Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience

Yao, Mylene W. M.; Nguyen, Elizabeth T.; Retzloff, Matthew G.; Gago, Laura April; Copland, Susannah; Nichols, John E.; Payne, John F.; Opsahl, Michael; Cadesky, Ken; Meriano, Jim; Donesky, Barry W.; Bird, Joseph; Peavey, Mary; Beesley, Ronald; Neal, Gregory; Bird, Joseph S.; Swanson, Trevor; Chen, Xiaocong; Walmer, David K.

doi:10.3390/jcm13123560

Cited by 3 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have reported results from a retrospective study measuring patients’ treatment utilization after receiving MLCS-counseling, the results of which informed the design of a prospective trial (in progress). (30) In the context of clinical workflow, MLCS models can support an evolving, more diverse range of healthcare providers -- such as advanced practice providers (APPs), nurse practitioners and general obstetrician-gynecologists -- to perform patient counseling, further improving scalability and accessibility of IVF treatments. (31, 32)…”

Section: Discussionmentioning

confidence: 99%

Predicting IVF live birth probabilities using machine learning, center-specific models: validation results and potential benefits over national registry-based models

Nguyen,

Retzloff,

Gago

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Structured AbstractObjectiveTo compare the performance of machine learning based, center-specific (MLCS) models and the US national registry-based, multicenter model (SART model) in predicting IVF live birth probabilities (LBPs) for 6 unrelated, geographically diverse US fertility centers.DesignRetrospective observational design.SubjectsTest sets comprised first IVF cycle data (2013-2022) extracted from a retrospective cohort of 4,645 patients at 6 fertility centers.Intervention or ExposureThe initial (MLCS1) and updated (MLCS2) models were compared against age control. MLSC2 and SART models were compared.Main Outcome MeasuresModel validation metrics, reported in median and interquartile range (IQR), were compared using Wilcoxon signed-rank test: ROC AUC, posterior log-likelihood of odds ratio compared to age (PLORA), Precision-Recall (PR) AUC, F1 score and continuous net reclassification improvement (NRI).ResultsMLCS1 and MLCS2 models showed improved AUC and PLORA compared to age control; MLCS1 models were validated using out-of-time test data. MLCS2 models showed improved PLORA 23.9 (IQR 10.2, 39.4) compared to 7.2 (IQR 3.6, 11.8) for MLCS1, p<0.05. MLCS2 showed higher median PR AUC at 0.75 (IQR 0.73, 0.77) compared to 0.69 (IQR 0.68, 0.71) for SART, p<0.05. In addition, the median F1 Score was higher for MLCS2 compared to SART model across predicted live birth probability (LBP) thresholds sampled at deciles at ≥40%, ≥50%, ≥60%, ≥70%. For example, at the 50% LBP threshold, MLCS2 had a median F1 score of 0.74 (IQR 0.72, 0.78) compared to 0.71 (IQR 0.68, 0.73) for SART.At these six centers, using the LBP threshold of ≥ 50%, MLCS2 models can identify ∼84% of patients who would go on to have IVF live births, while the SART model can only identify ∼75%. That means for every 100 patients who will have a first IVF cycle live birth, using LBR ≥ 50% as threshold, the MLCS2 model can identify 9 more such patients without overcalling or overestimating LBPs compared to the SART model.ConclusionMLCS models accurately assign higher IVF LBPs to more patients compared to the SART model at 6 US fertility centers. We recommend testing a larger sample of fertility centers to evaluate generalizability of MLCS model benefits.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting IVF live birth probabilities using machine learning, center-specific models: validation results and potential benefits over national registry-based models

Nguyen,

Retzloff,

Gago

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Yao,

Jenkins,

Nguyen

et al. 2024

Semin Reprod Med

View full text Add to dashboard Cite

Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report delivery require specialized human and platform expertise. This review article takes a pragmatic approach to review relevant reports of IVF prognostic models and draws from extensive experience meeting patients' and providers' needs with the development of data and model pipelines to implement validated ML models at scale, at the point-of-care. Requirements of using ML-based IVF prognostics at point-of-care will be considered alongside clinical ML implementation factors critical for success. Finally, we discuss health, social, and economic objectives that may be achieved by leveraging combined human expertise and ML prognostics to expand fertility care access and advance health and social good.

show abstract

Listing Day Returns of Select Nse-Listed Ipos in India With Respect to Different Listing Delays: An Empirical Analysis

Samanta,

Saha

2024

IJIRA

View full text Add to dashboard Cite

Capital formation through the primary capital market is indispensable for the economic development of a nation. New securities in the form of Initial Public Offerings (IPOs) are issued in the primary capital market. Securities issued through IPOs get listed on a recognised stock exchange mandatorily within a stipulated time from the date of issuance. The IPOs are subscribed by the interested investors at the offer price or issue price decided by the merchant bankers as book runner lead managers (BRLMs). After the listing of IPOs, they may display under-pricing or over-pricing on the listing day. Many factors govern the market performance of IPOs. In this context, the current study analyses the first day price performance of IPOs based on the listing delays incorporated in the issue process. The paper also studies the various measures of first day returns, like the average initial return, average MAAR, average annualised initial return, and average annualised MAAR on the basis of the different groups formed on the basis of difference in listing delays for the sample IPOs companies. Such measures of average returns are observed for statistical significance with the application of one-sample t-test. The study ultimately finds the sample IPOs are underpriced when studied on the basis of different categories of listing delays. Listing delays between 8 days to 12 days repeatedly shows higher under-pricing, which is significant. However, IPOs belonging to the group where listing delays are less than 8 days do not show significant under-pricing.

show abstract

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective Multicenter Experience

Cited by 3 publications

References 38 publications

Predicting IVF live birth probabilities using machine learning, center-specific models: validation results and potential benefits over national registry-based models

Predicting IVF live birth probabilities using machine learning, center-specific models: validation results and potential benefits over national registry-based models

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Listing Day Returns of Select Nse-Listed Ipos in India With Respect to Different Listing Delays: An Empirical Analysis

Contact Info

Product

Resources

About