Deep Attention Model for Triage of Emergency Department Patients

Gligorijevic, Djordje; Stojanović, Jelena; Satz, Wayne A.; Stojković, Ivan; Schreyer, Kathrin; Portal, Daniel Del; Obradović, Zoran

doi:10.1137/1.9781611975321.34

Cited by 28 publications

(32 citation statements)

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“…There are other ways of obtaining compact representations s, such as sum, average or max of individual event vectors. However, our experiments, as well as available literature [7,20], demonstrate that such strategies are inferior to using attention machanisms.…”

Section: Temporal Attention Learningmentioning

confidence: 55%

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Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Gligorijevic

Gligorijević

Flores

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Prospective display advertising poses a particular challenge for large advertising platforms. The existing machine learning algorithms are easily biased towards the highly predictable retargeting events that are often non-eligible for the prospective campaigns, thus exhibiting a decline in advertising performance. To that end, efforts are made to design powerful models that can learn from signals of various strength and temporal impact collected about each user from different data sources and provide a good quality and early estimation of users' conversion rates. In this study, we propose a novel deep time-aware approach designed to model sequences of users' activities and capture implicit temporal signals of users' conversion intents. On several real-world datasets, we show that the proposed approach consistently outperforms other, previously proposed approaches by a significant margin while providing interpretability of signal impact to conversion probability. CCS CONCEPTS • Information systems → Display advertising; Web mining; • Computing methodologies → Machine learning.

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Section: Temporal Attention Learningmentioning

confidence: 55%

“…To tap into the explainability of the models we randomly selected a hundred converters and analyzed attentions of their events for the communications advertiser. We compare DTAIN model primarily against the GRU+Attn model, which has shown properties of explainability in the past [7]. From Fig.…”

Section: Attention Analysis and Interpretationmentioning

confidence: 99%

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Gligorijevic

Gligorijević

Flores

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…It is possible that when some clinical events occur simultaneously, it may lead to a sharp increase to risk while each event alone does not cause high risk. In this study, we adopt self-attention mechanism to capture clinical significant event patterns [23].…”

Section: Interpretabilitymentioning

confidence: 99%

An interpretable risk prediction model for healthcare with pattern attention

Kamal

Yin

Qian

et al. 2020

BMC Med Inform Decis Mak

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Background The availability of massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (1) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias. The models’ performance is highly dependent on the imputation accuracy. (2) Lots of existing studies just take Boolean value medical events (e.g. diagnosis code) as inputs, but ignore real value medical events (e.g., lab tests and vital signs), which are more important for acute disease (e.g., sepsis) and mortality prediction. (3) Existing interpretable models can illustrate which medical events are conducive to the output results, but are not able to give contributions of patterns among medical events. Methods In this study, we propose a novel interpretable Pattern Attention model with Value Embedding (PAVE) to predict the risks of certain diseases. PAVE takes the embedding of various medical events, their values and the corresponding occurring time as inputs, leverage self-attention mechanism to attend to meaningful patterns among medical events for risk prediction tasks. Because only the observed values are embedded into vectors, we don’t need to impute the missing values and thus avoids the imputations bias. Moreover, the self-attention mechanism is helpful for the model interpretability, which means the proposed model can output which patterns cause high risks. Results We conduct sepsis onset prediction and mortality prediction experiments on a publicly available dataset MIMIC-III and our proprietary EHR dataset. The experimental results show that PAVE outperforms existing models. Moreover, by analyzing the self-attention weights, our model outputs meaningful medical event patterns related to mortality. Conclusions PAVE learns effective medical event representation by incorporating the values and occurring time, which can improve the risk prediction performance. Moreover, the presented self-attention mechanism can not only capture patients’ health state information, but also output the contributions of various medical event patterns, which pave the way for interpretable clinical risk predictions. Availability The code for this paper is available at: https://github.com/yinchangchang/PAVE.

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“…Claims data, as 1 source of information in this work, were successfully used to address several high-impact health care tasks, 6,7 especially through deep learning models. 8,9 An additional challenge to clinical trial planning is the nuanced nature of clinical trials: no 2 studies are alike, and studies are very difficult to parameterize. Understanding the nuances by learning from free-text data that describe existing medical processes has been a longstanding challenge of research communities, as there are no clear standard methods or tools for analyzing medical text data yet.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Gligorijevic et al proposed a deep learning model that outperformed a hospital's triage staff at predicting triage resource allocation in emergency departments. 9 Finally, since many predictive problems can be formulated as matching problems, deep learning models that perform matching explicitly were developed. For matching of 2 items, siamese network architectures were proposed.…”

Section: Introductionmentioning

confidence: 99%

Optimizing clinical trials recruitment via deep learning

Gligorijević

Gligorijevic

Pavlovski

et al. 2019

Journal of the American Medical Informatics Association

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Objective Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the primary drivers of drug development cost. Materials and Methods To facilitate clinical trials optimization, we propose DeepMatch (DM), a novel approach that builds on top of advances in deep learning. DM is designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials. Results Large-scale evaluation conducted on 2618 studies provides evidence that the proposed ranking-based framework improves the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials. Discussion The extensive experimental section suggests that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators. Conclusion Due to the great significance of the problem at hand, related research efforts are set to shift the paradigm of how investigators are chosen for clinical trials, thereby optimizing and automating them and reducing the cost of new therapies.

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Deep Attention Model for Triage of Emergency Department Patients

Cited by 28 publications

References 0 publications

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

Prospective Modeling of Users for Online Display Advertising via Deep Time-Aware Model

An interpretable risk prediction model for healthcare with pattern attention

Optimizing clinical trials recruitment via deep learning

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