A long short-temory relation network for real-time prediction of patient-specific ventilator parameters

Accurate prediction of patient-specific ventilator parameters is crucial for optimizing patient-ventilator interaction. Current approaches encounter difficulties in concurrently observing long-term, time-series dependencies and capturing complex, significant features that influence the ventilator treatment process, thereby hindering the achievement of accurate prediction of ventilator parameters. To address these challenges, we propose a novel approach called the long short-term memory relation network (LSTMRnet). Our approach uses a long, short-term memory bank to store rich information and an important feature selection step to extract relevant features related to respiratory parameters. This information is obtained from the prior knowledge of the follow up model. We also concatenate the embeddings of both information types to maintain the joint learning of spatio-temporal features. Our LSTMRnet effectively preserves both time-series and complex spatial-critical feature information, enabling an accurate prediction of ventilator parameters. We extensively validate our approach using the publicly available medical information mart for intensive care (MIMIC-III) dataset and achieve superior results, which can be potentially utilized for ventilator treatment (i.e., sleep apnea-hypopnea syndrome ventilator treatment and intensive care units ventilator treatment

DEPOSITION OF CHEMICALLY-MODIFIED APATITES AS COATINGS FOR BIOMEDICAL APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH

A Comparative Study on the In-vitro Antibacterial Behaviour of Chemically-Modified Calcium Phosphate Coatings

10.1080/10667857.2018.1487173Materials Technology3511-12734-74

A model-based hybrid soft actor-critic deep reinforcement learning algorithm for optimal ventilator settings

Chen S, Qiu X, Tan X, Fang Z, Jin Y. A model-based hybrid soft actor-critic deep reinforcement learning algorithm for optimal ventilator settings. Information Sciences. 2022;611:47-64.A ventilator is a device that mechanically assists in pumping air into the lungs, which is a life-saving supportive therapy in an intensive care unit (ICU). In clinical scenarios, each patient has unique physiological circumstances and specific respiratory diseases, thus requiring individualized ventilator settings. Long-term supervision by experienced clini-cians is essential to perform the task of precisely adjusting ventilator parameters and mak-ing timely modifications. Moreover, a tiny clinical error can result in severe lung injury, induce multi-system organ dysfunction, and increase mortality. To reduce the workload of clinicians and prevent medical errors, machine learning (ML), or more specifically, rein-forcement learning (RL) methods, have been developed to automatically adjust the venti-lator's parameters and select optimal strategies. However, the ventilator settings contain both continuous (e.g., frequency) and discrete parameters (e.g., ventilation mode), making it challenging for conventional RL-based approaches to handle such problems. Meanwhile, it is necessary to develop models with high data efficiency to overcome medical data insuf-ficiency. In this paper, we propose a model-based hybrid soft actor-critic (MHSAC) algo-rithm that is developed based on the classic soft actor-critic (SAC) and model-based policy optimization (MBPO) framework. This algorithm can learn both continuous and dis-crete policies according to the current and predictive state of patient's physiological infor-mation with high data efficiency. Results reveal that our proposed model significantly outperforms the baseline models, achieving superior efficiency and high accuracy in the OpenAI Gym simulation environment. Our proposed model is capable of resolving mixed action space problems, enhancing data efficiency, and accelerating convergence, which can generate practical optimal ventilator settings, minimize possible medical errors, and provide clinical decision support.(c) 2022 Elsevier Inc. All rights reserved

FAPR: An Adaptive Approach to Link Failure Recovery in SDN with High Speed and Low Interruption Rate

Link failures are the most common type of fault in software-defined networking (SDN), which is an extremely crucial aspect of SDN fault tolerance. Existing strategies include proactive and reactive approaches. Proactive schemes pre-deploy backup paths for fast recovery but may exhaust resources, while reactive schemes calculate paths upon failure, resulting in longer recovery but better outcomes. This paper proposes a single link failure recovery strategy that combines these two schemes, termed as flow-aware pro-reactive (FAPR), with the aim of achieving high-speed recovery while ensuring high-quality backup paths. Specifically, the controller adopts pro-VLAN to install backup paths for each link into switches, and precalculates multiple backup paths for each link in the controller before any link failures. In case of a link failure, pro-VLAN, i.e., a method based on the proactive approach, is initially utilized for swift recovery automatically without the involvement of the controller. Simultaneously, the controller analyzes types of affected flows based on the transport layer data, obtains several key network indicators of the backup paths, and then selects the most suitable path for different flows on the basis of the current network view. Simulation results and theoretical analysis show that the recovery time of the FAPR scheme reduces by over 65% compared with the reactive scheme. The interruption rate of flows after fault recovery is reduced by 20% and 50% compared with the reactive and proactive schemes, respectively. In addition, due to the principle of pro-VLAN, the number of backup flow rules required is at most 85% less than that required by the proactive scheme. In conclusion, FAPR promises the highest failure recovery speed and the lowest interruption rate among three methods, and helps to improve the quality of network services

Deposition of Substituted Apatite Coatings at Different Coating Patterns via Drop-on-Demand Micro-Dispensing Technique

10.1080/10667857.2018.1456782Materials Technology336406-41

A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support

Qiu X, Tan X, Li Q, Chen S, Ru Y, Jin Y. A latent batch-constrained deep reinforcement learning approach for precision dosing clinical decision support. Knowledge-Based Systems . 2022;237: 107689.Precise prescription of medication dosing is crucial to patients, especially among Intensive Care Unit (ICU) patients. However, improper administration of some sensitive therapeutic medications (e.g., heparin) might place patients at unneeded risk, even life-threatening. Numerous factors such as a patient's clinical phenotype, genotype, and environmental factors will affect the heparin dose response. As a result, it is challenging to prescribe the optimal initial dose of heparin. In this paper, an individualized dosing policy is proposed to determine the optimal initial dose and minimize the risk of mis-dosing, as well as preventing the patients from late complications associated with medications dosing. A latent batch-constrained deep reinforcement learning (RL) algorithm is proposed to guarantee the safety of the medication recommendation system. The agent can observe a latent representation of patents and generate medication dosing solutions in successive and limited action spaces. The individualized dosing policy aims to reduce the extrapolation errors in the off-policy algorithms, by evaluating over-dosing and under-dosing of heparin in patients. Our results evaluated on Medical Information Mart for Intensive Care III (MIMIC-III) database demonstrate that the latent batch-constrained RL algorithm can work effectively from the retrospective data, showing promise to be used in future medication dosing policies.(C)& nbsp;2021 Elsevier B.V. All rights reserved

Model-Based Off-Policy Deep Reinforcement Learning With Model-Embedding

Tan X, Qu C, Xiong J, Zhang J, Qiu X, Jin Y. Model-Based Off-Policy Deep Reinforcement Learning With Model-Embedding. IEEE Transactions on Emerging Topics in Computational Intelligence. 2024:1-13.Model-based reinforcement learning (MBRL) has shown its advantages in sample efficiency over model-free reinforcement learning (MFRL) by leveraging control-based domain knowledge. Despite the impressive results it achieves, MBRL is still outperformed by MFRL due to the lack of unlimited interactions with the environment. While imaginary data can be generated by imagining the trajectories of future states, a trade-off between the usage of data generation and the influence of model bias remains to be resolved. In this paper, we propose a simple and elegant off-policy model-based deep reinforcement learning algorithm with a model embedded in the framework of probabilistic reinforcement learning, called MEMB. To balance the sample-efficiency and model bias, we exploit both real and imaginary data in training. In particular, we embed the model in the policy update and learn value functions from the real data set. We also provide a theoretical analysis of MEMB with the Lipschitz continuity assumption on the model and policy, proving the reliability of the short-term imaginary rollout. Finally, we evaluate MEMB on several benchmarks and demonstrate that our algorithm can achieve state-of-the-art performance

Sparse-attentive meta temporal point process for clinical decision support

Ru Y, Qiu X, Tan X, Chen B, Gao Y, Jin Y. Sparse-attentive meta temporal point process for clinical decision support. Neurocomputing . 2022;485:114-123.In the study of clinical decision-making, prediction of future clinical events of patients has become an important task, especially for variant disease predictions. In previous studies, the disease prediction prob-lems are considered as binary classification based on the patients' electronic health records (EHRs), which lack the capacity to predict multiple types of diseases. In this paper, we propose a method which can pre-dict both the patients' disease types among various candidate diseases and patients' next hospital visit time. The next hospital visit time is crucial for medical experts in making decisions, because it reflects the onset time information of disease and provides sufficient information on the severity of the disease. Our proposed method is implemented based on the point process framework, which utilizes meta-learning to gain the prior knowledge of the individual patient's clinical data with context information, adopts sparse-attention to determine the importance of past major clinical events, and simulates the intensity of clinical events through Hawkes process to predict the types of diseases diagnosed by the doc-tor and patient' next hospital visit time. The experimental data are extracted from the public datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-II) and Medical Information Mart for Intensive Care (MIMIC-III). Compared with the baseline time series models, our proposed method has achieved superior results, with a higher F1-score (66.67%) and a lower root-mean-square error (RMSE) (6.69) on the test set, which proves the effectiveness of the proposed method. We further study the self-attention mechanism based on Transformer and sparse-attention methods to demonstrate the valid-ity of our model. Our proposed method provides empirical evidence of its ability in facilitating the decision-making process of clinicians, which can be potentially utilized as effective clinical decision sup-port tools to better improve the quality of medical services and reduce medical errors.(c) 2022 Elsevier B.V. All rights reserved

Anisotropic coating of a-plane oriented hydroxyapatite via a drop-on-demand micro-dispensing technique

10.1080/10667857.2018.1469261Materials Technology3511-12713-71