Improving Diagnostics And Treatment of Female Reproductive Health Conditions

女性生殖问题很常见，在某些情况下会大大降低女性的生活质量。一些最常见的生殖健康问题是子宫内膜异位症，多囊卵巢综合征（PCOS）和子宫肌瘤[1]。据估计，大约十分之一的女性患有子宫内膜异位症，十分之一的女性患有PCOS。子宫内膜异位症和PCOS可以同时发生，也可以单独发生。由于估计有10%的育龄妇女可能患有这两种疾病，因此可以估计全世界约有1.76亿妇女可能患有每种疾病[2][3]。子宫肌瘤甚至更普遍，估计有1/3的女性在生命中的某个时候发展为肌瘤[4]有时这些疾病会导致轻微或没有症状，但在其他情况下，女性健康状况会导致主要症状，如剧烈疼痛，不孕症，甚至会影响其他器官[5]。有了这些常见的条件，以及如此严重的症状，人们会期望在这一领域进行大量的研究。看来情况并非如此。在医学领域，关于女性生殖条件及其发生原因的知识并不多。在计算机科学领域，对这些条件的研究特别缺乏，大多数论文几乎完全集中在机器学习方法上。这不是我想采取的方法，因此我提出的研究似乎是相当新颖的。生殖问题可能特别难以诊断，有些情况需要数年才能得到明确的诊断。特别令人担忧的是，对于子宫内膜异位症，从症状发作到诊断的平均时间为7.5年[6]。有几个原因为什么女性健康状况的诊断可能是漫长的，特别是它们往往表现为一些其他可能的潜在医疗问题[7]。除此之外，我相信有一个未开发的机会，使用计算技术和应用程序来帮助医疗领域提供更快的诊断结果。在对计算机科学在这一领域的应用进行了一些背景研究后，我没有发现太多以患者为中心的工作，也就是说，直接关注改善患有或可能患有这些疾病的患者的健康的研究。出于这个原因，我想建议我的目标是研究计算机科学可以用来帮助诊断和/或治疗疑似或确诊生殖健康问题的患者的方法。我最初的想法包括可能建立一个移动的应用程序，使妇女能够跟踪她们正在经历的症状，以获得更快的诊断结果，或者可能与不同女性生殖健康状况的正式临床途径合作，并为患者发现最佳治疗方法。一些机器学习将补充我的解决方案;然而，它不会是它的全部。数据挖掘可以用来从医学文本中提取有用的信息，约束编程可以用于解决内部存在的组合问题，同时试图概率地确定一个女人有任何这些条件的可能性。这种方法理论上可以转移到其他医疗领域，其基本技术能够帮助诊断许多医疗状况，而不仅仅是与女性健康有关的疾病。Bowles和Caminati [8]的论文《Balancing Prescriptions with Constraint Solvers》采用了类似的方法。本文探讨了一种自动化的方法来找到一个疗程，使用约束求解器和定理证明。虽然侧重于不同的基本健康状况，本文可能证明有价值的评估可能的实施路线。

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.