Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics.

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.

A narrative review of medical image processing by deep learning models: origin to COVID-19

A rapid diagnostic system is a primary role in the healthcare system exclusively during a pandemic situation to control contagious diseases like coronavirus disease-2019 (COVID-19). Many countries remain lacking to spot COVID cases by the reverse transcription-polymerase chain reaction (RT-PCR) test. On this stretch, deep learning algorithms have been strengthened the medical image processing system to analyze the infection, categorization, and further diagnosis. It is motivated to discover the alternate way to identify the disease using existing medical implications. Hence, this review narrated the character and attainment of deep learning algorithms at each juncture from origin to COVID-19. This literature highlights the importance of deep learning and further focused the medical image processing research on handling the data of magnetic resonance imaging (MRI), computed tomography (CT) scan, and electromagnetic radiation (X-ray) images. Additionally, this systematic review tabulates the popular deep learning networks with operational parameters, peer-reviewed research with their outcomes, popular nets, and prevalent datasets, and highlighted the facts to stimulate future research. The consequence of this literature ascertains convolutional neural network-based deep learning approaches work better in the medical image processing system, and especially it is very supportive of sorting out the COVID-19 complications.

Explaining Causal Influence of External Factors on Incidence Rate of Covid-19.

Classical susceptible-infected-removed model with constant transmission rate and removal rate may not capture real world dynamics of epidemic due to complex influence of multiple external factors on the spread and spatio-temporal variation of transmission rate. Also, explainability of a model is of prime necessity to understand the influence of multiple factors on transmission rate. Thus, we modified discrete global susceptible-infected-removed model with time-varying transmission rate, recovery rate and multiple spatially local models. We have derived the criteria for disease-free equilibrium within a specific time period. A convolutional LSTM model is created and trained to map multiple spatiotemporal features to transmission rate. The model achieved 8.39% mean absolute percent error in terms of cumulative infection cases in each locality in a region in USA for a 10-day prediction period. Comparison with current state of the art methods reveals performance superiority of the proposed method. A perturbation-based spatio-temporal model interpretation method is proposed which generates spatio-temporal local interpretations. Global interpretations are generated by statistically accumulating the local interpretations. Global interpretations of transmission rate for a region in USA shows consistent positive influence of population density, whereas, temperature and humidity have very minor influence. An experiment with what-if scenario reveals locality specific quick identification of positive cases, rapid isolation and improving healthcare facilities are keys to rapid convergence to disease-free equilibrium. A long-term forecasting of 160 days predicts new infection cases in a region in USA with a median error of 455 cases.