Information Entropy Measures for Evaluation of Reliability of Deep Neural Network Results

Deep neural networks (DNN) try to analyze given data, to come up with decisions regarding the inputs. The decision-making process of the DNN model is not entirely transparent. The confidence of the model predictions on new data fed into the network can vary. We address the question of certainty of decision making and adequacy of information capturing by DNN models during this process of decision-making. We introduce a measure called certainty index, which is based on the outputs in the most penultimate layer of DNN. In this approach, we employed iEEG (intracranial electroencephalogram) data to train and test DNN. When arriving at model predictions, the contribution of the entire information content of the input may be important. We explored the relationship between the certainty of DNN predictions and information content of the signal by estimating the sample entropy and using a heatmap of the signal. While it can be assumed that the entire sample must be utilized for arriving at the most appropriate decisions, an evaluation of DNNs from this standpoint has not been reported. We demonstrate that the robustness of the relationship between certainty index with the sample entropy, demonstrated through sample entropy-heatmap correlation, is higher than that with the original signal, indicating that the DNN focuses on information rich regions of the signal to arrive at decisions. Therefore, it can be concluded that the certainty of a decision is related to the DNN’s ability to capture the information in the original signal. Our results indicate that, within its limitations, the certainty index can be used as useful tool in estimating the confidence of predictions. The certainty index appears to be related to how effectively DNN heatmaps captured the information content in the signal

Risk of Transmission of infection to Healthcare Workers delivering Supportive Care for Coronavirus Pneumonia; A Rapid GRADE Review [preprint]

Background: Avenues of treatment currently implemented for Covid-19 pandemic are largely supportive in nature. Non -availability of an effective antiviral treatment makes supportive care for acute hypoxic respiratory failure is the most crucial intervention. Highly contagious nature of Covid-19 had created stress and confusion among front line Health Care Workers (HCWs) regarding infectious risk of supportive interventions and best preventive strategies. Purpose: To analyze and summarize key evidence from published literature exploring the risk of transmission of Covid-19 related to common supportive care interventions in hospitalized patients and effectiveness of currently used preventive measures in hospital setting. Data Sources: Curated Covid-19 literature from NCBI Computational Biology Branch ,Embase and Ovid till May 20,2020.Longitudinal and reference search till June 28,2020 Study Selection: Studies pertaining to risk of infection to HCWs providing standard supportive care of hospitalized Covid-19 mainly focusing on respiratory support interventions.Indirect studies from SARS,MERS or other ARDS pathology caused by infectious agents based on reference tracking and snow ball search . Clinical, Healthy volunteer and mechanistic studies were included. Two authors independently screened studies for traditional respiratory supportive-care (Hypoxia management, ventilatory support and pulmonary toileting) related transmission of viral or bacterial pneumonia to HCWs. Data Extraction: Two authors (TK and SP) independently screened articles and verified for consensus. Quality of studies and level of evidence was assessed using Oxford Center for Evidence Based Medicine (OCEBM) , Newcastle - Ottawa quality assessment Scale for observational studies and Grading of Recommendations Assessment, Development and Evaluation (GRADE) system for grading evidence. Data Synthesis: 21 studies were eligible for inclusion. In 11 mechanistic studies, 7 were manikin based,1 was in the setting of GNB pneumonia ,2 were healthy volunteer study and 1 was heterogenous setting.Out of 10 clinical studies ,5 were case controlled and 6 were cohort studies. Risk of corona virus transmission was significantly high in HCWs performing or assisting endotracheal intubation or contact with respiratory secretion.(Moderate certainty evidence, GRADE B) Safety of nebulization treatment in corona virus pneumonia patients are questionable(Low certainty evidence, GRADE C).Very low certainty evidence exist for risk of transmission with conventional HFNC (GRADE D) and NIV (GRADE D),CPR (GRADE D),Bag and mask ventilation(GRADE D).Moderate certainty evidence exist for protective effect of wearing a multilayered mask, gown , eye protection and formal training for PPE use (GRADE B).Low certainty evidence exist for transmission risk with bag and mask ventilation, suctioning before and after intubation and prolonged exposure (GRADE C).Certainty of evidence for wearing gloves,post exposure hand washing and wearing N 95 mask is low(GRADE C). Limitations: This study was limited to articles with English abstract. Highly dynamic nature of body of literature related to Covid-19, frequent updates were necessary even during preparation of manuscript and longitudinal search was continued even after finalizing initial search. Due to the heterogeneity and broad nature of the search protocol, quantitative comparisons regarding the effectiveness of included management strategies could not be performed. Direct evidence was limited due to poor quality and non-comparative nature of available Covid-19 reporting. Conclusions: Major risk factors for transmission of corona virus infection were, performing or assisting endotracheal intubation and contact with respiratory secretion. Risk of transmission with HFNC or NIV can be significantly decreased by helmet interface, modified exhalation circuit or placing a properly fitting face mask over patient interface of HFNC. Evidence for risk of transmission with CPR, suctioning before or after intubation or bag and mask ventilation of very low certainty. Significant protective factors are Formal training for PPE use, consistently wearing mask, gown and eye protection. Primary Funding Source: None Disclosure: None of the authors have any conflict of interest to disclose