Annual Volume 2022

By Ewa J. Kleczyk

In recent years, ensuring patient privacy has become an important part of healthcare and medical research. With the vast amount of healthcare data available for research and the increasing ability to link and combine multiple healthcare datasets to enhance understanding of patient diagnostic and treatment journey, the need for the assessment of analytical datasets and output from the patient privacy side has become yet another step of the study protocol. The types of methods applied in the evaluation vary and include internal evaluation of sensitive personal and healthcare information through a statistical expert determination of the combined dataset. In addition, collaborating with a third-party expert in the privacy area can help ensure an objective assessment of meeting patient privacy requirements. As a result of the importance of patient privacy in healthcare research, this chapter will review the variety of methods leveraged in ensuring patient privacy protection during the healthcare analytics and research journey.

Part of the book: Ethics

By Ewa J. Kleczyk, Julie Mallory Crawford and Laura Dalfonso

This chapter discusses the current landscape of Atopic Dermatitis treatment pathways and management of disease progression. Data leveraged for these types of analyses can come from disease-based communities, otherwise known as patient registries. Disease-based communities can work with participating healthcare institutions and enroll qualified patients to aid understanding of the population and sub-cohorts’ characteristics, as well as therapy protocols and regimens, time to and on therapy, discontinuation trends and reasons, and healthcare outcomes. Since the data collection includes Electronic Healthcare Records (EHRs) with the ability to append patient reported outcome questionnaires, as well as any other data sources relevant to the condition being studied, the resulting datasets provide in-depth insights on the patient population and their treatment pathways. Treating physicians are the Principal Investigators, managing and monitoring the patients’ progression and treatment. The reader is able, as a result, to learn the current preferred treatment pathways in treating Atopic Dermatitis, management of disease progression, as well as understand the underlying patient characteristics and historical medical information that drive the selection of therapies.

Part of the book: Latest Breakthroughs in the Treatment of Atopic Dermatitis