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Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
[article]
2018
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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]
2012
Studies in Computational Intelligence
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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]
2017
Proceedings of the 2017 SIAM International Conference on Data Mining
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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
2018
Journal of Biomedical Informatics
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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
2022
ACM Transactions on Management Information Systems
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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]
2017
arXiv
pre-print
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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
2020
IEEE Access
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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
2017
Epilepsia
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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]
2021
arXiv
pre-print
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2021 pre-print arXiv:2006.12777v2 fatcat:tugn2evlpjbs5cjub2bgxoqe3yOther Versions
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
2020
BMC Medicine
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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 (https://www.ru.nl/bsi/research/group-pages/complex-systems-group/) 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]
2021
arXiv
pre-print
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2021 pre-print arXiv:2101.09986v1 fatcat:3czlymxa6bh45etu5tqajwwge4
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]
2020
arXiv
pre-print
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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
2019
Cognition
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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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