Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology

Reddy, B. Krishna; Delen, Dursun

doi:10.1016/j.compbiomed.2018.08.029

Cited by 127 publications

(73 citation statements)

References 25 publications

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“…Additionally, and current data enrichment techniques are not able to deal well with categorical variables. The reported applications of deep learning to readmission prediction are restricted to a specific disease, that is, lupus patients (Reddy & Delen, ), for which there are long clinical histories per patient accessible through the EHR, so that the abundance of data allows for the training of deep models. Therefore, we focus on the following well‐known machine‐learning classification methods (Artetxe, Beristain, Graña and Besga ; Artetxe, Ayerdi, Graña & Rios ; Artetxe, Ayerdi, Graña, & Beristain ; Artetxe et al, ; Garmendia et al, ; Garmendia et al, ): •Linear discrimination analysis (LDA) and variants quadratic discriminant analysis (QDA), and mixture discriminant analysis (MDA) are the most standard linear models that provide a baseline result from linear discriminant theory, which is well grounded and accepted by the medical researchers. •Support vector machines (SVM) are the most standard machine‐learning algorithm in the biosciences literature used for predictive analysis, we explore both linear and non‐linear approaches, the later using the so‐called kernel trick.…”

Section: Methodsmentioning

confidence: 99%

Section: Classification Methodsmentioning

confidence: 99%

“…Recent works favor the application of predictive machine‐learning approaches, formulating readmission prediction as a binary classification problem (Artetxe et al, ; Ottenbacher et al, ). Instances of classifier models used for readmission prediction are support vector machines (SVM) (Besga et al, ; Futoma et al, ; Zheng et al, ; Cui et al, ) deep learning (Reddy & Delen, ; Xiao et al, ), artificial neural network (Ottenbacher et al, ), and naïve Bayes (Vukicevic et al, ; Shameer et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Readmission prediction in the case of adult patients has been tackled with diverse statistical approaches (Artetxe et al, 2018;Kansagara et al, 2011) such as logistic regression (Garmendia et al, 2017;Ottenbacher et al, 2001) and survival analysis (Garmendia et al, 2019). machines (SVM) (Besga et al, 2015;Futoma et al, 2015;Zheng et al, 2015;Cui et al, 2018) deep learning (Reddy & Delen, 2018;Xiao et al, 2018), artificial neural network (Ottenbacher et al, 2001), and naïve Bayes (Vukicevic et al, 2015;Shameer et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Modelling hospital readmissions under frailty conditions for healthy aging

et al. 2019

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In the current context of an aging population in many developed countries, the issue of healthy aging is at the forefront of the political, scientific, and technological concerns. The frailty accompanying the late years of elderly people (>70 years old) deserves special consideration due to its great economical and personal costs and the workload imposed on the health care system. Hospital readmissions under a short time after hospital discharge are one of the sources of concern, and much effort is being devoted to their prediction for better care of the elder and optimized resource management. In this paper, we consider the prediction of readmissions for patients that are evaluated positively in the frailty scales. The computational experiments are carried out over a gender‐balanced cohort of 645 patients recruited at the University Hospital of Alava. We report machine‐learning prediction results of the readmission before the standard readmission limit of 30 days. We apply an upsampling technique to correct for class imbalance. Results are positive, encouraging further research and the creation of larger cohorts in international efforts.

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Section: Methodsmentioning

confidence: 99%

Section: Classification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling hospital readmissions under frailty conditions for healthy aging

et al. 2019

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“…11,12 In order to realize the purpose of automatically diagnosing diseases using text-based medical data, long-short time memory (LSTM) neural network was proposed. 13,14 The physiological parameters obtained in the clinic are usually a vector rather than image data. Sequential data also play an important role in clinical diagnosis.…”

Section: Introductionmentioning

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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Hybrid Text Feature Modeling for Disease Group Prediction Using Unstructured Physician Notes

Krishnan

Kamath

2020

Lecture Notes in Computer Science

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Existing Clinical Decision Support Systems (CDSSs) largely depend on the availability of structured patient data and Electronic Health Records (EHRs) to aid caregivers. However, in case of hospitals in developing countries, structured patient data formats are not widely adopted, where medical professionals still rely on clinical notes in the form of unstructured text. Such unstructured clinical notes recorded by medical personnel can also be a potential source of rich patient-specific information which can be leveraged to build CDSSs, even for hospitals in developing countries. If such unstructured clinical text can be used, the manual and time-consuming process of EHR generation will no longer be required, with huge person-hours and cost savings. In this article, we propose a generic ICD9 disease group prediction CDSS built on unstructured physician notes modeled using hybrid word embeddings. These word embeddings are used to train a deep neural network for effectively predicting ICD9 disease groups. Experimental evaluation showed that the proposed approach outperformed the state-of-the-art disease group prediction model built on structured EHRs by 15% in terms of AUROC and 40% in terms of AUPRC, thus proving our hypothesis and eliminating dependency on availability of structured patient data.

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Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology

Cited by 127 publications

References 25 publications

Modelling hospital readmissions under frailty conditions for healthy aging

Modelling hospital readmissions under frailty conditions for healthy aging

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

Hybrid Text Feature Modeling for Disease Group Prediction Using Unstructured Physician Notes

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