Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis.

This research addresses the problem of detecting acute respiratory, urinary tract, and other infectious diseases in elderly nursing home residents using machine learning algorithms. The study analyzes data extracted from multiple vital signs and other contextual information for diagnostic purposes. The daily data collection process encounters sampling constraints due to weekends, holidays, shift changes, staff turnover, and equipment breakdowns, resulting in numerous nulls, repeated readings, outliers, and meaningless values. The short time series generated also pose a challenge to analysis, preventing the extraction of seasonal information or consistent trends. Blind data collection results in most of the data coming from periods when residents are healthy, resulting in excessively imbalanced data. This study proposes a data cleaning process and then builds a mechanism that reproduces the basal activity of the residents to improve the classification of the disease. The results show that the proposed basal module-assisted machine learning techniques allow anticipating diagnostics 2, 3 or 4 days before doctors decide to start treatment with antibiotics, achieving a performance measured by the area-under-the-curve metric of 0.857. The contributions of this work are: (1) a new data cleaning process; (2) the analysis of contextual information to improve data quality; (3) the generation of a baseline measure for relative comparison; and (4) the use of either binary (disease/no disease) or multiclass classification, differentiating among types of infections and showing the advantages of multiclass versus binary classification. From a medical point of view, the anticipated detection of infectious diseases in institutionalized individuals is brand new.

Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices.

BACKGROUND: treating infectious diseases in elderly individuals is difficult; patient referral to emergency services often occurs, since the elderly tend to arrive at consultations with advanced, serious symptoms. AIM: it was hypothesized that anticipating an infectious disease diagnosis by a few days could significantly improve a patient's well-being and reduce the burden on emergency health system services. METHODS: vital signs from residents were taken daily and transferred to a database in the cloud. Classifiers were used to recognize patterns in the spatial domain process of the collected data. Doctors reported their diagnoses when any disease presented. A flexible microservice architecture provided access and functionality to the system. RESULTS: combining two different domains, health and technology, is not easy, but the results are encouraging. The classifiers reported good results; the system has been well accepted by medical personnel and is proving to be cost-effective and a good solution to service disadvantaged areas. In this context, this research found the importance of certain clinical variables in the identification of infectious diseases. CONCLUSIONS: this work explores how to apply mobile communications, cloud services, and machine learning technology, in order to provide efficient tools for medical staff in nursing homes. The scalable architecture can be extended to big data applications that may extract valuable knowledge patterns for medical research.

Predicting the Appearance of Hypotension During Hemodialysis Sessions Using Machine Learning Classifiers.

A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

Algorithm and Distributed Computing for the Internet of Things.

The ongoing generalization of Internet of Things and its presence and application in multiple fields is generating a large amount of data that can be used to extract knowledge, among other purposes. In this context, algorithmic techniques and efficient computer systems provide an opportunity to successfully address efficient data processing and intelligent data analysis. As a result, multiple services can be improved, resources can be optimized and real-world problems of interest can be solved. This Special Issue on Algorithm and Distributed Computing for the Internet of Things gives the opportunity to know recent advances in the application of modern technologies hardware and software to the Internet of Things.

Toward a Robust Multi-Objective Metaheuristic for Solving the Relay Node Placement Problem in Wireless Sensor Networks.

During the last decade, Wireless sensor networks (WSNs) have attracted interest due to the excellent monitoring capabilities offered. However, WSNs present shortcomings, such as energy cost and reliability, which hinder real-world applications. As a solution, Relay Node (RN) deployment strategies could help to improve WSNs. This fact is known as the Relay Node Placement Problem (RNPP), which is an NP-hard optimization problem. This paper proposes to address two Multi-Objective (MO) formulations of the RNPP. The first one optimizes average energy cost and average sensitivity area. The second one optimizes the two previous objectives and network reliability. The authors propose to solve the two problems through a wide range of MO metaheuristics from the three main groups in the field: evolutionary algorithms, swarm intelligence algorithms, and trajectory algorithms. These algorithms are the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Multi-Objective Artificial Bee Colony (MO-ABC), Multi-Objective Firefly Algorithm (MO-FA), Multi-Objective Gravitational Search Algorithm (MO-GSA), and Multi-Objective Variable Neighbourhood Search Algorithm (MO-VNS). The results obtained are statistically analysed to determine if there is a robust metaheuristic to be recommended for solving the RNPP independently of the number of objectives.

Fine-grained parallelization of fitness functions in bioinformatics optimization problems: gene selection for cancer classification and biclustering of gene expression data.

BACKGROUND: Metaheuristics are widely used to solve large combinatorial optimization problems in bioinformatics because of the huge set of possible solutions. Two representative problems are gene selection for cancer classification and biclustering of gene expression data. In most cases, these metaheuristics, as well as other non-linear techniques, apply a fitness function to each possible solution with a size-limited population, and that step involves higher latencies than other parts of the algorithms, which is the reason why the execution time of the applications will mainly depend on the execution time of the fitness function. In addition, it is usual to find floating-point arithmetic formulations for the fitness functions. This way, a careful parallelization of these functions using the reconfigurable hardware technology will accelerate the computation, specially if they are applied in parallel to several solutions of the population. RESULTS: A fine-grained parallelization of two floating-point fitness functions of different complexities and features involved in biclustering of gene expression data and gene selection for cancer classification allowed for obtaining higher speedups and power-reduced computation with regard to usual microprocessors. CONCLUSIONS: The results show better performances using reconfigurable hardware technology instead of usual microprocessors, in computing time and power consumption terms, not only because of the parallelization of the arithmetic operations, but also thanks to the concurrent fitness evaluation for several individuals of the population in the metaheuristic. This is a good basis for building accelerated and low-energy solutions for intensive computing scenarios.

Fast decision algorithms in low-power embedded processors for quality-of-service based connectivity of mobile sensors in heterogeneous wireless sensor networks.

When a mobile wireless sensor is moving along heterogeneous wireless sensor networks, it can be under the coverage of more than one network many times. In these situations, the Vertical Handoff process can happen, where the mobile sensor decides to change its connection from a network to the best network among the available ones according to their quality of service characteristics. A fitness function is used for the handoff decision, being desirable to minimize it. This is an optimization problem which consists of the adjustment of a set of weights for the quality of service. Solving this problem efficiently is relevant to heterogeneous wireless sensor networks in many advanced applications. Numerous works can be found in the literature dealing with the vertical handoff decision, although they all suffer from the same shortfall: a non-comparable efficiency. Therefore, the aim of this work is twofold: first, to develop a fast decision algorithm that explores the entire space of possible combinations of weights, searching that one that minimizes the fitness function; and second, to design and implement a system on chip architecture based on reconfigurable hardware and embedded processors to achieve several goals necessary for competitive mobile terminals: good performance, low power consumption, low economic cost, and small area integration.