Prediction of intradialytic hypotension using pre-dialysis features - a deep learning-based artificial intelligence model.

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Abstract

Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD) associated with increased risks of cardiovascular morbidity and mortality. However, its accurate prediction remains a clinical challenge. The aim of this study was to develop a deep learning-based artificial intelligence (AI) model to predict IDH using pre-dialysis features.

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SciPy 1.0: fundamental algorithms for scientific computing in Python

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant … (2020)

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

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A Unified Approach to Interpreting Model Predictions

Scott R. Lundberg, Su-In Lee (2017)

Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches. To appear in NIPS 2017

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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

Davide Chicco, Giuseppe Jurman (2020)

Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.

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Author and article information

Journal

Journal ID (iso-abbrev): Nephrol Dial Transplant

Title: Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association

Publisher: Oxford University Press (OUP)

ISSN (Electronic): 1460-2385

ISSN (Print): 0931-0509

Publication date (Electronic): Apr 05 2023

Affiliations

[1 ] Transplantation Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

[2 ] Division of Nephrology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

[3 ] APEXAI Co., Ltd., Seongnam-si, Republic of Korea.

[4 ] Department of Internal Medicine, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea.

[5 ] Department of Internal Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

[6 ] Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Republic of Korea.

[7 ] Department of Internal Medicine, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, Republic of Korea.

[8 ] Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea.

[9 ] Division of Nephrology, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

Article

Publisher Item ID: 7109281

DOI: 10.1093/ndt/gfad064

PubMed ID: 37019834

SO-VID: c0fe0a1a-939f-450b-8d24-7a801d245489

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Keywords: intradialytic hypotension,artificial intelligence,clinical data warehouse,deep learning,hemodialysis

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Keywords: intradialytic hypotension, artificial intelligence, clinical data warehouse, deep learning, hemodialysis

Prediction of intradialytic hypotension using pre-dialysis features - a deep learning-based artificial intelligence model.

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Abstract

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Artificial Intelligence in Medicine

Most cited references 33

SciPy 1.0: fundamental algorithms for scientific computing in Python

A Unified Approach to Interpreting Model Predictions

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

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