A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set

Rasmy, Laila; Wu, Yonghui; Wang, Ningtao; Geng, Xin; Zheng, W. Jim; Wang, Fei; Wu, Hulin; Xu, Hua; Zhi, Degui

doi:10.1016/j.jbi.2018.06.011

Cited by 92 publications

(60 citation statements)

References 10 publications

(8 reference statements)

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“…Four of the studies compared model performance with social determinants and without social determinants; three showed social determinants significantly improved prediction, others showed improved prediction by addition of age, gender and race. 40,41 The study that showed decreased performance aimed to forecast the pattern of the demand for hemorrhagic stroke healthcare services based on air quality; it is possible that the relationship between specific variable tested and outcome have little direct relationship. 42…”

Section: Resultsmentioning

confidence: 99%

Machine Learning for Integrating Social Determinants in Cardiovascular Disease Prediction Models: A Systematic Review

Zhao

Wood

Mirin

et al. 2020

Preprint

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Background: Cardiovascular disease (CVD) is the number one cause of death worldwide, and CVD burden is increasing in low-resource settings and for lower socioeconomic groups worldwide. Machine learning (ML) algorithms are rapidly being developed and incorporated into clinical practice for CVD prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with addressing the social determinants of cardiovascular outcomes. We sought to review how social determinants of health (SDoH) and variables along their causal pathway are being included in ML algorithms in order to develop best practices for development of future machine learning algorithms that include social determinants. Methods: We conducted a systematic review using five databases (PubMed, Embase, Web of Science, IEEE Xplore and ACM Digital Library). We identified English language articles published from inception to April 10, 2020, which reported on the use of machine learning for cardiovascular disease prediction, that incorporated SDoH and related variables. We included studies that used data from any source or study type. Studies were excluded if they did not include the use of any machine learning algorithm, were developed for non-humans, the outcomes were bio-markers, mediators, surgery or medication of CVD, rehabilitation or mental health outcomes after CVD or cost-effective analysis of CVD, the manuscript was non-English, or was a review or meta-analysis. We also excluded articles presented at conferences as abstracts and the full texts were not obtainable. The study was registered with PROSPERO (CRD42020175466). Findings: Of 2870 articles identified, 96 were eligible for inclusion. Most studies that compared ML and regression showed increased performance of ML, and most studies that compared performance with or without SDoH/related variables showed increased performance with them. The most frequently included SDoH variables were race/ethnicity, income, education and marital status. Studies were largely from North America, Europe and China, limiting the diversity of included populations and variance in social determinants. Interpretation: Findings show that machine learning models, as well as SDoH and related variables, improve CVD prediction model performance. The limited variety of sources and data in studies emphasize that there is opportunity to include more SDoH variables, especially environmental ones, that are known CVD risk factors in machine learning CVD prediction models. Given their flexibility, ML may provide opportunity to incorporate and model the complex nature of social determinants. Such data should be recorded in electronic databases to enable their use.

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Section: Resultsmentioning

confidence: 99%

Machine Learning for Integrating Social Determinants in Cardiovascular Disease Prediction Models: A Systematic Review

Zhao

Wood

Mirin

et al. 2020

Preprint

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“…6 Laila Rasmy et al used recurrent neural networks to predict the risk of heart failure based on a large number of mixed EHR data. 7 Sasank Chilamkurthy et al used natural language processing model to recognize noncontrast head CT scan to identify various head diseases, such as intracranial haemorrhages and cranial fractures et al 8 Kang Zhang et al used transfer learning algorithm and Google's Inception-V3 model to rapidly diagnose many kinds of diseases of eye and children pulmonary diseases. 9 Michael A. Schwemmer et al used a deep neural network decoding framework to classify intracortical recording, and then controlled the motor to help patients complete corresponding actions, according to the classification results.…”

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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“…While there does not exist an intuitive method for risk stratification of patients with penile cancer, there is strong evidence of the potential of machine learning for stratification of patients in other diseases such as heart failure [7][8][9][10], kidney disease [11], and critical care [12][13][14][15][16][17]. Furthermore, machine learning has been shown to be effective for readmission prediction [17][18][19], drug adverse event prediction [20].…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithm for early mortality prediction in patients with advanced penile cancer

Chen

Kudelka

Rosado

et al. 2020

Preprint

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Penile cancer remains a rare cancer with an annual incidence of 1 in 100,000 men in the United States, accounting for 0.4-0.6% of all malignancies. Furthermore, to date there are no predictive models of early mortality in penile cancer. Meanwhile, machine learning has potential to serve as a prognostic tool for patients with advanced disease. We developed a machine learning model for predicting early mortality in penile cancer (survival less than 11 months after initial diagnosis. A cohort of 88 patients with advanced penile cancer was extracted from the Surveillance, Epidemiology and End Results (SEER) program. In the cohort, patients with advanced penile cancer exhibited a median overall survival of 21 months, with the 25th percentila of overall survival being 11 months. We constructed predictive features based on patient demographics, staging, metastasis, lymph node biopsy criteria, and metastatic sites. We trained a multivariate logistic regression model, tuning parameters with respect to regularization, and feature selection criteria. Upon evaluation with 5-fold cross validation, our model achieved 68.2% accuracy with AUC 0.696. Criteria for advanced staging (T4, group stage IV), as well as higher age, white race and squamous cell histology, were the most predictive of early mortality. Tumor size was the strongest negative predictor of early mortality. Our study showcases the first known predictive model for early mortality in patients with advanced penile cancer and should serve as a framework for approaching the clinical problem in future studies. Future work should aim to incorporate other data sources such as genomic and metabolomic data, increase patient counts, incorporate clinical characteristics such as ECOG and RECIST criteria, and assess the performance of the model in a prospective fashion.

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A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set

Cited by 92 publications

References 10 publications

Machine Learning for Integrating Social Determinants in Cardiovascular Disease Prediction Models: A Systematic Review

Machine Learning for Integrating Social Determinants in Cardiovascular Disease Prediction Models: A Systematic Review

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

Machine learning algorithm for early mortality prediction in patients with advanced penile cancer

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