Predicting Kidney Transplant Survival Using Multiple Feature Representations for HLAs

Nemati, Mohammadreza; Zhang, Haonan; Sloma, Michael; Bekbolsynov, Dulat; Wang, Hong; Stepkowski, Stanislaw M.; Xu, Kevin S.

doi:10.1007/978-3-030-77211-6_6

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…A number of ML-and non-ML-based prediction tools have been developed using national and international collaborative data sets [35][36][37]. In Taiwan, the National Health Insurance Research Database exemplifies a population-level data source for research in health care, with strict requirement for privacy and data confidentiality [38].…”

Section: Data Sharingmentioning

confidence: 99%

Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?

Mashar¹,

Chawla²,

Chen³

et al. 2023

JMIR AI

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Given the growing use of machine learning (ML) technologies in health care, regulatory bodies face unique challenges in governing their clinical use. Under the regulatory framework of the Food and Drug Administration, approved ML algorithms are practically locked, preventing their adaptation in the ever-changing clinical environment, defeating the unique adaptive trait of ML technology in learning from real-world feedback. At the same time, regulations must enforce a strict level of patient safety to mitigate risk at a systemic level. Given that ML algorithms often support, or at times replace, the role of medical professionals, we have proposed a novel regulatory pathway analogous to the regulation of medical professionals, encompassing the life cycle of an algorithm from inception, development to clinical implementation, and continual clinical adaptation. We then discuss in-depth technical and nontechnical challenges to its implementation and offer potential solutions to unleash the full potential of ML technology in health care while ensuring quality, equity, and safety. References for this article were identified through searches of PubMed with the search terms “Artificial intelligence,” “Machine learning,” and “regulation” from June 25, 2017, until June 25, 2022. Articles were also identified through searches of the reference list of the articles. Only papers published in English were reviewed. The final reference list was generated based on originality and relevance to the broad scope of this paper.

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Section: Data Sharingmentioning

confidence: 99%

Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?

Mashar¹,

Chawla²,

Chen³

et al. 2023

JMIR AI

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show abstract

“…Figure 2 indicates that the use of C-index is steadily increasing and it has become a dominant predictive measure recently (Jing et al, 2019;Amico et al, 2021). On the one hand, this implies that the lack of handy evaluation metrics tools since other measures are also very important in survival analysis but do not have an adequate seat in survival prediction practices (Nemati et al, 2021;Ensor et al, 2021). On the other hand, it reminds us that providing an accurate C-index measure which can take various situations into account is extremely important (H Ea Gerty and Zheng, 2005;Kang et al, 2015;Zadeh and Schmid, 2020;.…”

Section: Introductionmentioning

confidence: 99%

SurvMetrics: An R package for Predictive Evaluation Metrics in Survival Analysis

Zhou

Wang

et al. 2023

The R Journal

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Recently, survival models have found vast applications in biostatistics, bioinformatics, reliability engineering, finance and related fields. But there are few R packages focusing on evaluating the predictive power of survival models. This lack of handy software on evaluating survival predictions hinders further applications of survival analysis for practitioners. In this research, we want to fill this gap by providing an "all-in-one" R package which implements most predictive evaluation metrics in survival analysis. In the proposed SurvMetrics R package, we implement concordance index for both untied and tied survival data; we give a new calculation process of Brier score and integrated Brier score; we also extend the applicability of integrated absolute error and integrated square error for real data. For models that can output survival time predictions, a simplified metric called mean absolute error is also implemented. In addition, we test the effectiveness of all these metrics on simulated and real survival data sets. The newly developed SurvMetrics R package is available on CRAN at https://CRAN.R-project.org/package=SurvMetrics and GitHub at https://github.com/skyee1/SurvMetrics.

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The prospect for the diagnostic management team to streamline virtual and physical crossmatching

Liwski

Stepkowski

2022

Transplant Immunology

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Predicting Kidney Transplant Survival Using Multiple Feature Representations for HLAs

Cited by 5 publications

References 19 publications

Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?

Artificial Intelligence Algorithms in Health Care: Is the Current Food and Drug Administration Regulation Sufficient?

SurvMetrics: An R package for Predictive Evaluation Metrics in Survival Analysis

The prospect for the diagnostic management team to streamline virtual and physical crossmatching

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