Kant on God’s Intuitive Understanding: Interpreting<i>CJ</i>§76’s Modal Claims

Winegar, Reed

doi:10.1017/s1369415417000061

Cited by 4 publications

(3 citation statements)

References 8 publications

(13 reference statements)

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“…Third, given the diversity of anatomic diagnosis and varying types of pediatric cardiac surgery, subgroup analysis based on surgical types was not performed; thus, the clinical application of predicting postoperative complications based on specific surgery types remains unclear 8 , 35 . Fourth, the most advanced model interpretation approaches, including SHAP and their alternatives, do not take into account dependencies between features and inevitably introduce correlation biases 33 . Fifth, the validation of the present findings in future research endeavors will necessitate the incorporation of multicenter cohorts.…”

Section: Discussionmentioning

confidence: 99%

“…The interpretations of ML models were evaluated using SHAP by ranking feature importance (the contribution of a feature to predicting the risk of a complication) and representing them as patient-specific visualizations 33 . A positive SHAP value showed that the corresponding feature leads to a higher risk of the complication, whereas a negative SHAP value showed that the corresponding feature contributes to a lower risk of that complication.…”

Section: Methodsmentioning

confidence: 99%

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Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study

Tong,

Du,

Chen

et al. 2024

International Journal of Surgery

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Background: Major adverse postoperative outcomes (APOs) can greatly affect mortality, hospital stay, care management and planning, and quality of life. This study aimed to evaluate the performance of five machine learning (ML) algorithms for predicting four major APOs after pediatric congenital heart surgery and their clinically meaningful model interpretations. Methods: Between August 2014 and December 2021, 23,000 consecutive pediatric patients receiving congenital heart surgery were enrolled. Based on the split date of 1 January 2019, we selected 13,927 participants for the training cohort, and 9,073 participants for the testing cohort. Four predefined major APOs including low cardiac output syndrome (LCOS), pneumonia, renal failure, and deep venous thrombosis (DVT) were investigated. 39 clinical and laboratory features were inputted in five ML models: light gradient boosting machine (LightGBM), logistic regression (LR), support vector machine, random forest, and CatBoost. The performance and interpretations of ML models were evaluated using the area under the receiver operating characteristic curve (AUC) and Shapley Additive Explanations (SHAP). Results: In the training cohort, CatBoost algorithms outperformed others with the mean AUCs of 0.908 for LCOS and 0.957 for renal failure, while LightGBM and LR achieved the best mean AUCs of 0.886 for pneumonia and 0.942 for DVT, respectively. In the testing cohort, the best-performing ML model for each major APOs with the following mean AUCs: LCOS (LightGBM), 0.893 (95% confidence interval (CI), 0.884-0.895); pneumonia (LR), 0.929 (95% CI, 0.926-0.931); renal failure (LightGBM), 0.963 (95% CI, 0.947-0.979), and DVT (LightGBM), 0.970 (95% CI, 0.953-0.982). The performance of ML models using only clinical variables was slightly lower than those using combined data, with the mean AUCs of 0.873 for LCOS, 0.894 for pneumonia, 0.953 for renal failure, and 0.933 for DVT. The SHAP showed that mechanical ventilation time was the most important contributor of four major APOs. Conclusions: In pediatric congenital heart surgery, the established ML model can accurately predict the risk of four major APOs, providing reliable interpretations for high-risk contributor identification and informed clinical decisions making.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study

Tong,

Du,

Chen

et al. 2024

International Journal of Surgery

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“…In Winegar (), I examine in more detail the relationship between God's intuitive understanding and possibility, including the relationship between God's intuitive understanding and God's will in creation.…”

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Kant on intuitive understanding and things in themselves

Winegar

2017

European J of Philosophy

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Kant claims that an intuitive understanding—such as God would possess—could cognize things in themselves. This claim has prompted many interpreters of Kant's theoretical philosophy to propose that things in themselves correspond to how an intuitive understanding would cognize things. In contrast, I argue that Kant's theoretical philosophy does not endorse the common proposal that all things in themselves correspond to how an intuitive understanding would cognize things. Instead, Kant's theoretical philosophy maintains that things in themselves might or might not correspond to how an intuitive understanding would cognize things. I then consider whether Kant's moral philosophy and theory of reflecting judgment might provide alternative grounds for claiming that we should regard things in themselves as corresponding to how an intuitive understanding would cognize things. I argue that Kant's moral philosophy does not provide such grounds, but his theory of reflecting judgment does. Thus, interpretations of Kant's transcendental idealism should attend to the differences between Kant's theoretical philosophy, moral philosophy, and theory of reflecting judgment in assessing the relationship between intuitive understanding and things in themselves.

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Noumenal Freedom and Kant’s Modal Antinomy

Abaci

2021

Kantian rev.

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Kant states in §76 of the third Critique that the divine intuitive intellect would not represent modal distinctions. Kohl (2015) and Stang (2016) claim that this statement entails that noumena lack modal properties, which, in turn, conflicts with Kant’s attribution of contingency to human noumenal wills. They both propose resolutions to this conflict based on conjectures regarding how God might non-modally represent what our discursive intellects represent as modally determined. I argue that (i) these proposals fail; (ii) the viable resolution consists in recognizing that we modalize human noumenal wills as a merely regulative-practical principle in our judgements of imputation.

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Kant on God’s Intuitive Understanding: InterpretingCJ§76’s Modal Claims

Cited by 4 publications

References 8 publications

Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study

Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study

Kant on intuitive understanding and things in themselves

Noumenal Freedom and Kant’s Modal Antinomy

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