Modeling asynchronous event sequences with RNNs

Wu, Stephen; Liu, Sijia; Sohn, Sunghwan; Moon, Sungrim; Wi, Chung‐Il; Juhn, Young J.; Liu, Hongfang

doi:10.1016/j.jbi.2018.05.016

Cited by 41 publications

(35 citation statements)

References 37 publications

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“…We review several limitations of deep learning tools illustrated with specific examples from prior work: prediction of postoperative bleeding following colorectal surgery (CRS-POB), 20 prediction of childhood asthma diagnosis, remission, and reoccurance (A-DRR), 21 prediction of time to first treatment for patients diagnosed with chronic lymphocytic leukemia (CLL-TFT), 22 prediction of ICU mortality (ICU-M) using a publically available dataset, 21,23 and finally prediction of opioid mis-use (Opioid). 24 Each study was approved by Mayo Clinic’s Institutional Review Board.…”

Section: Introductionmentioning

confidence: 99%

Deep learning and alternative learning strategies for retrospective real-world clinical data

et al. 2019

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In recent years, there is increasing enthusiasm in the healthcare research community for artificial intelligence to provide big data analytics and augment decision making. One of the prime reasons for this is the enormous impact of deep learning for utilization of complex healthcare big data. Although deep learning is a powerful analytic tool for the complex data contained in electronic health records (EHRs), there are also limitations which can make the choice of deep learning inferior in some healthcare applications. In this paper, we give a brief overview of the limitations of deep learning illustrated through case studies done over the years aiming to promote the consideration of alternative analytic strategies for healthcare.

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Section: Introductionmentioning

confidence: 99%

Deep learning and alternative learning strategies for retrospective real-world clinical data

et al. 2019

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“…However, we speculate that human physiological features are not independent, it is not sufficient to consider only one parameter which is the reason for the better performance of LSTM that can analyze joint characteristics. 38,40 Therefore, LSTM is a better choice for dealing with physiological parameter sequences with complex intrinsic relationships, similar to the recognition of semantic environments or voice signals. Further, the above studies on the auxiliary diagnostic system show good performance in the test set, but do not provide the basis for model classification data.…”

Section: Discussionmentioning

confidence: 99%

<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>

Liu¹,

Zhang²,

Zhao³

et al. 2020

DMSO

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Introduction: The research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques. Methods: Here, we established an auxiliary diagnostic tool based on attention deep learning algorithm to diagnostic hyperlipemia and automatically predict the corresponding diagnostic markers using hematological parameters. In this paper, we not only demonstrated the ability of the proposed model to automatically diagnose diseases using text-based medical data, such as physiological parameters, but also demonstrated its ability to forecast disease diagnostic markers. Human physiological parameters are used as input to the model, and the doctor's diagnosis results as an output. Through the attention layer, the degree of attention of the model to different physiological parameters can be obtained, that is, the model provides a diagnostic basis. Results: It achieved 94% ACC, 97.48% AUC, 96% sensitivity and 92% specificity with the test dataset. All the above samples are drawn from clinical practice. Moreover, the model predicted the diagnostic markers of hyperlipidemia by the attention mechanism, and the results were fully agreeable to the golden criteria. Discussion: The auxiliary diagnosis system proposed in this paper not only achieves the accurate and robust performance, and can be used for the preliminary diagnosis of patients, but also showing its great potential to discover new diagnostic markers. Therefore, it not only can improve the efficiency of clinical diagnosis but also shorten the research period of researching a diagnosis basis to an extent. It has a positive significance to the development of the medical diagnosis level.

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“…Wu et al [103] introduced event sequences and their properties (evenness, synchronicity, and co-cardinality) for the classification of pediatric asthma (chronic disease). They also determined how the inverse of these properties, i.e.…”

Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using 'HIPAA Safe Harbor' technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

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Modeling asynchronous event sequences with RNNs

Cited by 41 publications

References 37 publications

Deep learning and alternative learning strategies for retrospective real-world clinical data

Deep learning and alternative learning strategies for retrospective real-world clinical data

<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

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