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Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach

Autor: Li, Yubo, Padman, Rema

Objective: To improve prediction of Chronic Kidney Disease (CKD) progression to End Stage Renal Disease (ESRD) using machine learning (ML) and deep learning (DL) models applied to an integrated clinical and claims dataset of varying observation windo

Externí odkaz: http://arxiv.org/abs/2410.01859

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Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques

Autor: Li, Yubo, Al-Sayouri, Saba, Padman, Rema

This study explores the potential of utilizing administrative claims data, combined with advanced machine learning and deep learning techniques, to predict the progression of Chronic Kidney Disease (CKD) to End-Stage Renal Disease (ESRD). We analyze

Externí odkaz: http://arxiv.org/abs/2409.12087

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Asymptotically Optimal Sequence Sets With Low/Zero Ambiguity Zone Properties

Autor: Tian, Liying, Song, Xiaoshi, Liu, Zilong, Li, Yubo

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful for modern wireless communication and radar systems operating in mobile environments. This paper first presents a new family of ZAZ sequence sets by generalizing an earlier constr

Externí odkaz: http://arxiv.org/abs/2401.00683

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