Neural Natural Language Processing for Long Texts: A Survey of the State-of-the-Art

The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural Language Processing (NLP) during the past decade. However, the demands of long document analysis are quite different from those of shorter texts, while the ever increasing size of documents uploaded on-line renders automated understanding of long texts a critical area of research. This article has two goals: a) it overviews the relevant neural building blocks, thus serving as a short tutorial, and b) it surveys the state-of-the-art in long document NLP, mainly focusing on two central tasks: document classification and document summarization. Sentiment analysis for long texts is also covered, since it is typically treated as a particular case of document classification. Additionally, this article discusses the main challenges, issues and current solutions related to long document NLP. Finally, the relevant, publicly available, annotated datasets are presented, in order to facilitate further research.Comment: 53 pages, 2 figures, 171 citation

Implementation and Experimental Verification of Resistorless Fractional-Order Basic Filters

Novel structures of fractional-order differentiation and integration stages are presented in this work, where passive resistors are not required for their implementation. This has been achieved by considering the inherent resistive behavior of fractional-order capacitors. The implementation of the presented stages is performed using a current feedback operational amplifier as active element and fractional-order capacitors based on multi-walled carbon nano-tubes. Basic filter and controller stages are realized using the introduced fundamental blocks, and their behavior is evaluated through experimental results

Bayesian Networks to Support the Management of Patients with ASCUS/LSIL Pap Tests

In the majority of cases, cervical cancer (CxCa) develops as a result of underestimated abnormalities in the Pap test. Nowadays, there are ancillary molecular biology techniques providing important information related to CxCa and the Human Papillomavirus (HPV) natural history, including HPV DNA test, HPV mRNA tests and immunocytochemistry tests. However, these techniques have their own performance, advantages and limitations, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this paper we present a risk assessment model based on a Bayesian Network which, by combining the results of Pap test and ancillary tests, may identify women at true risk of developing cervical cancer and support the management of patients with ASCUS or LSIL cytology. The model, following the paradigm of other implemented systems, can be integrated into existing platforms and be available on mobile terminals for anytime/anyplace medical consultation

A Digital Mental Health Support Program for Depression and Anxiety in Populations With Attention-Deficit/Hyperactivity Disorder: Feasibility and Usability Study

BackgroundA total of 1 in 2 adults with attention-deficit/hyperactivity disorder (ADHD) struggles with major depressive or anxiety disorders. The co-occurrence of these disorders adds to the complexity of finding utility in as well as adherence to a treatment option. Digital therapeutic solutions may present a promising alternative treatment option that could mitigate these challenges and alleviate symptoms. ObjectiveThis study aims to investigate (1) the feasibility and acceptance of a digital mental health intervention, (2) participants’ engagement and retention levels, and (3) the potential efficacy with respect to anxiety and depression symptoms in a population with ADHD. Our main hypothesis was that a digital, data-driven, and personalized intervention for adults with coexisting ADHD and depressive or anxiety symptoms would show high engagement and adherence, which would be accompanied by a decrease in depressive and anxiety symptoms along with an increase in quality of life and life satisfaction levels. MethodsThis real-world data, single-arm study included 30 adult participants with ADHD symptomatology and coexisting depressive or anxiety symptoms who joined a 16-week digital, data-driven mental health support program. This intervention is based on a combination of evidence-based approaches such as cognitive behavioral therapy, mindfulness, and positive psychology techniques. The targeted symptomatology was evaluated using the Patient Health Questionnaire–9, Generalized Anxiety Disorder–7, and Barkley Adult ADHD Rating Scale–IV. Quality of life aspects were evaluated using the Satisfaction With Life Scale and the Life Satisfaction Questionnaire, and user feedback surveys were used to assess user experience and acceptability. ResultsThe study retention rate was 97% (29/30), and high engagement levels were observed, as depicted by the 69 minutes spent on the app per week, 5 emotion logs per week, and 11.5 mental health actions per week. An average decrease of 46.2% (P<.001; r=0.89) in depressive symptoms and 46.4% (P<.001; r=0.86) in anxiety symptoms was observed, with clinically significant improvement for more than half (17/30, 57% and 18/30, 60%, respectively) of the participants. This was followed by an average increase of 23% (P<.001; r=0.78) and 20% (P=.003; r=0.8) in Satisfaction With Life Scale and Life Satisfaction Questionnaire scores, respectively. The overall participant satisfaction level was 4.3 out of 5. ConclusionsThe findings support the feasibility, acceptability, and value of the examined digital program for adults with ADHD symptomatology to address the coexisting depressive or anxiety symptoms. However, controlled trials with larger sample sizes and more diverse participant profiles are required to provide further evidence of clinical efficacy

Feasibility, engagement, and preliminary clinical outcomes of a digital biodata-driven intervention for anxiety and depression.

HypothesisThe main hypothesis is that a digital, biodata-driven, and personalized program would exhibit high user retention and engagement, followed by more effective management of their depressive and anxiety symptoms.ObjectiveThis pilot study explores the feasibility, acceptability, engagement, and potential impact on depressive and anxiety and quality of life outcomes of the 16-week Feel Program. Additionally, it examines potential correlations between engagement and impact on mental health outcomes.MethodsThis single-arm study included 48 adult participants with mild or moderate depressive or anxiety symptoms who joined the 16-week Feel Program, a remote biodata-driven mental health support program created by Feel Therapeutics. The program uses a combination of evidence-based approaches and psychophysiological data. Candidates completed an online demographics and eligibility survey before enrolment. Depressive and anxiety symptoms were measured using the Patient Health Questionnaire and Generalized Anxiety Disorder Scale, respectively. The Satisfaction with Life Scale and the Life Satisfaction Questionnaire were used to assess quality of life. User feedback surveys were employed to evaluate user experience and acceptability.ResultsIn total, 31 participants completed the program with an overall retention rate of 65%. Completed participants spent 60 min in the app, completed 13 Mental Health Actions, including 5 Mental Health Exercises and 4.9 emotion logs on a weekly basis. On average, 96% of the completed participants were active and 76.8% of them were engaged with the sensor during the week. Sixty five percent of participants reported very or extremely high satisfaction, while 4 out of 5 were very likely to recommend the program to someone. Additionally, 93.5% of participants presented a decrease in at least one of the depressive or anxiety symptoms, with 51.6 and 45% of participants showing clinically significant improvement, respectively. Finally, our findings suggest increased symptom improvement for participants with higher engagement throughout the program.ConclusionsThe findings suggest that the Feel Program may be feasible, acceptable, and valuable for adults with mild or moderate depressive and/or anxiety symptoms. However, controlled trials with bigger sample size, inclusion of a control group, and more diverse participant profiles are required in order to provide further evidence of clinical efficacy