Ewa J. KleczykThe University of Maine, USA
Title: Applying Machine Learning Techniques to Predict Endometriosis Onset
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 the reproductive organs. In women, it is one of the most common causes of pelvic pain and infertility. 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: The objective of the study was 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 can be predicted well in advance. As a result, the proper medical care and treatment can be given to the impacted patients well in advance. Logistic Regression (LR) and extreme Gradient Boosting (XGB) algorithms leveraged 36 months of medical history data to demonstrate the feasibility. Conclusions: Leveraging selected machine learning approaches can aid in early prediction of the disease and offer an opportunity for patients to receive the needed medical treatment earlier in the patient journey. Several direct and indirect features were identified as important to accurate prediction, including selected diagnosis and procedure codes. 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 and informing the diagnostic processes that would result in a timely and precise diagnosis, ultimately increasing patient care and their quality of life.
Ewa J. Kleczyk is an analytics leader with a proven record for establishing high performing analytics teams and delivering innovative analytics solutions to the healthcare industry. Currently, Dr. Kleczyk is a Vice President, leading the Advanced & Custom Analytics group at Symphony Health, ICON, plc. Her experience spans across data science, health economics, outcomes research, digital & media analytics, as well as forecasting & promotional impact measurement. Dr. Kleczyk is also a highly sought-after conference speaker with experience speaking at leading industry conferences, including Pharmaceutical Marketing Sciences Association, DTC Perspectives, CDM Media, Pharmaceutics & Novel Drug Delivery Systems, and Conference for Business and Economics at the Harvard University, etc. She also has published in multiple academic & industry journals and is a board member of several peer-reviewed publications. Dr. Kleczyk has been an active advocate of mentoring future women leaders of the pharmaceutical industry for which she has been recognized with multiple leadership awards, including HBA’s ‘Rising Star’ & ‘Luminary’ recognitions. She is also a Board Member on the Community Cancer Council for the Northern Light Health Network that provides advocacy for cancer patients.