14 research outputs found
A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom
Multimodal medical data fusion has emerged as a transformative approach in
smart healthcare, enabling a comprehensive understanding of patient health and
personalized treatment plans. In this paper, a journey from data to information
to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart
healthcare. We present a comprehensive review of multimodal medical data fusion
focused on the integration of various data modalities. The review explores
different approaches such as feature selection, rule-based systems, machine
learning, deep learning, and natural language processing, for fusing and
analyzing multimodal data. This paper also highlights the challenges associated
with multimodal fusion in healthcare. By synthesizing the reviewed frameworks
and theories, it proposes a generic framework for multimodal medical data
fusion that aligns with the DIKW model. Moreover, it discusses future
directions related to the four pillars of healthcare: Predictive, Preventive,
Personalized, and Participatory approaches. The components of the comprehensive
survey presented in this paper form the foundation for more successful
implementation of multimodal fusion in smart healthcare. Our findings can guide
researchers and practitioners in leveraging the power of multimodal fusion with
the state-of-the-art approaches to revolutionize healthcare and improve patient
outcomes.Comment: This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
Machine Unlearning is an emerging field that addresses data privacy issues by
enabling the removal of private or irrelevant data from the Machine Learning
process. Challenges related to privacy and model efficiency arise from the use
of outdated, private, and irrelevant data. These issues compromise both the
accuracy and the computational efficiency of models in both Machine Learning
and Unlearning. To mitigate these challenges, we introduce a novel framework,
Attention-based Machine Unlearning using Federated Reinforcement Learning
(FRAMU). This framework incorporates adaptive learning mechanisms, privacy
preservation techniques, and optimization strategies, making it a well-rounded
solution for handling various data sources, either single-modality or
multi-modality, while maintaining accuracy and privacy. FRAMU's strength lies
in its adaptability to fluctuating data landscapes, its ability to unlearn
outdated, private, or irrelevant data, and its support for continual model
evolution without compromising privacy. Our experiments, conducted on both
single-modality and multi-modality datasets, revealed that FRAMU significantly
outperformed baseline models. Additional assessments of convergence behavior
and optimization strategies further validate the framework's utility in
federated learning applications. Overall, FRAMU advances Machine Unlearning by
offering a robust, privacy-preserving solution that optimizes model performance
while also addressing key challenges in dynamic data environments.Comment: This work has been submitted to the IEEE for possible publication.
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AI-Driven Patient Monitoring with Multi-Agent Deep Reinforcement Learning
Effective patient monitoring is vital for timely interventions and improved
healthcare outcomes. Traditional monitoring systems often struggle to handle
complex, dynamic environments with fluctuating vital signs, leading to delays
in identifying critical conditions. To address this challenge, we propose a
novel AI-driven patient monitoring framework using multi-agent deep
reinforcement learning (DRL). Our approach deploys multiple learning agents,
each dedicated to monitoring a specific physiological feature, such as heart
rate, respiration, and temperature. These agents interact with a generic
healthcare monitoring environment, learn the patients' behavior patterns, and
make informed decisions to alert the corresponding Medical Emergency Teams
(METs) based on the level of emergency estimated. In this study, we evaluate
the performance of the proposed multi-agent DRL framework using real-world
physiological and motion data from two datasets: PPG-DaLiA and WESAD. We
compare the results with several baseline models, including Q-Learning, PPO,
Actor-Critic, Double DQN, and DDPG, as well as monitoring frameworks like
WISEML and CA-MAQL. Our experiments demonstrate that the proposed DRL approach
outperforms all other baseline models, achieving more accurate monitoring of
patient's vital signs. Furthermore, we conduct hyperparameter optimization to
fine-tune the learning process of each agent. By optimizing hyperparameters, we
enhance the learning rate and discount factor, thereby improving the agents'
overall performance in monitoring patient health status. Our AI-driven patient
monitoring system offers several advantages over traditional methods, including
the ability to handle complex and uncertain environments, adapt to varying
patient conditions, and make real-time decisions without external supervision.Comment: This work has been submitted to the IEEE for possible publication.
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longer be accessible. arXiv admin note: text overlap with arXiv:2309.1057
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
Artificial Intelligence techniques can be used to classify a patient's
physical activities and predict vital signs for remote patient monitoring.
Regression analysis based on non-linear models like deep learning models has
limited explainability due to its black-box nature. This can require
decision-makers to make blind leaps of faith based on non-linear model results,
especially in healthcare applications. In non-invasive monitoring, patient data
from tracking sensors and their predisposing clinical attributes act as input
features for predicting future vital signs. Explaining the contributions of
various features to the overall output of the monitoring application is
critical for a clinician's decision-making. In this study, an Explainable AI
for Quantitative analysis (QXAI) framework is proposed with post-hoc model
explainability and intrinsic explainability for regression and classification
tasks in a supervised learning approach. This was achieved by utilizing the
Shapley values concept and incorporating attention mechanisms in deep learning
models. We adopted the artificial neural networks (ANN) and attention-based
Bidirectional LSTM (BiLSTM) models for the prediction of heart rate and
classification of physical activities based on sensor data. The deep learning
models achieved state-of-the-art results in both prediction and classification
tasks. Global explanation and local explanation were conducted on input data to
understand the feature contribution of various patient data. The proposed QXAI
framework was evaluated using PPG-DaLiA data to predict heart rate and mobile
health (MHEALTH) data to classify physical activities based on sensor data.
Monte Carlo approximation was applied to the framework to overcome the time
complexity and high computation power requirements required for Shapley value
calculations.Comment: This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring
Reinforcement learning has been increasingly applied in monitoring
applications because of its ability to learn from previous experiences and can
make adaptive decisions. However, existing machine learning-based health
monitoring applications are mostly supervised learning algorithms, trained on
labels and they cannot make adaptive decisions in an uncertain complex
environment. This study proposes a novel and generic system, predictive deep
reinforcement learning (PDRL) with multiple RL agents in a time series
forecasting environment. The proposed generic framework accommodates virtual
Deep Q Network (DQN) agents to monitor predicted future states of a complex
environment with a well-defined reward policy so that the agent learns existing
knowledge while maximizing their rewards. In the evaluation process of the
proposed framework, three DRL agents were deployed to monitor a subject's
future heart rate, respiration, and temperature predicted using a BiLSTM model.
With each iteration, the three agents were able to learn the associated
patterns and their cumulative rewards gradually increased. It outperformed the
baseline models for all three monitoring agents. The proposed PDRL framework is
able to achieve state-of-the-art performance in the time series forecasting
process. The proposed DRL agents and deep learning model in the PDRL framework
are customized to implement the transfer learning in other forecasting
applications like traffic and weather and monitor their states. The PDRL
framework is able to learn the future states of the traffic and weather
forecasting and the cumulative rewards are gradually increasing over each
episode.Comment: This work has been submitted to the Springer for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion
Federated Learning (FL) is currently one of the most popular technologies in
the field of Artificial Intelligence (AI) due to its collaborative learning and
ability to preserve client privacy. However, it faces challenges such as
non-identically and non-independently distributed (non-IID) and data with
imbalanced labels among local clients. To address these limitations, the
research community has explored various approaches such as using local model
parameters, federated generative adversarial learning, and federated
representation learning. In our study, we propose a novel Clustered FedStack
framework based on the previously published Stacked Federated Learning
(FedStack) framework. The local clients send their model predictions and output
layer weights to a server, which then builds a robust global model. This global
model clusters the local clients based on their output layer weights using a
clustering mechanism. We adopt three clustering mechanisms, namely K-Means,
Agglomerative, and Gaussian Mixture Models, into the framework and evaluate
their performance. We use Bayesian Information Criterion (BIC) with the maximum
likelihood function to determine the number of clusters. The Clustered FedStack
models outperform baseline models with clustering mechanisms. To estimate the
convergence of our proposed framework, we use Cyclical learning rates.Comment: This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
AI enabled RPM for mental health facility
Mental healthcare is one of the prominent parts of the healthcare industry with alarming concerns related to patients' depression, stress leading to self-harm and threat to fellow patients and medical staff. To provide a therapeutic environment for both patients and staff, aggressive or agitated patients need to be monitored remotely and track their vital signs and physical activities continuously. Remote patient monitoring (RPM) using non-invasive technology could enable contactless monitoring of acutely ill patients in a mental health facility. Enabling the RPM system with AI unlocks a predictive environment in which future vital signs of the patients can be forecasted. This paper discusses an AI-enabled RPM system framework with a non-invasive digital technology RFID using its in-built NCS mechanism to retrieve vital signs and physical actions of patients. Based on the retrieved time series data, future vital signs of patients for the upcoming 3 hours and classify their physical actions into 10 labelled physical activities. This framework assists to avoid any unforeseen clinical disasters and take precautionary measures with medical intervention at right time. A case study of a middle-aged PTSD patient treated with the AI-enabled RPM system is demonstrated in this study.</p
Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities
Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward
AI enabled RPM for Mental Health Facility
Mental healthcare is one of the prominent parts of the healthcare industry with alarming concerns related to patients' depression, stress leading to self-harm and threat to fellow patients and medical staff. To provide a therapeutic environment for both patients and staff, aggressive or agitated patients need to be monitored remotely and track their vital signs and physical activities continuously. Remote patient monitoring (RPM) using non-invasive technology could enable contactless monitoring of acutely ill patients in a mental health facility. Enabling the RPM system with AI unlocks a predictive environment in which future vital signs of the patients can be forecasted. This paper discusses an AI-enabled RPM system framework with a non-invasive digital technology RFID using its in-built NCS mechanism to retrieve vital signs and physical actions of patients. Based on the retrieved time series data, future vital signs of patients for the upcoming 3 hours and classify their physical actions into 10 labelled physical activities. This framework assists to avoid any unforeseen clinical disasters and take precautionary measures with medical intervention at right time. A case study of a middle-aged PTSD patient treated with the AI-enabled RPM system is demonstrated in this study
Exploring the Landscape of Machine Unlearning: A Survey and Taxonomy
Machine unlearning (MU) is a field that is gaining increasing attention due
to the need to remove or modify predictions made by machine learning (ML)
models. While training models have become more efficient and accurate, the
importance of unlearning previously learned information has become increasingly
significant in fields such as privacy, security, and fairness. This paper
presents a comprehensive survey of MU, covering current state-of-the-art
techniques and approaches, including data deletion, perturbation, and model
updates. In addition, commonly used metrics and datasets are also presented.
The paper also highlights the challenges that need to be addressed, including
attack sophistication, standardization, transferability, interpretability,
training data, and resource constraints. The contributions of this paper
include discussions about the potential benefits of MU and its future
directions in Natural Language Processing, Computer vision, and Recommender
Systems. Additionally, the paper emphasizes the need for researchers and
practitioners to continue exploring and refining unlearning techniques to
ensure that ML models can adapt to changing circumstances while maintaining
user trust. The importance of unlearning is further highlighted in making
Artificial Intelligence (AI) more trustworthy and transparent, especially with
the increasing importance of AI in various domains that involve large amounts
of personal user dataComment: This work has been submitted to the IEEE for possible publication.
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longer be accessibl