Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record.

OBJECTIVE: Obtain clinicians' perspectives on early warning scores (EWS) use within context of clinical cases. MATERIAL AND METHODS: We developed cases mimicking sepsis situations. De-identified data, synthesized physician notes, and EWS representing deterioration risk were displayed in a simulated EHR for analysis. Twelve clinicians participated in semi-structured interviews to ascertain perspectives across four domains: (1) Familiarity with and understanding of artificial intelligence (AI), prediction models and risk scores; (2) Clinical reasoning processes; (3) Impression and response to EWS; and (4) Interface design. Transcripts were coded and analyzed using content and thematic analysis. RESULTS: Analysis revealed clinicians have experience but limited AI and prediction/risk modeling understanding. Case assessments were primarily based on clinical data. EWS went unmentioned during initial case analysis; although when prompted to comment on it, they discussed it in subsequent cases. Clinicians were unsure how to interpret or apply the EWS, and desired evidence on its derivation and validation. Design recommendations centered around EWS display in multi-patient lists for triage, and EWS trends within the patient record. Themes included a "Trust but Verify" approach to AI and early warning information, dichotomy that EWS is helpful for triage yet has disproportional signal-to-high noise ratio, and action driven by clinical judgment, not the EWS. CONCLUSIONS: Clinicians were unsure of how to apply EWS, acted on clinical data, desired score composition and validation information, and felt EWS was most useful when embedded in multi-patient views. Systems providing interactive visualization may facilitate EWS transparency and increase confidence in AI-generated information.

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction.

Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.

A Conceptual Framework of Climate Action Needs of the Least Developed Party Countries of the Paris Agreement.

This article provides a framework for conceptualizing climate action needs grounded in the nationally determined contributions (NDCs) of the least developed party countries (LDPCs) of the Paris Agreement (PA). It examines the NDCs of 35 LDPCs recorded in the NDC public registry of the United Nations Framework Convention for Climate Change (UNFCCC). A grounded theory approach is adopted to assess what these countries need to materialize their NDCs under the PA. A conceptual framework of needs is figured out through an iterative process of data collection and analysis in three cycles: (1) open and in vivo coding; (2) axial coding; and (3) theoretical or selective coding. The data are analyzed with the help of NVIVO software. The results provide a verifiable framework of needs for climate action, which includes 55 saturated need factors extracted from the writing excerpts of NDCs, 17 sub-categories (axial codes) with climate finance and technology transfer as the most prominent, and 7 theoretical or selective categories with mobilize, educate, governmental, synergic, levels, equity, and public health. It provides a baseline for policy, research, and action from the developed party countries to uphold their PA obligations.

Carbon Emissions in the SAARC Countries with Causal Effects of FDI, Economic Growth and Other Economic Factors: Evidence from Dynamic Simultaneous Equation Models.

South Asian Association for Regional Cooperation (SAARC) countries like other developing countries are the major destination for foreign investors. At the same time, these countries are facing different climate change challenges. This study aims to inspect the economic determinants of carbon emissions (CE) and dynamic causal interaction of CE with foreign direct investment (FDI), economic growth (EG), and other economic factors using panel cointegration test, dynamic ordinary least squares (DOLS) and vector error correction model (VECM) for the SAARC countries. To make the homogenous analysis, we examined the association among variables for the individual country and as a group for the period 1990 to 2016. The panel results of this study confirmed the presence of the unidirectional causal association of EG with CE. The panel results of other economic factors confirmed the causality of urban population (UP) and energy consumption (EC) with CE. Moreover, the panel results of domestic capital (DS) and inflation rate (INF) confirmed the causal association with EG. Finally, the panel results of DS revealed a causality with FDI. Based on the above results, some policy guidelines are proposed.

Conceptualizing COVID-19 and Public Panic with the Moderating Role of Media Use and Uncertainty in China: An Empirical Framework.

Uncertainty puts people in a binary state of mind, where every piece of external information can positively or negatively affect their state of health. Given the uncertain situation created by the new coronavirus pandemic, this study claims to be the first empirical analysis of the real-time status of public panic in China. It frames peoples' intrinsic and extrinsic stimuli, creating a psychosocial analysis of public panic. We conducted an online survey of WeChat and QQ users in February 2020 and collected 1613 samples through a QR code questionnaire. We used the ordinary least squares (OLS) regression equation model to conceptualize public panic pathways in different gender and age groups. This underlines the psychological origins of fear and anxiety and points out how the media uses socially constructed public panic. The results show that the outbreak of COVID-19 created uncertainty among the public, and the official media intensified it because of the late dissemination of news about the outbreak's real-time status. Hence, unofficial media remained faster in news reporting, but the news reporting remained contradictory with official reports. This created doubts about the authenticity of the given information and caused public mental health abnormalities. The study provides a conceptual framework based on lessons learned from physiology, psychology, and social psychology and real-time public analysis to inform policymakers and public administrators about the contextual dynamics of public panic in China. It provides useful insights into the wise handling of this uncertain time and controlling the fatal conditions of public panic created by COVID-19. It has implications for other countries as well.