Machine learning for clinician-interpretable algorithm development: pressure injury safety surveillance using explainable-Ai

2021 ◽  
Author(s):  
Jonathan Dimas ◽  
Andrew Wilson ◽  
Jenny Alderden ◽  
Sergey Krikov ◽  
Amanda Shields ◽  
...  
Keyword(s):  
Machine Learning ◽  
Algorithm Development ◽  
Safety Surveillance ◽  
Explainable Ai ◽  
Pressure Injury ◽  
Development Pressure

Understanding Machine Learning for Diversified Portfolio Construction by Explainable AI

2020 ◽  
Author(s):  
Markus Jaeger ◽  
Stephan Krügel ◽  
Dimitri Marinelli ◽  
Jochen Papenbrock ◽  
Peter Schwendner
Keyword(s):  
Machine Learning ◽  
Portfolio Construction ◽  
Explainable Ai ◽  
Diversified Portfolio

Explainable AI Models of Stock Crashes: A Machine-Learning Explanation of the Covid March 2020 Equity Meltdown

2021 ◽  
Author(s):  
Jean-Jacques Ohana ◽  
Steve Ohana ◽  
Eric Benhamou ◽  
David Saltiel ◽  
Beatrice Guez
Keyword(s):  
Machine Learning ◽  
Explainable Ai

scholarly journals Using Machine Learning Technologies in Pressure Injury Management：a Systematic Review (Preprint)

10.2196/25704 ◽  
2020 ◽  
Author(s):  
Mengyao Jiang ◽  
Yuxia Ma ◽  
Siyi Guo ◽  
Liuqi Jin ◽  
Lin Lv ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Technologies ◽  
Pressure Injury

scholarly journals Predicting the Development of Surgery-Related Pressure Injury Using a Machine Learning Algorithm Model

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Ji-Yu CAI ◽  
Man-Li ZHA ◽  
Yi-Ping SONG ◽  
Hong-Lin CHEN
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Pressure Injury

scholarly journals PyDL8.5: a Library for Learning Optimal Decision Trees

2020 ◽  
Author(s):  
Gaël Aglin ◽  
Siegfried Nijssen ◽  
Pierre Schaus
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Efficient Algorithm ◽  
Optimal Decision ◽  
Learning Tasks ◽  
Explainable Ai ◽  
Classification Tasks ◽  
Interpretable Models ◽  
Limited Depth

Decision Trees (DTs) are widely used Machine Learning (ML) models with a broad range of applications. The interest in these models has increased even further in the context of Explainable AI (XAI), as decision trees of limited depth are very interpretable models. However, traditional algorithms for learning DTs are heuristic in nature; they may produce trees that are of suboptimal quality under depth constraints. We introduce PyDL8.5, a Python library to infer depth-constrained Optimal Decision Trees (ODTs). PyDL8.5 provides an interface for DL8.5, an efficient algorithm for inferring depth-constrained ODTs. The library provides an easy-to-use scikit-learn compatible interface. It cannot only be used for classification tasks, but also for regression, clustering, and other tasks. We introduce an interface that allows users to easily implement these other learning tasks. We provide a number of examples of how to use this library.

Towards Human-Centered AI: Psychological concepts as foundation for empirical XAI research

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Katharina Weitz
Keyword(s):  
Machine Learning ◽  
Mental Models ◽  
Empirical Studies ◽  
End Users ◽  
User Needs ◽  
Specific Task ◽  
Explainable Ai ◽  
First Results ◽  
Shed Light ◽  
Psychological Concepts

Abstract Human-Centered AI is a widely requested goal for AI applications. To reach this is explainable AI promises to help humans to understand the inner workings and decisions of AI systems. While different XAI techniques have been developed to shed light on AI systems, it is still unclear how end-users with no experience in machine learning perceive these. Psychological concepts like trust, mental models, and self-efficacy can serve as instruments to evaluate XAI approaches in empirical studies with end-users. First results in applications for education, healthcare, and industry suggest that one XAI does not fit all. Instead, the design of XAI has to consider user needs, personal background, and the specific task of the AI system.

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