Taxonomy-based prompt engineering to generate synthetic drug-related patient portal messages.

Objective: The objectives of this study were to: (1) create a corpus of synthetic drug-related patient portal messages to address the current lack of publicly available datasets for model development, (2) assess differences in language used and linguistics among the synthetic patient portal messages, and (3) assess the accuracy of patient-reported drug side effects for different racial groups.

Methods: We leveraged a taxonomy for patient- and clinician-generated content to guide prompt engineering for synthetic drug-related patient portal messages. We generated two groups of messages: the first group (200 messages) used a subset of the taxonomy relevant to a broad range of drug-related messages and the second group (250 messages) used a subset of the taxonomy relevant to a narrow range of messages focused on side effects.

Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases.

Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, since infrastructures around the world are overworked and falling apart. These new technologies can be used to fight COVID-19 because they are flexible and can be changed.

Evaluation of symptoms along with depression, anxiety and stress levels of Sars-CoV-2 positive patients during the second wave in the city of Indore, Central India - a cross sectional study.

Introduction: The consequences of the second wave hitting India have drastically laid a huge impact on the mental state of patients. The second wave had proven to be far more dangerous and hence the psychological evaluation needed to be conducted to know the scenario of patients suffering from SARS-CoV-2.

Objective: This study was undertaken to evaluate the symptoms of SARS-CoV-2 patients along with the existing depression, anxiety and stress levels amongst them.

Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning.

The Coronavirus Disease-2019 (COVID-19) pandemic persists to have a mortifying impact on the health and well-being of the global population. A continued rise in the number of patients testing positive for COVID-19 has created a lot of stress on governing bodies across the globe and they are finding it difficult to tackle the situation. We have developed an outbreak prediction system for COVID-19 for the top 10 highly and densely populated countries.