Monitoring of advanced cancer patients' health, treatment, and supportive care is essential for improving cancer survival outcomes. Traditionally, oncology has relied on clinical metrics such as survival rates, time to disease progression, and clinician-assessed toxicities. In recent years, patient-reported outcome measures (PROMs) have provided a complementary perspective, offering insights into patients' health-related quality of life (HRQoL). However, collecting PROMs consistently requires frequent clinical assessments, creating important logistical challenges. Wearable devices combined with artificial intelligence (AI) present an innovative solution for continuous, real-time HRQoL monitoring. While deep learning models effectively capture temporal patterns in physiological data, most existing approaches are unimodal, limiting their ability to address patient heterogeneity and complexity. This study introduces a multimodal deep learning approach to estimate HRQoL in advanced cancer patients. Physiological data, such as heart rate and sleep quality collected via wearable devices, are analyzed using a hybrid model combining convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks with an attention mechanism. The BiLSTM extracts temporal dynamics, while the attention mechanism highlights key features, and CNNs detect localized patterns. PROMs, including the Hospital Anxiety and Depression Scale (HADS) and the Integrated Palliative Care Outcome Scale (IPOS), are processed through a parallel neural network before being integrated into the physiological data pipeline. The proposed model was validated with data from 204 patients over 42 days, achieving a mean absolute percentage error (MAPE) of 0.24 in HRQoL prediction. These results demonstrate the potential of combining wearable data and PROMs to improve advanced cancer care.

• Non-pharmacological interventions for people with dementia are of great importance. • Virtual Reality found to enhance symptom management in people with dementia. • Virtual Reality found to improve the quality of life of people with dementia. • Virtual Reality was found to be a possible solution for pain management. Emerging research supports that institutionalisation may contribute to the development of the behavioural and psychological symptoms of dementia. Many studies have documented that Virtual Reality can enhance symptom management in people diagnosed with dementia. We design a Virtual Reality system to improve the symptom management of people diagnosed with dementia residing in long-term care services. Twenty people with dementia were enrolled in the study to evaluate the developed solution. Following semi-structured interviews and observations, a thematic analysis was conducted to analyse the results of the system. Heart Rate and Eye-tracking data were recorded to enhance the reliability of the findings. Our findings indicate that Virtual Reality might be able to improve the quality of life of people diagnosed with dementia, as it is highly effective in reducing behavioral and psychological symptoms associated with dementia, including aggression, agitation, anxiety, apathy, and depression. Additionally, Virtual Reality was found to be a possible solution for pain management, reminiscence therapy, and dementia diagnosis.

Time series are ubiquitous in domains ranging from medicine to marketing and finance. Frequent Pattern Mining (FPM) from a time series has thus received much attention. This general problem has been studied under different matching relations determining whether two time series match or not. Recently, it has been studied under the order-preserving (OP) matching relation stating that a match occurs when two time series have the same relative order (i.e., ranks) on their elements. Thus, a frequent OP pattern captures a trend shared by sufficiently many parts of the input time series. Here, we propose exact, highly scalable algorithms for FPM in the OP setting. Our algorithms employ an OP suffix tree (OPST) as an index to store and query time series efficiently. Unfortunately, there are no practical algorithms for OPST construction. Thus, we first propose a novel and practical$\mathcal{O}(n\sigma\log\sigma)$-time and$\mathcal{O}(n)$- space algorithm for constructing the OPST of a length-n time series over an alphabet of size$\sigma$. We also propose an alternative faster OPST construction algorithm running in$\mathcal{O}(n\log\sigma)$time using$\mathcal{O}(n)$space; this algorithm is mainly of theoretical interest. Then, we propose an exact$\mathcal{O}(n)$-time and$\mathcal{O}(n)$-space algorithm for mining all maximal frequent OP patterns, given an OPST. This significantly improves on the state of the art, which takes$\Omega(n^{3})$time in the worst case. We also formalize the notion of closed frequent OP patterns and propose an exact$\mathcal{O}(n)$-time and$\mathcal{O}(n)$-space algorithm for mining all closed frequent OP patterns, given an OPST. We conducted experiments using real-world, multi-million letter time series showing that our$\mathcal{O}(n\sigma\log\sigma)$- time OPST construction algorithm runs in$\mathcal{O}(n)$time on these datasets despite the$\mathcal{O}(n\sigma\log\sigma)$bound; that our frequent pattern mining algorithms are up to orders of magnitude faster than the state of the art and natural Apriori-like baselines; and that OP pattern-based clustering is effective.

Recent years have seen a growing research interest towards designing computer-assisted health interventions aiming to improve mental health services. Digital technologies are becoming common methods for diagnosis, therapy, and training. With the advent of lower-cost VR head-mounted-displays (HMDs) and high internet data transfer capacity, there is a new opportunity for applying immersive VR tools to augment existing interventions. This study is among the first to explore the use of a Multi-User Virtual Reality (MUVR) system as a therapeutic medium for participants at high-risk for developing Eating Disorders. This paper demonstrates the positive effect of using MUVR remote psychotherapy to enhance traditional therapeutic practices. The study capitalises on the opportunities which are offered by a MUVR remote psychotherapeutic session to enhance the outcome of Acceptance and Commitment Therapy, Play Therapy and Exposure Therapy for sufferers with body shape and weight concerns. Moreover, the study presents the design opportunities and challenges of such technology, while strengths on the feasibility, and the positive user acceptability of introducing MUVR to facilitate remote psychotherapy. Finally, the appeal of using VR for remote psychotherapy and its observed positive impact on both therapists and participants is discussed.

People living with Dementia (PwD) are amongst the most vulnerable populations in society, often depending on caregivers for their quality of life (QoL). Emerging research confirms the need for technological solutions to support non-pharmacological interventions that can enhance the QoL of PwD. This paper posits that Virtual Reality (VR) is a useful technology for boosting the physical training of PwD. In a study with PwD, we compared the conventional physical training PwD receive at a nursing home, against Semi Immersive VR (SIVR) and Fully Immersive VR (FIVR) training paradigms. We recorded the emotional behaviour, task-specific metrics, and level of independence of PwD during the training. The presence and usability of the systefm by both medical staff and PwD was also assessed. Results indicated that FIVR can improve the training of PwD leading to more accurate execution of the exercises while preventing external distractions. Beyond the findings, this article discusses the opportunities, challenges, along with the feasibility and acceptability of VR, to facilitate physical training for PwD who reside in restricted health care environments.

This article presents a study on the validation of gamified versions of three established cognitive tasks, namely the Corsi task, the Visual Search task, and the Whack a Mole task. Short versions of these tasks were created using gamification techniques and tested on healthy adults. Results showed that these new gamified versions produce similar patterns of results with those obtained from the traditional versions. Concluding, game-based cognitive assessment for visuospatial tasks is promising, however, this needs to be further evaluated on larger scale studies as well as under different cognitive conditions.

3D medical imaging provides an invaluable tool to the radiologist in visualizing normal and abnormal tissue and structure for the assessment of disease and treatment planning. Moreover, in difficult image disease assessment cases, as well as in the assessment of the early stages of the disease the need exists for a real time 3D collaborative platform. The objective of this paper was to develop a multi-user Virtual Reality (VR) application for visualization and analysis in medical imaging. The proposed platform is based on the Unity VR platform that is integrated with the very well-known and popular image Visualization Toolkit- VTK. The platform was evaluated successfully in the 3D visualization of images of the ADNI dataset. Future work will focus in integrating in the platform the visualization of quantitative imaging analytics as well as the evaluation of the platform in a more-wide spectrum of imaging cases.

Many people with dementia (PWD) residing in long-term care may face barriers in accessing experiences beyond their physical premises; this may be due to location, mobility constraints, legal mental health act restrictions, or offence-related restrictions. In recent years, there have been research interests towards designing non-pharmacological interventions aiming to improve the Quality of Life (QoL) for PWD within long-term care. We explored the use of Virtual Reality (VR) as a tool to provide 360°-video based experiences for individuals with moderate to severe dementia residing in a locked psychiatric hospital. We discuss at depth the appeal of using VR for PWD, and the observed impact of such interaction. We also present the design opportunities, pitfalls, and recommendations for future deployment in healthcare services. This paper demonstrates the potential of VR as a virtual alternative to experiences that may be difficult to reach for PWD residing within locked setting.

Abstract Image recognition algorithms that automatically tag or moderate content are crucial in many applications but are increasingly opaque. Given transparency concerns, we focus on understanding how algorithms tag people images and their inferences on attractiveness. Theoretically, attractiveness has an evolutionary basis, guiding mating behaviors, although it also drives social behaviors. We test image-tagging APIs as to whether they encode biases surrounding attractiveness. We use the Chicago Face Database, containing images of diverse individuals, along with subjective norming data and objective facial measurements. The algorithms encode biases surrounding attractiveness, perpetuating the stereotype that “what is beautiful is good.” Furthermore, women are often misinterpreted as men. We discuss the algorithms’ reductionist nature, and their potential to infringe on users’ autonomy and well-being, as well as the ethical and legal considerations for developers. Future services should monitor algorithms’ behaviors given their prevalence in the information ecosystem and influence on media.