ArticleJournal of multidisciplinary healthcare2025
Construction of a Prediction Model for Sleep Quality in Embryo Repeated Implantation Failure Patients Undergoing Assisted Reproductive Technology Based on Machine Learning: A Single-Center Retrospective Study.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Constructing a predictive model for sleep quality in embryo Repeated Implantation Failure(RIF) patients using multiple machine learning algorithms, verifying its performance, and selecting the optimal model. Methods: Retrospective collection of clinical data from RIF patients who underwent assisted reproductive technology at the Reproductive Medicine Center of Tongji University Affiliated Obstetrics and Gynecology Hospital from January 2022 to June 2022, divided into a training set and a validation set in an 8:2 ratio. Use Lasso regression to screen variables and construct a risk prediction model using six machine learning algorithms. Evaluate the validity of the model using the area under the curve (AUC), and comprehensively evaluate the performance of the model based on F1 score, accuracy, sensitivity, and specificity. Use SHAP method to explain the contribution of each variable in the optimal model to the occurrence of sleep disorders. Results: A total of 404 RIF patients were included in the study. The incidence of sleep disturbances was 48.76%. After LASSO regression analysis, nine variables were selected for inclusion in the model. The RF model has an AUC of 0.941, Accuracy of 0.938, Specification of 0.950, and F1 score of 0.938 in the validation set, making it the optimal model for this study. According to the SHAP feature importance ranking of the RF model, the factors influencing sleep quality in RIF patients were E2, SDS, Fertiqol, FSH, daily exercise time, weekly shift work hours, coffee consumption, sunbathing, and SAS. Conclusion: The RF model is the optimal model for predicting the sleep quality of RIF patients. Its sleep quality is not only affected by physiological factors, but also by psychological and lifestyle factors. Medical personnel should implement intervention strategies as early as possible based on relevant risk factors to improve the sleep quality of this population.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.