Qini-based uplift regression

Belbahri, Mouloud; Murua, Alejandro; Gandouet, Olivier; Nia, Vahid Partovi

doi:10.1214/21-aoas1465

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…The uplift in the qini curve demonstrated the gain in 28-day survival that resulted from patients being randomized to the lower Sp o 2 group relative to the ordering of patients by their predicted likelihood to benefit from a lower Sp o 2 target (eFigure 5 in Supplement 1). The adjusted qini value was 2.27 and C-for-benefit was 0.55 (bootstrapped 95% CI, 0.50 to 0.60), consistent with the model’s ability to discriminate treatment effects better than random chance. The model was well calibrated (eFigure 6 in Supplement 1).…”

Section: Resultssupporting

confidence: 84%

Individualized Treatment Effects of Oxygen Targets in Mechanically Ventilated Critically Ill Adults

Buell,

Spicer,

Casey

et al. 2024

JAMA

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ImportanceAmong critically ill adults, randomized trials have not found oxygenation targets to affect outcomes overall. Whether the effects of oxygenation targets differ based on an individual’s characteristics is unknown.ObjectiveTo determine whether an individual’s characteristics modify the effect of lower vs higher peripheral oxygenation-saturation (Spo2) targets on mortality.Design, Setting, and ParticipantsA machine learning model to predict the effect of treatment with a lower vs higher Spo2 target on mortality for individual patients was derived in the Pragmatic Investigation of Optimal Oxygen Targets (PILOT) trial and externally validated in the Intensive Care Unit Randomized Trial Comparing Two Approaches to Oxygen Therapy (ICU-ROX) trial. Critically ill adults received invasive mechanical ventilation in an intensive care unit (ICU) in the United States between July 2018 and August 2021 for PILOT (n = 1682) and in 21 ICUs in Australia and New Zealand between September 2015 and May 2018 for ICU-ROX (n = 965).ExposuresRandomization to a lower vs higher Spo2 target group.Main Outcome and Measure28-Day mortality.ResultsIn the ICU-ROX validation cohort, the predicted effect of treatment with a lower vs higher Spo2 target for individual patients ranged from a 27.2% absolute reduction to a 34.4% absolute increase in 28-day mortality. For example, patients predicted to benefit from a lower Spo2 target had a higher prevalence of acute brain injury, whereas patients predicted to benefit from a higher Spo2 target had a higher prevalence of sepsis and abnormally elevated vital signs. Patients predicted to benefit from a lower Spo2 target experienced lower mortality when randomized to the lower Spo2 group, whereas patients predicted to benefit from a higher Spo2 target experienced lower mortality when randomized to the higher Spo2 group (likelihood ratio test for effect modification P = .02). The use of a Spo2 target predicted to be best for each patient, instead of the randomized Spo2 target, would have reduced the absolute overall mortality by 6.4% (95% CI, 1.9%-10.9%).Conclusion and relevanceOxygenation targets that are individualized using machine learning analyses of randomized trials may reduce mortality for critically ill adults. A prospective trial evaluating the use of individualized oxygenation targets is needed.

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Section: Resultssupporting

confidence: 84%

Individualized Treatment Effects of Oxygen Targets in Mechanically Ventilated Critically Ill Adults

Buell,

Spicer,

Casey

et al. 2024

JAMA

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“…A resource optimization model using classification results was developed. Belbahri et al (2021) proposed a Qini-based uplift model based on logistic regressions, which are used to identify customers who are more likely to respond positively to targeted marketing activities to retain them, i.e., to avoid unnecessary costs for customers who are more likely to switch on competitors. The Qini coefficient measures the model performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer churn prediction model: a case of the telecommunication market

et al. 2022

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The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

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M³TN: Multi-Gate Mixture-of-Experts Based Multi-Valued Treatment Network for Uplift Modeling

Sun,

Chen

2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Qini-based uplift regression

Cited by 5 publications

References 28 publications

Individualized Treatment Effects of Oxygen Targets in Mechanically Ventilated Critically Ill Adults

Individualized Treatment Effects of Oxygen Targets in Mechanically Ventilated Critically Ill Adults

Customer churn prediction model: a case of the telecommunication market

M³TN: Multi-Gate Mixture-of-Experts Based Multi-Valued Treatment Network for Uplift Modeling

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Qini-based uplift regression

Cited by 5 publications

References 28 publications

Individualized Treatment Effects of Oxygen Targets in Mechanically Ventilated Critically Ill Adults

Individualized Treatment Effects of Oxygen Targets in Mechanically Ventilated Critically Ill Adults

Customer churn prediction model: a case of the telecommunication market

M3TN: Multi-Gate Mixture-of-Experts Based Multi-Valued Treatment Network for Uplift Modeling

Contact Info

Product

Resources

About

M³TN: Multi-Gate Mixture-of-Experts Based Multi-Valued Treatment Network for Uplift Modeling