No abstract

Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.

Abstract. We present GP-HH, a framework for evolving local-search 3-SAT heuristics based on GP. The aim is to obtain "disposable" heuristics which are evolved and used for a specific subset of instances of a problem. We test the heuristics evolved by GP-HH against well-known local-search heuristics on a variety of benchmark SAT problems. Results are very encouraging.

This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a good solution is found. The framework is tested on some of the most widely used benchmarks in the field of exam timetabling and compared with the best state-of-the-art approaches. Results show that the framework is very competitive with other constructive techniques, and did outperform other hyper-heuristic frameworks on many occasions.

Question-answering has become one of the most popular information retrieval applications. Despite that most questionanswering systems try to improve the user experience and the technology used in finding relevant results, many difficulties are still faced because of the continuous increase in the amount of web content. Questions Classification (QC) plays an important role in question-answering systems, with one of the major tasks in the enhancement of the classification process being the identification of questions types. A broad range of QC approaches has been proposed with the aim of helping to find a solution for the classification problems; most of these are approaches based on bag-of-words or dictionaries. In this research, we present an analysis of the different type of questions based on their grammatical structure. We identify different patterns and use machine learning algorithms to classify them. A framework is proposed for question classification using a grammar-based approach (GQCC) which exploits the structure of the questions. Our findings indicate that using syntactic categories related to different domain-specific types of Common Nouns, Numeral Numbers and Proper Nouns enable the machine learning algorithms to better differentiate between different question types. The paper presents a wide range of experiments the results show that the GQCC using J48 classifier has outperformed other classification methods with 90.1% accuracy.

Current mortality prediction models and scoring systems for intensive care unit patients are generally usable only after at least 24 or 48 h of admission, as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of intensive care unit admission. This study aims to investigate how early hospital mortality can be predicted for intensive care unit patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 h of intensive care unit admission. The results showed that the discrimination power of the machine-learning classification methods after 6 h of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 h of admission.

Classification is one of the most researched problems in machine learning, since the 1960s a myriad of different techniques have been proposed. The purpose of a classification algorithm, also known as a 'classifier', is to identify what class, or category an observation belongs to. In many real-world scenarios, datasets tend to suffer from class imbalance, where the number of observations belonging to one class greatly outnumbers that of the observations belonging to other classes. Class imbalance has been shown to hinder the performance of classifiers, and several techniques have been developed to improve the performance of imbalanced classifiers. Using a cost matrix is one such technique for dealing with class imbalance, however it requires a matrix to be either pre-defined, or manually optimized. This paper proposes an approach for automatically generating optimized cost matrices using a genetic algorithm. The genetic algorithm can generate matrices for classification problems with any number of classes, and is easy to tailor towards specific use-cases. The proposed approach is compared against unoptimized classifiers and alternative cost matrix optimization techniques using a variety of datasets. In addition to this, storage system failure prediction datasets are provided by Seagate UK, the potential of these datasets is investigated.

In this paper, we introduce a new approach that computes the shortest-reliable end-to-end paths for centrally controlled networks like software-defined networks (SDNs). The proposed method aims to find the correlation between the routing mechanism and reliability with the purpose of decreasing the required time of backup path installation through reducing the number of required rules at the moment of failure towards guarantee the fast restoration of the affected path, hence leading to the reduction of the overhead on SDN network controller and the probability of the loss of packets. We also investigate the correlation between the network topology and its reliability and demonstrate the benefits from this relation through experiments using well known SDN network simulation tools.