Decoding the Black Box: Extracting Explainable Decision Boundary Approximations from Machine Learning Models for Real Time Safety Assurance of the National Airspace

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

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Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

Applied Clinical Informatics ◽

10.1055/s-0041-1726422 ◽

2021 ◽

Vol 12 (02) ◽

pp. 372-382

Author(s):

Christine Xia Wu ◽

Ernest Suresh ◽

Francis Wei Loong Phng ◽

Kai Pik Tai ◽

Janthorn Pakdeethai ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Real Time ◽

Risk Score ◽

Patient Specific ◽

Learning Models ◽

Medicine Department ◽

High Risk Patients ◽

Risk Patients ◽

Machine Learning Models

Abstract Objective To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

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Machine Learning Models for Stock Prediction Using Real-Time Streaming Data

Learning and Analytics in Intelligent Systems - Biologically Inspired Techniques in Many-Criteria Decision Making ◽

10.1007/978-3-030-39033-4_10 ◽

2020 ◽

pp. 101-108

Author(s):

Monalisa Jena ◽

Ranjan Kumar Behera ◽

Santanu Kumar Rath

Keyword(s):

Machine Learning ◽

Real Time ◽

Streaming Data ◽

Learning Models ◽

Stock Prediction ◽

Machine Learning Models

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Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis

Diagnostics ◽

10.3390/diagnostics12010040 ◽

2021 ◽

Vol 12 (1) ◽

pp. 40

Author(s):

Meike Nauta ◽

Ricky Walsh ◽

Adam Dubowski ◽

Christin Seifert

Keyword(s):

Machine Learning ◽

Clinical Practice ◽

Skin Cancer ◽

Cancer Diagnosis ◽

Image Inpainting ◽

Relevant Information ◽

Black Box ◽

Training Dataset ◽

Learning Models ◽

Machine Learning Models

Machine learning models have been successfully applied for analysis of skin images. However, due to the black box nature of such deep learning models, it is difficult to understand their underlying reasoning. This prevents a human from validating whether the model is right for the right reasons. Spurious correlations and other biases in data can cause a model to base its predictions on such artefacts rather than on the true relevant information. These learned shortcuts can in turn cause incorrect performance estimates and can result in unexpected outcomes when the model is applied in clinical practice. This study presents a method to detect and quantify this shortcut learning in trained classifiers for skin cancer diagnosis, since it is known that dermoscopy images can contain artefacts. Specifically, we train a standard VGG16-based skin cancer classifier on the public ISIC dataset, for which colour calibration charts (elliptical, coloured patches) occur only in benign images and not in malignant ones. Our methodology artificially inserts those patches and uses inpainting to automatically remove patches from images to assess the changes in predictions. We find that our standard classifier partly bases its predictions of benign images on the presence of such a coloured patch. More importantly, by artificially inserting coloured patches into malignant images, we show that shortcut learning results in a significant increase in misdiagnoses, making the classifier unreliable when used in clinical practice. With our results, we, therefore, want to increase awareness of the risks of using black box machine learning models trained on potentially biased datasets. Finally, we present a model-agnostic method to neutralise shortcut learning by removing the bias in the training dataset by exchanging coloured patches with benign skin tissue using image inpainting and re-training the classifier on this de-biased dataset.

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