Fundamentals of Clinical Data Science

Kubben, Pieter L.; Dumontier, Michel; Dekker, André

doi:10.1007/978-3-319-99713-1

Cited by 71 publications

(12 citation statements)

References 24 publications

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“…Among frequency-domain methods, the most popular is the fast Fourier transform (FFT). Auto-regressive (AR) modeling is an alternative to Fourier-based methods for computing the frequency spectrum of a signal [62,63].…”

Section: Feature Extractionmentioning

confidence: 99%

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EEG-Based BCI Emotion Recognition: A Survey

Torres

Hernández-Álvarez

et al. 2020

Sensors

193

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Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

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Section: Feature Extractionmentioning

confidence: 99%

“…The time-frequency domain exploits variations in time and frequency, which are very descriptive of the neural activities. For this, wavelet transform (WT) and wavelet packet decomposition (WPD) are used [62].…”

Section: Feature Extractionmentioning

confidence: 99%

EEG-Based BCI Emotion Recognition: A Survey

Torres

Hernández-Álvarez

et al. 2020

Sensors

193

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“…Machine learning (ML) powered Artificial intelligence (AI) methods are increasingly applied in the form of Clinical Decision Support Systems (CDSSs) to assist healthcare professionals (HCPs) in predicting patient outcomes. These novel CDSSs have the capacity to propose recommendations based on a plethora of patient data at a much greater speed than HCPs [1]. In doing so, they have the potential to pave the way for personalized treatments, improved patient outcomes, and reduced health care costs.…”

Section: Introductionmentioning

confidence: 99%

“…In laboratory settings, proof-of-concept AI-based CDSSs show promising performance [2,3]. In practice, however, AI-based CDSSs often yield limited improvements [1,[4][5][6][7][8][9]. A possible explanation for this might be that in cases where the AI system's suggested course of action deviates from established clinical guidelines or medical intuition, it can be difficult to convince HCPs to consider the systems' recommendations rather than dismissing them a priori.…”

Section: Introductionmentioning

confidence: 99%

“…It is important to note that many AI algorithms, especially the popular artificial neural networks (ANN), are so-called "black boxes," because the inner workings of the algorithm remain opaque to the user. This situation can lead to a lack of trust in the black-box AI system, which is an important barrier to CDSS adoption [1]. Consequently, finding ways to foster HCPs' trust in CDSSs is critical to enable their wide-range adoption in clinical practice [1,10,11].…”

Section: Introductionmentioning

confidence: 99%

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To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

et al. 2022

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Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the system is implemented, the designated role in the decision-making process, and the key user group(s). Thus, each CDSS will require an individualized assessment of explainability needs and we provide an example of how such an assessment could look like in practice.

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Basic Steps in Machine Learning-Based Modeling

Bhattacharya

2021

A Primer on Machine Learning in Subsurface Geosciences

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Fundamentals of Clinical Data Science

Cited by 71 publications

References 24 publications

EEG-Based BCI Emotion Recognition: A Survey

EEG-Based BCI Emotion Recognition: A Survey

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

Basic Steps in Machine Learning-Based Modeling

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