Complex Valued Deep Neural Networks for Nonlinear System Modeling

López-Pacheco, Mario; Yu, Wen

doi:10.1007/s11063-021-10644-1

Cited by 8 publications

(4 citation statements)

References 45 publications

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“…The SVM classifier can work well with small data sets and be robust to outliers. While the FCNN can learn complex patterns and nonlinear relationships from data and be flexible to different architectures [ 51 ]. These classifiers use the decision tree ensemble method, kernel method, and approximation theorem, respectively, for classification, and they are well contrasted with each other.…”

Section: Methodsmentioning

confidence: 99%

Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach

Wang,

Yu,

Liu

et al. 2023

JMIR Ment Health

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Background After the COVID-19 pandemic, the conflict between limited mental health care resources and the rapidly growing number of patients has become more pronounced. It is necessary for psychologists to borrow artificial intelligence (AI)–based methods to analyze patients’ satisfaction with drug treatment for those undergoing mental illness treatment. Objective Our goal was to construct highly accurate and transferable models for predicting the satisfaction of patients with mental illness with medication by analyzing their own experiences and comments related to medication intake. Methods We extracted 41,851 reviews in 20 categories of disorders related to mental illnesses from a large public data set of 161,297 reviews in 16,950 illness categories. To discover a more optimal structure of the natural language processing models, we proposed the Unified Interchangeable Model Fusion to decompose the state-of-the-art Bidirectional Encoder Representations from Transformers (BERT), support vector machine, and random forest (RF) models into 2 modules, the encoder and the classifier, and then reconstruct fused “encoder+classifer” models to accurately evaluate patients’ satisfaction. The fused models were divided into 2 categories in terms of model structures, traditional machine learning–based models and neural network–based models. A new loss function was proposed for those neural network–based models to overcome overfitting and data imbalance. Finally, we fine-tuned the fused models and evaluated their performance comprehensively in terms of F1-score, accuracy, κ coefficient, and training time using 10-fold cross-validation. Results Through extensive experiments, the transformer bidirectional encoder+RF model outperformed the state-of-the-art BERT, MentalBERT, and other fused models. It became the optimal model for predicting the patients’ satisfaction with drug treatment. It achieved an average graded F1-score of 0.872, an accuracy of 0.873, and a κ coefficient of 0.806. This model is suitable for high-standard users with sufficient computing resources. Alternatively, it turned out that the word-embedding encoder+RF model showed relatively good performance with an average graded F1-score of 0.801, an accuracy of 0.812, and a κ coefficient of 0.695 but with much less training time. It can be deployed in environments with limited computing resources. Conclusions We analyzed the performance of support vector machine, RF, BERT, MentalBERT, and all fused models and identified the optimal models for different clinical scenarios. The findings can serve as evidence to support that the natural language processing methods can effectively assist psychologists in evaluating the satisfaction of patients with drug treatment programs and provide precise and standardized solutions. The Unified Interchangeable Model Fusion provides a different perspective on building AI models in mental health and has the potential to fuse the strengths of different components of the models into a single model, which may contribute to the development of AI in mental health.

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Section: Methodsmentioning

confidence: 99%

Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach

Wang,

Yu,

Liu

et al. 2023

JMIR Ment Health

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“…Our study introduces a Stochastic Differential Equation (SDE) model to accurately depict an individual's health progression and likelihood of survival at any given time. Current models, which employ either weight vectors (Lopez-Pacheco et al 2022;Somers et al 2009) or pairwise interaction matrices (Somers et al 2009), tend to suffer from low predictive accuracy. The SDE approach is a burgeoning field in aging research.…”

Section: Introductionmentioning

confidence: 99%

“…The Weighted Network Model (WNM) creates trajectories for 10 'deficits' and forecasts survival (Zhbannikov et al 2017), yet it simplifies these 'deficits' to binary variables, reducing its complexity. The Joint Model (JM) presents a combined framework for analyzing both longitudinal and survival data dynamics (Lopez-Pacheco et al 2022). Additionally, the Stochastic Process Model (SPM) employs a Stochastic Differential Equation…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Longitudinal Modeling of Aging Health and Predicting Mortality Rates

Guan

2024

AAAI

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Aging is a complex stochastic process that affects healthy functioning through various pathways. In contrast to the more commonly used cross-sectional methods, our research focuses on longitudinal modeling of aging, a less explored but crucial area. We have developed a Stochastic Differential Equation (SDE) model, at the forefront of aging research, designed to accurately forecast the health trajectories and survival rates of individuals. This model adeptly delineates the connections between different health indicators and provides clear, interpretable results. Our approach utilizes the SDE framework to encapsulate the inherent uncertainty in the aging process. Moreover, it incorporates a Recurrent Neural Network (RNN) to integrate past health data into future health projections. We plan to train and test our model using a comprehensive dataset tailored for aging studies. This model is not only computationally cost-effective but also highly relevant in assessing health risks in older populations, particularly for those at high risk. It can serve as an essential tool in anticipating and preparing for challenges like infectious disease outbreaks. Overall, our research aims to improve health equity and global health security significantly, offering substantial benefits to public health and deepening our understanding of the aging process.

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“…The structure of the model (linear or non-linear), which may be difficult to define due to the complexity and time variability of the system and its environment, is not necessary in this case. The dominant approach, in this case, is the use of neural networks, ranging from simple perceptron networks (shallow and deep) 29 – 33 , convolutional networks (CNN) 34 , 35 to recurrent networks (RNN) 36 – 39 , mainly Long Short Term Memory (LSTM) 40 – 44 and Gated Recurrent Unit (GRU) 45 networks.…”

Section: Introductionmentioning

confidence: 99%

Linear matrix genetic programming as a tool for data-driven black-box control-oriented modeling in conditions of limited access to training data

Praczyk,

Szymkowiak

2024

Sci Rep

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In the paper, a new evolutionary technique called Linear Matrix Genetic Programming (LMGP) is proposed. It is a matrix extension of Linear Genetic Programming and its application is data-driven black-box control-oriented modeling in conditions of limited access to training data. In LMGP, the model is in the form of an evolutionarily-shaped program which is a sequence of matrix operations. Since the program has a hidden state, running it for a sequence of input data has a similar effect to using well-known recurrent neural networks such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU). To verify the effectiveness of the LMGP, it was compared with different types of neural networks. The task of all the compared techniques was to reproduce the behavior of a nonlinear model of an underwater vehicle. The results of the comparative tests are reported in the paper and they show that the LMGP can quickly find an effective and very simple solution to the given problem. Moreover, a detailed comparison of models, generated by LMGP and LSTM/GRU, revealed that the former are up to four times more accurate than the latter in reproducing vehicle behavior.

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Complex Valued Deep Neural Networks for Nonlinear System Modeling

Cited by 8 publications

References 45 publications

Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach

Predicting Patients' Satisfaction With Mental Health Drug Treatment Using Their Reviews: Unified Interchangeable Model Fusion Approach

A Novel Approach for Longitudinal Modeling of Aging Health and Predicting Mortality Rates

Linear matrix genetic programming as a tool for data-driven black-box control-oriented modeling in conditions of limited access to training data

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