Cikes, M. , Sanchez‐Martinez, S. , Claggett, B. , Duchateau, N. , Piella, G. , Butakoff, C. , Pouleur, A. C., Knappe, D. , Biering‐Sørensen, T. , Kutyifa, V. , Moss, A. , Stein, K. , Solomon, S. D., Bijnens, B. Machine learning‐based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. European Journal of Heart Failure
We have relevant datasets, repositories, frameworks and tools of relevance for research and technology transfer initiatives related to knowledge extraction. This section provides an overview on a selection of them and links to download or contact details.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository
as well as at the UPF e-repository 
. Below a non-exhaustive list of datasets representative of the research in the Department.
As part of the promotion of the availability of resources, the creation of specific communities in Zenodo has also been promoted, at level of research communities (for instance, MIR and Educational Data Analytics) or MSc programs (for instance, the Master in Sound and Music Computing)
Cikes, M. , Sanchez‐Martinez, S. , Claggett, B. , Duchateau, N. , Piella, G. , Butakoff, C. , Pouleur, A. C., Knappe, D. , Biering‐Sørensen, T. , Kutyifa, V. , Moss, A. , Stein, K. , Solomon, S. D., Bijnens, B. Machine learning‐based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. European Journal of Heart Failure
Cikes, M. , Sanchez‐Martinez, S. , Claggett, B. , Duchateau, N. , Piella, G. , Butakoff, C. , Pouleur, A. C., Knappe, D. , Biering‐Sørensen, T. , Kutyifa, V. , Moss, A. , Stein, K. , Solomon, S. D., Bijnens, B. Machine learning‐based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy. European Journal of Heart Failure
Aims
We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT).
Methods and results
We studied 1106 HF patients from the Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy (MADIT‐CRT) (left ventricular ejection fraction ≤ 30%, QRS ≥ 130 ms, New York Heart Association class ≤ II) randomized to CRT with a defibrillator (CRT‐D, n = 677) or an implantable cardioverter defibrillator (ICD, n = 429). An unsupervised ML algorithm (Multiple Kernel Learning and K‐means clustering) was used to categorize subjects by similarities in clinical parameters, and left ventricular volume and deformation traces at baseline into mutually exclusive groups. The treatment effect of CRT‐D on the primary outcome (all‐cause death or HF event) and on volume response was compared among these groups. Our analysis identified four phenogroups, significantly different in the majority of baseline clinical characteristics, biomarker values, measures of left and right ventricular structure and function and the primary outcome occurrence. Two phenogroups included a higher proportion of known clinical characteristics predictive of CRT response, and were associated with a substantially better treatment effect of CRT‐D on the primary outcome [hazard ratio (HR) 0.35; 95% confidence interval (CI) 0.19–0.64; P = 0.0005 and HR 0.36; 95% CI 0.19–0.68; P = 0.001] than observed in the other groups (interaction P = 0.02).
Conclusions
Our results serve as a proof‐of‐concept that, by integrating clinical parameters and full heart cycle imaging data, unsupervised ML can provide a clinically meaningful classification of a phenotypically heterogeneous HF cohort and might aid in optimizing the rate of responders to specific therapies.