Healthcare documentation or nursing documentation as often used in practice is the name of an indispensable part of a patient’s medical documentation, and documentation is an integral part of a nurse’s daily work. Documenting health care in the hospital means recording data on all procedures performed, during the entire health care process for the individual, all for the purpose of systematic monitoring, planning and evaluation of the quality of health care. Nursing documentation serves as a means of communication between the team and is of great importance for the quality and continuity of health care.

ABSTRACT The thermal power plant systems are one of the most complex dynamical systems which must function properly all the time with least amount of costs. More sophisticated monitoring systems with early detection of failures and abnormal behaviour of the power plants are required. The detection of anomalies in historical data using machine learning techniques can lead to system health monitoring. The goal of the research is to build a neural network-based data-driven model that will be used for anomaly detection in selected sections of thermal power plant. Selected sections are Steam Superheaters and Steam Drum. Inputs for neural networks are some of the most important process variables of these sections. All of the inputs are observable from installed monitoring system of thermal power plant, and their anomaly/normal behaviour is recognized by operator's experiences. The results of applying three different types of neural networks (MLP, recurrent and probabilistic) to solve the problem of anomaly detection confirm that neural network-based data-driven modelling has potential to be integrated in real-time health monitoring system of thermal power plant.

— Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. It is very important to timely detect parameter anomalies in real-world running thermal power plant system, which is one of the most complex dynamical systems. Artificial neural networks are one of anomaly detection techniques. This paper presents the Elman recurrent neural network as method to solve the problem of parameter anomaly detection in selected sections of thermal power plant (steam superheaters and steam drum). Inputs for neural networks are some of the most important process variables of these sections. In addition to the implementation of this network for anomaly detection, the effect of key parameter change on anomaly detection results is also shown. Results confirm that recurrent neural network is good approach for anomaly detection problem, especially in real-time industrial applications.

Anomalies are integral part of every system's behavior and sometimes cannot be avoided. Therefore it is very important to timely detect such anomalies in real-world running power plant system. Artificial neural networks are one of anomaly detection techniques. This paper gives a type of neural network (probabilistic) to solve the problem of anomaly detection in selected sections of thermal power plant. Selected sections are steam superheaters and steam drum. Inputs for neural networks are some of the most important process variables of these sections. It is noteworthy that all of the inputs are observable in the real system installed in thermal power plant, some of which represent normal behavior and some anomalies. In addition to the implementation of this network for anomaly detection, the effect of key parameter change on anomaly detection results is also shown. Results confirm that probabilistic neural network is excellent solution for anomaly detection problem, especially in real-time industrial applications.

Evolutionary strategies is a heuristic, guided-search based evolutionary algorithm, widely used as optimization technique for computationally intensive problems. Python is a high-level programming language known for code readability, reusability and the ease of use, making it preferable choice for quick and robust software development, although it is lacking in performance and concurrency area. Emerging technologies such as Anaconda Accelerate Python compiler attempt to combine Python's ease of use with both declarative and explicit parallelization and high performance in computationally intensive problems. In this paper an example of master - slave parallel Evolutionary strategy ES(μ,λ) implementation in Python is given, and its performance on CPU and GPU are analyzed.