Applications of sentiment analysis and machine learning techniques in disease outbreak prediction – A review

Prediction of the facts about any topic always sounds fascinating. It becomes more important and useful when it can predict the facts about healthcare information. In computer science, machine learning techniques/models can be used to predict important health care facts, further the study of the sentiments related to health care information can also play a significant role in the generation of important information. There are many international and national organizations like United States Centers for Disease Control and Prevention (CDC), World Health Organization (WHO), etc. that publish important healthcare data. These data sets can be used to predict important facts about the disease. Further, the datasets related to sentiments of the people like tweets on Twitter, posts on Facebook, and blogs are useful to study the sentiments of the people on a particular topic. Sentiments related to health care information, diseases, and epidemics are very useful. The study of these sentiments can help make better prediction systems and to generate fruitful health care facts. Further, the real datasets related to infectious diseases like COVID-19 can be used for regression analysis for making predictions regarding the upcoming cases. In this paper, we have presented a survey on the application of different machine learning techniques, that are used in the classification and regression analysis related to health care predictions and the study of sentiments. We have presented a review of major research studies related to healthcare between the years 2010 to 2020.

Imprint Cytology for Intra-operative Assessment of Surgical Margins for Skeletal Tumours in COVID-19 Pandemic.

In the current COVID-19 pandemic, tremendous pressure is been exerted on the existing health infrastructure of many developing nations. Limb salvage surgeries in bone and soft tissue sarcomas cannot be delayed beyond a certain time-period and constitute an orthopaedic emergency in certain situations. Evaluation of intra-operative surgical margins forms an important step especially in cases with planned close margins or intercalary resections. Techniques such as imprint cytology can be preferred over frozen sections for evaluation of the surgical margins especially in COVID-19 positive patients. The advantages it offers such as completion of the procedure within the operation room complex, no generation of aerosols, and almost equal sensitivity and specificity when compared with frozen section method do warrant a modification of current surgical practice in the current health crisis especially in resource-constrained nations. Awareness and better communication regarding the same from the pathologist to the surgical team will go a long way in conserving resources and avoid unnecessary exposure to potentially infected aerosols.