•Early Identification or Prediction of COVID-19 cases.•Real-time Monitoring of COVID-19.•Treatment Response of COVID-19 confirmed cases.•An IoT-based Framework for COVID-19.
The world has been facing ...the challenge of COVID-19 since the end of 2019. It is expected that the world will need to battle the COVID-19 pandemic with precautious measures, until an effective vaccine is developed. This paper proposes a real-time COVID-19 detection and monitoring system. The proposed system would employ an Internet of Things (IoTs) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components: Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infrastructure. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine (SVM), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-NN), Decision Table, Decision Stump, OneR, and ZeroR. An experiment was conducted to test these eight algorithms on a real COVID-19 symptom dataset, after selecting the relevant symptoms. The results show that five of these eight algorithms achieved an accuracy of more than 90 %. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of COVID-19, and the framework would then document the treatment response for each patient who has contracted the virus.
Established therapeutic interventions effectively mitigate the risk and progression of chronic kidney disease (CKD). Countries and regions have a compelling need for organizational structures that ...enable early identification of people with CKD who can benefit from these proven interventions. We report the current global status of CKD detection programs.
A multinational cross-sectional survey.
Stakeholders, including nephrologist leaders, policymakers, and patient advocates from 167 countries, participating in the International Society of Nephrology (ISN) survey from June to September 2022.
Structures for the detection and monitoring of CKD, including CKD surveillance systems in the form of registries, community-based detection programs, case-finding practices, and availability of measurement tools for risk identification.
Descriptive statistics.
Of all participating countries, 19% (n=31) reported CKD registries, and 25% (n=40) reported implementing CKD detection programs as part of their national policies. There were variations in CKD detection program, with 50% (n=20) using a reactive approach (managing cases as identified) and 50% (n=20) actively pursuing case-finding in at-risk populations. Routine case-finding for CKD in high-risk populations was widespread, particularly for diabetes (n=152; 91%) and hypertension (n=148; 89%). Access to diagnostic tools, estimated glomerular filtration rate (eGFR), and urine albumin-creatinine ratio (UACR) was limited, especially in low-income (LICs) and lower-middle-income (LMICs) countries, at primary (eGFR: LICs 22%, LMICs 39%, UACR: LICs 28%, LMICs 39%) and secondary/tertiary health care levels (eGFR: LICs 39%, LMICs 73%, UACR: LICs 44%, LMICs 70%), potentially hindering CKD detection.
A lack of detailed data prevented an in-depth analysis.
This comprehensive survey highlights a global heterogeneity in the organization and structures (surveillance systems and detection programs and tools) for early identification of CKD. Ongoing efforts should be geared toward bridging such disparities to optimally prevent the onset and progression of CKD and its complications.
Early detection and management of chronic kidney disease (CKD) is crucial to prevent progression to kidney failure. A multinational survey across 167 countries revealed disparities in CKD detection programs. Only 19% reported CKD registries, and 25% implemented detection programs as part of their national policy. Half used a reactive approach while others actively pursued case-finding in at-risk populations. Routine case-finding was common for individuals with diabetes and hypertension. However, limited access to gold standard tools such as estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (UACR), especially in low-income and lower-middle income countries, may hinder CKD detection. A global effort to bridge these disparities is needed to optimally prevent the onset and progression of CKD and its complications.
Display omitted
The goal of the present study was to compare profiles among Black families of autistic youth who were identified Early (≤ 2 years of age), Mid (age 3 or 4), and Delayed (≥ 5 years of age) to better ...identify the characteristics that contribute to early ASD identification and delayed ASD identification. Black caregivers with autistic youth (N = 101) were divided into Early (N = 34), Mid (N = 39), and Delayed (N = 28) groups and compared on (a) the age at which signs of autism signs were first noticed, (b) wait times, (c) previous misdiagnoses rates, and (d) racial barriers experienced during the diagnostic process. The results revealed differences between the diagnostic profiles. Specifically, (a) Delayed families noticed the first signs of autism significantly later, (b) Early families had significantly smaller wait times between age of noticing signs of autism and age of receiving the diagnosis, (c) the odds of receiving a later or delayed autism diagnosis was nearly three times higher for caregivers who reported receiving a misdiagnosis, and (d) there were no significant differences in racial barriers experienced between Early, Mid, and Delayed families. Challenges in receiving a timely diagnosis remain for some Black autistic youth. To improve early identification for Black autistic youth who are at risk for receiving delayed diagnostic care, further research should examine factors and practices that improve autism knowledge among professionals and caregivers, enhance assessment practices, and integrate culturally responsive practices into assessment and screening procedures.
Brain differences linked to autism spectrum disorder (ASD) can manifest before observable symptoms. Studying these early neural precursors in larger and more diverse cohorts is crucial for advancing ...our understanding of developmental pathways and potentially facilitating earlier identification. EEG is an ideal tool for investigating early neural differences in ASD, given its scalability and high tolerability in infant populations. In this context, we integrated EEG into an existing multi-site MRI study of infants with a higher familial likelihood of developing ASD. This paper describes the comprehensive protocol established to collect longitudinal, high-density EEG data from infants across five sites as part of the Infant Brain Imaging Study (IBIS) Network and reports interim feasibility and data quality results. We evaluated feasibility by measuring the percentage of infants from whom we successfully collected each EEG paradigm. The quality of task-free data was assessed based on the duration of EEG recordings remaining after artifact removal. Preliminary analyses revealed low data loss, with average in-session loss rates at 4.16% and quality control loss rates at 11.66%. Overall, the task-free data retention rate, accounting for both in-session issues and quality control, was 84.16%, with high consistency across sites. The insights gained from this preliminary analysis highlight key sources of data attrition and provide practical considerations to guide similar research endeavors.
•Early brain differences linked to autism spectrum disorder (ASD) have been identified through EEG and MRI studies, which have the potential to inform markers for earlier ASD identification.•Multimodal studies with larger and more diverse cohorts are needed to effectively utilize these structural and functional brain differences as neural indicators.•An EEG protocol was integrated into an existing multi-site MRI study of infants with higher familial likelihood of developing ASD.•Preliminary analyses show a 96% success rate in EEG data collection, with an average of 480seconds of clean, task-free data retained across sites (89% of total recording time).•Feasibility and data quality metrics were highly consistent across sites, reinforcing the adaptability and scalability of the protocol.
Developmental monitoring/screening predict early identified autism spectrum disorders (ASD), but studies have not yet robustly controlled for a key health care service impacting early identification: ...medical home. National Surveys of Children's Health (NSCH; 2016-2020) were used to determine the relationship between medical home, developmental monitoring/screening, and identified ASD. NSCH overall medical home variable had a minimal relationship with ASD (under 5 years of age, under 5 identified in last year, under 5 identified over a year prior). Usual source of care was positively, and care coordination negatively, associated with ASD identified in last year, suggesting the overall medical home variable may mask variance from subscales. Research is needed to determine how medical home relates to identification in applied settings.
Purpose
Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), ...and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.
Design/methodology/approach
Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.
Findings
The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.
Research limitations/implications
The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.
Practical implications
The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.
Social implications
Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.
Originality/value
Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.
Institutions struggle with successful use of sepsis alerts within electronic health records.
Test the association of sepsis screening measurement criteria in discrimination of mortality and detection ...of sepsis in a large dataset.
Retrospective, cohort study using a large United States (U.S.) intensive care database. The Institutional Review Board exempt status was obtained from Kansas University Medical Center Human Research Protection Program (10-1-2015).
334 U.S. hospitals participating in the eICU Research Institute.
Nine hundred twelve thousand five hundred and nine adult intensive care admissions from 183 hospitals.
Exposures included: systemic inflammatory response syndrome criteria ≥ 2 (Sepsis-1); systemic inflammatory response syndrome criteria with organ failure criteria ≥ 3.5 points (Sepsis-2); and sepsis-related organ failure assessment score ≥ 2 and quick score ≥ 2 (Sepsis-3). Discrimination of outcomes was determined with/without (adjusted/unadjusted) baseline risk exposure to a model. The receiver operating characteristic curve (AUROC) and odds ratios (ORs) for each decile of baseline risk of sepsis or death were assessed.
Within the eligible cohort of 912,509, a total of 86,219 (9.4 %) patients did not survive their hospital stay and 186,870 (20.5 %) met the definition of suspected sepsis. For suspected sepsis discrimination, Sepsis-2 (unadjusted AUROC 0.67, 99 % CI: 0.66–0.67 and adjusted AUROC 0.77, 99 % CI: 0.77–0.77) outperformed Sepsis-3 (SOFA unadjusted AUROC 0.61, 99 % CI: 0.61–0.61 and adjusted AUROC 0.74, 99 % CI: 0.74–0.74) (qSOFA unadjusted AUROC 0.59, 99 % CI: 0.59–0.60 and adjusted AUROC 0.73, 99 % CI: 0.73–0.73). Sepsis-2 also outperformed Sepsis-1 (unadjusted AUROC 0.58, 99 % CI: 0.58–0.58 and adjusted AUROC 0.73, 99 % CI: 0.73–0.73). In between differences of AUROCs were statistically significantly different. Sepsis-2 ORs were higher for the outcome of suspected sepsis when considering deciles of risk than the other measurement systems.
Sepsis-2 outperformed other systems in suspected sepsis detection and was comparable to SOFA in prognostic accuracy of mortality in adult intensive care patients.
Acute respiratory distress syndrome (ARDS) is a major cause of death in patients with severe acute pancreatitis (SAP). Although a series of prediction models have been developed for early ...identification of such patients, the majority are complicated or lack validation. A simpler and more credible model is required for clinical practice.
To develop and validate a predictive model for SAP related ARDS.
Patients diagnosed with AP from four hospitals located at different regions of China were retrospectively grouped into derivation and validation cohorts. Statistically significant variables were identified using the least absolute shrinkage and selection operator regression method. Predictive models with nomograms were further built using multiple logistic regression analysis with these picked predictors. The discriminatory power of new models was compared with some common models. The performance of calibration ability and clinical utility of the predictive models were evaluated.
Out of 597 patients with AP, 139 were diagnosed with SAP (80 in derivation cohort and 59 in validation cohort) and 99 with ARDS (62 in derivation cohort and 37 in validation cohort). Four identical variables were identified as independent risk factors for both SAP and ARDS: heart rate odds ratio (OR) = 1.05; 95%CI: 1.04-1.07;
< 0.001; OR = 1.05, 95%CI: 1.03-1.07,
< 0.001, respiratory rate (OR = 1.08, 95%CI: 1.0-1.17,
= 0.047; OR = 1.10, 95%CI: 1.02-1.19,
= 0.014), serum calcium concentration (OR = 0.26, 95%CI: 0.09-0.73,
= 0.011; OR = 0.17, 95%CI: 0.06-0.48,
= 0.001) and blood urea nitrogen (OR = 1.15, 95%CI: 1.09-1.23,
< 0.001; OR = 1.12, 95%CI: 1.05-1.19,
< 0.001). The area under receiver operating characteristic curve was 0.879 (95%CI: 0.830-0.928) and 0.898 (95%CI: 0.848-0.949) for SAP prediction in derivation and validation cohorts, respectively. This value was 0.892 (95%CI: 0.843-0.941) and 0.833 (95%CI: 0.754-0.912) for ARDS prediction, respectively. The discriminatory power of our models was improved compared with that of other widely used models and the calibration ability and clinical utility of the prediction models performed adequately.
The present study constructed and validated a simple and accurate predictive model for SAP-related ARDS in patients with AP.
The current demand for autism diagnostic services exceeds the ability of the workforce to assess and diagnose children in a timely manner. One solution may be to equip primary care providers (PCPs) ...with the tools and expertise needed to diagnose autism within their practice. PCPs are often trusted professionals who have many touchpoints with children during early development, in which they can identify early signs of autism. Recent initiatives have focused on bolstering PCPs' diagnostic capabilities; however, no studies have examined how the rates of autism diagnosis in primary care have changed over time. We aimed to evaluate whether autism diagnosis in primary care has changed over time and how diagnosis in primary care relates to a child's age at the time of diagnosis. We found that the likelihood of a child being diagnosed by a PCP decreased by about 2% with every passing year from 2004 to 2019 when accounting for demographic characteristics. In our sample, PCPs diagnosed children approximately 1 year earlier than non-PCPs (e.g., psychologists and psychiatrists). Further research is needed to understand why the proportion of children diagnosed by PCPs decreases over time. However, this decrease suggests more work is needed to get capacity-building initiatives into community primary care practice. Though we must continue to find effective ways to build community PCPs' ability to diagnose autism, the present findings support the crucial role PCPs can play in early autism diagnosis.
PDF
