Relevant bibliographies by topics / Time-Aware LSTM

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Time-Aware LSTM'

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Published: 29 March 2025

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Journal articles on the topic "Time-Aware LSTM"

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Cheng, Lin, Yuliang Shi, Kun Zhang, Xinjun Wang, and Zhiyong Chen. "GGATB-LSTM: Grouping and Global Attention-based Time-aware Bidirectional LSTM Medical Treatment Behavior Prediction." ACM Transactions on Knowledge Discovery from Data 15, no. 3 (May 2021): 1–16. http://dx.doi.org/10.1145/3441454.

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In China, with the continuous development of national health insurance policies, more and more people have joined the health insurance. How to accurately predict patients future medical treatment behavior becomes a hotspot issue. The biggest challenge in this issue is how to improve the prediction performance by modeling health insurance data with high-dimensional time characteristics. At present, most of the research is to solve this issue by using Recurrent Neural Networks (RNNs) to construct an overall prediction model for the medical visit sequences. However, RNNs can not effectively solve the long-term dependence, and RNNs ignores the importance of time interval of the medical visit sequence. Additionally, the global model may lose some important content to different groups. In order to solve these problems, we propose a Grouping and Global Attention based Time-aware Bidirectional Long Short-Term Memory (GGATB-LSTM) model to achieve medical treatment behavior prediction. The model first constructs a heterogeneous information network based on health insurance data, and uses a tensor CANDECOMP/PARAFAC decomposition method to achieve similarity grouping. In terms of group prediction, a global attention and time factor are introduced to extend the bidirectional LSTM. Finally, the proposed model is evaluated by using real dataset, and conclude that GGATB-LSTM is better than other methods.
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Wiessner, Paul, Grigor Bezirganyan, Sana Sellami, Richard Chbeir, and Hans-Joachim Bungartz. "Uncertainty-Aware Time Series Anomaly Detection." Future Internet 16, no. 11 (October 31, 2024): 403. http://dx.doi.org/10.3390/fi16110403.

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Traditional anomaly detection methods in time series data often struggle with inherent uncertainties like noise and missing values. Indeed, current approaches mostly focus on quantifying epistemic uncertainty and ignore data-dependent uncertainty. However, consideration of noise in data is important as it may have the potential to lead to more robust detection of anomalies and a better capability of distinguishing between real anomalies and anomalous patterns provoked by noise. In this paper, we propose LSTMAE-UQ (Long Short-Term Memory Autoencoder with Aleatoric and Epistemic Uncertainty Quantification), a novel approach that incorporates both aleatoric (data noise) and epistemic (model uncertainty) uncertainties for more robust anomaly detection. The model combines the strengths of LSTM networks for capturing complex time series relationships and autoencoders for unsupervised anomaly detection and quantifies uncertainties based on the Bayesian posterior approximation method Monte Carlo (MC) Dropout, enabling a deeper understanding of noise recognition. Our experimental results across different real-world datasets show that consideration of uncertainty effectively increases the robustness to noise and point outliers, making predictions more reliable for longer periodic sequential data.
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Yadulla, Akhila Reddy, Mounica Yenugula, Vinay Kumar Kasula, Bhargavi Konda, Santosh Reddy Addula, and Sarath Babu Rakki. "A time-aware LSTM model for detecting criminal activities in blockchain transactions." International Journal of Communication and Information Technology 4, no. 2 (July 1, 2023): 33–39. https://doi.org/10.33545/2707661x.2023.v4.i2a.108.

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Yang, Xuan, and James A. Esquivel. "Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence." Tsinghua Science and Technology 29, no. 1 (February 2024): 185–96. http://dx.doi.org/10.26599/tst.2023.9010025.

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Chen, Long, Zhiyao Tian, Shunhua Zhou, Quanmei Gong, and Honggui Di. "Attitude deviation prediction of shield tunneling machine using Time-Aware LSTM networks." Transportation Geotechnics 45 (March 2024): 101195. http://dx.doi.org/10.1016/j.trgeo.2024.101195.

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Chen, Jie, Chang Liu, Jiawu Xie, Jie An, and Nan Huang. "Time–Frequency Mask-Aware Bidirectional LSTM: A Deep Learning Approach for Underwater Acoustic Signal Separation." Sensors 22, no. 15 (July 26, 2022): 5598. http://dx.doi.org/10.3390/s22155598.

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Underwater acoustic signal separation is a key technique for underwater communications. The existing methods are mostly model-based, and cannot accurately characterize the practical underwater acoustic communication environment. They are only suitable for binary signal separation and cannot handle multivariate signal separation. However, recurrent neural networks (RNNs) show a powerful ability to extract the features of temporal sequences. Inspired by this, in this paper, we present a data-driven approach for underwater acoustic signal separation using deep learning technology. We use a bidirectional long short-term memory (Bi-LSTM) approach to explore the features of a time–frequency (T-F) mask, and propose a T-F-mask-aware Bi-LSTM for signal separation. Taking advantage of the sparseness of the T-F image, the designed Bi-LSTM network is able to extract the discriminative features for separation, which further improves the separation performance. In particular, this method breaks through the limitations of the existing methods and not only achieves good results in multivariate separation but also effectively separates signals when they are mixed with 40 dB Gaussian noise signals. The experimental results show that this method can achieve a 97% guarantee ratio (PSR), and the average similarity coefficient of the multivariate signal separation is stable above 0.8 under high noise conditions. It should be noted that our model can only handle known signals such as test signals for calibration.
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Zhang, Jinkai, Wenming Ma, En Zhang, and Xuchen Xia. "Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation." Sensors 24, no. 4 (February 11, 2024): 1185. http://dx.doi.org/10.3390/s24041185.

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Technological progress has led to significant advancements in Earth observation and satellite systems. However, some services associated with remote sensing face issues related to timeliness and relevance, which affect the application of remote sensing resources in various fields and disciplines. The challenge now is to help end-users make precise decisions and recommendations for relevant resources that meet the demands of their specific domains from the vast array of remote sensing resources available. In this study, we propose a remote sensing resource service recommendation model that incorporates a time-aware dual LSTM neural network with similarity graph learning. We further use the stream push technology to enhance the model. We first construct interaction history behavior sequences based on users’ resource search history. Then, we establish a category similarity relationship graph structure based on the cosine similarity matrix between remote sensing resource categories. Next, we use LSTM to represent historical sequences and Graph Convolutional Networks (GCN) to represent graph structures. We construct similarity relationship sequences by combining historical sequences to explore exact similarity relationships using LSTM. We embed user IDs to model users’ unique characteristics. By implementing three modeling approaches, we can achieve precise recommendations for remote sensing services. Finally, we conduct experiments to evaluate our methods using three datasets, and the experimental results show that our method outperforms the state-of-the-art algorithms.
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Zheng, Ruixuan, Yanping Bao, Lihua Zhao, and Lidong Xing. "Prediction of steelmaking process variables using K-medoids and a time-aware LSTM network." Heliyon 10, no. 12 (June 2024): e32901. http://dx.doi.org/10.1016/j.heliyon.2024.e32901.

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Subapriya Vijayakumar and Rajaprakash Singaravelu. "Time Aware Long Short-Term Memory and Kronecker Gated Intelligent Transportation for Smart Car Parking." Journal of Advanced Research in Applied Sciences and Engineering Technology 44, no. 1 (April 26, 2024): 134–50. http://dx.doi.org/10.37934/araset.44.1.134150.

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Technology desires to improve quality of life and impart citizen’s health as well as happiness. The concept of Internet of Things (IoT) refers to smart world where prevailing objects are said to be embedded and hence interact with each other (i.e., between objects and human beings) to achieve an objective. In the period of IoT as well as smart city, there is requirement for Intelligent Transport System-based (ITS) ingenious smart parking or car parking space prediction (CPSP) for more feasible cities. With the increase in population and mushroom growth in vehicles are bringing about several distinct economic as well as environmental issues. One of pivotal ones is optimal parking space identification. To address on this problem, in this work, Time-aware Long Short-Term Memory and Kronecker product Gated Recurrent Unit (TLSTM-KGRU) for smart parking in internet of transportation things is proposed. The TLSTM-KGRU method is split into two sections. In the first section, smart parking occupancy is derived using Time-aware Long Short-Term Memory (Time-aware LSTM) for Kuala Lumpur Convention Centre car parking sensor dataset. Following which the resultant smart car occupancy results are subjected to Linear Interpolations and Kronecker product Gated Recurrent Unit for predicting smart parking. When compared against other predictive methods such as SGRU-LSTM and CPSP using DELM, our experimental outcomes denote that TLSTM-KGRU method has improved performance for smart parking occupancy forecast as it not only enhances sensitivity and specificity but also reduces prediction time with minimum delay.
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Gui, Zhipeng, Yunzeng Sun, Le Yang, Dehua Peng, Fa Li, Huayi Wu, Chi Guo, Wenfei Guo, and Jianya Gong. "LSI-LSTM: An attention-aware LSTM for real-time driving destination prediction by considering location semantics and location importance of trajectory points." Neurocomputing 440 (June 2021): 72–88. http://dx.doi.org/10.1016/j.neucom.2021.01.067.

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Dissertations / Theses on the topic "Time-Aware LSTM"

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Cissoko, Mamadou Ben Hamidou. "Adaptive time-aware LSTM for predicting and interpreting ICU patient trajectories from irregular data." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD012.

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En médecine prédictive personnalisée, modéliser avec précision la maladie et les processus de soins d'un patient est crucial en raison des dépendances temporelles à long terme inhérentes. Cependant, les dossiers de santé électroniques (DSE) se composent souvent de données épisodiques et irrégulières, issues des admissions hospitalières sporadiques, créant des schémas uniques pour chaque séjour hospitalier.Par conséquent, la construction d'un modèle prédictif personnalisé nécessite une considération attentive de ces facteurs pour capturer avec précision le parcours de santé du patient et aider à la prise de décision clinique.LSTM sont efficaces pour traiter les données séquentielles comme les DSE, mais ils présentent deux limitations majeures : l'incapacité à interpréter les résultats des prédictions et à prendre en compte des intervalles de temps irréguliers entre les événements consécutifs. Pour surmonter ces limitations, nous introduisons de nouveaux réseaux neuronaux à mémoire dynamique profonde appelés Multi-Way Adaptive et Adaptive Multi-Way Interpretable Time-Aware LSTM (MWTA-LSTM etAMITA), conçus pour les données séquentielles collectées de manière irrégulière.L'objectif principal des deux modèles est de tirer parti des dossiers médicaux pour mémoriser les trajectoires de maladie et les processus de soins, estimer les états de maladie actuels et prédire les risques futurs, offrant ainsi un haut niveau de précision et de pouvoir prédictif
In personalized predictive medicine, accurately modeling a patient's illness and care processes is crucial due to the inherent long-term temporal dependencies. However, Electronic Health Records (EHRs) often consist of episodic and irregularly timed data, stemming from sporadic hospital admissions, which create unique patterns for each hospital stay. Consequently, constructing a personalized predictive model necessitates careful consideration of these factors to accurately capture the patient's health journey and assist in clinical decision-making. LSTM networks are effective for handling sequential data like EHRs, but they face two significant limitations: the inability to interpret prediction results and to take into account irregular time intervals between consecutive events. To address limitations, we introduce novel deep dynamic memory neural networks called Multi-Way Adaptive and Adaptive Multi-Way Interpretable Time-Aware LSTM (MWTA-LSTM and AMITA) designed for irregularly collected sequential data. The primary objective of both models is to leverage medical records to memorize illness trajectories and care processes, estimate current illness states, and predict future risks, thereby providing a high level of precision and predictive power
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Gaddari, Abdelhamid. "Analysis and Prediction of Patient Pathways in the Context of Supplemental Health Insurance." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10299.

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Ce travail de thèse s'inscrit dans la catégorie de la recherche en informatique de santé, en particulier l'analyse et la prédiction des parcours patients, qui sont les séquences des actes médicaux consommés par les patients au fil du temps. Notre objectif est de proposer une approche innovante pour l'exploitation des données de parcours de soins afin de réaliser non seulement une classification binaire, mais aussi multi-label. Nous concevons également une nouvelle approche de vectorisation et représentation sémantique exclusivement pour le domaine médical français, qui permettra d'exploiter un autre aspect des parcours patients afin d'améliorer la performance prédictive de notre approche proposée. Notre recherche s'inscrit dans le cadre des travaux de CEGEDIM ASSURANCES, une business unit du groupe CEGEDIM qui fournit des logiciels et des services pour les secteurs de l'assurance maladie complémentaire et de la gestion des risques en France. En analysant le parcours de soins et en utilisant l'approche que nous proposons, nous pouvons extraire des informations précieuses et identifier des patterns dans les parcours médicaux des patients afin de prédire des événements médicaux potentiels ou la consommation médicale à venir. Cela permettra aux assureurs de prévoir les futures demandes de soins de santé et donc de négocier de meilleurs tarifs avec les prestataires de soins de santé, ce qui permettra une planification financière précise, des modèles de tarification équitables et une réduction des coûts. En outre, ça permettra aux assureurs privés de concevoir des plans de santé personnalisés qui répondent aux besoins spécifiques des patients, en veillant à ce qu'ils reçoivent les soins adéquats au bon moment afin de prévenir la progression de la maladie. Enfin, l'offre de programmes de soins préventifs et de produits et services de santé personnalisés renforce les relations avec les clients, améliore leur satisfaction et réduit l'attrition. Dans ce travail, nous visons à développer une approche permettant d'analyser les parcours patients et de prédire les événements médicaux ou les traitements à venir, sur la base d'un large portefeuille de remboursements. Pour atteindre cet objectif, nous proposons tout d'abord un nouveau modèle basé sur les LSTM qui tient compte de la notion temporelle et qui permet de réaliser de la classification binaire et multi-label. Le modèle proposé est ensuite étendu par un autre aspect des parcours de soins, à savoir des informations supplémentaires provenant d'un clustering flou du même portefeuille. Nous démontrons que l'approche proposée est plus performante que les méthodes traditionnelles et d'apprentissage profond dans la prédiction médicale binaire et multi-label. Par la suite, nous améliorons la performance prédictive de l'approche proposée en exploitant un aspect supplémentaire des parcours patients, qui consiste en une description textuelle détaillée des traitements médicaux consommés. Ceci est réalisé grâce à la conception de F-BERTMed, une nouvelle approche de vectorisation et de représentation sémantique de phrases pour le domaine médical français. Celle-ci présente des avantages significatifs par rapport aux méthodes de l'état de l'art du traitement automatique du langage naturel (TAL). F-BERTMed est basé sur FlauBERT, dont le pré-entraînement utilisant la tâche MLM (Modélisation Masqué du Langage) a été étendu sur des textes médicaux français avant d'être fine-tuné sur les tâches NLI (Inférence du Langage Naturel) et STS (Similarité Sémantique Textuelle). Nous démontrons enfin que l'utilisation de F-BERTMed pour générer une nouvelle représentation des parcours patients améliore les performances prédictives de notre modèle proposé pour les tâches de classification binaire et multi-label
This thesis work falls into the category of healthcare informatics research, specifically the analysis and prediction of patients’ care pathways, which are the sequences of medical services consumed by patients over time. Our aim is to propose an innovative approach for the exploitation of patient care trajectory data in order to achieve not only binary, but also multi-label classification. We also design a new sentence embedding framework exclusively for the french medical domain, which will harness another view of the patients’ care pathways in order to enhance the predictive performance of our proposed approach. Our research is part of the work of CEGEDIM ASSURANCES, a business unit of the CEGEDIM Group that provides software and services for the french supplementary healthcare insurance and risk management sectors. By analyzing the patient care pathway and leveraging our proposed approach, we can extract valuable insights and identify patterns within the patients’ medical journeys in order to predict potential medical events or upcoming medical consumption. This will allow insurers to forecast future healthcare claims and therefore negotiate better rates with healthcare providers, allowing for accurate financial planning, fair pricing models and cost reductions. Furthermore, it enables private healthcare insurers to design personalized health plans that meet the specific needs of the patients, ensuring they receive the right care at the right time to prevent disease progression. Ultimately, offering preventive care programs and customized health products and services enhances client relationship, improving their satisfaction and reducing churn. In this work, we aim to develop an approach to analyze patient care pathways and predict medical events or upcoming treatments, based on a large portfolio of reimbursed medical records. To achieve this goal, we first propose a new time-aware long-short term memory based framework that can achieve both binary and multi-label classification. The proposed framework is then extended with another aspect of the patient healthcare trajectories, namely additional information from a fuzzy clustering of the same portfolio. We show that our proposed approach outperforms traditional and deep learning methods in medical binary and multi-label prediction. Subsequently, we enhance the predictive performance of our proposed approach by exploiting a supplementary view of the patient care pathways that consists of a detailed textual description of the consumed medical treatments. This is achieved through the design of F-BERTMed, a new sentence embedding framework for the french medical domain that presents significant advantages over the natural language processing (NLP) state-of-the-art methods. F-BERTMed is based on FlauBERT, whose pre-training using MLM (Masked Language Modeling) was extended on french medical texts before being fine-tuned on NLI (Natural Language Inference) and STS (Semantic Textual Similarity) tasks. We finally show that using F-BERTMed to generate a new representation of the patient care pathways enhances the performance of our proposed medical predictive framework on both binary and multi-label classification tasks
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Book chapters on the topic "Time-Aware LSTM"

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Lee, Jeong Min, and Milos Hauskrecht. "Recent Context-Aware LSTM for Clinical Event Time-Series Prediction." In Artificial Intelligence in Medicine, 13–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21642-9_3.

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Sahu, Parth, S. Raghavan, K. Chandrasekaran, and Divakarla Usha. "Time-Aware Online QoS Prediction Using LSTM and Non-negative Matrix Factorization." In Algorithms for Intelligent Systems, 369–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2248-9_35.

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Nguyen, An, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, and Bjoern Eskofier. "Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring." In Lecture Notes in Business Information Processing, 112–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72693-5_9.

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Mishra, Abhinav. "Public Opinion Regarding COVID-19 Analyzed for Emotion Using Deep Learning Techniques." In Demystifying Emerging Trends in Machine Learning, 350–62. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815305395125020034.

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As a result of the COVID-19 epidemic, many individuals are experiencing extreme worry, dread, and other difficult emotions. Since coronavirus immunizations were first introduced, people's reactions have gotten more nuanced and varied. In this study, we will use deep learning methods to decode their emotions. Twitter provides a glimpse into what is popular and what is on people's minds at any given time, and social media is presently the finest means to convey sentiments and emotions. Our goal while conducting this study was to have a better grasp of how different groups of individuals feel about vaccinations. The research period for this research's tweet was from December 21st to July 21st. Of the most talked-about vaccines that have recently been available in various regions of the world were the subject of several tweets. The term Valence Aware Sentiment Dictionary An NLP program called Believed (VADER) was used to examine people's sentiments on certain vaccines. We were better able to see the big picture after categorizing the collected attitudes into positive (33.96 percent), negative (17.55 percent), and neutral (48.49 percent) camps. We also included into our study an examination of the tweets' chronology, given that attitudes changed over time. The performance of the forecasting models was evaluated using an RNN-oriented design that included bidirectional LSTM (Bi-LSTM) as well as long short-term memory (LSTM); LSTM attained an accuracy of 90.59% as well as BiLSTM of 90.83%. Additional performance metrics, such as Precision, F1-score, as well as a matrix of confusion, were used to confirm our hypotheses as well as outcomes. The findings of this research provide credence to efforts to eradicate coronavirus across the globe by expanding our knowledge of public opinion on COVID-19 vaccines.
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Conference papers on the topic "Time-Aware LSTM"

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Baytas, Inci M., Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, and Jiayu Zhou. "Patient Subtyping via Time-Aware LSTM Networks." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3097983.3097997.

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Zhang, Yuan, Xi Yang, Julie Ivy, and Min Chi. "ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/607.

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Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. Long-Short Term Memory (LSTM) is an effective model to handle sequential data, such as EHRs, but it encounters two major limitations when applied to EHRs: it is unable to interpret the prediction results and it ignores the irregular time intervals between consecutive events. To tackle these limitations, we propose an attention-based time-aware LSTM Networks (ATTAIN), to improve the interpretability of LSTM and to identify the critical previous events for current diagnosis by modeling the inherent time irregularity. We validate ATTAIN on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs. Our results demonstrate that the proposed framework outperforms the state-of-the-art models such as RETAIN and T-LSTM. Also, the generated interpretative time-aware attention weights shed some lights on the progression behaviors of septic shock.
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Liu, Lucas Jing, Victor Ortiz-Soriano, Javier A. Neyra, and Jin Chen. "KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9994931.

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Chen, Zhiqi, Yao Wang, Gadi Wollstein, Maria de los Angeles Ramos-Cadena, Joel Schuman, and Hiroshi Ishikawa. "Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM." In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020. http://dx.doi.org/10.1109/isbi45749.2020.9098614.

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Navarin, Nicolo, Beatrice Vincenzi, Mirko Polato, and Alessandro Sperduti. "LSTM networks for data-aware remaining time prediction of business process instances." In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. http://dx.doi.org/10.1109/ssci.2017.8285184.

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Yin, Changchang, Sayoko E. Moroi, and Ping Zhang. "Predicting Age-Related Macular Degeneration Progression with Contrastive Attention and Time-Aware LSTM." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3539163.

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Yamamura, Tatsuya, Ismail Arai, Masatoshi Kakiuchi, Arata Endo, and Kazutoshi Fujikawa. "Bus Ridership Prediction with Time Section, Weather, and Ridership Trend Aware Multiple LSTM." In 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). IEEE, 2023. http://dx.doi.org/10.1109/percomworkshops56833.2023.10150218.

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Chen, Dehua, Liping Zhang, Ming Zuo, and Qiao Pan. "Risk Assessment Model for Diabetic Cardiovascular Disease Via Personality and Time-Aware LSTM Network." In International Conference on Biotechnology and Biomedicine. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0012032600003633.

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Abdelhamid, Gaddari, Elghazel Haytham, Jaziri Rakia, Hacid Mohand-Saïd, and Comble Pierre-Henri. "A New Time-Aware LSTM based Framework for Multi-label Classification on Healthcare Data." In 2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA). IEEE, 2023. http://dx.doi.org/10.1109/aiccsa59173.2023.10479260.

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Perera, Dilruk, and Roger Zimmermann. "LSTM Networks for Online Cross-Network Recommendations." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/532.

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Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance. Existing models (1) fail to capture complex non-linear relationships in user interactions, and (2) are designed for offline settings hence, not updated online with incoming interactions to capture the dynamics in the recommender environment. We propose a novel multi-layered Long Short-Term Memory (LSTM) network based online solution to mitigate these issues. The proposed model contains three main extensions to the standard LSTM: First, an attention gated mechanism to capture long-term user preference changes. Second, a higher order interaction layer to alleviate data sparsity. Third, time aware LSTM cell gates to capture irregular time intervals between user interactions. We illustrate our solution using auxiliary information from Twitter and Google Plus to improve recommendations on YouTube. Extensive experiments show that the proposed model consistently outperforms state-of-the-art in terms of accuracy, diversity and novelty.
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