Implementation and sustainability of public health approaches for mental health and wellbeing in schools

Aims: With increasing prevalence of emotional difficulties in children and young people in England, there is a growing emphasis on prevention programs delivered in school settings. The Education for Wellbeing program is working with around 400 schools in England to evaluate five universal mental health and wellbeing interventions Here, we present an overview of the Education for Wellbeing program and describe patterns across different school settings in barriers and facilitators to sustaining intervention delivery Methods: This study draws on interviews with staff and pupils from eight schools over multiple timepoints, including during the COVID- 19 pandemic. Qualitative methods were used to cluster schools with similar 'journeys' over a three-year period in terms of staff members' experiences of intervention delivery and perceived barriers and facilitators to sustaining delivery Results: The analysis demonstrated patterns in schools' journeys over time, each underpinned by a range of barriers and facilitators to the sustainability of the interventions. Four clusters of schools were identified each representing one overarching pattern: 'Spreading and embedding', 'Trialled and moved on', 'Everything's changed', and 'Built into the curriculum for now' Conclusion(s): The variety in schools' experiences highlights the complexity of both school settings and the process of implementing and sustaining interventions. These findings suggest that the environment and conditions into which a public health intervention is placed may be as, if not more, important than the intervention itself, and provoke important questions regarding future research and intervention development.

Evaluation of Hotel Performance with Sentiment Analysis by Deep Learning Techniques

The subject of sentiment analysis through social media sites has witnessed significant development due to the increasing reliance of people on social media in advertising and marketing, especially after the Corona pandemic. There is no doubt that the prevalence of the Arabic language makes it considered one of the most important languages all over the world. Through human comments, it can know things if they are positive or negative. But in fact, the comments are many, and it takes work to evaluate the place or the product through a detailed reading of each comment. Therefore, this study applied deep learning approaches to this issue to provide final results that could be utilized to differentiate between the comments in the dataset. Arabic Sentiment Analysis was used and gave a percentage for each positive and negative commentary. This work used eight methods of deep learning techniques after using Fast Text as embedding, except Ara BERT. These techniques are the transformer (AraBERT), RNN (Long short-term memory (LSTM), Bidirectional long-short term memory (BILSTM), Gated recurrent units (GRUs), Bidirectional Gated recurrent units (BIGRU)), CNN (like ALEXNET, proposed CNN), and ensemble model (CNN with BI-GRU). The Hotel Arabic Reviews Dataset was utilized to test the models. This paper obtained the following results. In the Ara BERT model, the accuracy is 96.442%. In CNN, like the Alex Net model, the accuracy is 93.78%. In the suggested CNN model, the accuracy is 94.43%. In the suggested LSTM model, the accuracy is 95%. In the suggested BI-LSTM model, the accuracy is 95.11%. The accuracy of the suggested GRU model is 95.07%. The accuracy of the suggested BI-GRU model is 95.02%. The accuracy is 94.52% in the Ensemble CNN with BI-GRU model that has been proposed. Consequently, the AraBERT outperformed the other approaches in terms of accuracy. Because the AraBERT has already been trained on some Arabic Wikipedia entries. The LSTM, BI-LSTM, GRU, and BI-GRU, on the other hand, had comparable outcomes. © 2023, International Journal of Interactive Mobile Technologies. All Rights Reserved.

The challenges of embedding advanced care planning into a Haematology service

Following an increased need for individual patient escalation plans during the COVID-19 pandemic we recently created a working group to embed Advance Care Planning (ACP) into our service. Modern ACP is not only about end-of- life planning, it involves meaningful conversations and supporting patients to make decisions throughout all stages of their disease and treatment. With early conversations and the opportunity to pre plan, the stress and anxiety attached to the difficult decisions at a time when someone may be acutely unwell should become easier (from the perspective of both staff and patients). We carried out a preproject audit to ascertain patient opinions on how we could embed ACP into our service. 50 patients were offered a questionnaire, 38 chose to partake. Result(s): 100% (38) of patients had never been approached by a nurse to discuss ACP. 82% (31 out of 38) said they would not want to be approached about ACP. 18% (7 pts) would like to be approached but 13% (5pts) noted only if end of life. On asking when the best time to be approached: three patients said 'at diagnosis', two said 'anytime', one said 'never', five said 'when ready' and seven said 'end of life only'. Suggestions on the best way to raise ACP issues, five said poster displays (one noting the need for this poster to be positive and also available in Welsh), four suggested routine discussions in the current appointment and five people suggested a separate appointment. Other comments included, I only want to discuss these issues with my solicitor, I would like my family to be involved, make sure the nurses can answer questions. Average age was 68.5 years (range 49-85). Discussion(s): This was a limited audit on an outpatient population, but it raised a number of important issues. The actual carrying out of an audit on an emotive subject highlighted one the difficulties of embedding ACP initiatives into a service with a quarter of patients choosing not to answer the questionnaire, and of those who chose to answer 83% said they did not necessarily want to be approached to discuss ACP. In addition, the terminology 'ACP' appeared confusing, many people linking it to end-of- life discussions or to legal aspects such as writing of Wills. The barriers this audit has highlighted, has helped to shape the future direction of our working group and highlighted the need for increased training.

Combating the infodemic: COVID-19 induced fake news recognition in social media networks.

COVID-19 has caused havoc globally due to its transmission pace among the inhabitants and prolific rise in the number of people contracting the disease worldwide. As a result, the number of people seeking information about the epidemic via Internet media has increased. The impact of the hysteria that has prevailed makes people believe and share everything related to illness without questioning its truthfulness. As a result, it has amplified the misinformation spread on social media networks about the disease. Today, there is an immediate need to restrict disseminating false news, even more than ever before. This paper presents an early fusion-based method for combining key features extracted from context-based embeddings such as BERT, XLNet, and ELMo to enhance context and semantic information collection from social media posts and achieve higher accuracy for false news identification. From the observation, we found that the proposed early fusion-based method outperforms models that work on single embeddings. We also conducted detailed studies using several machine learning and deep learning models to classify misinformation on social media platforms relevant to COVID-19. To facilitate our work, we have utilized the dataset of "CONSTRAINT shared task 2021". Our research has shown that language and ensemble models are well adapted to this role, with a 97% accuracy.

Using a manifold-based approach to extract clinical codes associated with winter respiratory viruses at an emergency department

Every winter, respiratory viruses put most Emergency Departments (ED) around the world under intense pressure. To reduce the consequent stress for hospitals, anticipation of the massive increase of intakes for illness-based symptoms is essential. As the Covid-19 2020 pandemic clearly illustrates, patients are not systematically tested. The ED staff therefore has no real-time knowledge of the presence of the virus in the patients flow. To address this issue, we propose here to use the hospital's laboratory-confirmed database as an attractor for the manifold-based approach for clustering the clinical codes associated with respiratory viruses. We propose a new framework based on the embedding of time series onto the Stiefel manifold, coupled with a density-based clustering algorithm (HDBSCAN) enhanced by a reduction of dimension (UMAP) for the clustering on that manifold. In particular, we show, based on real data sets of two academic hospitals in France, the significant benefits of using geometrical approaches for time series clustering as compared to traditional methods.

Developing Multidisciplinary Models of Care in Rheumatology at University College London Hospital: 18 Months of Learning from Conception to Delivery and Beyond

Background/Aims The COVID-19 pandemic has placed unprecedented pressures on NHS departments, with demand rapidly outstripping capacity. The British Society for Rheumatology 'Rheumatology Workforce: a crisis in numbers (2021)' highlighted the need to provide innovative ways of delivering rheumatology specialist care. At University College London Hospitals (UCLH) we created a rheumatology multidisciplinary team (MDT) clinic to meet rising demands on our service. The aims of the Rheumatology MDT clinic were to: reduce new appointment/follow-up waiting times, increase clinic capacity, incorporate musculoskeletal (MSK) point of care ultrasound, reduce number of hospital visits and add value to each clinic encounter. Methods We ran a 6-month pilot, supported by our outpatient transformation team, incorporating a Rheumatology Advanced Practice Physiotherapist (APP), Clinical Nurse Specialist (CNS) and MSK ultrasound within a Consultant clinic. The success of the pilot helped secure funding for a further 12 months. Over 18 months we have implemented: APP/Consultant enhanced triage - up to 40% of referrals were appropriate for APP assessment, including regional MSK problems and back pain. This increased capacity for consultant-led appointments. Standardisation of time-lapse between CNS and consultant follow-up appointments to ensure appropriate spacing between patient encounters. Facilitated overbooking of urgent cases afforded by additional capacity provided by the APP. MSK ultrasound embedded in the clinic template. 'Zoom' patient education webinars facilitated by MDT members and wider disciplines e.g. dietetics, to empower self-management and reduce the administrative burden of patient emails/phone calls occurring outside the clinic. Patient participation sessions and feedback to help shape the service. Results During the 6-month pilot we reduced our waiting time for follow-up appointments from 9 months to 2. We now have capacity to book 1-2 urgent cases each week. Pre-MDT the average wait from consultant referral to physiotherapist appointment was 55 days. The MDT allows for same day assessment (reducing 2-3 patient journeys a clinic) and where suitable, facilitates discharge or onwards referral to the appropriate service. A dedicated MDT CNS has shortened treatment times, reduced email traffic between CNS and consultant and allows for same day, joint decision-making resulting in fewer appointments. Zoom webinar feedback has been positive. Patients value the broad expertise of allied health professionals which supports self-management. Embedding ultrasound allows for same day diagnostics, decreased referrals to radiology and reduced hospital visits. Conclusion Our MDT model has reduced waiting lists, decreased treatment delays and cut hospital attendances. Point of care ultrasound allows for same day decision making and abolishes the cost and diagnostic delay associated with referrals to radiology or outsourced providers. Shared decision-making adds value to outpatient attendances, which is reflected in patients' positive feedback. The MDT model maximises the existing workforce skill set by enhancing the APP and CNS role, allowing patients immediate access to their expertise.

'Don't forget the children' - even in a pandemic

Background: BHIVA's 'Don't Forget the Children' and Standards of Care (SoC) documents highlight the importance of routine HIV testing for children of people living with HIV (PLWH). Our HIV service audited child testing in 2008, 2009 and 2010 with 46%, 78% and 82% respectively of children requiring testing having a documented result. Having evolved a child testing pathway and MDT, with dedicated Health Advisor and Paediatric nurse support, we wanted to re-evaluate our child testing performance during the COVID-19 pandemic. Method(s): Newly diagnosed PLWH, 01/08/2020 - 31/12/2021, were identified via our HARS dataset. All 32 identified individuals case notes were reviewed and the relevant auditable outcomes from BHIVA's SoC document used. Result(s): 32/32 (100%) had documented evidence that child testing had been considered within 4 weeks of diagnosis (BHIVA target 95%). 13/32 had a total of 35 children, 29 of whom did not require testing. 20/29 had documented evidence their mother was not living with HIV post childbirth, 9/29 were >18 years and all but 1, not living in the UK, had either tested in sexual health or antenatal settings. 6/35 (17%) children required testing. 6/6 (100%) had a documented test result within 6 months of their parent's diagnosis, 1 of whom tested negative prior to parental diagnosis (BHIVA target 90%). 5/6 tested aged >18 months. 1 child <18 months, whose parent was diagnosed antenatally, awaits final 4th generation testing at 18 months. Conclusion(s): Our service has a robust mechanism in place for asking all newly diagnosed individuals, and those new to our service, about children during their first consultation. Where children without documented evidence of HIV testing are identified our child testing pathway ensures timely investigation and documentation - all child testing was completed within one month of parental diagnosis in this audit sample. Our service surpassed the BHIVA standards for child testing for all new diagnoses during the COVID-19 pandemic. Future planned work includes a re-audit of child testing for those already known to our HIV service. As neither parental status nor child location is static regular enquiry in relation to children needs embedding into routine HIV care. (Table Presented).

Machine learning uncovers blood test patterns subphenotypes at hospital admission discerning increased 30-day ICU mortality rates in COVID-19 elderly patients

Introduction: Since March 2020, a number of SARS-CoV-2 patients have frequently required intensive care unit (ICU) admission, associated with moderate survival outcomes and an increasing economic burden. Elderly patients are among the most numerous, due to previous comorbidities and complications they develop during hospitalization [1]. For this reason, a reliable early risk stratification tool could help estimate an early prognosis and allow for an appropriate resources allocation in favour of the most vulnerable and critically ill patients. Method(s): This retrospective study includes data from two Spanish hospitals, HU12O (Madrid) and HCUV (Valencia), from 193 patients aged > 64 with COVID-19 between February and November 2020 who were admitted to the ICU. Variables include demographics, full-blood-count (FBC) tests and clinical outcomes. Machine learning applied a non-linear dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) [2];then hierarchical clustering on the t-SNE output was performed. The number of clinically relevant subphenotypes was chosen by combining silhouette and elbow coefficients, and validated through exploratory analysis. Result(s): We identified five subphenotypes with heterogeneous interclustering age and FBC patterns (Fig. 1). Cluster 1 was the 'healthiest' phenotype, with 2% 30-day mortality and characterized by moderate leukocytes and eosinophils. Cluster 5, the severe phenotype, showed 44% 30-day mortality and was characterized by the highest leukocyte, neutrophil and platelet count and minimal monocytes and lymphocyte count. Clusters 2-4 displayed intermediate mortality rates (20-28%). Conclusion(s): The findings of this preliminary report of Eld-ICUCOV19 patients suggest the patient's FBC and age can display discriminative patterns associated with disparate 30-day ICU mortality rates.

Tweets and PCR Test-based Analysis and Prediction of Social Response to a Future Pandemic. A Case Study

The COVID-19 pandemic has greatly affected our society badly. It has been a subject of discussion since 2019 due to the increased prevalence of social media and its extensive use, and it has been a source of tension, fear, and disappointment for people all over the world. In this research, we took data from COVID-19 tweets from 10 different regions from July 25, 2020, to August 29, 2020. Using the well-known word embedding technique count-vectorizer, we experimented with different machine learning classifiers on data to train deep neural networks to improve the accuracy of predicted opinions with a low elapsed time. In addition, we collected PCR results from these regions for the same time interval. We compared the opinions in the form of positive or negative responses with the results of the PCR tests per million people. With the help of the results, We figured out a real-time international measure to detect these regions' behaviors for any future pandemic. If we know how a region thinks about an upcoming pandemic, then we can predict the region's real-time behavior for the particular pandemic. This would happen if we had past case studies to compare, like in our proposed research. Copyrights © 2023 The Institute of Electronics and Information Engineers.

Histomorphological Spectrum of Lung Lesions at Autopsy-A Tertiary Care Centre Experience

Background and aims: A wide variety of pathological conditions involve the lungs. In autopsy, the lungs are examined for disease, injury and other findings suggesting cause of death or related changes.Aims & Objectives: The present study aimed to study the histomorphological spectrum of lung lesions at autopsy and to assess the frequency of different types of lesions;and to associate histomorphological changes with cause of death.Material and Methods: It was a one-year observational study conducted in the Department of Pathology, Govt. Medical College, Jammu. Lung tissue pieces from all medicolegal autopsies received were fixed, examined grossly, processed;paraffin embedded sections obtained were stained with Hematoxylin and Eosin stain and examined under microscope. Findings were recorded and tabulated. Result(s): Out of 264 cases, males were predominantly affected (84%);median age was 38 years. The various changes observed were congestion (68%), edema (45.4%), pneumonia (5%), granulomatous inflammation (3%), diffuse alveolar damage (1.5%), haemorrhage (14.4%), interstitial changes (60%), malaria (0.4%) and malignancy (0.4%). Natural deaths were the commonest cause (75, 28%) followed by asphyxial deaths (65, 24.6%). Conclusion(s): Histopathological examination of lung autopsies highlights many incidental findings, establishes underlying cause of death, serves as a learning tool and also holds scope for detection of newer diseases.Copyright © 2023 JK Science.

Propagandization of Relative Gratification: How Chinese State Media Portray the International Pandemic

While many previous studies have investigated propaganda in connection with misinformation, disinformation, or "fake news” campaigns, they have given insufficient attention to the political messages which are not squarely factually inaccurate but manipulated. This study identifies a political communication strategy, the propagandization of relative gratification, through which propaganda media 1) highlight global chaos to nudge the public's downward comparison to a relatively stable domestic situation;2) portray the nation's adversaries as worse than its allies;and 3) leverages the public's anti-foreign attitude. This study empirically examines Chinese state media's approach to the coverage of the COVID-19 pandemic in 46 countries in 2020 by analyzing more than 3 million Chinese social media posts using the semantic similarity found in word embedding models. The results reveal that the global pandemic was depicted by the state media as generally more severe than China's domestic situation. The more distant a foreign country's relationship with China, the more severe its COVID-19 representation in China's propaganda, deviating from the country's actual epidemiological severity and what the Chinese general public thinks about it, indicating that a country's relationship with China is an important predictor of how its COVID-19 severity was presented in China's state media. This study extends the understanding of the sophisticated nature of propaganda in the current era. [ FROM AUTHOR] Copyright of Political Communication is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Deep Metric Learning for Transparent Classification of Covid-19 X-Ray Images

This work proposes an interpretable classifier for automatic Covid-19 classification using chest X-ray images. It is based on a deep learning model, in particular, a triplet network, devoted to finding an effective image embedding. Such embedding is a non-linear projection of the images into a space of reduced dimension, where homogeneity and separation of the classes measured by a predefined metric are improved. A K-Nearest Neighbor classifier is the interpretable model used for the final classification. Results on public datasets show that the proposed methodology can reach comparable results with state of the art in terms of accuracy, with the advantage of providing interpretability to the classification, a characteristic which can be very useful in the medical domain, e.g. in a decision support system. © 2022 IEEE.

Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter

The number of social media users has increased. These users share and reshare their ideas in posts and this information can be mined and used by decision-makers in different domains, who analyse and study user opinions on social media networks to improve the quality of products or study specific phenomena. During the COVID-19 pandemic, social media was used to make decisions to limit the spread of the disease using sentiment analysis. Substantial research on this topic has been done;however, there are limited Arabic textual resources on social media. This has resulted in fewer quality sentiment analyses on Arabic texts. This study proposes a model for Arabic sentiment analysis using a Twitter dataset and deep learning models with Arabic word embedding. It uses the supervised deep learning algorithms on the proposed dataset. The dataset contains 51,000 tweets, of which 8,820 are classified as positive, 37,360 neutral, and 8,820 as negative. After cleaning it will contain 31,413. The experiment has been carried out by applying the deep learning models, Convolutional Neural Network and Long Short-Term Memory while comparing the results of different machine learning techniques such as Naive Bayes and Support Vector Machine. The accuracy of the AraBERT model is 0.92% when applying the test on 3,505 tweets.

Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction.

Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human-SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy.

Interspecies analysis to dissect cellular transcriptomic signatures of humans and hamsters in COVID-19

The ongoing corona virus disease 2019 (COVID-19) pandemic has led to an urgent demand for appropriate models depicting host-pathogen interactions and disease severity-dependent immune responses. Amongst various animal models, hamster species are particularly valuable as they are permissive to develop a moderate (Mesocricetus auratus) or severe (Phodopus roborovskii) disease course following infection. Here, we use single-cell ribonucleic acid sequencing of white blood cells to dissect cell-specific changes in moderate and severe disease courses of hamsters infected with severe acute respiratory syndrome coronavirus 2. To determine universal and species-specific transcriptional responses, the generated datasets were integrated with two publicly available datasets of human COVID-19 patients (Schulte-Schrepping et al. 2020 and Su et al. 2020) featuring all disease severities. Datasets were integrated using the R package Harmony and the Python package scGen enabling the prediction of disease states through different species using an autoencoder neural network architecture. Specifically, application of a low dimensional latent space embedding allows capturing most relevant transcriptome data structures, identifying shift vectors from healthy to diseased cells as well as interspecies differences. Preliminary results show that interspecies integration of hamster and human data is achievable, and major cell types were identified throughout the datasets. Training of a neuronal network on human blood monocytes enables the prediction of transcriptomic disease severity specific patterns, paving the way for extended analyses involving several cell types and species. In addition to in-depth analysis of COVID-19 signatures in blood of hamsters and humans, successfully established workflows could subsequently be used to study the pathology of extensive lung diseases, shedding light on cellular mechanisms in the transition from healthy to diseased cellular states.

PRISTINE: Semi-supervised Deep Learning Opioid Crisis Detection on Reddit

The drug abuse epidemic has been on the rise in the past few years, particularly after the start of COVID-19 pandemic. Our preliminary observations on Reddit alone show that discussions on drugs from 2018 to 2020 increased between a range of 45% to 200%, and so has the number of unique users participating in those discussions. Existing efforts focused on utilizing social media to distinguish potential drug abuse chats from unharmful chats regardless of what drug is being abused. Others focused on understanding the trends and causes of drug abuse from social media. To this end, we introduce PRISTINE (opioid crisis detection on reddit), our work dynamically detects-and extracts evolving misleading drug names from Reddit comments using reinforced Dynamic Query Expansion (DQE) and constructs a textual Graph Convolutional Network with the aid of powerful pre-trained embeddings to detect which type of drug class a Reddit comment corresponds to. Further, we perform extensive experiments to investigate the effectiveness of our model. © 2022 IEEE.

Building social capital in cruise travel via social network sites

The purpose of this study is to investigate what type of Facebook posts help cruise lines build bridging and bonding social capital. The study applies the Chi-Square Automatic Interaction Detection (CHAID) method to identify which types of posts establish bridging and bonding social capital. The analysis is conducted on an international cruise line's official Facebook posts posted between 1 January 2018 and 1 January 2020 before the Covid-19 pandemic. The results highlight that media type, embedding passenger motivation, and a ship image help establish both bridging and bonding social capital, while content type helps establish bridging social capital. The paper is original because it helps understand how cruise lines can improve bonding and bridging social capital via social media. The paper also enhances understanding of social capital theory in the travel industry by investigating the relationship between Facebook post types and social capital in cruise shipping.

The Embedment of Palliative Care Services on a Neuro-Surgical Unit (CS359)

Outcomes: 1. A better understanding of the benefits of embedded palliative care into a neuro surgery unit at a large academic hospital 2. An understanding of the financial impact related to embedding a palliative care APC into the neuro surgery unit at a large academic hospital Problem: Palliative care needs of patients admitted to neurology ICUs are often unmet. Patients with palliative care needs identified were more likely to die in an ICU setting or be transferred to the floor with comfort measures only. These patients were noted to have a longer length of stay. Because of the known benefits of palliative care, specifically, with this vulnerable population of patients, there was a desire to increase the palliative care presence on the neuro surgical service. Intervention(s): One APC palliative care position specific to the neuro ICU team was created. Responsibilities included symptom management, family support, medical decision making, managing conflicts over care goals, and disposition planning. Outcome(s): Outcomes included involvement in interdisciplinary rounds, increased donor opportunities, and increased billing by 28% in 2021. There was a 46% increase in palliative care consults from 2020 to 2021 and an increase in percentage of DNR/DNI orders obtained during admission from 2020 to 2021. An increase in deaths during hospitalization with active palliative care consults on comfort care was noted. Statistics were collected specific to mortality, ICU LOS, diagnosis, COVID status, social work involvement, as well as spiritual care involvement. Conclusion(s): Patients are seen earlier in their hospitalization and their medical wishes are now widely known and discussed by all interdisciplinary team members. The need for the involvement of the APC in these cases has only solidified with increased exposure to the palliative care team as consults increase. Patients are benefitting from the quality care being provided that now better aligns with their personal medical goals. Implications for nursing: There are many vulnerable patient populations for whom palliative care could be just as impactful;additional research should be completed to investigate further. Palliative care embedded on an ICU improves collaboration and increases exposure and understanding of the intent of palliative care.Copyright © 2023

ICD2Vec: Mathematical representation of diseases.

BACKGROUND: The International Classification of Diseases (ICD) codes represent the global standard for reporting disease conditions. The current ICD codes connote direct human-defined relationships among diseases in a hierarchical tree structure. Representing the ICD codes as mathematical vectors helps to capture nonlinear relationships in medical ontologies across diseases. METHODS: We propose a universally applicable framework called "ICD2Vec" designed to provide mathematical representations of diseases by encoding corresponding information. First, we present the arithmetical and semantic relationships between diseases by mapping composite vectors for symptoms or diseases to the most similar ICD codes. Second, we investigated the validity of ICD2Vec by comparing the biological relationships and cosine similarities among the vectorized ICD codes. Third, we propose a new risk score called IRIS, derived from ICD2Vec, and demonstrate its clinical utility with large cohorts from the UK and South Korea. RESULTS: Semantic compositionality was qualitatively confirmed between descriptions of symptoms and ICD2Vec. For example, the diseases most similar to COVID-19 were found to be the common cold (ICD-10: J00), unspecified viral hemorrhagic fever (ICD-10: A99), and smallpox (ICD-10: B03). We show the significant associations between the cosine similarities derived from ICD2Vec and the biological relationships using disease-to-disease pairs. Furthermore, we observed significant adjusted hazard ratios (HR) and area under the receiver operating characteristics (AUROC) between IRIS and risks for eight diseases. For instance, the higher IRIS for coronary artery disease (CAD) can be the higher probability for the incidence of CAD (HR: 2.15 [95% CI 2.02-2.28] and AUROC: 0.587 [95% CI 0.583-0.591]). We identified individuals at substantially increased risk of CAD using IRIS and 10-year atherosclerotic cardiovascular disease risk (adjusted HR: 4.26 [95% CI 3.59-5.05]). CONCLUSIONS: ICD2Vec, a proposed universal framework for converting qualitatively measured ICD codes into quantitative vectors containing semantic relationships between diseases, exhibited a significant correlation with actual biological significance. In addition, the IRIS was a significant predictor of major diseases in a prospective study using two large-scale datasets. Based on this clinical validity and utility evidence, we suggest that publicly available ICD2Vec can be used in diverse research and clinical practices and has important clinical implications.