Medical Informatics and Data Analysis

During recent years, the use of advanced data analysis methods has increased in clinical and epidemiological research. This book emphasizes the practical aspects of new data analysis methods, and provides insight into new challenges in biostatistics, epidemiology, health sciences, dentistry, and clinical medicine. This book provides a readable text, giving advice on the reporting of new data analytical methods and data presentation. The book consists of 13 articles. Each article is self-contained and may be read independently according to the needs of the reader. The book is essential reading for postgraduate students as well as researchers from medicine and other sciences where statistical data analysis plays a central role

Efficient Synchronization for GPGPU

High-performance General Purpose Graphics processing units (GPGPUs) have exposed bottlenecks in synchronizations of threads and cores. The massively parallel computing cores and complex hierarchies of threads present new challenges for synchronizations at different granularities. Performance of GPU is hindered by inefficient global and local synchronizations. I propose hardware-software cooperative frameworks for efficient synchronization of GPGPU to address the following issues. To provide efficient global synchronization (Gsync), an API with direct hardware support is proposed. The GPU cores are synchronized by an on-chip Gsync controller. Partial context switch is employed to guarantee deadlock-free execution. The proposed Gsync avoids expensive API calls and alleviates data thrashing. Prioritized warp scheduling is used to increase the overlap of context switch with kernel execution. To efficiently exploit the inherent parallelism of producer-consumer problems, a flexible wait-signal scheme is proposed at thread-block level. I propose dedicated APIs to express fine-grained static and dynamic dependencies with hardware support. The proposed scheme can accelerate wavefront, graph and machine learning applications. The architectural design of on-chip wait-signal controller eliminates busy wait loop and long-latency memory operations. I also propose thread block dispatch scheduling to address the problem of load imbalance and large context switch overhead. To reduce stall due to synchronizations, a synchronization-aware warp scheduling is proposed to coordinate multiple warp schedulers upon synchronization events. Both performance and hardware utilization are improved by resolving the barrier sooner

Castration-Resistant Prostate Cancer Outcome Prediction Using Phased Long Short-Term Memory with Irregularly Sampled Serial Data

It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients’ likely outcomes to support physicians’ decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) model with a special module called a “time-gate” is used to process the irregularly sampled data sets. A synthetic minority oversampling technique is used to overcome the data imbalance between two patient groups: those with and without CRPC treatment. The phased-LSTM model is able to predict the CRPC outcome with an accuracy of 88.6% (precision-recall: 91.6%) using 120 days of data or 94.8% (precision-recall: 96.9%) using 360 days of data. The validation loss converged slowly with 120 days of data and quickly with 360 days of data. In both cases, the prediction model takes four epochs to build. The overall CPRC outcome prediction model using irregularly sampled serial medical data is accurate and can be used to support physicians’ decision-making, which saves time compared to cumbersome serial data reviews. This study can be extended to realize clinically meaningful prediction models

Predicting Blood glucose levels with Phased LSTM

In this thesis, a phased long short-term memory model is implemented to predict the blood glucose level in patients with type-1 diabetes with a 30-minute forecast. We will continue previous work by extending the standard long short-term memory deep neural network model with a phased LSTM cell. The model is trained on the OhioT1DM dataset from the BGLP challenge. This study will try to solve a standard LSTM model’s bottlenecks by using a phased LSTM model. Furthermore, an attention-based phased LSTM model will be implemented to achieve explainability to this research topic’s models. An attention-based phased LSTM model performs best if trained on a larger dataset. The performance is on par with previously implemented methods for predicting blood glucose levels from the BGLP dataset