Abstract:
"Pulmonary fibrosis is a progressive lung condition caused by damaged or
scarred lung tissue obstructing the exchange of carbon dioxide and oxygen gasses in
the alveoli Thereby, leaving the body deprived of the oxygen required for blood
oxygenation and less lung volume. As per state-of-the-art medical practice, the
deterioration/ scarring of the lung tissue is not entirely reversible or correctable, merely
leaving patients with symptom management using therapy and clinical drug trials. An
accurate judgment of the lung function decline is crucial for the management and trial
treatment of the patient.
This research project endeavors to automate the process of prognosis
prediction of pulmonary fibrosis using a hybrid-classical quantile regression hybrid
model built using a variational quantum circuit. Although quantum computing is still
in its formative years, research activities done in similar domains have proved to have
immaculate success in both the correctness and speed of the results. The project
explores the advantages one might gain by utilizing the developing quantum
computing over the use of classical computational approaches, which will in return
facilitate and encourage more optimization of machine learning using quantum
computing.
The model has shown promising results so far, with a Laplace Log Likelihood
matrix of -7.13, and a mean absolute error of just 212.31. For a regression model
trained with a small dataset such as the OSIC dataset with just 700+ DICOM images
with its metadata, the evaluations are noticeable and promising."
