Pressure Prediction System in Lung Circuit Using Deep Learning

A massive number of patients infected with SARS-CoV2 and Delta variant of COVID-19 have generated acute respiratory distress syndrome (ARDS) which needs intensive care, which includes mechanical ventilation. But due to the huge no of patients, the workload and stress on healthcare infrastructure and related personnel have grown exponentially. This has resulted in huge demand for innovation in the field of automated health care which can help reduce the stress on the current healthcare infrastructure. This work gives a solution for the issue of pressure prediction in mechanical ventilation. The algorithm suggested by the researchers tries to predict the pressure in the respiratory circuit for various lung conditions. Prediction of pressure in the lungs is a type of sequence prediction problem. Long short-term memory (LSTM) is the most efficient solution to the sequence prediction problem. Due to its ability to selectively remember patterns over the long term, LSTM has an edge over normal RNN. RNN is good for short-term patterns but for sequence prediction problems, LSTM is preferred. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Not Another Hardcoded Solution to the Student Dropout Prediction Problem: A Novel Approach Using Genetic Algorithms for Feature Selection

Preventing student dropout is a challenge for higher education institutions (HEIs) that have worsened with COVID-19 and online classes. Despite several research attempts to understand and reduce dropout rates in HEIs, the solutions found in the literature are often hardcoded, making reuse difficult and therefore slowing progress in the area. In an effort to advance the area, this paper introduces a novel portable approach based on genetic algorithms to automatically select the optimal subset of features for dropout prediction in HEIs. Our approach is validated on a dataset containing approx. 248k student records from a Brazilian university. The results show that the proposed approach significantly increases the accuracy in dropout prediction, outperforming previous work in the literature. Our contributions in this paper are fourfold: the implementation of a (i) novel efficient and accurate automatic feature selector that does not require expert knowledge;(ii) an adaptive deep learning model for dropout prediction in sequential data sets;(iii) a portable solution that can be applied to other data sets/degrees;and, (iv) an analysis and discussion of the performance of feature selection and predictive models for dropout prediction. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Discovering missing edges in drug-protein networks: Repurposing drugs for SARS-CoV-2

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, led to a global health crisis, with more than 157 million cases confirmed infected by May 2021. Effective medication is desperately needed. Predicting drug-target interaction (DTI) is an important step to discover novel uses of chemical structures. Here, we develop a pipeline to predict novel DTIs based on the proteins of the coronavirus. Different datasets (human/SARSCoV-2 Protein-Protein interaction (PPI), Drug-Drug similarity (DD sim), and DTIs) are used and combined. After mapping all datasets onto a heterogeneous graph, path-related features are extracted. We then applied various machine learning (ML) algorithms to model our dataset and predict novel DTIs among unlabeled pairs. Possible drugs identified by the models with a high frequency are reported. In addition, evidence of the efficiency of the predicted medicines by the models against COVID-19 are presented. The proposed model can then be generalized to contain other features that provide a context to predict medicine for different diseases. © 2021 IEEE.

Coventry University telehealth coaching – A pilot

Keywords: Telehealth;Health-coaching;Behaviour change Purpose: COVID-19 has changed the face of healthcare, including student placement provision in the UK and internationally. There is a national placement capacity crisis. The Coventry University telehealth coaching clinic provided second year BSc Physiotherapy students with a placement on the University site. Telehealth enables students to work with clients to achieve their physical activity goals at a time when University staff working at home may need more well-being support. To our knowledge, on-site telehealth clinics are being used at one other UK University. Methods: Using problem-based learning, ten students used a ‘telehealth trigger scenario’ and worked collaboratively to brainstorm and search the evidence for key concepts. Students were facilitated by visiting tutors and clinical educators to replicate a practice placement. Students led the development of infrastructure (booking system, IT support), governance (record keeping, GDPR compliance, informed consent) and teamworking (weekly strategy meetings). Students modified the ‘improving health: changing health behaviour’ NHS health trainer handbook to use as an assessment tool, focussing on physical activity (removing eating, smoking and drinking habits). We evaluated the clinic using mixed methods: (i) placement capacity statistics, (ii) student satisfaction survey, (iii) student performance (pass/fail rate), (iv) client satisfaction survey, (v) student's experience (themes extracted from team debrief sessions). Results: Students saw a total of 15 clients, and 12 of those received a follow up appointment. (i) Placement capacity: 29 of the 120 second year BSc Physiotherapy students could not be placed during March–April 2021. Ten (35%) of these students were placed on the telehealth clinic. (ii) Student satisfaction survey: of 10 respondents, 90% agreed they gained new and transferable skills, 90% agreed they felt part of a community with staff and students, 40% agreed the placement was organised and running smoothly. (iii) Student performance: at the halfway evaluation, 100% of students had passed. (iv) Client satisfaction: of seven respondents, 85% were satisfied with the service, 100% agreed it was easy to schedule appointments at convenient times. (v) Student experience: key themes were learning achieved (development of clinical knowledge and skills, quality assurance skills, and emotional intelligence) and limitations of the placement (time constraints, limited learning opportunities). Conclusion(s): The Coventry University telehealth clinic is an innovative and high-quality service, which is appropriate to be managed by second- and third-year BSc physiotherapy students. However, there is room for improvements such as increasing structure and organisation of the clinic, which can be done by addressing feedback. Impact: Currently, 44 students do not have placements for May 2021, but 45% of these students will be placed by scaling up the telehealth clinic to take 20 students. This could reduce the current predictions that 50% of Coventry University Physiotherapy students will graduate late (due to sub-optimal placement hours), by at least one-third. Telehealth Students can gain new skills in organisation, administration, management, governance and communication (in virtual environments) that will impact confidence and ability to execute audits and service delivery projects after graduation. The telehealth service could be scaled up to include psychology, occupational therapy and dietetics students. Funding acknowledgements: No external funding was received.