Quantitative Computed Tomography Imaging and Machine Learning for Evaluating Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is characterized mainly by irreversible airflow limitation due to underlying pathological changes such as emphsysema and airway disease. Despite decades of research, there remain major challenges for managing patients with COPD, including: 1) inability to predict episodes of worsening symptoms, known as exacerbations, that may result in emergency room visits or hospitalizations, 2) differentiating COPD from asthma in patients that have features of both diseases, and, 3) prediction of COPD patients at an increased risk of disease progression. Spirometry test measurements are a simple and inexpensive approach to diagnosis COPD and categorize the severity of lung disease. Force expiratory volume in one second (FEV )1is one the most important spirometry test measurement . However, it is a global measurement that does not provide any information about the underlying disease – information that could guide therapy decisions. Computed tomography (CT) imaging, in contrast, provides information about the underlying disease pathology (emphysema, airway disease, etc) and heterogeneity within the lung. In more recent years, predictive models play a significant role in predicting COPD outcomes, such as disease progression, hospitalization, acute exacerbation, emergency room visit and mortality. Machine learning algorithms are widely utilized as models to predict outcomes with maximum accuracy and minimal error. Therefore, the overaching objective of this thesis was to construct a comprehensive feature-set that carries global and regional lung information using combinations of demographics, spirometry test measurements and CT lung features. Then, the machine learning algorithms, including support vector machine (SVM) and neural networks, were applied to predict COPD outcomes, which can be both classification problems such as hospitalization prediction or classification of COPD/asthma, and regression problems such as predicting COPD progression as measured byFEV . Fea1ure selection also plays an important role in identifing the most important features, and for dimensionality reduction to reduce the complexity of the learning algorithm and the probability of overfitting. In this regard, this thesis proposed novel hybrid features selection with the aim of finding the most important predictors. Additionally, a feature selection algorithm based on nonnegative matrix factorization (NMF) with geometry structure preserving and sparsity consideration was proposed to find most important features. In each of our studies, we demonstrated the performance of the learning algorithms were considerably increased by using CT pulmonary imaging features. Key words: COPD hospitalization, Spirometry test, CT lung biomarkers, Machine learning and Feature selection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".