Predicting the Effect of Proton Beam Therapy Technology on Pulmonary Toxicities for Patients With Locally Advanced Lung Cancer Enrolled in the Proton Collaborative Group Prospective Clinical Trial.

PURPOSE: This study aimed to predict the probability of grade ≥2 pneumonitis or dyspnea within 12 months of receiving conventionally fractionated or mildly hypofractionated proton beam therapy for locally advanced lung cancer using machine learning. METHODS AND MATERIALS: Demographic and treatment characteristics were analyzed for 965 consecutive patients treated for lung cancer with conventionally fractionated or mildly hypofractionated (2.2-3 Gy/fraction) proton beam therapy across 12 institutions. Three machine learning models (gradient boosting, additive tree, and logistic regression with lasso regularization) were implemented to predict Common Terminology Criteria for Adverse Events version 4 grade ≥2 pulmonary toxicities using double 10-fold cross-validation for parameter hyper-tuning without leak of information. Balanced accuracy and area under the curve were calculated, and 95% confidence intervals were obtained using bootstrap sampling. RESULTS: The median age of the patients was 70 years (range, 20-97), and they had predominantly stage IIIA or IIIB disease. They received a median dose of 60 Gy in 2 Gy/fraction, and 46.4% received concurrent chemotherapy. In total, 250 (25.9%) had grade ≥2 pulmonary toxicity. The probability of pulmonary toxicity was 0.08 for patients treated with pencil beam scanning and 0.34 for those treated with other techniques (P = 8.97e-13). Use of abdominal compression and breath hold were highly significant predictors of less toxicity (P = 2.88e-08). Higher total radiation delivered dose (P = .0182) and higher average dose to the ipsilateral lung (P = .0035) increased the likelihood of pulmonary toxicities. The gradient boosting model performed the best of the models tested, and when demographic and dosimetric features were combined, the area under the curve and balanced accuracy were 0.75 ± 0.02 and 0.67 ± 0.02, respectively. After analyzing performance versus the number of data points used for training, we observed that accuracy was limited by the number of observations. CONCLUSIONS: In the largest analysis of prospectively enrolled patients with lung cancer assessing pulmonary toxicities from proton therapy to date, advanced machine learning methods revealed that pencil beam scanning, abdominal compression, and lower normal lung doses can lead to significantly lower probability of developing grade ≥2 pneumonitis or dyspnea.

A domain-agnostic approach for characterization of lifelong learning systems.

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of (1) Continuous Learning, (2) Transfer and Adaptation, and (3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications.

Building more accurate decision trees with the additive tree.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.

Artificial intelligence for pediatric ophthalmology.

PURPOSE OF REVIEW: Despite the impressive results of recent artificial intelligence applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric patients and how artificial intelligence techniques can address these challenges, surveys recent applications to pediatric ophthalmology, and discusses future directions. RECENT FINDINGS: The most significant advances involve the automated detection of retinopathy of prematurity, yielding results that rival experts. Machine learning has also been applied to the classification of pediatric cataracts, prediction of postoperative complications following cataract surgery, detection of strabismus and refractive error, prediction of future high myopia, and diagnosis of reading disability. In addition, machine learning techniques have been used for the study of visual development, vessel segmentation in pediatric fundus images, and ophthalmic image synthesis. SUMMARY: Artificial intelligence applications could significantly benefit clinical care by optimizing disease detection and grading, broadening access to care, furthering scientific discovery, and improving clinical efficiency. These methods need to match or surpass physician performance in clinical trials before deployment with patients. Owing to the widespread use of closed-access data sets and software implementations, it is difficult to directly compare the performance of these approaches, and reproducibility is poor. Open-access data sets and software could alleviate these issues and encourage further applications to pediatric ophthalmology.

MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine.

Machine learning algorithms that are both interpretable and accurate are essential in applications such as medicine where errors can have a dire consequence. Unfortunately, there is currently a tradeoff between accuracy and interpretability among state-of-the-art methods. Decision trees are interpretable and are therefore used extensively throughout medicine for stratifying patients. Current decision tree algorithms, however, are consistently outperformed in accuracy by other, less-interpretable machine learning models, such as ensemble methods. We present MediBoost, a novel framework for constructing decision trees that retain interpretability while having accuracy similar to ensemble methods, and compare MediBoost's performance to that of conventional decision trees and ensemble methods on 13 medical classification problems. MediBoost significantly outperformed current decision tree algorithms in 11 out of 13 problems, giving accuracy comparable to ensemble methods. The resulting trees are of the same type as decision trees used throughout clinical practice but have the advantage of improved accuracy. Our algorithm thus gives the best of both worlds: it grows a single, highly interpretable tree that has the high accuracy of ensemble methods.

Increased severity of alcoholic liver injury in female verses male rats: a microarray analysis.

Alcoholic liver disease (ALD) is an increasingly recognized condition that may progress to end-stage liver disease. In addition to alcohol consumption, genetic factors, dietary fatty acids, gender and viral infection potentiate the severity of alcoholic liver injury. In humans, significant gender differences in susceptibility to ALD are observed. In the intragastric infusion rat model of ALD, female rats developed more severe liver injury than males. To understand the effect of gender on the development of more severe ALD in female rats, we performed a microarray based expression profiling of genes in rats fed with fish oil and ethanol diet. A large number of genes showed significant changes in female livers compared to males. The upregulated genes in female liver were involved in proteosome endopeptidase activity, catalytic activity, lipid metabolism, alcohol metabolism, mitochondrial and oxidoreductase activity. The downregulated genes were involved in oxidoreductase activity, chaperone activity, and electron transport activity in the female liver as demonstrated by biological theme analysis. Ingenuity computational pathway analysis tools were used to identify specific regulatory networks of genes operative in promoting liver injury. These networks allowed us to identify a large cluster of genes involved in lipid metabolism, development, cellular growth and proliferation, apoptosis, carcinogenesis and various signaling pathways. Genes listed in this article that were significantly increased or decreased (expression two fold or more) were assigned to pathological functional groups and reviewed for relevance to establish hypotheses of potential mechanisms involved in ALD in female liver injury.

Transcriptional networks in a rat model for nonalcoholic fatty liver disease: a microarray analysis.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a common hepatic condition that may progress to end-stage liver disease. High-fat diets in animals reproduce many of the features found in nonalcoholic steatohepatitis. OBJECTIVE: To understand how various dietary or genetic factors influence the development of fatty liver and consequently NAFLD, we performed microarray-based expression profiling of genes, induced by fish oil and dextrose diet, a putative mediator of alcohol-like effects on the liver of the female rat. DESIGN: Male and age-matched female rats were fed fish oil and dextrose for 4 weeks. Hepatic RNA from each sample was extracted and used for microarray analysis. RESULTS: A large number of genes underwent significant changes in the female liver as compared to male controls. In the female rat liver, biological theme analysis demonstrated a shift in the transcriptional program which included upregulation of genes involved in lipid metabolism, chaperone activity, mitochondrial and oxidoreductase activity combined with downregulation of genes involved in nucleic acid metabolism. The differential expression of genes of interest identified by microarray technique was validated by real-time reverse transcription-polymerase chain reaction. Ingenuity computational pathway analysis tools were used to identify specific regulatory networks of genes operative in promoting liver injury. CONCLUSIONS: The use of networks stated above allowed us to identify genes involved in cell death, apoptosis, peroxisome proliferator-activated receptor alpha-regulated lipid metabolism and mitogen-activated protein kinase signaling pathways.