NARMAX model as a sparse, interpretable and transparent machine learning approach for big medical and healthcare data analysis

Influenza and influenza-like illnesses are one of the leading causes of death in the world, resulting in heavy losses to individual families and nations. Accurate and timely forecasts of seasonal influenza would therefore crucially important to inform and facilitate public health decision-making for presenting and intervening influenza epidemics. System identification and data-driven modelling approaches play an indispensable role in analyzing and understanding complex processes including medical, healthcare and environmental time series. This paper aims to present a type of sparse, interpretable and transparent (SIT) model, which cannot only be used for future behavior prediction but more importantly for understanding the dependent relationship between the response variables of a system on potential independent variables (also known as input variables or predictors). An ideal candidate for such a SIT representation is the well-known NARMAX (nonlinear autoregressive moving average with exogenous inputs) model, which can be established based on input and output data of the system of interest, and the final refined model is usually simple, parsimonious and easy to interpret. The general framework of the NARMAX model is presented, and the state-of-the-art algorithms for such a SIT model estimation are described. Two case studies are provided to illustrate how well the SIT-NARMAX model can work for medical, healthcare and related data

Human lower extremity joint moment prediction: A wavelet neural network approach

Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics. To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient (ρ). Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE 0.94) compared to FFANN (NRMSE 0.89). A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation

Complex systems modelling for statistical forecasting of winter North Atlantic atmospheric variability: a new approach

Seasonal forecasts of winter North Atlantic atmospheric variability have until recently shown little skill. Here we present a new technique for developing both linear and non‐linear statistical forecasts of the winter North Atlantic Oscillation (NAO) based on complex systems modelling, which has been widely used in a range of fields, but generally not in climate research. Our polynomial NARMAX models demonstrate considerable skill in out‐of‐sample forecasts and their performance is superior to that of linear models, albeit with small sample sizes. Predictors can be readily identified and this has the potential to inform the next generation of dynamical models and models allow for the incorporation of non‐linearities in interactions between predictors and atmospheric variability. In general there is more skill in forecasts developed over a shorter training period from 1980 compared with an equivalent forecast using training data from 1956. This latter point may relate to decreased inherent predictability in the period 1955‐1980, a wider range of available predictors since 1980 and/or reduced data quality in the earlier period and is consistent with previously identified decadal variability of the NAO. A number of predictors such as sea‐level pressure over the Barents Sea, and a clear tropical signal are commonly selected by both linear and polynomial NARMAX models. Tropical signals are modulated by higher latitude boundary conditions. Both approaches can be extended to developing probabilistic forecasts and to other seasons and indices of atmospheric variability such as the East Atlantic pattern and jet stream metrics

Previsão de carga em redes de mapas acoplados

Resumo: As previsões de carga são foco de interesse por parte das operadoras e concessionárias de energia elétrica, pois com base nessas projeções é feita a tomada de decisões no âmbito de planejamento, operação e controle de sistemas de potência, ressaltando sua importância econômica. Ao longo dos anos, diversos métodos foram aplicados ao problema de previsão, alguns inclusive utilizando variáveis externas que influenciam indiretamente nas cargas, porém normalmente não considerando a topologia do sistema. Para unir a previsão por barramento às características da rede elétrica podem-se aplicar conceitos de sistemas dinâmicos como a modelagem por redes de mapas acoplados, um modelo que abrange sistemas espacialmente extensos que possuam dinâmica no tempo e espaço. Ao se modelar um sistema de potência por esta técnica são adicionadas variáveis que especificam características da rede, tais como conexões entre as barras e impedâncias das linhas, teoricamente tornando a previsão de carga elétrica mais precisa que os modelos atuais. Pelo fato de considerar a influência de elementos da rede, para a aplicação da técnica, não é necessário um vasto histórico para a realização das previsões. Uma dinâmica local e superficial do problema é detectada com apenas um instantâneo do sistema, o qual representa um atraso de tempo. Os resultados são comparados com um modelo de regressão linear. Os valores de erro obtidos sugerem que com pouco histórico disponível o método proposto possui um desempenho adequado, porém com a vantagem de realizar um passo da previsão de demanda para todas as barras de um dado sistema em apenas uma iteração, independente do número de barramentos considerados

Computational Fracture Prediction in Steel Moment Frame Structures with the Application of Artificial Neural Networks

Damage to steel moment frames in the 1994 Northridge and 1995 Hyogken-Nanbu earthquakes subsequently motivated intensive research and testing efforts in the US, Japan, and elsewhere on moment frames. Despite extensive past research efforts, one important problem remains unresolved: the degree of panel zone participation that should be permitted in the inelastic seismic response of a steel moment frame. To date, a fundamental computational model has yet to be developed to assess the cyclic rupture performance of moment frames. Without such a model, the aforementioned problem can never be resolved. This dissertation develops an innovative way of predicting cyclic rupture in steel moment frames by employing artificial neural networks. First, finite element analyses of 30 notched round bar models are conducted, and the analytical results in the vicinity of the notch root are extracted to form the inputs for either a single neural network or a competitive neural array. After training the neural networks, the element with the highest potential to initiate a fatigue crack is identified, and the time elapsed up to the crack initiation is predicted and compared with its true synthetic answer. Following similar procedures, a competitive neural array comprising dynamic neural networks is established. Two types of steel-like materials are created so that material identification information can be added to the input vectors for neural networks. The time elapsed by the end of every stage in the fracture progression is evaluated based on the synthetic allocation of the total initiation life assigned to each model. Then, experimental results of eight beam-to-column moment joint specimens tested by four different programs are collected. The history of local field variables in the vicinity of the beam flange - column flange weld is extracted from hierarchical finite element models. Using the dynamic competitive neural array that has been established and trained, the time elapsed to initiate a low cycle fatigue crack is predicted and compared with lab observations. Finally, finite element analyses of newly designed specimens are performed, the strength of their panel zone is identified, and the fatigue performance of the specimens with a weak panel zone is predicted