Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks

Esteban, Cristóbal; Staeck, Oliver; Baier, Stephan; Yang, Yinchong; Tresp, Volker

doi:10.1109/ichi.2016.16

Cited by 117 publications

(68 citation statements)

References 7 publications

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“…Here, we considered recurrent neural networks (RNNs), which allow an individual's latent state to be represented by a vector of numbers, thus providing a richer encoding of an individual's "disease state" beyond a single integer (as in the case of discrete state hidden Markov models). In the context of medical applications, RNNs have been used to model electronic health records (Lipton et al, 2016a;Choi et al, 2016;Esteban et al, 2016;Pham et al, 2017;Rajkomar et al, 2018;Suo et al, 2018) and AD disease progression (Nguyen et al, 2018;Ghazi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting Alzheimer’s disease progression using deep recurrent neural networks

Nguyen

et al. 2019

Preprint

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Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed a recurrent neural network (RNN) model and applied it to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al. 2018) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared the performance of the RNN model and three baseline algorithms up to 6 years into the future.Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a "preprocessing" issue, by imputing the missing data using the previous timepoint ("forward filling") or linear interpolation ("linear filling). The third strategy utilized the RNN model itself to fill in the missing data both during training and testing ("model filling"). Our analyses suggest that the RNN with "model filling" was better than baseline algorithms, including support vector machine/regression and linear state space (LSS) models. However, there was no statistical difference between the RNN and LSS for predicting cognition and ventricular volume.Importantly, although the training procedure utilized longitudinal data, we found that the trained RNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data.An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 56 entries as of August 12th, 2019.

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Section: Introductionmentioning

confidence: 99%

Predicting Alzheimer’s disease progression using deep recurrent neural networks

Nguyen

et al. 2019

Preprint

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“…(ii) The complexity associated with the sequential data in engineering design is more than that of the data handled in the existing literature. For example, in Esteban et al (2016), each data in the sequence represents one of the three endpoints that a kidney patient may face after kidney transplantation, i.e., kidney rejection, kidney loss and death of the patient. The inputs are laboratory analysis results obtained at different dates.…”

Section: Using Recurrent Neural Network To Model Sequential Datamentioning

confidence: 99%

Predicting human design decisions with deep recurrent neural network combining static and dynamic data

et al. 2020

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Computational modeling of the human sequential design process and successful prediction of future design decisions are fundamental to design knowledge extraction, transfer, and the development of artificial design agents. However, it is often difficult to obtain designer-related attributes (static data) in design practices, and the research based on combining static and dynamic data (design action sequences) in engineering design is still underexplored. This paper presents an approach that combines both static and dynamic data for human design decision prediction using two different methods. The first method directly combines the sequential design actions with static data in a recurrent neural network (RNN) model, while the second method integrates a feed-forward neural network that handles static data separately, yet in parallel with RNN. This study contributes to the field from three aspects: (a) we developed a method of utilizing designers’ cluster information as a surrogate static feature to combine with a design action sequence in order to tackle the challenge of obtaining designer-related attributes; (b) we devised a method that integrates the function–behavior–structure design process model with the one-hot vectorization in RNN to transform design action data to design process stages where the insights into design thinking can be drawn; (c) to the best of our knowledge, it is the first time that two methods of combining static and dynamic data in RNN are compared, which provides new knowledge about the utility of different combination methods in studying sequential design decisions. The approach is demonstrated in two case studies on solar energy system design. The results indicate that with appropriate kernel models, the RNN with both static and dynamic data outperforms traditional models that only rely on design action sequences, thereby better supporting design research where static features, such as human characteristics, often play an important role.

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“…Lipton et al [24] presented LSTMs for multi-label classification to classify 128 diagnoses given 13 frequently but irregularly sampled clinical measurements. This model was evaluated on a dataset consisting of 10 401 ICU episodes, where each episode consists of multivariate time series of 13 variables.…”

Section: Ehrmentioning

confidence: 99%

“…The aggregated F1-score is 0.809, compared to the averaged F1score of 6 certified cardiologists is 0.751. RNN [32] Mortality prediction IML(GBT mimics SDAE&RNN) [22] RNN [26,33] Ensemble of deep NN [34] Medical event prediction IML(GBT mimics SDAE&RNN) [22] RNN [23,25,26] Ensemble of deep NN [34] Intelligent diagnosis RNN [24,32] Ensemble of deep NN [34] Disease risk prediction CNN [30] SDAE [12] RNN [31,32] Hospital readmission prediction DNN [28] Ensemble of deep NN [34] Pattern and association rule discovery DBM [27] EHR Deidentification RNN [35] Chronological age prediction…”

Section: Ecgmentioning

confidence: 99%

Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey

Yao

Chen

2018

Int. J. Autom. Comput.

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In the recent years, deep learning models have addressed many problems in various fields. Meanwhile, technology development has spawned the big data in healthcare rapidly. Nowadays, application of deep learning to solve the problems in healthcare is a hot research direction. This paper introduces the application of deep learning in healthcare extensively. We focus on 7 application areas of deep learning, which are electronic health records (EHR), electrocardiography (ECG), electroencephalogram (EEG), community healthcare, data from wearable devices, drug analysis and genomics analysis. The scope of this paper does not cover medical image processing since other researchers have already substantially reviewed it. In addition, we analyze the merits and drawbacks of the existing works, analyze the existing challenges, and discuss future trends.

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Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks

Cited by 117 publications

References 7 publications

Predicting Alzheimer’s disease progression using deep recurrent neural networks

Predicting Alzheimer’s disease progression using deep recurrent neural networks

Predicting human design decisions with deep recurrent neural network combining static and dynamic data

Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey

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