healthcare technology

Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.

Objective: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.

Methods: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.

Results: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.

Conclusions: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems.

Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether.

The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus.

Such systems may aid future efforts to prevent and contain the spread of infectious diseases.

read the study at https://publichealth.jmir.org/2021/3/e26719

A model has been developed by researchers at Indian Institute of Technology ,Kharagpur predicting alteration in metabolic reaction rates of lung cells post SARS-CoV-2 infection.

"We have used the gene expression of normal human bronchial cells infected with SARS-CoV-2 along with the macromolecular make-up of the virus to create this integrated genome-scale metabolic model. The growth rate predicted by the model showed a very high agreement with experimentally and clinically reported effects of SARS-CoV-2," said Dr Amit Ghosh, Assistant Professor, School of Energy Science and Engineering, IIT Kharagpur who coauthored the paper

The research would lead to a better understanding of metabolic reprogramming and aid the development of better therapeutics to deal with viral pandemics,

Summary:

Metabolic flux analysis in disease biology is opening up new avenues for therapeutic interventions. Numerous diseases lead to disturbance in the metabolic homeostasis and it is becoming increasingly important to be able to quantify the difference in interaction under normal and diseased condition.

While genome-scale metabolic models have been used to study those differences, there are limited methods to probe into the differences in flux between these two conditions. Our method of conducting a differential flux analysis can be leveraged to find which reactions are altered between the diseased and normal state.

We applied this to study the altered reactions in the case of SARS-CoV-2 infection. We further corroborated our results with other multi-omics studies and found significant agreement.

read the paper at https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008860