Improving Diagnostics And Treatment of Female Reproductive Health Conditions

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Female reproductive problems are common, and in some cases can drastically decrease a woman's quality of life. Some of the most common reproductive health concerns are Endometriosis, Polycystic Ovarian Syndrome (PCOS), and Uterine Fibroids [1]. It is estimated that roughly 1 in 10 women have Endometriosis, and that 1 in 10 women have PCOS. Endometriosis and PCOS can co-occur, but also may occur distinctly. Since it is estimated that 10% of women of reproductive age may suffer from either of these conditions, it can be approximated that roughly 176 million women worldwide may suffer from each [2][3]. Uterine Fibroids are even more prevalent, with an estimated 1 in 3 women developing fibroids at some point in their life [4] Sometimes these conditions can cause minor or no symptoms, but in other situations female health conditions can cause major symptoms such as severe pain, infertility, and can even affect other organs [5]. With such common conditions, and such severe symptoms, one would expect there to be a large amount of research in this area. This does not appear to be the case. There is not a massive amount of knowledge in the medical domain on female reproductive conditions and why they occur. There is a particular lack of research on these conditions within the Computer Science field, with most papers focussing almost purely on machine learning methods. This is not the approach I wish to take, and therefore the research I propose seems to be rather novel. Reproductive issues can be particularly difficult to diagnose, with some conditions taking years to receive a definitive diagnosis. It is particularly alarming that for Endometriosis, the average time from onset of symptoms to diagnosis is 7.5 years [6]. There are several reasons why diagnosis for female health conditions can be lengthy, particularly that they tend to manifest themselves as a number of possible other potential medical problems [7]. In addition to this, I believe that there is an unexplored opportunity to use computational techniques and applications to aid the medical field in providing quicker diagnostic outcomes. Having conducted some background research on the applications of Computer Science in this area, I did not find much patient-centric work, that is, research which is directly focused on improving the wellbeing of the patients who are suffering from, or potentially suffering from, one of these conditions. For this reason, I would like to propose that I aim to research ways that Computer Science can be used to aid in the diagnosis and/or treatment of patients with suspected or confirmed reproductive health issues. My initial ideas involve potentially building a mobile application which allows women to track symptoms they are experiencing, for quicker diagnostic outcomes, or perhaps working with the formal clinical pathways of different female reproductive health conditions and discovering the best methods of treatment for a patient. Some Machine Learning will be complementary to my solution; however, it will not be the entirety of it. Data Mining may be used to extract useful information from medical text, and Constraint Programming may be useful in solving the combinatorial problems which will internally exist while trying to probabilistically determine the likelihood that a woman has any of these conditions. This approach would theoretically be transferrable to other medical areas, with the underlying techniques being able to aid the diagnosis process of many medical conditions, not just those relating to female health. A paper which takes a similar approach is `Balancing Prescriptions with Constraint Solvers' by Bowles and Caminati [8]. The paper explores an automated method to finding a course of treatment, using constraint solvers and theorem provers. Though focusing on different underlying health conditions, this paper may prove valuable when evaluating possible implementation routes.