Improving Diagnostics And Treatment of Female Reproductive Health Conditions

女性生殖问题很常见，在某些情况下会极大地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，大约每10名妇女中有1人患有子宫内膜异位症，每10名妇女中就有1人患有多囊卵巢综合征。子宫内膜异位症和多囊卵巢综合征可以同时发生，也可以单独发生。据估计，10%的育龄妇女可能患有这两种疾病中的任何一种，因此可以估计，全世界大约有1.76亿妇女可能分别患有这两种疾病。子宫肌瘤甚至更为普遍，据估计，每3名妇女中就有1人在一生中的某个时候患上子宫肌瘤[4]有时这些情况会导致轻微症状或没有症状，但在其他情况下，女性的健康状况会导致严重疼痛、不孕不育等主要症状，甚至会影响其他器官[5]。在如此常见的情况下，如此严重的症状，人们可以预料到在这一领域将会有大量的研究。情况似乎并非如此。在医学领域，关于女性生殖状况及其发生的原因，并没有大量的知识。在计算机科学领域特别缺乏对这些条件的研究，大多数论文几乎纯粹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诊断，有些情况需要数年时间才能得到明确的诊断。尤其令人担忧的是，子宫内膜异位症从出现症状到确诊的平均时间为7.5年[6]。女性健康状况的诊断可能很漫长，有几个原因，特别是她们往往表现为一些可能的其他潜在的医疗问题[7]。除此之外，我认为还有一个尚未开发的机会，即使用计算机技术和应用程序来帮助医学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，即直接专注于改善患有或可能患有其中一种疾病的患者的健康状况的研究。出于这个原因，我想建议我致力于研究如何利用计算机科学来帮助诊断和/或治疗疑似或确诊的生殖健康问题的患者。我最初的想法是，可能会开发一个移动应用程序，允许女性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路径合作，发现患者的最佳治疗方法。一些机器学习将是对我的解决方案的补充；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，约束编程可能有助于解决内部存在的组合问题，同时试图以概率方式确定女性患有这些疾病的可能性。从理论上讲，这种方法可以转移到其他医学领域，其基本技术能够帮助许多医学疾病的诊断过程，而不仅仅是那些与女性健康有关的疾病。一篇采用类似方法的论文是鲍尔斯和卡米纳蒂的《用约束解算器平衡处方》[8]。本文探索了一种使用约束求解器和定理证明器来自动寻找治疗过程的方法。尽管本文侧重于不同的潜在健康状况，但在评估可能的实施路线时，这篇论文可能会被证明是有价值的。

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.