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expose/src/expose.tex

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 % Packages
 \usepackage{amsmath}
 % biber bibliography
-\usepackage[backend=biber]{biblatex}
+\usepackage[style=ieee, backend=biber]{biblatex}
 \addbibresource{../../main.bib}
 
 % Document
@@ -20,24 +20,92 @@
 
 
     \section{Introduction}\label{sec:introduction}
-    Women's health is a topic that has been neglected for a long time.
-    The menstrual cycle and its corresponding ovulation are essential for the reproductive health,
+    Women's health is a topic that has been neglected for a long time, even though it has direct implications for
+    more than half of the world's population.
+    The menstrual cycle and its corresponding ovulation are essential for reproductive health,
     family planning and natural contraception.
-
-        v
+    Part of the challenge lies in the lack of comprehensive datasets, but even more critically,
+    in the incomplete understanding of the underlying physiological mechanisms.
+    New advances in machine learning have shown, that there is potential in predicting the menstrual cycle and ovulation
+    with high accuracy based on physiological data, such as basal body temperature,
+    heart rate or cervicovaginal fluid\cite{noauthor_cervicovaginal_2005, luo_detection_2020, masuda_machine_2025, yu_tracking_2022}.
+    A problem with the current landscape of research is often the lack of interpretability of the models as well as insufficient
+    data to train the models.
+    This lack of understanding is compounded by the opacity of many machine learning models, making their predictions difficult to trust and validate in clinical settings.
+    In female health, a wrong prediction can have severe consequences, such as unwanted pregnancies, health risks or even death.
+    This thesis aims to improve upon that by using a large dataset of body core temperature and markers to train an
+    interpretable model based on attention mechanisms to predict ovulation and to identify the most important predictors for ovulation.
 
 
     \section{Related Work}\label{sec:related-work}
+    Research on fertility prediction goes back as far as the 1990s, where the first purely statistical models were
+    developed to predict both the ovulation and the fertility over time\cite{royston_identifying_1991}.
+    Since then, there has been a long pause in research until the rise of machine learning in the late 2010s.
+    Both data quality and quantity have improved significantly, which has led to more accurate models\cite{maman_prediction_2023, noauthor_pdf_2024}.
+    However, since the female menstrual cycle is a biological process, prone to noise and irregularities, the models
+    often lack generalizability and interpretability, especially for women with irregular cycles.
+    There is a variety of biological features that can be used to predict ovulation, but most of it have shown to be
+    impractical for large scale use, as they require daily blood samples or ultrasound measurements.
+    The most promising are models based on temperature data, as it is a quantity that can be recorded on a large scale
+    without intrusive methods (ear-piece or vaginal sensor monitoring)\cite{alexander_fertilitatsmonitoring_2014, luo_detection_2020}.
+    However, body temperature alone is an imperfect predictor of ovulation,
+    as it is influenced by external factors such as stress, sport, illness, and sleep disturbances.
+    While external measurement (e.g.\ skin or earpiece sensors) are even less reliable,
+    even vaginal temperature measurements can be influenced by these factors.
+    This is where this research aims to improve upon, by incorporating more predictors, such as markers for illness, sport,
+    stress or similar things.
+
 
     \section{Research Questions and Objectives}\label{sec:research-questions-and-objectives}
+    The main research objective is to create a model that can predict ovulation and fertile days with high accuracy
+    while offering a high level of interpretability in its underlying attention mechanisms.
 
+    \begin{itemize}
+        \item Can attention-based models improve the prediction of ovulation?
+        \item Can attention-based models improve the interpretability of the female menstrual cycle?
+        \item What are the most important predictors for ovulation, both in space and time?
+        \item How does an attention-based model compare to other models in terms of accuracy and interpretability?
+        \item Can we account for external influences (e.g. stress, illness, sport) to improve prediction robustness?
+        \item Optional: Use a non-interpretable transformer model to see impact of interpretability on prediction accuracy.
+    \end{itemize}
 
 
     \section{Methodology}\label{sec:methodology}
+    The research will be based on an extensive dataset, generously provided by the FemTech company\cite{noauthor_vivosens_nodate},
+    which incorporates 50,000 cycles with temperature measurements every 5 minutes by a intravaginal sensor ring.
+    The dataset contains a wide variety of cycle types and predispositions, including regular and irregular cycles,
+    PCOS, endometriosis, and other conditions.
+    Additionally, the dataset contains both user generated and automatically generated markers for cervical fluid, menstruation,
+    mood and other relevant factors.
 
+    The model will be based on a transformer architecture, which has shown to be very effective in time series prediction\cite{vaswani_attention_2017}.
+    This architecture is particularly suited for ovulation prediction because it can capture dependencies across different time points—identifying,
+    for instance, how temperature fluctuations in the follicular phase influence predictions in the luteal phase.
+    Transformer-based models have been further improved for specific use cases.
+    Here we will use a model that has shown to be effective in time-series analysis with the addition of an
+    interpretable attention mechanism, called the \emph{Temporal Fusion Transformer}(TFT)\cite{lim_temporal_2020}.
+    TFT is specifically designed for multivariate time-series forecasting, allowing it to incorporate static covariates
+    (e.g., age) alongside dynamic features (e.g., temperature, stress markers) to improve prediction robustness.
+    By training an interpretable attention mechanism, we will identify the most crucial factors for ovulation and
+    fertility prediction.
+    These factors will then be presented to a reproductive health expert to validate and interpret the findings.
+    Given the sensitivity of fertility prediction, particular attention
+    will be paid to data privacy and ethical considerations in model deployment.
+
+    The model will be evaluated on its accuracy and interpretability and compared to other models in the field such as
+    OvulaRing's previous methods\cite{alexander_fertilitatsmonitoring_2014} other machine learning models (LSTM, GRU, etc.)
+    and simpler statistical models ((S)ARIMA, etc.).
+    Next to standard metrics, such as accuracy, precision, recall, F1-score, we will also use fertility-specific metrics,
+    that incorporate the probability curve of the fertile window according to \citeauthor{dunson_day-specific_1999}\cite{dunson_day-specific_1999},
+    and the severity of the consequences of a wrong prediction.
 
 
     \section{Conclusion}
+    The research aims to improve the prediction of ovulation and fertility by using an interpretable attention-based model.
+    The model will be trained on a large dataset of intravaginal temperature measurements and set of time-varying and constant features.
+    The model will be evaluated on its accuracy and interpretability and compared to other models in the field.
+    The most important predictors for ovulation will be identified and presented to a reproductive health expert for validation.
+
     % bibliography
     \printbibliography