Machine Learning Models for 6-Month Survival Prediction after Surgical Resection of Glioblastoma

Introduction: The role of surgical resection for the treatment of glioblastoma multiforme is well established. Survival analysis after resective surgery in the literature comprises mostly of traditional statistical models. Machine learning models offer powerful predictive and analytical capability for varied datasets and offer improved generalizability and scalability. We analyzed survival data of patients with glioblastoma with various machine learning algorithms and compared it to binary logistic regression. Methods: We retrospectively identified cases of glioblastoma treated with surgical resection at our institution from 2012-2018. Feature scaling and one-hot encoding was used to better fit the models to the data and used the formula X’ = (X – Xmin)/(Xmax – Xmin). Feature selection was performed using chi-squared analysis (features with p Results: 582 patients fit the inclusion criteria and were used to build these models. 6-month mortality was 43.13%. Accuracy scores (AUC) for models used were 0.670 (logistic regression), 0.704 (Random Forest), 0.585 (Support Vector Machine), 0.560 (Naïve Bayes), 0.650 (XG Boost), 0.585 (Stochastic Gradient Descent Classifier), and 0.740 (Neural Network). 5-fold cross validation was used to ensure generalizability to an independent dataset. Conclusion: Machine learning methods for prediction of six-month survival for glioblastoma are promising analytical tools that we show can approach or exceed the accuracy of traditional logistic regression, particularly neural networks and the random forest algorithm. Improved prediction of 6-month survival using machine learning offers increased capabilities for patient education, adjuvant chemotherapy or radiation planning, and post-operative counseling, while maintaining increased adaptability and generalizability compared to regression models

Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds

We describe a method to use discrete human feedback to enhance the performance of deep learning agents in virtual three-dimensional environments by extending deep-reinforcement learning to model the confidence and consistency of human feedback. This enables deep reinforcement learning algorithms to determine the most appropriate time to listen to the human feedback, exploit the current policy model, or explore the agent's environment. Managing the trade-off between these three strategies allows DRL agents to be robust to inconsistent or intermittent human feedback. Through experimentation using a synthetic oracle, we show that our technique improves the training speed and overall performance of deep reinforcement learning in navigating three-dimensional environments using Minecraft. We further show that our technique is robust to highly innacurate human feedback and can also operate when no human feedback is given

Design of a Metadata Framework for the Environmental Models with an Example Hydrologic Application in HydroShare

Environmental modelers rely on a variety of computational models to make predictions, test hypotheses, and address specific problems related to environmental science and natural resource management. Scientists and engineers must devote significant effort to preparing these computational models. While significant attention has been devoted to sharing and reusing environmental data, less attention has been devoted to sharing and reusing environmental models. A first step toward increasing environmental model sharing and reuse is to define a general metadata framework for models that is flexible and, therefore, applicable across the wide variety of models used by environmental modelers. This paper proposes a general approach for representing environmental model metadata that extends the Dublin Core metadata framework. The framework is implemented within the HydroShare system and applied for a hydrologic model sharing use case. This example application demonstrates how the metadata framework implemented within HydroShare can assist in model sharing, publication, reuse, and reproducibility

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations from other sites to inform focal sites. In this paper, we evaluate two different DL model structures, a long short-term memory neural network (LSTM) and a recurrent graph convolutional neural network (RGCN), both with and without data assimilation for forecasting daily maximum stream temperature 7 days into the future at monitored and unmonitored locations in a 70-segment stream network. All our DL models performed well when forecasting stream temperature as the root mean squared error (RMSE) across all models ranged from 2.03 to 2.11°C for 1-day lead times in the validation period, with substantially better performance at gaged locations (RMSE = 1.45–1.52°C) compared to ungaged locations (RMSE = 3.18–3.27°C). Forecast uncertainty characterization was near-perfect for gaged locations but all DL models were overconfident (i.e., uncertainty bounds too narrow) for ungaged locations. Our results show that the RGCN with data assimilation performed best for ungaged locations and especially at higher temperatures (>18°C) which is important for management decisions in our study location. This indicates that the networked model structure and data assimilation techniques may help borrow information from nearby monitored sites to improve forecasts at unmonitored locations. Results from this study can help guide DL modeling decisions when forecasting other important environmental variables