RETRACTED ARTICLE: LSTM Model for Prediction of Heart Failure in Big Data

Maragatham, G.; Devi, S. Prasanna

doi:10.1007/s10916-019-1243-3

Cited by 130 publications

(68 citation statements)

References 39 publications

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“…from high throughput molecular profiling may provide a practical foundation for the sort of precision phenotyping aspired to by Triposkiadis et al [42] and for ‘hyper-local analytics’ [47]. Deep learning, based on neural networks, is another aspect of the machine learning and AI revolution likely to find applications in cardiology [56,57].…”

Section: Will the Data Revolution Break The Logjam?mentioning

confidence: 99%

Short-Term Therapies for Treatment of Acute and Advanced Heart Failure—Why so Few Drugs Available in Clinical Use, Why Even Fewer in the Pipeline?

Pollesello

Gal

Béttex

et al. 2019

JCM

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Both acute and advanced heart failure are an increasing threat in term of survival, quality of life and socio-economical burdens. Paradoxically, the use of successful treatments for chronic heart failure can prolong life but—per definition—causes the rise in age of patients experiencing acute decompensations, since nothing at the moment helps avoiding an acute or final stage in the elderly population. To complicate the picture, acute heart failure syndromes are a collection of symptoms, signs and markers, with different aetiologies and different courses, also due to overlapping morbidities and to the plethora of chronic medications. The palette of cardio- and vasoactive drugs used in the hospitalization phase to stabilize the patient’s hemodynamic is scarce and even scarcer is the evidence for the agents commonly used in the practice (e.g., catecholamines). The pipeline in this field is poor and the clinical development chronically unsuccessful. Recent set backs in expected clinical trials for new agents in acute heart failure (AHF) (omecamtiv, serelaxine, ularitide) left a field desolately empty, where only few drugs have been approved for clinical use, for example, levosimendan and nesiritide. In this consensus opinion paper, experts from 26 European countries (Austria, Belgium, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Israel, Italy, The Netherlands, Norway, Poland, Portugal, Russia, Slovenia, Spain, Sweden, Switzerland, Turkey, U.K. and Ukraine) analyse the situation in details also by help of artificial intelligence applied to bibliographic searches, try to distil some lesson-learned to avoid that future projects would make the same mistakes as in the past and recommend how to lead a successful development project in this field in dire need of new agents.

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Section: Will the Data Revolution Break The Logjam?mentioning

confidence: 99%

Short-Term Therapies for Treatment of Acute and Advanced Heart Failure—Why so Few Drugs Available in Clinical Use, Why Even Fewer in the Pipeline?

Pollesello

Gal

Béttex

et al. 2019

JCM

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“…Alternatively, handling vital signs data as time series model inputs without overaggregating may yield improved results. A sliding window approach with real time series data and more powerful machine learning methods would allow for subsequent predictions to be made well after admission and throughout a patient stay [ 31 ]. This alternative approach would address the temporal relationship between the decompensation event (heat failure onset) and the input data used to make the prediction.…”

Section: Discussionmentioning

confidence: 99%

Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset

Essay¹,

Balkan²,

Subbian³

2020

JMIR Med Inform

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Background Heart failure is a leading cause of mortality and morbidity worldwide. Acute heart failure, broadly defined as rapid onset of new or worsening signs and symptoms of heart failure, often requires hospitalization and admission to the intensive care unit (ICU). This acute condition is highly heterogeneous and less well-understood as compared to chronic heart failure. The ICU, through detailed and continuously monitored patient data, provides an opportunity to retrospectively analyze decompensation and heart failure to evaluate physiological states and patient outcomes. Objective The goal of this study is to examine the prevalence of cardiovascular risk factors among those admitted to ICUs and to evaluate combinations of clinical features that are predictive of decompensation events, such as the onset of acute heart failure, using machine learning techniques. To accomplish this objective, we leveraged tele-ICU data from over 200 hospitals across the United States. Methods We evaluated the feasibility of predicting decompensation soon after ICU admission for 26,534 patients admitted without a history of heart failure with specific heart failure risk factors (ie, coronary artery disease, hypertension, and myocardial infarction) and 96,350 patients admitted without risk factors using remotely monitored laboratory, vital signs, and discrete physiological measurements. Multivariate logistic regression and random forest models were applied to predict decompensation and highlight important features from combinations of model inputs from dissimilar data. Results The most prevalent risk factor in our data set was hypertension, although most patients diagnosed with heart failure were admitted to the ICU without a risk factor. The highest heart failure prediction accuracy was 0.951, and the highest area under the receiver operating characteristic curve was 0.9503 with random forest and combined vital signs, laboratory values, and discrete physiological measurements. Random forest feature importance also highlighted combinations of several discrete physiological features and laboratory measures as most indicative of decompensation. Timeline analysis of aggregate vital signs revealed a point of diminishing returns where additional vital signs data did not continue to improve results. Conclusions Heart failure risk factors are common in tele-ICU data, although most patients that are diagnosed with heart failure later in an ICU stay presented without risk factors making a prediction of decompensation critical. Decompensation was predicted with reasonable accuracy using tele-ICU data, and optimal data extraction for time series vital signs data was identified near a 200-minute window size. Overall, results suggest combinations of laboratory measurements and vital signs are viable for early and continuous prediction of patient decompensation.

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“…70 Cardiovascular medicine is an area with a long history of embracing predictive modeling to assess patient risk. 71 Recent work has uncovered methods to predict heart failure 72 and other serious cardiac events in asymptomatic individuals. 73 When combined with personalized prevention strategies, 74,75 these models may positively impact disease incidence and sequela.…”

Section: Future Synergies Between Ai and Precision Medicinementioning

confidence: 99%

Precision Medicine, AI, and the Future of Personalized Health Care

Johnson

Wei

Weeraratne

et al. 2020

Clinical Translational Sci

565

227

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The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

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RETRACTED ARTICLE: LSTM Model for Prediction of Heart Failure in Big Data

Cited by 130 publications

References 39 publications

Short-Term Therapies for Treatment of Acute and Advanced Heart Failure—Why so Few Drugs Available in Clinical Use, Why Even Fewer in the Pipeline?

Short-Term Therapies for Treatment of Acute and Advanced Heart Failure—Why so Few Drugs Available in Clinical Use, Why Even Fewer in the Pipeline?

Decompensation in Critical Care: Early Prediction of Acute Heart Failure Onset

Precision Medicine, AI, and the Future of Personalized Health Care

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