Artificial Intelligence in Personalized Healthcare Analysis for Womens’ Menstrual Health Disorders

Abstract

The paper presents an AI-based model which depending on the input of a woman for a finite number of menstrual cycles helps in determining the possible ovulation dates as well as possibility of some health risks e.g., Premenstrual Syndrome, Luteal Phase Defect etc. The architecture of the model consists of three layers, namely analyzing and detecting the features from a single cycle, analyzing cycle level concepts based on the analyzed features, and analyzing the user's health risks based on the cycle level concepts accumulated over a finitely many cycles.

Editorial – Wissenschaftlicher WIG2 Jahresrückblick 2022

“…Ebenso schloss Dr. Ein Forschungsteam aus Polen präsentierte eine KI-gestützte personalisierte Analyse bei Menstruationsbeschwerden zur Einschätzung von möglichen Gesundheitsrisiken. Die KI ist dabei in der App OvuFriend 2.0 integriert und analysiert zyklusbezogene Informationen und Symptome zyklusübergreifend hinsichtlich möglicher Korrelationen und Muster (Sosnowski, Żuławińska, Dutta, Szymusik, Zyguła & Bambul-Mazurek, 2022). Zwei Forscher der Universität Blida in Algerien befassten sich mit neuronalen Encoder-Decoder-Netzwerken mit Aufmerksamkeitsmechanismus zur Erkennung von Typen in verknüpften Daten (Hamel & Fareh, 2022).…”

Interactive, Personalized Decision Support in Analyzing Women’s Menstrual Disorders

“…At this stage all the detector level concepts are analysed. If through the primary analysis it is determinned that ovulation has been occurred, then an attempt is made to indicate two intervals of equal length falling into the follicular phase and the luteal phase of the concerned cycle respectively [11].…”

“…The schemes requiring a complete cycle data, namely PMS, LPD, polyp and fibroids, are already discussed in [11]. Though the basic formulas for calculating these health risks are different, the general form of the underlying algorithms is similar.…”

“…To analyze the risk of PMS, which is a combination of symptoms that many women get about a week or two before their period, the coefficients of occurrence of the physical symptoms and mood symptoms are calculated (see [11]). The set of symptoms and formulas for calculating the coefficients based on them are defined with the help of a team of medical experts.…”

“…The platform of OvuFriend 1.0 was developed as a part of R&D project where through a mobile app an user can put the data related to her physical and mental states during a specific menstrual cycle, and the underlying algorithm of the app helps to get an analysis of the possibility of conceiving or not conceiving. The second stage of OvuFriend's project, namely OvuFriend 2.0, focuses on the analysis of whether a particular user has the possibility of having the risk of Premenstrual Syndrome (PMS) Luteal Phase Defect (LPD), benign growths like polyps, fibroids in the uterus, Polycystic Ovary Syndrome (PCOS) or hypothyroidism Among them in [11] a detailed discussion regarding the schemes of PMS, LPD, and the risk from polyp and fibroids are discussed. Here we add the schemes corresponding to hypothyroidism and PCOS.…”