ABSTRACT
The main assay tool of COVID-19, as a pandemic, still has significant faults. To ameliorate the current situation, all facilities and tools in this realm should be implemented to encounter this epidemic. The current study has endeavored to propose a self-assessment decision support system (DSS) for distinguishing the severity of the COVID-19 between confirmed cases to optimize the patient care process. For this purpose, a DSS has been developed by the combination of the data-driven Bayesian network (BN) and the Fuzzy Cognitive Map (FCM). First, all of the data are utilized to extract the evidence-based paired (EBP) relationships between symptoms and symptoms' impact probability. Then, the results are evaluated in both independent and combined scenarios. After categorizing data in the triple severity levels by self-organizing map, the EBP relationships between symptoms are extracted by BN, and their significance is achieved and ranked by FCM. The results show that the most common symptoms necessarily do not have the key role in distinguishing the severity of the COVID-19, and extracting the EBP relationships could have better insight into the severity of the disease.
ABSTRACT
INTRODUCTION: Thrombocytopenia (TP) is associated with poor outcome in patients who are critically ill with pneumonia, burns, and H1N1 influenza. To our knowledge, no similar study in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) has been conducted to date. The aim of this study was to determine the impact of platelet count on the outcome of patients with AECOPD. MATERIAL AND METHODS: Patients admitted to our teaching hospital for AECOPD were divided into two cohorts, those with and without TP. The outcome of all patients was followed. RESULTS: Of the 200 patients with AECOPD, 55 (27.5%) had TP. Of these, 14 (25.5%) died in the hospital, whereas of the 145 non-TP patents, 11 (7.5%) died (p-value = 0.001). There was a significantly higher transfer rate to the ICU and mechanical ventilation in TP patients. The mean platelet count was significantly lower in patients who died than those who were discharged (161,672 vs. 203,005 cell/µL; p-value = 0.017). There was negative correlation between duration of hospitalization and platelet count. CONCLUSION: TP was associated with poor outcome in AECOPD. TP could be considered as a marker for the assessment of inï¬ammation and prognosis in AECOPD patients based on its cost-effective features.
