Striking a match between FHIR-based patient data and FHIR-based eligibility criteria

Inputs and Outputs: The Strike-a-Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria.Purpose: Ultimately, such a system will play a "matchmaker" role in facilitating point of-care recognition of patient-specific clinical decision support.Specifications: The eligibility criteria are defined in HL7 FHIR (version R5) Evidence Variable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record.Application: The Strike-a-Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human-readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https://fevir.net/110192 provides a method for sharing functions using the same information model.

A Novel Approach for Face Mask Detection using Tensorflow for Covid-19

Corona Virus Disease (COVID-19) pandemic has become a cause world health crisis. It is a disease that can spread from humans to humans through physical contact with the infected droplets or via airborne. It has been scientifically proven that wearing a face mask is the most effective method against the virus. This paper's aim is to develop a face mask detector which could be used to make mitigation, evaluation, prevention, and action plans against COVID-19 pandemic by the authorities. In this study, the face mask detection is developed based on the image classification method called Mobile-NetV2. The pseudo-steps for making the detector model are accumulating data, pre-processing, breakdown of the data, training the model, and implementation of the model. The proposed model is able to detect the people with or without a face mask with an accuracy of 96.85 percent. The experimental results of the model have been performed on real-time applications. The mask detector is also able to detect the face mask on a moving subject with expected accuracy. © 2021 IEEE.