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Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction [article]

Luchen Liu, Jianhao Shen, Ming Zhang, Zichang Wang, Jian Tang
2018 arXiv   pre-print
In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events.  ...  This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications.  ...  learn joint representations of heterogeneous temporal events for clinical endpoint prediction.  ... 
arXiv:1803.04837v4 fatcat:6uihvdvmnzfglewadzah66edlm

HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records [article]

Hanyang Liu, Sunny S. Lou, Benjamin C. Warner, Derek R. Harford, Thomas Kannampallil, Chenyang Lu
2022 arXiv   pre-print
We propose the Hierarchical burnout Prediction based on Activity Logs (HiPAL), featuring a pre-trained time-dependent activity embedding mechanism tailored for activity logs and a hierarchical predictive  ...  In contrast to prior approaches that exclusively relied on surveys for burnout measurement, our framework directly learns deep workload representations from large-scale clinician activity logs to predict  ...  This idea has been further extended and developed as a class of long-memory sequence models, Temporal Convolutional Networks (TCN), to model large-scale sequential data such as videos and discrete events  ... 
arXiv:2205.11680v1 fatcat:vkswcfuf4fhghcosenizxrqlpa

Multi-Task Prediction of Clinical Outcomes in the Intensive Care Unit using Flexible Multimodal Transformers [article]

Benjamin Shickel, Patrick J. Tighe, Azra Bihorac, Parisa Rashidi
2021 arXiv   pre-print
over multiple future time horizons.  ...  While some recent studies have implemented Transformers for clinical tasks using electronic health records data, they are limited in scope, flexibility, and comprehensiveness.  ...  Events + 0.895 0.843 0.697 0.978 0.983 0.953 0.923 0.892 Continuous Measure- ment Values GRU Resampled Multi- 0.869 0.75 0.686 0.960 0.972 0.938 0.907 0.872 variate Time Series GRU with Resampled Multi  ... 
arXiv:2111.05431v1 fatcat:vyncvuxqkrb5rcxzl2gn65v54y

On the Need of New Approaches for the Novel Problem of Long-Term Prediction over Multi-dimensional Data [chapter]

Rui Henriques, Cláudia Antunes
2012 Studies in Computational Intelligence  
Mining evolving behavior over multi-dimensional structures is increasingly critical for planning tasks.  ...  On one hand, well-studied techniques to mine temporal structures are hardly applicable to multi-dimensional data.  ...  Predictor's efficiency is measured in terms of memory and time cost for both the training and testing stages.  ... 
doi:10.1007/978-3-642-30454-5_9 fatcat:uo3dzaihsnbtxgm5qig2dzsqg4

An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease [chapter]

Chao Che, Cao Xiao, Jian Liang, Bo Jin, Jiayu Zho, Fei Wang
2017 Proceedings of the 2017 SIAM International Conference on Data Mining  
However, it is challenging to evaluate the clinical similarities among patients because of the longitudinality and temporality of their records.  ...  Evaluations on real world patient records demonstrate the promising utility and efficacy of the proposed architecture in personalized predictions.  ...  It is considered the best measure for time series pattern matching across a wide range of application domains [24] .  ... 
doi:10.1137/1.9781611974973.23 dblp:conf/sdm/CheXLJZW17 fatcat:ftmn4o6m4beg7jjgjrx7rtmob4

Modeling asynchronous event sequences with RNNs

Stephen Wu, Sijia Liu, Sunghwan Sohn, Sungrim Moon, Chung-il Wi, Young Juhn, Hongfang Liu
2018 Journal of Biomedical Informatics  
Our evaluation uses the temporally sensitive clinical use case of pediatric asthma, which is a chronic disease with symptoms (and lack thereof) evolving over time.  ...  accountings of relative time.  ...  Clinically, different types of events are not equally valuable for predicting or classifying asthma prognosis.  ... 
doi:10.1016/j.jbi.2018.05.016 pmid:29883623 pmcid:PMC6103779 fatcat:47xpyu2ftbd7te65bleyfuyuwu

Time Series Prediction Using Deep Learning Methods in Healthcare

Mohammad Amin Morid, Olivia R. Liu Sheng, Joseph Dunbar
2022 ACM Transactions on Management Information Systems  
In this paper we systematically reviewed research works that focused on advancing deep neural networks to leverage patient structured time series data for healthcare prediction tasks.  ...  Overall, we found that researchers have contributed to deep time series prediction literature in ten identifiable research streams: DL models, missing value handling, addressing temporal irregularity,  ...  deep time series prediction.  ... 
doi:10.1145/3531326 fatcat:w2mml2pc2fdnpazjkpi2hqpvgq

Attend and Diagnose: Clinical Time Series Analysis using Attention Models [article]

Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias
2017 arXiv   pre-print
With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data.  ...  In this paper, for the first time, we utilize attention models for clinical time-series modeling, thereby dispensing recurrence entirely.  ...  Figure 1 : An overview of the proposed approach for clinical time-series analysis.  ... 
arXiv:1711.03905v2 fatcat:lfnzbgyssfamtdrpqckyvnhmz4

GATE: Graph-Attention Augmented Temporal Neural Network for Medication Recommendation

Chenhao Su, Sheng Gao, Si Li
2020 IEEE Access  
as well as the lack of temporal prediction ability.  ...  For each admission record, a co-occurrence graph is constructed to establish the correlations among clinical events, and then a graph-attention augmented mechanism is used to learn the structural correlations  ...  FOR DATA OF DIFFERENT TEMPORAL LENGTH Since the number of admissions for each patient is different, the length of the time series to be processed should be considered.  ... 
doi:10.1109/access.2020.3007835 fatcat:cybpsj6onrgzpbu3bgwzw7gctu

Bursts of seizures in long-term recordings of human focal epilepsy

Philippa J. Karoly, Ewan S. Nurse, Dean R. Freestone, Hoameng Ung, Mark J. Cook, Ray Boston
2017 Epilepsia  
There was a complicated relationship between bursts and clinical seizures, although bursts were associated with multi-modal distributions of seizure duration, and poorer predictive outcomes.  ...  Methods-Chronic ambulatory intracranial EEG data collected for the purpose of seizure prediction were annotated to identify seizure events.  ...  From a statistical perspective, the presence of temporal clusters implies a structure or memory in event timings, which provides some degree of predictability.  ... 
doi:10.1111/epi.13636 pmid:28084639 pmcid:PMC5339053 fatcat:ulxpeubdhbagvkn4h4whsehjuu

Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning [article]

A. Tuan Nguyen, Hyewon Jeong, Eunho Yang, Sung Ju Hwang
2021 arXiv   pre-print
We validate our model on multiple clinical risk prediction tasks against various deep learning models for time-series prediction, which our model significantly outperforms, without any sign of negative  ...  In the case of time-series prediction tasks, knowledge learned for one task (e.g., predicting the sepsis onset) at a specific timestep may be useful for learning another task (e.g., prediction of mortality  ...  Clinical time-series analysis While our method is generic and applicable to any time-series prediction task, we mainly focus on clinical time-series analysis in this paper.  ... 
arXiv:2006.12777v4 fatcat:6v7ez4qobzdqpciolkry25bw6u

Complexity in psychological self-ratings: implications for research and practice

Merlijn Olthof, Fred Hasselman, Anna Lichtwarck-Aschoff
2020 BMC Medicine  
We examine this basic assumption of a complex systems approach to psychopathology by testing repeated self-ratings for three markers of complexity: memory, the presence of (time-varying) short- and long-range  ...  Promising first steps in this direction, such as research on real-time process monitoring, short-term prediction, and just-in-time interventions, are discussed.  ...  Acknowledgements We would like to thank the members of the Complex Systems Group at Radboud University ( for their input on the design  ... 
doi:10.1186/s12916-020-01727-2 pmid:33028317 fatcat:tmn5x2m4wbafxl2cfxaewszu54

Multi-view Integration Learning for Irregularly-sampled Clinical Time Series [article]

Yurim Lee, Eunji Jun, Heung-Il Suk
2021 arXiv   pre-print
Specifically, we devise a novel multi-integration attention module (MIAM) to extract complex information inherent in irregular time series data.  ...  In this work, we propose a multi-view features integration learning from irregular multivariate time series data by self-attention mechanism in an imputation-free manner.  ...  Figure 1 : 1 The overall framework of the proposed multi-view integration learning method for irregularly-sampled clinical time series.  ... 
arXiv:2101.09986v2 fatcat:u46qtifgbjer3odmjtmu3ynxfq

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping [article]

Michael Moor and Max Horn and Bastian Rieck and Damian Roqueiro and Karsten Borgwardt
2020 arXiv   pre-print
Our deep learning model employs a temporal convolutional network that is embedded in a Multi-task Gaussian Process Adapter framework, making it directly applicable to irregularly-spaced time series data  ...  Meanwhile, despite decades of clinical research, robust biomarkers for sepsis are missing.  ...  Raw, irregularly-spaced time series are provided to the multi-task Gaussian Process (MGP) for each patient.  ... 
arXiv:1902.01659v4 fatcat:24b4iwpih5d5rijci4chl5ybie

Scenes enable a sense of reliving: Implications for autobiographical memory

David C. Rubin, Samantha A. Deffler, Sharda Umanath
2019 Cognition  
We also hypothesized that a lack of layout underlies nonspecific autobiographical memories which are common in aging, future events, and clinical disorders, whereas currently such memories are most commonly  ...  Autobiographical memory has been defined by the phenomenological properties of reliving, vividness, and belief that an event occurred.  ...  We wish to thank Kaitlyn Brodar, Dorthe Berntsen, Rick Hoyle, Christin Ogle, and Lynn Watson for comments on the manuscript. p < .001. Gender is dummy coded, with a score of 1 indicating female.  ... 
doi:10.1016/j.cognition.2018.10.024 pmid:30412854 pmcid:PMC6322930 fatcat:5fu4pjwwjbfwxhjecjn3jcbejm
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