Personalised Medication Planning using PDDL+

Prescription medication is typically prescribed with a standardised set of instructions, to be followed regularly, with the aim being to manage symptoms while remaining within safe dosage limits. The caveat of such standardisation is that it is not tailored to the needs of the patient, in terms of their activities. In this paper, we take the first steps towards modelling medication pharmacokinetics as a PDDL+ hybrid planning problem. As pharmacokinetics are inherently non-linear, we present a planner-independent linearise--validate cycle, where tasks can be solved by iterative refinement of a linear approximation of the domain, by validation against the full non-linear semantics

Gamification as an educational tool to address antimicrobial resistance: A systematic review

Background Antimicrobial resistance (AMR) poses a serious threat to global healthcare, and inadequate education has been identified as a major challenge by the WHO. The human , animal and agricultural sectors contribute to the emergence of AMR. Gamification has emerged as an innovative tool to improve knowledge and change behaviours. Our study provides an overview of the literature on existing games in prescribers’ education across the One Health sectors, with a particular focus on the impact of gamification on learning. Methods Using the PRISMA guidelines, we searched Cochrane, PubMed, Scopus and Google Scholar for articles related to gamification for future prescribers of antimicrobials from inception until 28 March 2023. Retrieval and screening of articles was done using a structured search protocol with strict inclusion/exclusion criteria. Results A total of 120 articles were retrieved, of which 6 articles met the inclusion criteria for final analysis. High-income countries had the most studies, with one global study incorporating low- to middle-income countries. All games were evaluated in the human sector. Board and card games, featuring scoring and point systems, were the most prevalent game types. Most games focused on improving knowledge and prescribing behaviours of medical students, with bacteria or antibiotics as the only content. All studies highlighted the significant potential of gamification in mitigating AMR, promoting antimicrobial stewardship, and improving retention of information compared with conventional lectures. Conclusions Our review found an absence of studies in the animal and environmental sectors, disproportionately focused on medical students with questionable sample size, inadequate assessment of game content and effectiveness, and opportunities for game developers.publishedVersio

The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review

Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes. Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing. Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models. Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision. Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data

Association between rhesus and ABO blood group types and their impact on clinical outcomes in critically ill patients with COVID-19: A multi-center investigation

Background: There is increasing evidence suggesting that ABO blood type may play a role in the immunopathogenesis of COVID-19 infection. In addition to ABO blood type, the Rhesus (Rh) factor has also been implicated in various disease processes. Therefore, our study aimed to assess the association between both ABO and Rh blood types in critically ill patients with COVID-19 and their clinical outcomes. Methods: A multicenter retrospective cohort study conducted in Saudi Arabia between March 1, 2020, and July 31, 2021, involving adult COVID-19 patients admitted to Intensive Care Units, aimed to explore potential associations between rhesus blood group types (Positive versus Negative) and clinical outcomes. The primary endpoint assessed was the hospital length of stay (LOS). Other endpoints were considered secondary. Results: After propensity score matching (3:1 ratio), 212 patients were included in the final analysis. The hospital length of stay was longer in a negative Rh blood group compared with patients in the Rh-positive group (beta coefficient 0.26 (0.02, 0.51), p = 0.03). However, neither 30-day mortality (HR 0.28; 95% CI 0.47, 1.25, p = 0.28) nor in-hospital mortality (HR 0.74; 95% CI 0.48, 1.14, p = 0.17) reached statistical significance. Additionally, among the different ABO types, the A+ blood group exhibited a higher proportion of thrombosis/infarction and in-hospital mortality (28.1% and 31.2%, respectively). Conclusion: This study highlights the potential impact of blood group type on the prognosis of critically ill patients with COVID-19. It has been observed that patients with a negative Rh blood group type tend to have a longer hospital stay, while their mortality rates and complications during ICU stay are similar to the patients with a Rh-positive group

Indoor environmental monitoring of residential buildings in Saudi Arabia, Makkah: a case study

Abstract In Saudi Arabia, buildings require significant amounts of energy, especially during the cooling season, because of excessive air conditioning demands related to the high outdoor temperatures. Residential buildings consume more than 50% of electricity in the Kingdom of Saudi Arabia, where air conditioning loads represent 70% of the consumption. The main aim of the paper is to improve indoor thermal performance of existing residential buildings in Saudi Arabia, using Makkah as a case study. The methodology is to choose typical low-rise residential buildings to evaluate indoor thermal performance of existing residential buildings in Makkah, then calibrate this with the simulated results taken from thermal analysis software (TAS) to validate them, finally adding few energy efficiency measures to decrease the cooling load in the case study building. The result is expected to show similarity between the two results and also indicate that the energy conservation measures used can decrease the cooling load to as high as 34.5%.</jats:p

http://www.mejfm.com/February%202023/Seasonal%20influenza.htm

Background: Increasing vaccination rates and reducing the spread of influenza are both greatly improved by raising public knowledge about seasonal influenza. To promote acceptance and create awareness it is necessary to identify any potential barriers to vaccination. This study aimed to assess seasonal influenza awareness, knowledge, vaccination uptake, and barriers. Methods: A cross-sectional study was conducted as an online survey of 355 medical and non-medical students of Umm Al-Qura University. Results: Out of the total 355 participants, 175(49.3%) were medical students and 180 (50.7) were non-medical students. There was an almost equal distribution of males (178 (50.1%) and females 177 (49.9%) in both groups. Most of the students 208 (58.6%) were aged 21-24 and most of them were single 346 (97.5%). Awareness of seasonal influenza was 172 (98.3%) among medical students and 157 (87.2%) among non-medical students. The mean knowledge score was 7.75 ± 2.9, with a statistical difference between the two groups (P &lt; 0.001). Vaccination uptake was low in both groups 29 (16.6%) vs 46 (25.6%) in medical and non-medical respectively. The most prominent barriers to vaccination were the negative perceptions of the vaccine’s efficacy (53%) followed by accessibility (20%) and vaccine safety concerns (17%). More than one-third (37.7%) of the medical students and (8.9%) of non-medical students had good knowledge levels of seasonal influenza. Surprisingly, 145 (40.8%) had a poor knowledge level of seasonal influenza with a significant difference between the medical and non-medical groups (p &lt; 0.001). Conclusions: Despite the high level of awareness, the knowledge level and vaccine uptake were unsatisfactory. Negative perceptions of the vaccine’s efficacy, and accessibility were the most significant barriers to vaccination. Campaigns and health education programmes should be considered to encourage others to get vaccinated to reduce the burden of seasonal influenza. Keywords: Awareness, seasonal influenza, vaccine uptake, knowledge</jats:p