Standardizing New Diagnostic Tests to Facilitate Rapid Responses to The Covid-19 Pandemic

In order to enhance the data interoperability, an expeditious and accurate standardization solution is highly desirable for naming rapidly emerging novel lab tests, and thus diminishes confusion in early responses to pandemic outbreaks. This is a preliminary study to explore the roles and implementation of medical informatics technology, especially natural language processing and ontology methods, in standardizing information about emerging lab tests during a pandemic, thereby facilitating rapid responses to the pandemic. The ultimate goal of this study is to develop an informatics framework for rapid standardization of lab testing names during a pandemic to better prepare for future public health threats. We first constructed an information model for lab tests approved during the COVID-19 pandemic and built a named entity recognition tool that can automatically extract lab test information specified in the information model from the Emergency Use Authorization(EUA)documents of the U.S. Food and Drug Administration (FDA), thus creating a catalog of approved lab tests with detailed information. To facilitate the standardization of lab testing data in electronic health records, we further developed the COVID-19 TestNorm, a tool that normalizes the names of various COVID-19 lab testing used by different healthcare facilities into standard Logical Observation Identifiers Names and Codes (LOINC). The overall accuracy of COVID-19 TestNorm on the development set was 98.9%, and on the independent test set was 97.4%. Lastly, we conducted a clinical study on COVID-19 re-positivity to demonstrate the utility of standardized lab test information in supporting clinical research. We believe that the result of my study indicates great a potential of medical informatics technologies for facilitating rapid responses to both current and future pandemics

An ontology-driven approach for structuring scientific knowledge for predicting treatment adherence behaviour: a case study of tuberculosis in Sub-Saharan African communities.

Doctor of Philosophy in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, Durban 2016.Poor adherence to prescribed treatment is a complex phenomenon and has been identified as a major contributor to patients developing drug resistance and failing treatment in sub-Saharan African countries. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that may vary drastically between communities in different regions. Computer based predictive models can be used to identify individuals and communities at risk of non-adherence and aid in supporting resource allocation and intervention planning in disease control programs. However, constructing effective predictive models is challenging, and requires detailed expert knowledge to identify factors and determine their influence on treatment adherence in specific communities. While many clinical studies and abstract conceptual models exist in the literature, there is no known concrete, unambiguous and comprehensive computer based conceptual model that categorises factors that influence treatment adherence behaviour. The aim of this research was to develop an ontology-driven approach for structuring knowledge of factors that influence treatment adherence behaviour and for constructing adherence risk prediction models for specific communities. Tuberculosis treatment adherence in sub-Saharan Africa was used as a case study to explore and validate the approach. The approach provides guidance for knowledge acquisition, for building a comprehensive conceptual model, its formalisation into an OWL ontology, and generation of probabilistic risk prediction models. The ontology was evaluated for its comprehensiveness and correctness, and its effectiveness for constructing Bayesian decision networks for predicting adherence risk. The approach introduces a novel knowledge acquisition step that guides the capturing of influencing factors from peer-reviewed clinical studies and the scientific literature. Furthermore, the ontology takes an evidence based approach by explicitly relating each factor to published clinical studies, an important consideration for health practitioners. The approach was shown to be effective in constructing a flexible and extendable ontology and automatically generating the structure of a Bayesian decision network, a crucial step towards automated, computer based prediction of adherence risk for individuals in specific communities