added user specific metrics
This commit is contained in:
@@ -2267,7 +2267,7 @@ Conclusion(s): Measuring urinary LH levels is an excellent method for determinin
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month = mar,
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year = {2014},
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note = {arXiv:1312.4569 [cs]},
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keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
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keywords = {Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Computer Vision and Pattern Recognition},
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file = {Full Text PDF:/home/alex/Zotero/storage/IA52LNE8/Pham et al. - 2014 - Dropout improves Recurrent Neural Networks for Handwriting Recognition.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/LJ8EJ5PS/1312.html:text/html},
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}
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@@ -2282,7 +2282,7 @@ Conclusion(s): Measuring urinary LH levels is an excellent method for determinin
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journal = {IEEE Access},
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author = {Wen, Xianyun and Li, Weibang},
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year = {2023},
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keywords = {attention mechanisms, Autoregressive processes, Data models, Decoding, encoder and decoder model, Forecasting, Logic gates, long short-term memory networks, Predictive models, Time series analysis, Time series forecasting},
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keywords = {Predictive models, Time series analysis, Time series forecasting, attention mechanisms, Autoregressive processes, Data models, Decoding, encoder and decoder model, Forecasting, Logic gates, long short-term memory networks},
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pages = {48322--48331},
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file = {Full Text PDF:/home/alex/Zotero/storage/3M54PVSE/Wen and Li - 2023 - Time Series Prediction Based on LSTM-Attention-LSTM Model.pdf:application/pdf},
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}
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@@ -2299,7 +2299,65 @@ Conclusion(s): Measuring urinary LH levels is an excellent method for determinin
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month = apr,
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year = {2018},
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note = {arXiv:1706.02677 [cs]},
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keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning},
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keywords = {Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computer Vision and Pattern Recognition},
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annote = {Comment: Tech report (v2: correct typos)},
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file = {Full Text PDF:/home/alex/Zotero/storage/5MKPLWI7/Goyal et al. - 2018 - Accurate, Large Minibatch SGD Training ImageNet in 1 Hour.pdf:application/pdf;Snapshot:/home/alex/Zotero/storage/TRYJFLDW/1706.html:text/html},
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}
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@article{pearl_factors_1933,
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title = {{FACTORS} {IN} {HUMAN} {FERTILITY} {AND} {THEIR} {STATISTICAL} {EVALUATION}},
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volume = {222},
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copyright = {https://www.elsevier.com/tdm/userlicense/1.0/},
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issn = {01406736},
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url = {https://linkinghub.elsevier.com/retrieve/pii/S0140673601186484},
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doi = {10.1016/S0140-6736(01)18648-4},
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language = {en},
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number = {5741},
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urldate = {2025-07-30},
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journal = {The Lancet},
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author = {Pearl, Raymond},
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month = sep,
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year = {1933},
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pages = {607--611},
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}
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@article{gaskins_predictors_2018,
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title = {Predictors of sexual intercourse frequency among couples trying to conceive},
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volume = {15},
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issn = {1743-6095},
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url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882561/},
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doi = {10.1016/j.jsxm.2018.02.005},
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abstract = {Background
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Little is known about the predictors of sexual intercourse frequency (SIF) among couples trying to conceive despite the well-established link between SIF and fecundity.
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Aim
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To evaluate the male and female demographic, occupational, and lifestyle predictors of SIF among couples.
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Methods
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469 couples without a history of infertility participating in the Longitudinal Investigation of Fertility and the Environment Study (2005–2009) were followed for ≤1 year while trying to conceive. At enrollment, both partners were interviewed about demographic, occupational, lifestyle, and psychological characteristics using standardized questionnaires. Multivariable generalized linear mixed models with Poisson distribution was used to estimate the adjusted percent difference in SIF across exposure categories.
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Outcomes
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SIF was recorded in daily journals and summarized as average SIF per month.
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Results
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The median (interquartile range) SIF during follow-up was 6 (4–9) acts per month. For every year increase in female and male age, SIF decreased by −0.8\% (95\% CI −2.5, 1.0\%) and −1.7\% (95\% CI −3.1, −0.3\%). Women with high school education or less and those of non-White race had 34.4\% and 16.0\% higher SIF, respectively. A similar trend was seen for male education and race. Only couples where both partners (but not just one partner) worked rotating shifts had −39.1\% (95\% CI −61.0, −5.0\%) lower SIF compared to couples where neither partner worked rotating shifts. Male (but not female) exercise was associated with 13.2\% (95\% CI 1.7, 26.0\%) higher SIF. Diagnosis of a mood or anxiety disorder in the male (but not female) was associated with a 26.0\% (95\% CI −42.7, −4.4\%) lower SIF. Household income, smoking status, BMI, night work, alcohol intake, psychosocial stress were not associated with SIF.
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Clinical Implications
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Even among couples trying to conceive, there was substantial variation in SIF. Both partners’ age, education, race, and rotating shift work as well as male exercise and mental health play an important role in determining SIF.
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Strengths \& Limitations
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As this was a secondary analysis of an existing study, we lacked information on many pertinent psychological and relationship quality variables and the hormonal status of participants, which could have affected SIF. The unique population-based couple design, however, captured both partners’ demographics, occupational characteristics, lifestyle behaviors in advance of their daily, prospective reporting of SIF, which was a major strength.
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Conclusion
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Important predictors of SIF among couples attempting to conceive include male exercise and mental health and both partners’ age, education, race, and rotating shift work.},
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number = {4},
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urldate = {2025-07-30},
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journal = {The journal of sexual medicine},
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author = {Gaskins, Audrey J. and Sundaram, Rajeshwari and Buck Louis, Germaine M. and Chavarro, Jorge E.},
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month = apr,
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year = {2018},
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pmid = {29523477},
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pmcid = {PMC5882561},
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pages = {519--528},
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file = {Full Text PDF:/home/alex/Zotero/storage/S3JV4TU2/Gaskins et al. - 2018 - Predictors of sexual intercourse frequency among couples trying to conceive.pdf:application/pdf},
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}
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@@ -154,6 +154,27 @@ detecting the slight temperature rise that follows ovulation.
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Advances in wearable technology have further enabled continuous and automated temperature monitoring,
|
||||
improving accessibility and usability~\cite{alexander_fertilitatsmonitoring_2014, luo_detection_2020, yu_tracking_2022}.
|
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|
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\subsubsection{Use Cases of Fertility Prediction}\label{subsubsec:use_cases_of_ovulation_prediction}
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The prediction of ovulation and corresponding fertility within a menstrual cycle serves two distinct use cases.
|
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Specifically, we will focus on \emph{natural family planning} (NFP), which includes preventing and achieving pregnancy.
|
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Individuals aiming to avoid pregnancy identify fertile days to abstain from intercourse,
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whereas those seeking pregnancy aim to focus intercourse around days with the highest fertility probability.
|
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Both use cases revolve around accurately predicting ovulation.
|
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However, the implications of prediction errors differ significantly.
|
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A false-positive prediction indicates high fertility despite actual fertility being low or nonexistent,
|
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whereas a false-negative prediction implies low fertility when fertility is actually high.
|
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For women aiming to avoid pregnancy, minimizing false-negative predictions is crucial due to the risk of unintended pregnancy.
|
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Although false-positives may lead to unnecessary abstinence, this outcome is generally considered less severe.
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Consequently, prediction algorithms should be conservative, erring on the side of higher fertility estimates to prioritize safety.
|
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Conversely, for women aiming to conceive, false-positive predictions could misdirect efforts toward incorrect cycle days,
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causing frustration or delays.
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False-negatives have fewer negative consequences.
|
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Therefore, algorithms for this group should prefer cautious fertility estimates,
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reducing the risk of misdirected effort.
|
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\subsection{Data Source and Characteristics}\label{subsec:data_background}
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This study is based on a dataset collected from users of the \emph{OvulaRing}~\cite{noauthor_ovularing_nodate},
|
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an intravaginal wearable sensor developed by VivoSensMedical GmbH, located in Leipzig, Germany~\cite{noauthor_vivosens_nodate}.
|
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@@ -532,7 +553,6 @@ including time-series forecasting and biomedical modeling~\cite{wu_deep_nodate,z
|
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Its ability to model complex, long-range dependencies without recurrence makes it well-suited to domains like biomedical time-series,
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where signals are often irregular and span diverse temporal resolutions.
|
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\subsubsection{Convolutional Layers as Temporal Feature Extractors}
|
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For high-resolution time-series data, the input dimensionality can become large,
|
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especially in models like Transformers that process the entire sequence in parallel.
|
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@@ -573,6 +573,21 @@ The batch size was capped at 2048 to avoid OOM errors during data preparation.
|
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To meaningfully compare model performance, we define a set of metrics that capture both overall accuracy and behavior at key points in the prediction sequence.
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This includes metrics for different temporal segments, enabling a more detailed understanding of model strengths and limitations.
|
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\subsubsection{Baseline Comparisons}\label{subsubsec:baseline_comparisons}
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To place model results into context, a set of simple baseline models was established.
|
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These models rely on minimal assumptions and serve to evaluate whether the more complex models actually learn meaningful patterns beyond basic cycle regularities.
|
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Three types of baselines are considered:
|
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\begin{itemize}
|
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\item \textbf{Population-Mean OV Day}: predicts the mean ovulation day across the full population.
|
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\item \textbf{User-Mean OV Day}: predicts the user’s average ovulation day; uses the population mean for the first cycle.
|
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\item \textbf{Previous OV Day}: predicts the ovulation day from the previous cycle; uses the population mean for the first cycle.
|
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\end{itemize}
|
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These baselines also help assess performance on regular menstrual patterns, something often overlooked in related work (see Section~\ref{sec:related_work}).
|
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In many cases, it remains unclear whether proposed models genuinely outperform such simple heuristics.
|
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\subsubsection{Evaluation Metrics}\label{subsubsec:evaluation_metrics}
|
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The base metric used for all categories is the mean absolute error (MAE),
|
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@@ -682,20 +697,80 @@ For a user to be included in the analysis, they must have at least five complete
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Additionally, ovulation must occur no later than cycle day 150, as later values likely indicate measurement errors or
|
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biologically atypical cases that fall outside the scope of this study.
|
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\subsubsection{Baseline Comparisons}\label{subsubsec:baseline_comparisons}
|
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\subsubsection{Use Case Evaluation}
|
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We further evaluate the two distinct use cases introduced in Section~\ref{subsubsec:use_cases_of_ovulation_prediction}.
|
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For this purpose, two specialized evaluation algorithms were developed,
|
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enabling comparability between models and providing interpretable performance metrics for each scenario.
|
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To place model results into context, a set of simple baseline models was established.
|
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These models rely on minimal assumptions and serve to evaluate whether the more complex models actually learn meaningful patterns beyond basic cycle regularities.
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\paragraph{Contraception Use-Case:}
|
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For evaluating contraceptive effectiveness, we developed an algorithm inspired by the classical \emph{Pearl Index},
|
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initially proposed by~\citeauthor{pearl_factors_1933} in~\citeyear{pearl_factors_1933}.
|
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|
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Three types of baselines are considered:
|
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\begin{figure}[htbp]
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\centering
|
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\includegraphics[width=0.9\textwidth]{resources/figures/methodology/methodology_use_case_contraception_decision_diagram}
|
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\caption{
|
||||
Decision diagram outlining the evaluation procedure for the contraception use case.
|
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}
|
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\label{fig:methodology_use_case_contraception_decision_diagram}
|
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\end{figure}
|
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|
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Figure~\ref{fig:methodology_use_case_contraception_decision_diagram} illustrates the decision logic of the evaluation algorithm.
|
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The fertility threshold can be adjusted, as will be explored in Section~\ref{subsec:use_case_evaluation_results}.
|
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A day-specific probability of intercourse is computed for each user based on age distributions reported by~\cite{twenge_declines_2017}.
|
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We assume, that the users don't have any health-related or non-health-related issues affecting fertility.
|
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If a user's age is unknown, it is randomly drawn from the overall dataset distribution.
|
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Only users with at least one continuous year of data are included.
|
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|
||||
Each day of data for a full year is categorized by the algorithm into one of the following outcomes:
|
||||
\begin{itemize}
|
||||
\item \textbf{Population-Mean OV Day}: predicts the mean ovulation day across the full population.
|
||||
\item \textbf{User-Mean OV Day}: predicts the user’s average ovulation day; uses the population mean for the first cycle.
|
||||
\item \textbf{Previous OV Day}: predicts the ovulation day from the previous cycle; uses the population mean for the first cycle.
|
||||
\item \emph{No Sex}: no intercourse occurred.
|
||||
\item \emph{Correct Denial}: fertility prediction correctly indicated abstinence during a fertile period.
|
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\item \emph{Incorrect Denial}: fertility prediction incorrectly indicated abstinence during an infertile period.
|
||||
\item \emph{Pregnancy}: fertility prediction allowed intercourse during a potentially fertile period, and it led to a pregnancy.
|
||||
\item \emph{No Pregnancy}: fertility prediction allowed intercourse during an infertile or potentially fertile period, but it did not lead to a pregnancy.
|
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\end{itemize}
|
||||
|
||||
These baselines also help assess performance on regular menstrual patterns, something often overlooked in related work (see Section~\ref{sec:related_work}).
|
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In many cases, it remains unclear whether proposed models genuinely outperform such simple heuristics.
|
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This categorization captures both the contraceptive accuracy (avoiding pregnancy) and the practicality
|
||||
(minimizing unnecessary abstinence) of the predictive algorithm.
|
||||
An algorithm achieving perfect contraceptive accuracy by always recommending abstinence would score highly but
|
||||
significantly limit user acceptability and utility.
|
||||
|
||||
\paragraph{Pregnancy Use-Case:}
|
||||
|
||||
For the pregnancy use-case, we developed a complementary algorithm to evaluate model performance for users attempting to conceive.
|
||||
|
||||
\begin{figure}[htbp]
|
||||
\centering
|
||||
\includegraphics[width=0.9\textwidth]{resources/figures/methodology/methodology_use_case_pregnancy_decision_diagram}
|
||||
\caption{
|
||||
Decision diagram outlining the evaluation procedure for the pregnancy use case.
|
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}
|
||||
\label{fig:methodology_use_case_pregnancy_decision_diagram}
|
||||
\end{figure}
|
||||
|
||||
Figure~\ref{fig:methodology_use_case_pregnancy_decision_diagram} illustrates the decision logic of this algorithm.
|
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The fertility threshold is adjustable and is explored further in Section~\ref{subsec:use_case_evaluation_results}.
|
||||
Since sexual intercourse frequency differs slightly for couples trying to conceive~\cite{gaskins_predictors_2018},
|
||||
we assume an average frequency of six times per month.
|
||||
We derive a daily probability of intercourse based on remaining fertile days predicted for the month,
|
||||
ensuring that intercourse frequency averages out to this monthly rate.
|
||||
|
||||
We assume no health-related fertility impairments for comparative simplicity,
|
||||
though we acknowledge that real-world fertility is influenced by numerous complex factors.
|
||||
Similar to the contraception scenario, only users with at least one continuous year of data are considered.
|
||||
|
||||
Each day in a full year is classified into one of the following categories:
|
||||
\begin{itemize}
|
||||
\item \emph{No Sex}: Day predicted as fertile, but no intercourse occurred.
|
||||
\item \emph{Pregnancy}: Correct fertile prediction, intercourse occurred, resulting in pregnancy.
|
||||
\item \emph{No Pregnancy}: Correct fertile prediction, intercourse occurred, but no pregnancy occurred.
|
||||
\item \emph{Incorrect Deferral}: Incorrect non-fertile prediction, actual fertility was above threshold.
|
||||
\item \emph{Correct Deferral}: Correct non-fertile prediction.
|
||||
\end{itemize}
|
||||
|
||||
This classification measures both fertility prediction accuracy and the impact of incorrect deferrals.
|
||||
A model overly predicting fertility would increase pregnancy rates but negatively affect usability due to misdirected efforts.
|
||||
|
||||
\subsection{Ethical Considerations}\label{subsec:ethical_considerations}
|
||||
|
||||
|
||||
@@ -49,6 +49,13 @@ allowing for a nuanced comparison of approaches and their practical relevance to
|
||||
|
||||
\subsubsection{Influence of User History Depth}\label{subsubsec:influence_of_past_user_data}
|
||||
|
||||
\subsection{Use-Case Evaluation Results}\label{subsec:use_case_evaluation_results}
|
||||
|
||||
\subsubsection{Contraception Use-Case Results}\label{subsubsec:use_case_contraception_results}
|
||||
|
||||
\subsubsection{Pregnancy Use-Case Results}\label{subsubsec:use_case_pregnancy_results}
|
||||
|
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|
||||
% show, that past cycles might not directly be included but are indirectly included by the static features
|
||||
|
||||
\subsection{Summary of Key Findings}\label{subsec:summary_of_key_findings}
|
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|
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Reference in New Issue
Block a user