Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

The effect of mechanical feedback on outcome in self-care support tool based on solution-focused brief therapy.

In Psychotherapy research : journal of the Society for Psychotherapy Research

Objective: Little is known about the impact of mechanical feedback in self-care support tools. Technically, natural language processing and machine learning can provide mechanical feedback in self-care support tools. This study compared the differences between mechanical feedback and no feedback conditions in a self-care support tool based on solution-focused brief therapy. In the feedback condition, feedback was provided by mechanically determining the probability that the goal answered in goal setting was concrete or realistic. Methods: A total of 501 participants were recruited and randomly assigned to either the feedback (n = 268) or no feedback (n = 233) condition. Results: The results showed that the mechanical feedback increased the probability of problem-solving. In contrast, solution-building, positive and negative affect, and the probability of living an ideal life increased when using the self-care support tool based on solution-focused brief therapy, regardless of the feedback. In addition, the higher the probability of goal concreteness and reality, the greater the improvement in solution-building and positive affect. Conclusion: This study suggests that self-care support tools based on solution-focused brief therapy with feedback are more effective than those without feedback. Self-care support tools based on solution-focused brief therapy with feedback can be used as an easily accessible tool to maintain and promote mental health.

Takagi Gen

2023-Mar-13

goal, machine learning, mechanical feedback, natural language processing, self-care support tool, solution-focused brief therapy

Radiology Radiology

Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease.

In Frontiers in neurology

PURPOSE : Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms.

METHODS : Twenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles.

RESULTS : The combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 pp for FF and ± 1.8 ms for wT2.

CONCLUSION : This pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence.

Colelli Giulia, Barzaghi Leonardo, Paoletti Matteo, Monforte Mauro, Bergsland Niels, Manco Giulia, Deligianni Xeni, Santini Francesco, Ricci Enzo, Tasca Giorgio, Mira Antonietta, Figini Silvia, Pichiecchio Anna

2023

FSHD, machine learning, muscle MRI, radiomics, stir

General General

Toward Causal Understanding of Therapist-Client Relationships: A Study of Language Modality and Social Entrainment.

In Proceedings of the ... ACM International Conference on Multimodal Interaction. ICMI (Conference)

The relationship between a therapist and their client is one of the most critical determinants of successful therapy. The working alliance is a multifaceted concept capturing the collaborative aspect of the therapist-client relationship; a strong working alliance has been extensively linked to many positive therapeutic outcomes. Although therapy sessions are decidedly multimodal interactions, the language modality is of particular interest given its recognized relationship to similar dyadic concepts such as rapport, cooperation, and affiliation. Specifically, in this work we study language entrainment, which measures how much the therapist and client adapt toward each other's use of language over time. Despite the growing body of work in this area, however, relatively few studies examine causal relationships between human behavior and these relationship metrics: does an individual's perception of their partner affect how they speak, or does how they speak affect their perception? We explore these questions in this work through the use of structural equation modeling (SEM) techniques, which allow for both multilevel and temporal modeling of the relationship between the quality of the therapist-client working alliance and the participants' language entrainment. In our first experiment, we demonstrate that these techniques perform well in comparison to other common machine learning models, with the added benefits of interpretability and causal analysis. In our second analysis, we interpret the learned models to examine the relationship between working alliance and language entrainment and address our exploratory research questions. The results reveal that a therapist's language entrainment can have a significant impact on the client's perception of the working alliance, and that the client's language entrainment is a strong indicator of their perception of the working alliance. We discuss the implications of these results and consider several directions for future work in multimodality.

Vail Alexandria K, Girard Jeffrey M, Bylsma Lauren M, Cohn Jeffrey F, Fournier Jay, Swartz Holly A, Morency Louis-Philippe

2022-Nov

General General

Minimizing Viral Transmission in COVID-19 Like Pandemics: Technologies, Challenges, and Opportunities.

In IEEE sensors journal

Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.

Nisar Shibli, Wakeel Abdul, Tahir Wania, Tariq Muhammad

2023-Jan

COVID-19, artificial intelligence, cellular forensics, hidden patterns, pandemic, tracking systems

General General

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch.

In IEEE sensors journal

Ambient intelligence plays a crucial role in healthcare situations. It provides a certain way to deal with emergencies to provide the essential resources such as nearest hospitals and emergency stations promptly to avoid deaths. Since the outbreak of Covid-19, several artificial intelligence techniques have been used. However, situation awareness is a key aspect to handling any pandemic situation. The situation-awareness approach gives patients a routine life where they are continuously monitored by caregivers through wearable sensors and alert the practitioners in case of any patient emergency. Therefore, in this paper, we propose a situation-aware mechanism to detect Covid-19 systems early and alert the user to be self-aware regarding the situation to take precautions if the situation seems unlikely to be normal. We provide Belief-Desire-Intention intelligent reasoning mechanism for the system to analyze the situation after acquiring the data from the wearable sensors and alert the user according to their environment. We use the case study for further demonstration of our proposed framework. We model the proposed system by temporal logic and map the system illustration into a simulation tool called NetLogo to determine the results of the proposed system.

Saleem Kiran, Saleem Misbah, Ahmad Rana Zeeshan, Javed Abdul Rehman, Alazab Mamoun, Gadekallu Thippa Reddy, Suleman Ahmad

2023-Jan

Covid-19, NetLogo, Situation-awareness, ambient intelligence, belief-desire-intention (BDI), healthcare

General General

i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals.

In IEEE sensors journal

In this article, we propose a smart bedsheet-i-Sheet-for remotely monitoring the health of COVID-19 patients. Typically, real-time health monitoring is very crucial for COVID-19 patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-COVID effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of COVID-19 patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient. i-Sheet effectively categorizes the condition of the patient with an accuracy of 99.3% and utilizes 17.5 W of the power. Furthermore, the delay involved in monitoring the health of patients using i-Sheet is 2 s which is very diminutive and is acceptable.

Tapwal Riya, Misra Sudip, Deb Pallav Kumar

2023-Jan

Artificial intelligence, COVID-19, remote monitoring, sensors, smart bedsheet