Search

Objectives: This study aims to systematically review the literature on the computational processing of the language of pain, or pain narratives, whether generated by patients or physicians, identifying current trends and challenges. Methods: Following the PRISMA guidelines, a comprehensive literature search was conducted to select relevant studies on the computational processing of the language of pain and answer pre-defined research questions. Data extraction and synthesis were performed to categorize selected studies according to their primary purpose and outcome, patient and pain population, textual data, computational methodology, and outcome targets. Results: Physician-generated language of pain, specifically from clinical notes, was the most used data. Tasks included patient diagnosis and triaging, identification of pain mentions, treatment response prediction, biomedical entity extraction, correlation of linguistic features with clinical states, and lexico-semantic analysis of pain narratives. Only one study included previous linguistic knowledge on pain utterances in their experimental setup. Most studies targeted their outcomes for physicians, either directly as clinical tools or as indirect knowledge. The least targeted stage of clinical pain care was self-management, in which patients are most involved. Affective and sociocultural dimensions were the least studied domains. Only one study measured how physician performance on clinical tasks improved with the inclusion of the proposed algorithm. Discussion: This review found that future research should focus on analyzing patient-generated language of pain, developing patient-centered resources for self-management and patient-empowerment, exploring affective and sociocultural aspects of pain, and measuring improvements in physician performance when aided by the proposed tools., Comment: 36 pages, 16 tables, 2 figures, systematic review

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well., Comment: Accepted at IJCB 2023

Chronic pain is a multi-dimensional experience, and pain intensity plays an important part, impacting the patients emotional balance, psychology, and behaviour. Standard self-reporting tools, such as the Visual Analogue Scale for pain, fail to capture this burden. Moreover, this type of tools is susceptible to a degree of subjectivity, dependent on the patients clear understanding of how to use it, social biases, and their ability to translate a complex experience to a scale. To overcome these and other self-reporting challenges, pain intensity estimation has been previously studied based on facial expressions, electroencephalograms, brain imaging, and autonomic features. However, to the best of our knowledge, it has never been attempted to base this estimation on the patient narratives of the personal experience of chronic pain, which is what we propose in this work. Indeed, in the clinical assessment and management of chronic pain, verbal communication is essential to convey information to physicians that would otherwise not be easily accessible through standard reporting tools, since language, sociocultural, and psychosocial variables are intertwined. We show that language features from patient narratives indeed convey information relevant for pain intensity estimation, and that our computational models can take advantage of that. Specifically, our results show that patients with mild pain focus more on the use of verbs, whilst moderate and severe pain patients focus on adverbs, and nouns and adjectives, respectively, and that these differences allow for the distinction between these three pain classes., Comment: 29 pages, 6 figures, 7 tables

In this article, we develop analytical-numerical models for reconfigurable reflecting metasurfaces formed by chessboard-patterned arrays of metallic patches. These patch arrays are loaded with varactor diodes in order to enable surface impedance and reflection phase control. Two types of analytical models are considered. The first model based on the effective medium approach is used to predict the metasurface reflectivity. The second model is the Bloch wave dispersion model for the same structure understood as a two-dimensional transmission line metamaterial. The latter model is used to study ways to suppress parasitic resonances in finite-size beamforming metasurfaces. We validate the developed analytical models with full-wave numerical simulations. Finally, we outline a design of the metasurface control network with capacitive memory that may allow for independent programming of individual unit cells of the beamforming metasurface., Comment: 12 pages; 11 figures; submitted to New J. Phys. (Focus Issue on Tunable Metasurfaces)

Chronic pain is recognized as a major health problem, with impacts not only at the economic, but also at the social, and individual levels. Being a private and subjective experience, it is impossible to externally and impartially experience, describe, and interpret chronic pain as a purely noxious stimulus that would directly point to a causal agent and facilitate its mitigation, contrary to acute pain, the assessment of which is usually straightforward. Verbal communication is, thus, key to convey relevant information to health professionals that would otherwise not be accessible to external entities, namely, intrinsic qualities about the painful experience and the patient. We propose and discuss a topic modelling approach to recognize patterns in verbal descriptions of chronic pain, and use these patterns to quantify and qualify experiences of pain. Our approaches allow for the extraction of novel insights on chronic pain experiences from the obtained topic models and latent spaces. We argue that our results are clinically relevant for the assessment and management of chronic pain., Comment: 9 pages, 5 figures, 6 tables

Objective: Reveal and quantify qualities of reported experiences of chronic pain on social media, from multiple pathological backgrounds, by means of the novel Reddit Reports of Chronic Pain (RRCP) dataset, using Natural Language Processing techniques. Materials and Methods: Define and validate the RRCP dataset for a set of subreddits related to chronic pain. Identify the main concerns discussed in each subreddit. Model each subreddit according to their main concerns. Compare subreddit models. Results: The RRCP dataset comprises 86,537 Reddit submissions from 12 subreddits related to chronic pain (each related to one pathological background). Each RRCP subreddit has various main concerns. Some of these concerns are shared between multiple subreddits (e.g., the subreddit Sciatica semantically entails the subreddit backpain in their various concerns, but not the other way around), whilst some concerns are exclusive to specific subreddits (e.g., Interstitialcystitis and CrohnsDisease). Discussion: These results suggest that the reported experience of chronic pain, from multiple pathologies (i.e., subreddits), has concerns relevant to all, and concerns exclusive to certain pathologies. Our analysis details each of these concerns and their similarity relations. Conclusion: Although limited by intrinsic qualities of the Reddit platform, to the best of our knowledge, this is the first research work attempting to model the linguistic expression of various chronic pain-inducing pathologies and comparing these models to identify and quantify the similarities and differences between the corresponding emergent chronic pain experiences., Comment: 24 pages, 26 figures, 8 tables