ABSTRACT
The world we live in today, where technology has become a very integral part of our lives, has new, untapped resources that can bring about massive changes in the health sector. The Internet and social media have become the flag bearers of the tech-savvy world. Some of the services provided by the various social media platforms like chats, comments, blogs, captions, as well as reviews are starting to get studied for Natural Language Processing (NLP) and Text Analytics. A generation that scrounges up even the silliest of answers on the internet, it is very common for people to search for their health-related queries on social media. In this book chapter, we intend to propose a model for extracting data complying with health records and health-related text documents of the COVID-19 patients from some of the top social media forums and present a semantic framework. Given the fact that social media allows a more open and direct form of communication, among health workers, patients, and even curious students and researchers, it is a reliable source for addressing public health problems. In these tough times, when the entire world is suffering from the deadly Coronavirus pandemic, we hope our work gets the perfect infrastructure to grow on. Our book chapter puts forth a model to apply a semantic framework to retrieve healthcare data related to the Corona virus and COVID-19 pandemic from some of the social media forums available online and perform semantic analysis on the data. Social media can play a huge role in this aspect as people’s experiences with the pandemic and patients affected by the virus from the root of information that is circulated online. This research work tries to provide a useful and reliable method of data retrieval and data extraction, which would catch data in its unstructured form like patient questions and discussions among doctors and patients, people who have had a close look on the virus, analyze the semantic nature of the data, and present it in a more structured manner so that it can be put to effective and immediate use. The sole purpose of our framework is aiming to help detect and predict the symptoms of Coronavirus through the textual data extracted from the various social media platforms. © 2021 Scrivener Publishing LLC.
ABSTRACT
Objective: NA Background: Creutzfeldt-Jakob disease (CJD) is an extremely rare but fatal neurodegenerative disease with incidence of one in a million worldwide, and few than 1000 cases per year in the United States per year. Design/Methods: NA Case Summary: We present two probable CJD cases seen in the same hospital within one month. Case one was a 67-year-old white female, former psychology practice manager, presenting with worsening cognition, vertigo, behavioral changes and 15 lb weight loss over 6 months. Exam findings significant for MoCA of 17/30 (decreased to 15/30 after one week), constant right eye shut, mild dysmetria in both lower extremities, a wide based gait with small strides. Blood work and initial Spinal fluid studies were negative. Continuous EEG showing occasional right temporal slow, frequent generalized rhythmic theta and delta slowing. MRI brain findings were suggestive of CJD with hyperintensities in bilateral caudate nucleus and putamen. Patient did not respond to high dose steroid. Case two was a 78-year-old white male, admitted for deterioration in cognition, gait, speech, fatigue and intermittent body jerking. Progression of his symptoms was so rapid, from a highly functional retired funeral director, he became minimal speech, loss of ADL within 3 months. Exam was significant for orientation to self only, global aphasia, muscle weakness and startle myoclonus. Blood work and initial spinal fluid studies were negative. MRI brain showed asymmetric cortically based diffuse restriction within cingulate, caudate nucleus also left temporoparietal. EEG showed generalized rhythmic delta activity. CSF from both cases eventually showed positive RT-QuIC, 14-3-3 protein and highly elevated T-Tau protein. Conclusions: CJD is a transmittable, reportable disease. Two cases seen in the same hospital within one month skews from the previously known CJD prevalence. Surveillance and investigation on the reason of regional CJD arise during COVID-19 pandemic may prove to be important and urgent.
ABSTRACT
The healthcare system in the Indian subcontinent is plagued with numerous issues related to the access, transfer, and storage of patient's medical records. The lack of infrastructure to properly communicate and track records between all key participants has allowed the distribution of counterfeit drugs, dependency on unsafe methods of communication, and lack of trust between patients and providers. During the global COVID-19 pandemic, the need for a robust communication and record tracking system has been further emphasized. To facilitate efficient communication and mitigate the mentioned issues, a nationwide EHR (electronic health record) system must be introduced to bring the healthcare system into digital space. To further enhance security, efficiency, and cost, the innovation of Blockchain is introduced. Blockchain is a decentralized data structure that allows secure transactions between untrusted parties without needing a central authority. In this paper, a Hyperledger fabric-based Blockchain Electronic Healthcare Record (EHR) system is proposed. The system is integrated with technologies such as NLP (Natural Language Processing), and Machine Learning to provide users with practical features.