Interpretable Machine Learning In Healthcare

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An interpretable machine learning model based on a quick pre-scr…

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    Can interpretable machine learning models hold machine learning systems accountable in healthcare?
    Abstract—The drive towards greater penetration of machine learning in healthcare is being accompanied by increased calls for machine learning and AI based systems to be regulated and held accountable in healthcare. Interpretable machine learning models can be instrumental in holding machine learning systems accountable.

    (PDF) Interpretable Machine Learning in Healthcare

    (5 days ago) Interpretable Machine Learning in Healthcare. Muhammad Aurangzeb Ahmad, Carly Eckert, Ankur T eredesai, and Greg McKelve y. Abstract —The drive …

    https://www.researchgate.net/publication/328416903_Interpretable_Machine_Learning_in_Healthcare

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    Interpretable Machine Learning in Healthcare IEEE …

    (5 days ago) This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the …

    https://ieeexplore.ieee.org/document/8419428/

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    Interpretable Machine Learning in Healthcare - Google …

    (9 days ago) Interpretable Machine Learning in Healthcare ICML 202 1 Workshop Friday, July 23, 202 1 Virtual Worldwide. Overview. Applying machine learning (ML) in healthcare is gaining momentum rapidly. However, the black-box characteristics of the existing ML approach inevitably lead to less interpretability and verifiability in making clinical

    https://sites.google.com/view/imlh2021/

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    Interpretable Machine Learning in Healthcare - ICML

    (2 days ago) Abstract: Applying machine learning (ML) in healthcare is gaining momentum rapidly. However, the black-box characteristics of existing ML approaches inevitably lead to less interpretability and verifiability in making clinical predictions. To enhance the interpretability of medical intelligence, it becomes critical to develop methodologies to

    https://icml.cc/virtual/2021/workshop/8358

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    Interpretable Machine Learning in Healthcare

    (7 days ago) Interpretable Machine Learning in Healthcare Muhammad Aurangzeb Ahmad, Carly Eckert, Ankur Teredesai, and Greg McKelvey Abstract—The drive towards greater penetration of machine learning in healthcare is being accompanied by increased calls for machine learning and AI based systems to be regulated and held accountable in healthcare.

    https://www.comp.hkbu.edu.hk/~iib/2018/Aug/article1/iib_vol19no1_article1.pdf

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    Interpretable Machine Learning in Healthcare through …

    (4 days ago) We have investigated the risk factors that lead to severe retinopathy of prematurity using statistical analysis and logistic regression as a form of generalized additive model (GAM) with pairwise interaction terms (GA2M). In this process, we discuss the trade-off between accuracy and interpretability of these machine learning techniques on clinical data. We also …

    https://ieeexplore.ieee.org/document/8876951

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    Interpretable Machine Learning in Health Care - DCRI

    (3 days ago) Interpretable Machine Learning in Health Care. Posted on March 7, 2014 March 14, 2019 by Kaitlin Jansen. This entry was posted in Data in Clinical Research. Bookmark the permalink. Post navigation.

    https://dcri.org/interpretable-machine-learning-in-health-care/

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    Interpretable Machine Learning in Healthcare

    (2 days ago) This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of …

    https://www.computer.org/csdl/proceedings-article/ichi/2018/537701a447/12OmNApu5y9

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    Interpretability of machine learning‐based prediction models …

    (2 days ago) There is a need of ensuring that learning (ML) models are interpretable. Higher interpretability of the model means easier comprehension and explana-tion of future predictions for end-users. Further, interpretable ML models allow healthcare experts to make reasonable and data-driven decisions to pro-

    https://zitniklab.hms.harvard.edu/publications/papers/interpretableML-survey20.pdf

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    Interpretable AI or Explainable ML models for healthcare

    (7 days ago) Interpretable machine learning models can be used for accountability in machine learning. Healthcare offers unique challenges for machine learning where the demands for explainability, model fidelity, and performance, in general, are much higher as compared to most other domains. The time is not to address the notion of interpretability within

    https://www.kensci.com/research/explainable-ml/

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    Interpretable Machine Learning in Healthcare Publons

    (Just Now) See reviews and reviewers from Interpretable Machine Learning in Healthcare Interpretable Machine Learning in Healthcare's journal/conference profile on Publons, with several reviews by several reviewers - working with reviewers, publishers, institutions, and funding agencies to turn peer review into a measurable research output.

    https://publons.com/journal/985676/interpretable-machine-learning-in-healthcare/

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    Interpretable Machine Learning in Healthcare Request PDF

    (5 days ago) Further, interpretable machine learning models allow healthcare experts to make reasonable and data-driven decisions to provide personalized decisions that can ultimately lead to …

    https://www.researchgate.net/publication/326640633_Interpretable_Machine_Learning_in_Healthcare

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    Interpretable Machine Learning in Healthcare Request PDF

    (5 days ago) Having set the common goal of interpretability, in recent years the scientific community has fueled considerable interest in Interpretable Machine Learning, which today is an extremely open and

    https://www.researchgate.net/publication/327213007_Interpretable_Machine_Learning_in_Healthcare

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    INTERPRETABLE MACHINE LEARNING WITH APPLICATIONS …

    (4 days ago) INTERPRETABLE MACHINE LEARNING WITH APPLICATIONS IN HEALTH CARE By CHENGLIANG YANG A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT 2.1 Machine Learning Methods for Predicting Health Care Events . . . . . . . 21

    https://ufdcimages.uflib.ufl.edu/UF/E0/05/20/45/00001/YANG_C.pdf

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    Interpretable Machine Learning in Healthcare Semantic Scholar

    (6 days ago) The landscape of recent advances to address the challenges model interpretability in healthcare and also how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare are explored. This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and …

    https://www.semanticscholar.org/paper/Interpretable-Machine-Learning-in-Healthcare-Ahmad-Eckert/1c52a5630d9bd3457834ab0635f4aa44753ea991

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    Interpretable Machine Learning: A Case Study of Healthcare

    (4 days ago) With the evolution of artificial intelligence, Machine Learning (ML) techniques have become more powerful predictors, and accordingly, the use of ML techniques has become a part of our daily life in different application scenarios such as disease diagnosis, movie recommendation, monitoring system, or detection of malicious attacks. Although ML provides …

    https://ieeexplore.ieee.org/abstract/document/9615727/

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    Interpretable Machine Learning in Healthcare through Generalized

    (7 days ago) This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare.

    https://www.researchgate.net/publication/352262364_Interpretable_Machine_Learning_in_Healthcare_through_Generalized_Additive_Model_with_Pairwise_Interactions_GA2M_Predicting_Severe_Retinopathy_of_Prematurity

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    Interpretable Machine Learning in Healthcare Proceedings of the …

    (Just Now) This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed.

    https://dl.acm.org/doi/abs/10.1145/3233547.3233667

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    Stop Explaining Black Box Machine Learning Models for High …

    (1 days ago) Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace

    https://pubmed.ncbi.nlm.nih.gov/35603010/

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    Interpretable machine learning for genomics

    (3 days ago) Interpretable machine learning (iML)—also known as explainable artificial intelligence (xAI) or, more simply, explainability—is a fast-growing subfield of computational statistics devoted to helping users make sense of the predictions of ML models. Obermeyer et al. found evidence of significant racial bias in a healthcare screening

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527313/

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    A class-contrastive human-interpretable machine learning …

    (3 days ago) Machine learning (ML), one aspect of artificial intelligence (AI), involves computer algorithms that train themselves. They have been widely applied in the healthcare domain. Our work is a step towards personalised medicine and interpretable AI in mental health and has the potential to be applicable more broadly in healthcare.

    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654849/

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    Gaining Insights Into Patient Satisfaction Through Interpretable

    (1 days ago) A patient during his/her healthcare journey interacts with multipl … Gaining Insights Into Patient Satisfaction Through Interpretable Machine Learning IEEE J Biomed Health Inform. 2021 Jun;25(6):2215-2226. doi: 10.1109/JBHI.2020.3038194. Epub 2021 Jun 3. Authors

    https://pubmed.ncbi.nlm.nih.gov/33196445/

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    Interpretability of machine learning based prediction models in …

    (2 days ago) There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow healthcare experts to make reasonable and data-driven decisions to provide personalized decisions that …

    https://arxiv.org/abs/2002.08596

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    An interpretable deep learning workflow for discovering subvisual

    (Just Now) Respiratory complications after a COVID infection are a growing concern, but follow-up chest CT scans of COVID-19 survivors hardly present any recognizable lesions. A deep learning-based method

    https://www.nature.com/articles/s42256-022-00483-7

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    Interpretable image-based machine learning models in healthcare

    (8 days ago) Harper Shen developed a neural network that identifies skin cancer from photos and creates easily-interpretable images to show how it came to its conclusion. A doctor or even an untrained person can look at these images and see if the algorithm analysed the wrong part of the image. The algorithm is a convolutional neural network, a deep neural

    https://precisiondrivenhealth.com/interpretable-image-based-machine-learning-models-in-healthcare/

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    Making machine learning interpretable: a dialog with clinicians

    (6 days ago) Mihaela van der Schaar Nick Maxfield. June 9, 2021. 27 min read. This post distils and shares some fascinating insights from a series of wide-ranging discussions with clinicians on the topic of interpretable machine learning for healthcare. These insights could help change the way we design machine learning models for clinical applications.

    https://www.vanderschaar-lab.com/making-machine-learning-interpretable-a-dialog-with-clinicians/

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    Interpretable Machine Learning for Function Approximation in …

    (2 days ago) However, many resulting systems are opaque, making them neither interpretable nor trustworthy. Interpretable machine learning (IML) is an active new direction intended to match algorithm accuracy with transparency, enabling users to understand their systems. (2013) Structural health monitoring a machine learning perspective. Wiley, New York

    https://link.springer.com/chapter/10.1007/978-3-030-81716-9_18

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    Towards trustable machine learning Nature Biomedical Engineering

    (9 days ago) Clinical implementations of machine learning that are accurate, robust and interpretable will eventually gain the trust of healthcare providers and patients. Reports of machine-learning algorithms

    https://www.nature.com/articles/s41551-018-0315-x

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    Interpretable Machine Learning in Healthcare through Generalized

    (7 days ago) Interpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurity August 2019 DOI: 10.1109/Deep-ML

    https://www.researchgate.net/publication/336730669_Interpretable_Machine_Learning_in_Healthcare_through_Generalized_Additive_Model_with_Pairwise_Interactions_GA2M_Predicting_Severe_Retinopathy_of_Prematurity

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    Symmetry Free Full-Text A Review of Interpretable ML in …

    (5 days ago) The development of interpretable machine learning models in healthcare has recently become a trending research area to overcome the barriers in applying machine learning in real-world applications. The main goal of applying such models in healthcare is to shed light and provide insights to physicians about machine learning predictions.

    https://www.mdpi.com/2073-8994/13/12/2439/htm

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    Explainable Machine Learning for Healthcare - Medium

    (7 days ago) 1. Explainable Machine Learning. As described in [4], explainable machine learning in this article refers to post hoc analysis and methods that are used to understand the predictions of a pre-trained model. There are different post hoc methods such as reason code generation, local and global visualizations of model predictions, etc. [4].

    https://towardsdatascience.com/explainable-machine-learning-for-healthcare-7e408f8e5130

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    Explainable Machine Learning Models for Healthcare AI

    (7 days ago) Muhammad Aurangzeb Ahmad is the Principal Data Scientist at KenSci. In this role, his work is focused on applying machine learning to solve problems within healthcare. His research at KenSci is focused on interpretable machine learning, fairness in machine learning, and causal machine learning models within the context of healthcare.

    https://learning.acm.org/techtalks/healthcareai

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    Interpretable Machine Learning in Health Care - YouTube

    (3 days ago) David Carlson, PhD, shares insights on how to leverage machine learning to improve clinical research and health care delivery. It all starts with asking the

    https://www.youtube.com/watch?v=LCExpuNko9g

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    Early detection of septic shock onset using interpretable machine

    (9 days ago) Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient’s progression to septic shock is an active field of translational research.

    https://experts.arizona.edu/en/publications/early-detection-of-septic-shock-onset-using-interpretable-machine

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    Designing Machine Learning Systems - nanthealth.com

    (9 days ago) Fundamentally machine learning approaches can be distilled down to: a model, a goal task, and an optimization algorithm that runs through available data and tunes the model to perform well on the task. Biases we perceive as unfair may be introduced into a learning system based on how the task is defined, what restrictions a chosen model might

    https://nanthealth.com/resources/articles/designing-machine-learning-systems-in-the-presence-of-data-imbalance-sparsity-and-bias/

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    Interpretable machine learning for knowledge generation in

    (Just Now) Interpretable machine learning methods that merge the predictive capacity of black-box models with the physical interpretability of physics-based models offer an alternative to black-box models

    https://www.nature.com/articles/s41929-022-00744-z

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    Interpretable machine learning for high-dimensional trajectories of

    (8 days ago) We have developed a machine learning aging model, DJIN, to predict multidimensional health trajectories and survival given baseline information, and to generate realistic synthetic aging populations—while also learning interpretable network interactions that characterize the dynamics in terms of realistic physiological interactions.

    https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009746

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