Am I hurt?: Evaluating Psychological Pain Detection in Hindi Text using Transformer-based Models

The automated evaluation of pain is critical for developing effective pain management approaches that seek to alleviate while preserving patients’ functioning. Transformer-based models can aid in detecting pain from Hindi text data gathered from social media by leveraging their ability to capture complex language patterns and contextual information. By understanding the nuances and context of Hindi text, transformer models can effectively identify linguistic cues, sentiment and expressions associated with pain enabling the detection and analysis of pain-related content present in social media posts. The purpose of this research is to analyse the feasibility of utilizing NLP techniques to automatically identify pain within Hindi textual data, providing a valuable tool for pain assessment in Hindi-speaking populations. The research showcases the HindiPainNet model, a deep neural network that employs the IndicBERT model, classifying the dataset into two class labels {pain, no_pain} for detecting pain in Hindi textual data. The model is trained and tested using a novel dataset, दर्द-ए-शायरी (pronounced as Dard-e-Shayari) curated using posts from social media platforms. The results demonstrate the model’s effectiveness, achieving an accuracy of 70.5%. This pioneer research highlights the potential of utilizing textual data from diverse sources to identify and understand pain experiences based on psychosocial factors. This research could pave the path for the development of automated pain assessment tools that help medical professionals comprehend and treat pain in Hindi speaking populations. Additionally, it opens avenues to conduct further NLP-based multilingual pain detection research, addressing the needs of diverse language communities

Resolving data overload and latency issues in multivariate time-series IoMT data for mental health monitoring

Pervasive healthcare services have evolved substantially in the recent years with IoMT rapidly changing the pace and scale of healthcare delivery. A promising application of IoMT is to fetch patterns of mental behaviour symptomatology based on bio-signals and transfer it to the corresponding hospital or psychologist for remote monitoring. But the data volume performance, device diversity interoperability, hacking unauthorized use and acceptance adoption barriers still restrain the practical and competent use of these devices. This research presents a plausible solution to surmount the data overload and processing latency in real-time sensory data collected through wearable devices for mental health monitoring. We propose a modified k-medoid data clustering technique based on time-frame restricted intra-cluster similarity calculations to obtain a summarized version of the original benchmark WESAD dataset for which the degree of information lost is minimum. A CNN is then trained on this summarized dataset for classification of mental state into the baseline, stress and amusement categories. The results show a significant reduction in the average execution time by 34% with a comparable accuracy to the original dataset, thus offering prompt real-time healthcare analytics

Recommendations for the management of MPS IVA: systematic evidence- and consensus-based guidance.

IntroductionMucopolysaccharidosis (MPS) IVA or Morquio A syndrome is an autosomal recessive lysosomal storage disorder (LSD) caused by deficiency of the N-acetylgalactosamine-6-sulfatase (GALNS) enzyme, which impairs lysosomal degradation of keratan sulphate and chondroitin-6-sulphate. The multiple clinical manifestations of MPS IVA present numerous challenges for management and necessitate the need for individualised treatment. Although treatment guidelines are available, the methodology used to develop this guidance has come under increased scrutiny. This programme was conducted to provide evidence-based, expert-agreed recommendations to optimise management of MPS IVA.MethodsTwenty six international healthcare professionals across multiple disciplines, with expertise in managing MPS IVA, and three patient advocates formed the Steering Committee (SC) and contributed to the development of this guidance. Representatives from six Patient Advocacy Groups (PAGs) were interviewed to gain insights on patient perspectives. A modified-Delphi methodology was used to demonstrate consensus among a wider group of healthcare professionals with experience managing patients with MPS IVA and the manuscript was evaluated against the validated Appraisal of Guidelines for Research and Evaluation (AGREE II) instrument by three independent reviewers.ResultsA total of 87 guidance statements were developed covering five domains: (1) general management principles; (2) recommended routine monitoring and assessments; (3) disease-modifying interventions (enzyme replacement therapy [ERT] and haematopoietic stem cell transplantation [HSCT]); (4) interventions to support respiratory and sleep disorders; (5) anaesthetics and surgical interventions (including spinal, limb, ophthalmic, cardio-thoracic and ear-nose-throat [ENT] surgeries). Consensus was reached on all statements after two rounds of voting. The overall guideline AGREE II assessment score obtained for the development of the guidance was 5.3/7 (where 1 represents the lowest quality and 7 represents the highest quality of guidance).ConclusionThis manuscript provides evidence- and consensus-based recommendations for the management of patients with MPS IVA and is for use by healthcare professionals that manage the holistic care of patients with the intention to improve clinical- and patient-reported outcomes and enhance patient quality of life. It is recognised that the guidance provided represents a point in time and further research is required to address current knowledge and evidence gaps

Recommendations for the management of MPS VI: systematic evidence- and consensus-based guidance.

IntroductionMucopolysaccharidosis (MPS) VI or Maroteaux-Lamy syndrome (253200) is an autosomal recessive lysosomal storage disorder caused by deficiency in N-acetylgalactosamine-4-sulfatase (arylsulfatase B). The heterogeneity and progressive nature of MPS VI necessitates a multidisciplinary team approach and there is a need for robust guidance to achieve optimal management. This programme was convened to develop evidence-based, expert-agreed recommendations for the general principles of management, routine monitoring requirements and the use of medical and surgical interventions in patients with MPS VI.Methods26 international healthcare professionals from various disciplines, all with expertise in managing MPS VI, and three patient advocates formed the Steering Committee group (SC) and contributed to the development of this guidance. Members from six Patient Advocacy Groups (PAGs) acted as advisors and attended interviews to ensure representation of the patient perspective. A modified-Delphi methodology was used to demonstrate consensus among a wider group of healthcare professionals with expertise and experience managing patients with MPS VI and the manuscript has been evaluated against the validated Appraisal of Guidelines for Research and Evaluation (AGREE II) instrument by three independent reviewers.ResultsA total of 93 guidance statements were developed covering five domains: (1) general management principles; (2) recommended routine monitoring and assessments; (3) enzyme replacement therapy (ERT) and hematopoietic stem cell transplantation (HSCT); (4) interventions to support respiratory and sleep disorders; (5) anaesthetics and surgical interventions. Consensus was reached on all statements after two rounds of voting. The greatest challenges faced by patients as relayed by consultation with PAGs were deficits in endurance, dexterity, hearing, vision and respiratory function. The overall guideline AGREE II assessment score obtained for the development of the guidance was 5.3/7 (where 1 represents the lowest quality and 7 represents the highest quality of guidance).ConclusionThis manuscript provides evidence- and consensus-based recommendations for the management of patients with MPS VI and is for use by healthcare professionals that manage the holistic care of patients with the intention to improve clinical- and patient-reported outcomes and enhance patient quality of life. It is recognised that the guidance provided represents a point in time and further research is required to address current knowledge and evidence gaps

Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models

International audienceWe present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through user actions or behaviors. Our task is to infer the groups of users as well as their latent behavioral functions. We also propose a non-parametric version based on a Dirichlet Process to automatically infer the number of clusters. We test the properties and performance of the model on a synthetic dataset that represents the participation of users in the threads of an online forum. Experiments show that dual-view models outperform single-view ones when one of the views lacks information

Wetlands for wastewater treatment and subsequent recycling of treated effluent : a review

Due to water scarcity challenges around the world, it is essential to think about non-conventional water resources to address the increased demand in clean freshwater. Environmental and public health problems may result from insufficient provision of sanitation and wastewater disposal facilities. Because of this, wastewater treatment and recycling methods will be vital to provide sufficient freshwater in the coming decades, since water resources are limited and more than 70% of water are consumed for irrigation purposes. Therefore, the application of treated wastewater for agricultural irrigation has much potential, especially when incorporating the reuse of nutrients like nitrogen and phosphorous, which are essential for plant production. Among the current treatment technologies applied in urban wastewater reuse for irrigation, wetlands were concluded to be the one of the most suitable ones in terms of pollutant removal and have advantages due to both low maintenance costs and required energy. Wetland behavior and efficiency concerning wastewater treatment is mainly linked to macrophyte composition, substrate, hydrology, surface loading rate, influent feeding mode, microorganism availability, and temperature. Constructed wetlands are very effective in removing organics and suspended solids, whereas the removal of nitrogen is relatively low, but could be improved by using a combination of various types of constructed wetlands meeting the irrigation reuse standards. The removal of phosphorus is usually low, unless special media with high sorption capacity are used. Pathogen removal from wetland effluent to meet irrigation reuse standards is a challenge unless supplementary lagoons or hybrid wetland systems are used

Paradigm shifts: from pre-web information systems to recent web-based contextual information retrieval

As the types of user accessible data and information escalates, so does the variety of Information Retrieval (IR) practices which can match to achieve the challenges instigated. By expanding its applicability which can broaden the use, integrating technologies and methods and as long as the quest for the perfectly accurate system continues to exist it is quite possible and likely that Information Retrieval can become one of the key technology areas for current and future research and practice. This paper expounds the recent research advances in the area of Contextual Information Retrieval. It tracks and investigates the evolution of retrieval models from the pre-web (traditional) Information Retrieval paradigm and Web information retrieval to the most prominent interactive Web information retrieval field of contextual information retrieval focusing on developing models and strategies of contextual IR

Information retrieval and machine learning: Supporting technologies for web mining research and practice

With the enormous increase in recent years in the volume of information available on-line, and the consequent need for better techniques to access this information, there has been a strong resurgence of interest in Web Mining research. This paper expounds how research in Machine Learning (ML) and Information Retrieval (IR) will help develop applications that can drive the next generation of Web search with the key to support relevant search results by effectively and efficiently digging out user- centric information. This study attempts to probe the role of these two web mining supporting technologies (ML & IR). It reviews the web mining systems from the perspective of the Information Retrieval and illustrates how machine learning is likely to make substantial gains in web mining research and practice by developing standards and improving effectiveness