Predictive Analytics for Supporting Environmental Sustainability and Disaster Management

Bouttell, Adam B.; Lee, Sijin; Leung, Carson K.; Levesque, Martin J.; Son, Seunggon; Zhang, Weihong; Cuzzocrea, Alfredo

doi:10.1109/compsac54236.2022.00198

Cited by 4 publications

(1 citation statement)

References 57 publications

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“…The ability to do so would rely on predictive analytics, a data science approach that assesses behavior using ML approaches to detect patterns, score event likelihood or risk, present data are a digestible fashion, develop a statistical model, communicate the data effectively, and ultimately predict future behavior (70). Predictive analytics are currently principally used for natural disaster management preparedness, but have been recently informed by the SARS-CoV-2 pandemic (70)(71)(72)(73). In a related fashion, the American Red Cross has long monitored SoMe during natural disasters and extracted data to guide their immediate post-disaster responses (74).…”

Section: Role Of Machine Learning/ Augmented Intelligence In Violence...mentioning

confidence: 99%

In-Hospital Violence and Its Impact on Critical Care Practitioners

Bass,

Chang,

Winkle

et al. 2024

Critical Care Medicine

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Objectives: To provide a narrative review of hospital violence (HV) and its impact on critical care clinicians. Data Sources: Detailed search strategy using PubMed and OVID Medline for English language articles describing HV, risk factors, precipitating events, consequences, and mitigation strategies. Study Selection: Studies that specifically addressed HV involving critical care medicine clinicians or their practice settings were selected. The time frame was limited to the last 15 years to enhance relevance to current practice. Data Extraction: Relevant descriptions or studies were reviewed, and abstracted data were parsed by setting, clinician type, location, social media events, impact, outcomes, and responses (agency, facility, health system, individual). Data Synthesis: HV is globally prevalent, especially in complex care environments, and correlates with a variety of factors including ICU stay duration, conflict, and has recently expanded to out-of-hospital occurrences; online violence as well as stalking is increasingly prevalent. An overlap with violent extremism and terrorism that impacts healthcare facilities and clinicians is similarly relevant. A number of approaches can reduce HV occurrence including, most notably, conflict management training, communication initiatives, and visitor flow and access management practices. Rescue training for HV occurrences seems prudent. Conclusions: HV is a global problem that impacts clinicians and imperils patient care. Specific initiatives to reduce HV drivers include individual training and system-wide adaptations. Future methods to identify potential perpetrators may leverage machine learning/augmented intelligence approaches.

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