fixes and improvements

thesis/sections/discussion.tex

@@ -4,13 +4,13 @@
 
 \section{Discussion}\label{sec:discussion}
 
-\subsection{Objective 1: Model Architecture Evuation an Optimization}\label{subsec:discussion_rq1_model_evaluation}
+\subsection{Objective 1: Model Architecture Evaluation and Optimization}\label{subsec:discussion_rq1_model_evaluation}
 
 
 The first research objective considered different model architectures and how they perform
 under various hyperparameter configurations, with respect to the optimization goals of this study.
 
-We found that non-convolutional models performed best with medium resolutions and longer input windows,
+I found that non-convolutional models performed best with medium resolutions and longer input windows,
 where they also outperformed their convolutional counterparts.
 This suggests that including more historical context improves prediction accuracy.
 However, there appears to be a trade-off:
@@ -20,21 +20,21 @@ The strong performance at medium resolutions indicates a possible sweet spot—b
 The convolutional variants performed best with medium-length input windows, which was somewhat surprising.
 One would expect convolutional downsampling to enable more efficient data representation,
 allowing the model to extract relevant patterns from high-resolution data and to perform better on longer sequences.
-However, this was not observed in our experiments.
+However, this was not observed in my experiments.
 Instead, convolutional models performed worse than non-convolutional models on longer input windows,
 suggesting that the benefits of downsampling may be offset by limitations in capturing long-range dependencies.
 For medium and short input windows, however, convolutional models did outperform the non-convolutional
 variants—indicating that the convolution itself provides an advantage when input length is limited.
 
-To our knowledge, no prior studies have compared different deep learning architectures for real-time fertility prediction.
-This highlights both the novelty and the exploratory nature of our approach.
+To my knowledge, no prior studies have compared different deep learning architectures for real-time fertility prediction.
+This highlights both the novelty and the exploratory nature of my approach.
 
 All these results must be interpreted with caution, as they are based on single-shot experiments and are not statistically robust.
 A more rigorous evaluation would involve training all models multiple times to minimize the
 impact of random initialization and other stochastic processes in training.
 For most metrics, differences in performance were small, often less than 1\%, and could easily be attributed to such variability.
 
-We are also aware of potential losses in predictive quality due to class imbalance in the labels.
+I am also aware of potential losses in predictive quality due to class imbalance in the labels.
 Especially in long cycles, the targets are mostly zero, except for a narrow window around ovulation
 (for the fertility-probability target) or the area after ovulation (for the ov-over target).
 Positive-to-negative target ratios can reach 10–20:1 in long cycles, meaning for each non-zero target day,
@@ -42,12 +42,22 @@ there are 10 to 20 days with all-zero targets.
 This imbalance may introduce instability during training and reduce predictive performance.
 Future iterations of this study could incorporate class-weighting or loss-balancing mechanisms to address this issue.
 
-Future work should include a more thorough parameter search and statistically more robust evaluation methodology.
+A central limitation of this study is that the training labels were generated
+retrospectively from temperature curves, rather than being based on direct clinical
+confirmation of ovulation (e.g., ultrasound or hormone assays).
+Although the retrospective algorithm was validated against expert-labeled reference cycles,
+it remains a proxy and can be biased by ambiguous patterns, illness, or sensor dropout.
+The models therefore predict consistency with this retrospective labeling rather than
+the absolute physiological ground truth.
+Prospective validation against clinical reference standards will be essential to establish the reliability of
+the approach in real-world use.
+
+Future work should also include a more thorough parameter search and statistically more robust evaluation methodology.
 A more sophisticated hyperparameter search may be necessary to identify globally optimal configurations for each model architecture.
 Time and resource constraints could be alleviated using more efficient search algorithms such as Bayesian Optimization,
 Genetic Algorithms, or Neural Architecture Search (NAS) to better explore the joint parameter space.
 
-Additionally, we only focused on basic models—LSTMs and Transformers, with convolutional hybrids.
+Additionally, I habe only focused on basic models—LSTMs and Transformers, with convolutional hybrids.
 A closer look into alternative architectures, or tailoring architectures more specifically
 to the characteristics of menstrual cycle data, could significantly improve predictions.
 Recent Transformer variants for time series modeling, such as \emph{TimeXer}~\cite{wang_timexer_2024}
@@ -68,19 +78,19 @@ Finally, exploring alternative prediction targets may help address some of the c
 Beyond the fertility-probability and ov-over indicators used in this study,
 targets such as the number of days to the next ovulation (or since the last), or the ovulation day
 as a direct regression target, might prove more stable and informative.
-As we will see in the next section, the current targets are highly sensitive to anomalies,
+As can be seen in the next section, the current targets are highly sensitive to anomalies,
 which may not be the case for alternative formulations.
 
 \subsection{Objective 2: Factors and Patterns that Influence Prediction}\label{susbsec:discussion_rq2_factors_and_patterns}
 
-Our second research objective focused on identifying potential factors and patterns in the data
+My second research objective focused on identifying potential factors and patterns in the data
 that influence fertility prediction.
 
-To our knowledge, no prior work has explicitly analyzed the relationship between temperature patterns
+To my knowledge, no prior work has explicitly analyzed the relationship between temperature patterns
 and ovulation—including the corresponding fertile window—in a way that could directly inform machine learning models.
 This underscores the exploratory nature of this analysis.
 
-We identified a consistent pre-ovulatory temperature dip that aligned closely with the ground-truth fertility curve.
+I have identified a consistent pre-ovulatory temperature dip that aligned closely with the ground-truth fertility curve.
 This drop was particularly pronounced in short and regular cycles.
 In contrast, irregular cycles often exhibited too much variability and noise in the follicular phase
 for the models to reliably detect this temperature drop, or to associate it meaningfully with increased fertility probability.
@@ -89,13 +99,13 @@ larger dips tended to produce higher model confidence.
 
 This observed correlation may be coincidental, or it may indicate a meaningful biological marker
 for fertility and successful ovulation.
-We hypothesize that there is a real association between the magnitude of the pre-ovulatory temperature dip and the likelihood of ovulation.
+I hypothesize that there is a real association between the magnitude of the pre-ovulatory temperature dip and the likelihood of ovulation.
 
-However, our method has a critical limitation: the ground-truth labels used were not based on clinically confirmed ovulation events.
+However, my method has a critical limitation: the ground-truth labels used were not based on clinically confirmed ovulation events.
 This limitation may result not only in fertility-probability curves that are arbitrarily offset from their true value,
 but also in the inclusion of cycles labeled as ovulatory that in fact were anovulatory.
 As shown in Section~\ref{subsubsec:physiological_signs}, the only reliable indicator
-of ovulation is daily transvaginal ultrasound, which was not feasible given the scale of our dataset.
+of ovulation is daily transvaginal ultrasound, which was not feasible given the scale of my dataset.
 
 Future studies could apply the current model to a smaller subset of cycles with clinically
 confirmed ovulation and/or cycles exhibiting a clear temperature rise, to test the hypothesis
@@ -121,7 +131,7 @@ Better labels would also allow more reliable medical interpretation of the predi
 The third research objective of this study was to compare model performance across subsets of users
 with regular and irregular menstrual cycles.
 
-We found that predictive performance is clearly sensitive to cycle irregularity.
+I have found that predictive performance is clearly sensitive to cycle irregularity.
 Both overall accuracy and the improvement in performance over a growing user history
 were substantially better for regular cycles compared to irregular ones.
 This suggests that past cycles contain valuable information that helps the models
@@ -131,7 +141,7 @@ Some of this improvement can also be attributed to static features, several of w
 derived from aggregated past cycles, such as average cycle length or average ovulation day.
 These features appear to be more informative and stable in the regular cycle group.
 
-Based on the clear difference in performance, we hypothesize, that all models rely more on those static features
+Based on the clear difference in performance, I hypothesize, that all models rely more on those static features
 than on patterns identified in the temperature curves, since if that were the case, the difference would be smaller
 or non-existent.
 There might be additional factors that differentiate regular from irregular cycles, that might explain the results,
@@ -141,12 +151,12 @@ but this hypothesis needs to be investigated in further research.
 
 \citeauthor{yu_tracking_2022} also report a significant drop in performance for irregular cycles
 in their study~\cite{yu_tracking_2022}, particularly in terms of sensitivity.
-While we cannot directly compare results due to methodological differences, especially since
-our models do not perform explicit binary classification into fertile vs.\ non-fertile days, a similar trend is observable.
-In our case, the models tend to overestimate fertility probability when faced with uncertainty, particularly in irregular cycles
+While I cannot directly compare results due to methodological differences, especially since
+my models do not perform explicit binary classification into fertile vs.\ non-fertile days, a similar trend is observable.
+In my case, the models tend to overestimate fertility probability when faced with uncertainty, particularly in irregular cycles
 (see Figure~\ref{fig:results_rq2_irregular_cycle_predictions_example}).
-As a result, we do not report specificity or sensitivity values.
-However, the use-case scenarios in the next section offer a more binary evaluation framework.
+As a result, I do not report specificity or sensitivity values.
+However, the use case scenarios in the next section offer a more binary evaluation framework.
 
 A promising direction for future work would be to further investigate the causes of
 performance degradation in irregular cycle groups.
@@ -159,24 +169,24 @@ Ideally, the model would have access to the entire cycle history of a user for t
 However, this would likely exceed the memory and capacity constraints of the architectures used in this study.
 Overcoming this limitation may require more efficient or hierarchical time series architectures, as previously discussed.
 
-\subsection{Objective 4: Use-case Evaluations}\label{subsec:discussion_rq4_use_case_evaluations}
+\subsection{Objective 4: Use Case Evaluations}\label{subsec:discussion_rq4_use_case_evaluations}
 
-The fourth research objective was to evaluate two practical use-cases—natural contraception
+The fourth research objective was to evaluate two practical use cases—natural contraception
 and natural family planning (NFP), using predefined evaluation algorithms.
 These algorithms approximate the effectiveness of different models across varying fertility thresholds.
 
-We found that for both use-cases, all trained models outperformed the baselines.
+I have found that for both use cases, all trained models outperformed the baselines.
 There was notable variability between model types, with the Transformer model
 performing best, especially for low fertility thresholds, followed closely by the convolutional variants.
 The LSTM model consistently showed the weakest performance across all metrics.
 Still, both trained and baseline models yielded significant
 improvements over the control group, indicating that all model types offer meaningful predictive value.
 
-To our knowledge, no previous work has explicitly evaluated use-cases of natural
+To my knowledge, no previous work has explicitly evaluated use cases of natural
 contraception or NFP with machine learning, or compared them to traditional contraceptive methods.
 This highlights the exploratory nature of this analysis.
 
-\paragraph{Contraception Use-Case.}
+\paragraph{Contraception Use Case.}
 For the natural contraception scenario, Transformer-based architectures produced competitive results,
 achieving fewer than 5 pregnancies per 100 user-years for low thresholds.
 For context, the expected pregnancy rates per 100 user-years are approximately 12 for male condoms
@@ -186,7 +196,7 @@ In contrast, the control group, representing no contraception, resulted in 90 pr
 which closely aligns with literature values (85 pregnancies per 100 women per year)~\cite{trussell_contraceptive_2011}.
 
 As expected, the number of denials, i.e.,
-days on which a potential application would suggest abstinence,increases with the threshold.
+days on which a potential application would suggest abstinence, increases with the threshold.
 Therefore, a trade-off exists between minimizing pregnancies and minimizing denials.
 This trade-off is user-dependent: while some users prefer certainty and are comfortable with frequent denials,
 others may accept higher risk or use additional protective measures to mitigate it.
@@ -194,7 +204,7 @@ others may accept higher risk or use additional protective measures to mitigate
 This highlights a critical limitation: all results are based on a naive,
 rigid denial strategy that did not incorporate any additional measures or contextual information
 beyond the raw model predictions.
-When combined with established behavioral protocols,such as abstinence during the early cycle phase,
+When combined with established behavioral protocols, such as abstinence during the early cycle phase,
 as used in several commercial products~\cite{thigpen_oura_2025, bull_real-world_2019},
 significantly lower pregnancy rates are likely achievable.
 
@@ -207,8 +217,8 @@ Future work could explore models that combine predicted fertility probabilities
 with contextual features—such as current follicular phase length—to generate
 more nuanced contraceptive guidance.
 
-\paragraph{Pregnancy Use-Case.}
-For the NFP use-case (i.e., attempting to conceive), all trained models achieved
+\paragraph{Pregnancy Use Case.}
+For the NFP use case (i.e., attempting to conceive), all trained models achieved
 nearly double the pregnancy rate compared to the baselines.
 However, at low fertility thresholds, the baseline models showed higher efficiency in terms
 of pregnancies per 1000 intercourse events.
@@ -220,18 +230,13 @@ The number of correct deferrals, days correctly identified as non-fertile, rose
 again suggesting that users may benefit from personalized thresholds depending
 on the intensity of their conception efforts.
 
-Most related studies report pregnancy outcomes over 12 cycles, which we assume to represent roughly one year.
-We use the 100 user-years metric for consistency.
-Since fertility status of couples in our dataset is unknown, we assume that all included users are generally fertile.
-
-Related research usually refers to pregnancies per 100 women over 12 cycles.
-We assume that, on average 12 cycles represent one year, and thus use the 100 user-years metric as comparison.
-We cannot make any assumptions about the fertility of couples in our study, thus we assume that all users sampled
-are generally able to conceive.
+Most related studies report pregnancy outcomes over 12 cycles, which I assume to represent roughly one year.
+I will use the 100 user-years metric for consistency.
+Since fertility status of couples in my dataset is unknown,  assume that all included users are generally fertile.
 
 Among commercial fertility monitors, pregnancy rates over 12 cycles range
 from 68\% to 72\% (with additional cervical mucus tracking)~\cite{bouchard_achieving_2018}.
-Our method reaches approximately 80\%, without requiring additional measures,
+My method reaches approximately 80\%, without requiring additional measures,
 placing it at the upper end of current non-invasive fertility prediction tools.
 
 Interestingly, control group results show that high pregnancy rates are achievable
@@ -241,7 +246,7 @@ with literature estimates that ~85\% of couples conceive within a year without
 timing-based intervention~\cite{pfeifer_optimizing_2017}.
 
 The benefit of model-based prediction becomes more evident when examining efficiency.
-While most related work ignores intercourse frequency, our results show that
+While most related work ignores intercourse frequency, my results show that
 trained and baseline models significantly outperform the control group in pregnancies per 1000 intercourse events.
 At mid-range threshold values, trained models demonstrated a four-fold increase in efficiency,
 indicating that model guidance can substantially optimize the effort-to-outcome ratio for couples trying to conceive.
@@ -251,7 +256,7 @@ The same pregnancy rate over one year can represent conception in the first or t
 which has practical significance for users.
 Including this temporal dimension would allow for a more detailed comparative analysis.
 
-As in the contraception use-case, all results are based on statistical assumptions
+As in the contraception use case, all results are based on statistical assumptions
 that may not fully hold in real-world scenarios.
 Therefore, findings should be interpreted accordingly.
 A more detailed investigation of these assumptions, along with further medical interpretation,