Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations

BACKGROUND: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer term control. METHODS: We introduce explainable machine learning to make predictions of hypoglycemia (270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user. RESULTS: Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model. CONCLUSIONS: Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user’s glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications

Convertible bond valuation in a jump diffusion setting with stochastic interest rates

This paper proposes an integrated pricing framework for convertible bonds, which comprises firm value evolving as an exponential jump diffusion, correlated stochastic interest rates movements and an efficient numerical pricing scheme. By construction, the proposed stochastic model fits in the framework of affine jump diffusion processes of Duffie et al. [Econometrica, 2000, 68, 1343–1376] with tractable behaviour. We define the firm’s optimal call policy and investigate its impact on the computed convertible bond prices. We illustrate the performance of the numerical scheme and highlight the effects originated by the inclusion of jumps, stochastic interest rates and a non-zero correlation structure between firm value and interest rates

Participating in CaMKIN : impact on patients

Introduction: Managing long-term health conditions is a global challenge, which has necessitated developing innovative ways to deliver patient centred care. Social media allow patients to access and share personal experiences and peer support, with potential to feed back into the patient-centred development and improvement of healthcare services. The Cheshire and Merseyside Kidney Information Network (CaMKIN) was established in 2019 as part of the Kidney Information Network (KIN), providing CKD patients 24-hour online access to information and support regarding their condition. Methods A novel digital method (Vasilica et al., 2021) of a dataset retrieved from a CaMKIN patient Facebook micro-community (1,119 posts and 5,266 comments), complemented by a survey (61 CaMKIN members). The digital methods steps involved a framework analysis to create themes and subthemes, directed analysis of data, familiarisation and sense making. Findings Analysis of data identified four major themes and an additional 13 subthemes of impact. Patients used the group to ‘improve understanding of their condition’ (theme 1) through sharing useful information, accessing information from lived experiences and organizing Q&A with health professionals. The micro-community formed a peer support network with both, in person and online peer to peer support. This support extended to ‘encouragement of self-management of health’ (theme 2), including managing diet and fitness. Group members created their own healthy choices weight loss accountability group, adopting a supportive ‘weight watchers’ style. They encouraged each other to take proactive steps in self-managing their health recommending people to contact renal team or ring emergency line where necessary. CaMKIN positively contributed to ‘improved health and wellbeing outcomes’ (theme 3), by providing a safe space to air frustrations, quell anxieties, and support each other’s mental health. This was important during the COVID_19 pandemic. CaMKIN provided patients a ‘safe environment, outside clinical settings’ (theme 4), to share and receive health information related to their kidney disease or treatment. Throughout the pandemic, the group discussed or clarified information (with professionals on the network), reducing demand on the local services through the self-organisation that occurred within the group. Survey results reinforced the Facebook dataset findings; most respondents benefited from access to health care information (86.9 %), which made them feel more informed about their condition (77.1%). Patients received valuable support from peers (75.4%). Almost half of respondents agreed that it reduced isolation and it contributed to management of mental health (48.8%). Conclusion This innovative micro-community helps CKD patients understand their condition better and improve health awareness through information sharing (peer and professionally developed) and peer support that contribute to increased self-management. Demonstrated through self-reported mechanisms, CaMKIN improved mental health and reduced social isolation. During the pandemic it offered patients a safe environment to develop understanding of the volatile situation to manage their health safely. The data provides insight into an untapped opportunity, recommendations include utilising the CAMKIN to further develop service provision and communication between hospitals and patients. Further research is required to roll out and evaluate embedding KIN into local service provision, and developing a patient network at a regional and national level

Physically fit or physically literate? Children with special educational needs understanding of physical education

The role of physical literacy within physical education (PE) has become a widely debated topic in recent years. Its role in educating children about physicality through embodiment, skill acquisition and reading the environment is argued to be of great benefit to children. However, whether children understand the role of PE in the development of these competencies is not clear, and this is even truer for children who have special educational needs (SEN). Drawing on qualitative phenomenological data from 30 children in key stages 2 and three (7 to 14 years of age) who have SEN, this paper explores notions of physical fitness and physical literacy as understood by children in PE lessons. It aims to gain insight into the ways that children understand the purpose of PE, and places these perceptions within a physical literacy framework, using the National Curriculum for PE (NCPE) as a foundation. Findings demonstrate that children with SEN perceive PE as a means for improving physical fitness, whereas concepts surrounding physical literacy appear to be lost. The paper concludes by making recommendations for factoring physical literacy components more forcibly into the PE curriculum, and through initial teacher training and continued professional development

ESCAPADE: Encryption-type-ransomeware: system call based pattern detection

Encryption-type ransomware has risen in prominence lately as the go-to malware for threat actors aiming to compromise Android devices. In this paper, we present a ransomware detection technique based on behaviours observed in the system calls performed by the malware. We identify and present some common high-level system call behavioural patterns targeted at encryption-type ransomware and evaluate these patterns. We further present our repeatable and extensible methodology for extracting the system call log and patterns

An ecological method for the sampling of nonverbal signalling behaviours of young children with profound and multiple learning disabilities (PMLD)

- Background: Profound and multiple learning disabilities (PMLD) are a complex range of disabilities that affect the general health and wellbeing of the individual and their capacity to interact and learn. - Method: We developed a new methodology to capture the nonsymbolic signalling behaviours of children with PMLD within the context of a face-to-face interaction with a caregiver to provide analysis at a micro-level of descriptive detail incorporating the use of the ELAN digital video software. - Conclusion: The signalling behaviours of participants in a natural, everyday interaction can be better understood with the use of this innovation in methodology, which is predicated on the ecology of communication. Recognition of the developmental ability of the participants is an integral factor within that ecology. The method presented establishes an advanced account of the modalities through which a child affected by PMLD is able to communicate

Using Football Cultures as a Vehicle to Improve Mental Health in Men: The Case of the Redcar and Cleveland Boot Room

This paper sets out to appraise (from the perspective of members) the impact of a localized, football-based mental health intervention. Commissioned in late 2015, the ‘Redcar and Cleveland Boot Room (BR)’ was implemented in response to mass redundancy in the local area, coupled with regional suicide rates in men that exceed the national average. Interactive discussions with BR members revealed that: (a) the language of football and shared identity were important for initiating and sustaining engagement in the BR; (b) peer-support and mentoring combined with member-led activities were active ingredients of the BR and (c) that the BR was an effective vehicle for building mental health resilience. This evaluation adds to the evidence base on the value of football as a context to engage adult males in community-based interventions targeting mental health resilience

Treatment outcomes of patients with atopic dermatitis (AD) treated with dupilumab through the early access to medicines scheme (EAMS) in the UK

BACKGROUND Dupilumab, a monoclonal antibody against interleukin (IL)-4 receptor alpha that inhibits IL-4/IL-13 signalling is indicated in dermatology for the treatment of moderate-to-severe atopic dermatitis (AD) in adult and adolescent patients 12 years and older and severe AD in children 6-11 years, who are candidates for systemic therapy. Dupilumab received Early Access to Medicines Scheme (EAMS) approval for adults in March 2017. OBJECTIVES The purpose of this study was to assess the efficacy outcomes of treatment with dupilumab in EAMS. METHODS A retrospective analysis of adult patients enrolled in the dupilumab EAMS in the UK. Scores were assessed at baseline and follow up, including the Eczema Area and Severity Index (EASI), Investigator’s Global Assessment Score (IGA) and Dermatology Life Quality Index (DLQI). RESULTS Data were available for 57 adult patients treated with dupilumab for at least 12 weeks; 73.6% of patients had received prior treatment with 3 or 4 immunosuppressants. Baseline scores for the EASI and DLQI were 27.93 (standard deviation, SD 13.09) and 18.26 (SD 6.18) respectively. AD severity scores showed statistically significant improvement at week 16+4 weeks (p <0.001 for all). The mean change in EASI was 14.13 points with 66.7% and 36.7% achieving a 50% (EASI-50) and 75% (EASI-75) improvement in EASI, respectively at 16+/- 4 weeks. IGA scores improved by at least two categories for 75% patients. DLQI scores decreased by a mean of 9.0 points, with 80% patients demonstrating a MCID 4-point improvement. For 85% patients, clinicians rated the treatment response as being either ‘better’ (19%) or ‘much better’ (65%). CONCLUSIONS Dupilumab is associated with a significant and clinically relevant improvements in AD as measured by patient- and physician-reported outcome measures. Importantly, the clinical efficacy, despite the refractory disease of this EAMS cohort, is comparable to that previously reported in clinical trials

I-131 Dose Response for Incident Thyroid Cancers in Ukraine Related to the Chornobyl Accident

Background: Current knowledge about Chornobyl-related thyroid cancer risks comes from ecological studies based on grouped doses, case–control studies, and studies of prevalent cancers

Senescence Signatures Predict Hospitalization Risk and Severity in COVID-19 Patients

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a global pandemic associated with substantial morbidity and mortality worldwide, with a particular risk for severe disease and mortality in the elderly population. The more aged you are the higher the risk for mortality and severity due to COVID-19. Why age is the single largest risk factor for severity in COVID-19 is not known. Together virus-induced cell senesence and aging are believed to play a central role in COVID-19 severity and pathogenesis. A deeper understanding of COVID-19 pathophysiology and the involvement of senescence/aging proteins is therefore required. This can help identify patients, at an earlier stage, who are more susceptible to acquiring a severe COVID-19 infection and those who are most likely to go on to develop post-COVID-19 syndrome. This early detection remains a major challenge however largely due to limited understanding of SARS-CoV-2 pathogenesis.In this study, we investigate whether the levels of senescence-specific plasma proteins from COVID-19 patients can be utilized to predict severity and post-COVID-19 syndrome. We performed proteomic profiling of plasma from COVID-19 patients (n = 400) using the Olink Explore 384 Inflammation Panel. Data analysis identified differences in plasma concentrations of proteins, which are linked to senescence while considering patient hospitalization status, age, and their World Health Organization (WHO) clinical progression score.The statistically significant changes were found in the senescence-associated plasma proteome of COVID-19 patients who were hospitalized, more aged, and those with severe WHO classification (TPPI, CXCL10, HGF, VEGFA, SIRPB1, IL-6, TNFRSF11B, and B4GALT1; p &lt; 0.05) and which may be linked to post-COVID-19 syndrome. Epigenetic analysis of the methylome, using the GrimAge Clock, found that biological and chronological age did not correlate in hospitalized patients. We also identified that PTX3, CXCL10, KYNU, and SIRPB1 genes had increased promoter methylation in hospitalized patients.Machine learning analysis showed that characteristic protein changes perform with a similar accuracy to that of a whole panel biomarker signature in terms of hospitalization, age, and WHO clinical progression score.This study revealed senescence specific protein changes (sendotypes) in the plasma of COVID-19 patients, which can be used as determinants for predicting COVID-19 severity, viral signature persistence, and ultimately which may lead to post-COVID-19 syndrome. We propose that the identification of such sendotypes could be exploited for therapeutic intervention via senolytics in COVID-19