ATPT: Automate Typhoon Contingency Plan Generation from Text

Artificial intelligence (AI) planning models play an important role in decision support systems for disaster management e.g. typhoon contingency plan development. However, constructing an AI planning model always requires significant amount of manual effort, which becomes a bottleneck to emergency response in a time-critical situation. In this demonstration, we present a framework of automating a domain model of planning domain definition language from natural language input through deep learning techniques. We implement this framework in a typhoon response system and demonstrate automatic generation of typhoon contingency plan from official typhoon plan documents

Antibiotic susceptibility pattern, risk factors, and prediction of carbapenem-resistant Pseudomonas aeruginosa in patients with nosocomial pneumonia

Objectives: This study was aimed at describing antibiotic susceptibility patterns and developing a predictive model by assessing risk factors for carbapenem-resistant Pseudomonas aeruginosa (CRPA). Methods: A retrospective case-control study was conducted at a teaching hospital in China from May 2019 to July 2021. Patients were divided into the carbapenem-susceptible P. aeruginosa (CSPA) group and the CRPA group. Medical records were reviewed to find an antibiotic susceptibility pattern. Multivariate analysis results were used to identify risk factors and build a predictive model. Results: A total of 61 among 292 patients with nosocomial pneumonia were infected with CRPA. In the CSPA and CRPA groups, amikacin was identified as the most effective antibiotic, with susceptibility of 89.7%. The CRPA group showed considerably higher rates of resistance to the tested antibiotics. Based on the results of mCIM and eCIM, 28 (45.9%) of 61 isolates might be carbapenemase producers. Independent risk factors related to CRPA nosocomial pneumonia were craniocerebral injury, pulmonary fungus infection, prior use of carbapenems, prior use of cefoperazone-sulbactam, and time at risk (≥15 d). In the predictive model, a score >1 point indicated the best predictive ability. Conclusions: CRPA nosocomial pneumonia could be predicted by risk factor assessment particularly based on the underlying disease, antimicrobial exposure, and time at risk, which could help prevent nosocomial pneumonia