U.S. flag

An official website of the United States government

Here’s how you know
Here’s how you know

**Official websites use .gov**
A **.gov** website belongs to an official government organization in the United States.

**Secure .gov websites use HTTPS**
A **lock** ( A locked padlock) or **https://** means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Search Query:
			MAIN MENU

																				
              Health Topics
								All Health Topics A-Z
					
													
		

		Asthma


				

															Heart-Healthy Living


				

															High Blood Pressure


				

															Sickle Cell Disease


				

															Sleep Apnea


				

															Information & Resources on COVID-19
																				
              Health Education
								Education Programs and Initiatives
					
													
		

		The Heart Truth®


				

															Learn More Breathe Better®


				

															Blood Diseases and Disorders Education Program


				

															Publications and Resources
																				
              The Science
								Research Focus Areas
					
													
		

		Blood Disorders and Blood Safety


				

															Sleep Science and Sleep Disorders


				

															Lung Diseases


				

															Health Disparities and Inequities


				

															Heart and Vascular Diseases


				

															Precision Medicine Activities


				

															Obesity, Nutrition, and Physical Activity


				

															Population and Epidemiology Studies


				

															Women’s Health


				

															Research Topics


				

															Clinical Trials


				

															All Science A-Z
																				
              Grants and Training
								Grants and Training Home


				

															Policies and Guidelines


				

															Funding Opportunities and Contacts


				

															Training and Career Development
																				
              News and Events
								News and Events Home
					
													
		

		Email Alerts


				

															All News
					
													
		

		NHLBI News


				

															NHLBI in the Press


				

															Research Features


				

															All Events
					
													
		

		Past Events


				

															Upcoming Events
																				
              About NHLBI
								About NHLBI Home
					
													
		

		Mission and Strategic Vision


				

															Leadership


				

															Divisions, Offices and Centers


				

															Advisory Committees


				

															Budget and Legislative Information


				

															Jobs and Working at the NHLBI


				

															Contact and FAQs


				

															NIH Sleep Research Plan
						
						HOME
		Search Text: Home/Grants and Training/Funding Opportunities and Contacts/< Back ToNHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research/Heart Failure Challenge: Selected Solutions

Heart Failure Challenge: Selected Solutions
    
        **Development and Validation of a Machine Learning-based Phenomapping Tool to Identify Acute Decompensated Heart Failure Patients with Diuretic Resistance**

Ambarish Pandey, M.D., M.S.C.S. (team leader); Matthew W. Segar, M.D., M.S.; Duwyanne Willett, M.D.; Wilson Tang, M.D.;  Javed Butler, M.D., M.P.H., M.B.A.

This machine learning-based semi-supervised sub-phenotyping schema incorporates multiple disease-related factors among patients with acute decompensated heart failure (ADHF) to derive three phenogroups according to their diuretic efficiency. The phenomapping approach has identified key biological factors that underlie diuretic resistance and provide a tool to identify such patients for further phenotyping with proteomic and metabolomic analysis. The phenomapping tool will also help identify patients with a high probability of diuretic resistance for enrollment in clinical trials aimed at testing novel approaches to decongestion and symptom relief among patients with ADHF.

**A Deep Learning Classifier for Assessing Diastolic Dysfunction Severity in HFpEF**

Partho P Sengupta, M.D., D.M. (team leader); Ambarish Pandey, M.D., M.S.C.S.; Nobuyuki Kagiyama, M.D., Ph.D.; Matthew W. Segar, M.D., M.S.; Naveena Yanamala, Ph.D.

This is a rigorously validated and well-documented semi-supervised deep learning Solution for phenotyping patients with heart failure with preserved ejection fraction (HFpEF). The resulting classifier automatically integrates multidimensional echocardiographic data to grade the severity of diastolic dysfunction and segregates patients into subgroups with predominant cardiac versus extracardiac phenotypic patterns of HFpEF. The deep learning classifier offers a viable solution to overcome the limitations of the existing clinical standards for accurately characterizing the burden of diastolic dysfunction in HFpEF.

**Intelligently Characterizing Patient Hemodynamic Phenotypes for Advanced Heart Failure in the ESCAPE Trial Using Learned Multi Valued Decision Diagrams**

Josephine Lamp (team leader); Yuxin Wu; Steven Lamp; Lu Feng, Ph.D.; Sula Mazimba, M.D., M.P.H.

This machine learning Solution presents an open source risk stratification and phenotyping tool for hemodynamic phenotypes in advanced heart failure. The tool provides a hemodynamic risk score that takes in single point of care measures as input, and uses a diverse set of features (including invasive and composite hemodynamics and other clinical measures) to return a score of 1 (< 10% chance) to 5 (> 40% chance), indicating the probability of a specified outcome, such as mortality, rehospitalization, or readmission in 30 days.

**LEveraging Aggregated Data From Open Sources in Acute Heart Failure (LEAD-AHF)**

David Kao, M.D. (team leader); Pardeep Jhund, M.B., Ch.B., Ph.D.; Jennifer L. Hall, Ph.D.

This Solution proposes a harmonized dataset, the Precision Medicine Platform, that will enable researchers to construct virtual patient cohorts that can be used to support a wide range of phenotyping analyses. In accordance with recent calls to improve data sharing, the Solution will be reusable and will support the creation of virtual cohorts and extraction of datasets from acute heart failure clinical studies. The Solution will provide these publicly available datasets in a consistent and transparent manner for secondary analyses.

**Application of Machine Learning in Identifying Heterogeneity of Treatment Effects in Patients with Heart Failure and Preserved Ejection Fraction**

Rishi J Desai, Ph.D., M.S. (team leader); Sebastian Schneeweiss, M.D., Sc.D.; Muthiah Vaduganathan, M.D., M.P.H.

This Solution proposes to evaluate two novel machine learning-based predictive modeling approaches for grouping the HFpEF populations in order to identify subgroups that are most likely to derive benefit with certain treatments. The goal is to address key limitations of the traditional ‘one variable at a time’ subgroup analysis methods. Successful application of advanced machine learning approaches for identifying factors leading to differential treatment effects in heart failure with preserved ejection fraction (HFpEF) could be crucial in generating hypotheses regarding the role of certain etiologic factors in putative treatment benefits. This could facilitate planning and design of future investigations of targeted treatments for this heterogenous disease. Back To Top Link
  

        

        Back to top
  Web Policies & Notices
  

        

              Accessibility
                      No FEAR Act Data
                      Privacy Policy
                      Freedom of Information Act (FOIA)
                      HHS Vulnerability Disclosure
    
          Related Government Websites
  

        

              Health and Human Services
                      National Institutes of Health
                      Office of the Inspector General
                      USA.gov
    
          Stay Connected
    
      
            chat bubblesLive ChatLive Chat with us, Monday through Friday, 8:30 a.m. to 5:00 p.m. EST.envelopeGet Email AlertsReceive automatic alerts about NHLBI related news and highlights from across the Institute.
  Footer Bottom
  

        



Contact Us
                            Site Index
                            Jobs