Data Analysis of COVID-19 Hospital Records Using Contextual Patient Classification System

Abstract

Humanity today is suffering from one of the most dangerous pandemics in history, the Coronavirus Disease of 2019 (COVID-19). Although today there is immense advancement in the medical field with the latest technology, the COVID-19 pandemic has affected us severely. The virus is spreading rapidly, resulting in an escalation in the number of patients admitted. We propose a contextual patient classification system for better analysis of the data from the discharge summary available from the research hospital. The classification was done using the Knuth–Morris–Pratt algorithm. We have also analyzed the data of COVID-19 and non-COVID-19 patients. During the analysis, studies on the medicines, medical services and tests, pulse count, body temperature, and the overall effect of age and gender was done. The death versus survival ratio for the COVID-19 positive patients has also been studied. The classification accuracy of the contextual patient classification system achieved was 97.4%. The combination of data analysis and contextual patient classification will be helpful to all the sectors to be better prepared for any future waves of the COVID-19 pandemic.

Trends in psychiatric diagnoses by COVID-19 infection and hospitalization among patients with and without recent clinical psychiatric diagnoses in New York city from March 2020 to August 2021

“…Healthix provided a contextual patient classification system, where we applied a Knuth-Morris-Pratt (KMP) algorithm [ 30 ] to match the patterns of lab test results and to separate COVID-19 positive and negative patients. KMP algorithm can effectively filter data and extract the required features for classification [ 31 ]. Third, for all eligible patients with COVID-19 lab test results, we extracted their subsequent clinical psychiatric diagnoses using diagnosis codes after the encounter index date of their COVID-19 tests.…”

A Survey on Differential Privacy for Medical Data Analysis

“…During the COVID-19 epidemic prevention and control period [ 8 , 9 ], analyses on medical services and tests, pulse count, body temperature and the overall effect of age and gender was done [ 10 , 11 ]. Furthermore, the use of privacy computing technology such as multi-party security computing enables researchers from all over the world to jointly conduct genome analysis of case samples and share sequencing results without disclosing detailed personal information, so as to implement real-time tracking of the current virus situation and prediction of future strain evolution [ 1 , 12 ].…”