Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the multi-class classification task, where each instance can only be labeled as one class, the confusion matrix is a powerful tool for performance assessment by quantifying the classification overlap. However, in the multi-label classification task, where each instance can be labeled with more than one class, the confusion matrix is undefined. Performance assessment of the multi-label classifier is currently based on calculating performance averages, such as hamming loss, precision, recall, and F-score. While the current assessment techniques present a reasonable representation of each class and overall performance, their aggregate nature results in ambiguity when identifying false negative ( FN ) and false positive ( FP ) results. To address this gap, we define a method of creating the multi-label confusion matrix (MLCM) based on three proposed categories of multi-label problems. After establishing the shortcomings of current methods for identifying FN and FP , we demonstrate the usage of the MLCM with the classification of two publicly available multi-label data sets: i) a 12-lead ECG data set with nine classes, and ii) a movie poster data set with eighteen classes. A comparison of the MLCM results against statistics from the current techniques is presented to show the effectiveness in providing a concise and unambiguous understanding of a multi-label classifier behavior.

In this paper we present M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease, a system designed specifically for mobile devices that facilitates monitoring of cardiovascular disease (CVD). The system uses wearable sensors to collect observable trends of vital signs contextualized with data from clinical databases. Instead of transferring the raw data directly to the health care professionals, the system performs analysis on the local device by feeding the hybrid of collected data to a support vector machine (SVM) to monitor features extracted from clinical databases and wearable sensors to classify a patient as “continued risk” or “no longer at risk” for CVD. As a work in progress we evaluate a proof-of-concept M4CVD using a synthetic clinical database of 200 patients. The results of our experiment show the system was successful in classifying a patient's CVD risk with an accuracy of 90.5%.

Interpretable, highly accurate segmentation models have the potential to provide substantial benefit for automated clinical workflows. The recent advances and availability of computer hardware, software tools, and massive digital data archives have enabled the rapid development of artificial intelligence (AI) applications. Concern...

The increasing adoption of robot systems in industrial settings and teaming with humans have led to a growing interest in human-robot interaction (HRI) research. While many robots use sensors to avoid harming humans, they cannot elaborate on human actions or intentions, making them passive reactors rather than interactive collaborators. Intention-based systems can determine human motives and predict future movements, but their closer interaction with humans raises concerns about trust. This scoping review provides an overview of sensors, algorithms, and examines the trust aspect of intention-based systems in HRI scenarios. We searched MEDLINE, Embase, and IEEE Xplore databases to identify studies related to the forementioned topics of intention-based systems in HRI. Results from each study were summarized and categorized according to different intention types, representing various designs. The literature shows a range of sensors and algorithms used to identify intentions, each with their own advantages and disadvantages in different scenarios. However, trust of intention-based systems is not well studied. Although some research in AI and robotics can be applied to intention-based systems, their unique characteristics warrant further study to maximize collaboration performance. This review highlights the need for more research on the trust aspects of intention-based systems to better understand and optimize their role in human-robot interactions, at the same time establishes a foundation for future research in sensor and algorithm designs for intention-based systems.

There has been increased effort to understand the neurophysiological effects of concussion aimed to move diagnosis and identification beyond current subjective behavioural assessments that suffer from poor sensitivity. Recent evidence suggests that event-related potentials (ERPs) measured with electroencephalography (EEG) are persistent neurophysiological markers of past concussions. However, as such evidence is limited to group-level analyses, the extent to which they enable concussion detection at the individual-level is unclear. One promising avenue of research is the use of machine learning to create quantitative predictive models that can detect prior concussions in individuals. In this study we translate the recent group-level findings from ERP studies of concussed individuals into a machine learning framework for performing single-subject prediction of past concussion. We found that a combination of statistics of single-subject ERPs and wavelet features yielded a classification accuracy of 81% with a sensitivity of 82% and a specificity of 80%, improving on current practice. Notably, the model was able to detect concussion effects in individuals who sustained their last injury as much as 30 years earlier. However, failure to detect past concussions in a subset of individuals suggests that the clear effects found in group-level analyses may not provide us with a full picture of the neurophysiological effects of concussion.

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management. A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used. After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP. The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures. This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.