Toward automatic assessment of a risk of women’s health disorders based on ontology decision models and menstrual cycle analysis

Sosnowski, Łukasz; Wróblewski, Jakub

doi:10.1109/bigdata52589.2021.9671481

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…In continuation to a series of papers [7]- [10], here we present the developments made in the platform of OvuFriend 1 focusing on introducing the above mentioned aspects in an AI system for helping women in determining the possibility of conceiving and understanding the hidden risk of health problems based on their input. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Sosnowski,

Dutta,

Szymusik

et al. 2023

Annals of Computer Science and Information Systems

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This paper is in continuation to the paper published in FedCSIS 2022. In the earlier paper we presented the general scheme behind the AI based model for determining the possible ovulation dates as well as the possibility of some health risks.Here apart from the already discussed schemes for Premenstrual Syndrome (PMS), Luteal Phase Defect (LPD), and polyp and fibroids, a few additional schemes like hypothyroidism, polycystic ovary syndrom (PCOS) are included. Moreover, we attempt to throw light on the novelty of this AI based scheme from the perspective personalized, case sensitive, interactive medical support which does not depend only on a preset rule based system for diagnosing diseases.

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Section: Introductionmentioning

confidence: 99%