Phenotyping and Machine Learning Applications in Heart Failure Management

Heart failure (HF) represents a spectrum of syndromes characterised by diverse aetiologies, comorbidities and clinical trajectories. Traditional classifications—relying on ejection fraction, symptom severity and haemodynamic profiles—frequently overlook underlying heterogeneity. Phenotyping, the systematic identification of patient subgroups based on multidimensional data, seeks to refine diagnosis, risk stratification and therapy selection. Machine learning (ML) has emerged as a pivotal tool in this endeavour, enabling the integration of high-dimensional datasets from electronic health records, imaging modalities, biomarker assays, genomics and proteomics. Unsupervised algorithms detect latent structures without predefined labels, while supervised methods predict outcomes or treatment responses. The resulting phenogroups inform prognostic modelling, guide targeted interventions and can accelerate the design of stratified clinical trials. By illuminating mechanistic pathways—from inflammation and extracellular matrix remodelling to metabolic dysregulation—ML-driven phenotyping holds promise for precision management of HF and for reducing its global burden.

Research from Nature Portfolio

A study employed natural language processing to extract clinical complaints from unstructured electronic health records and generated vectorised representations of patient narratives. Unsupervised clustering of these vectors revealed a hierarchical taxonomy of HF subgroups, each characterised by distinct combinations of comorbid conditions, risk factors and symptom profiles. The methodology recovered well-known phenotypes—such as ischaemic cardiomyopathy, atrial fibrillation and valvular heart disease—while also exposing finer distinctions in clinical presentation. This entirely hypothesis-free approach demonstrated that data-driven phenotyping can yield clinically interpretable subpopulations and offers a scalable framework for discovery in other complex diseases.

Phenotyping and Machine Learning Applications in Heart Failure Management publication trend

The graph below shows the total number of articles in phenotyping and machine learning applications in heart failure management across all publications each year (not limited to Nature Index journals).

Technical terms

Phenotyping: The process of identifying and categorising patient subgroups based on observable clinical or molecular characteristics.

Machine learning (ML): A branch of artificial intelligence that enables computers to learn patterns from data and make predictions or classifications.

Unsupervised learning: A ML approach that discovers hidden structures or clusters within data without pre-labelled outcomes.

Clustering analysis: A set of algorithms that group similar data points together based on defined distance or similarity metrics.

Electronic health record (EHR): A digital system for collecting and storing patient health information, including diagnoses, laboratory results and clinical notes.

Subphenotype: A distinct subgroup within a broader disease category that shares specific clinical or molecular features and may require tailored management.

References

1. Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study. The Lancet Digital Health (2023).
2. Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis. BMC Medical Informatics and Decision Making (2024).
3. Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data. Scientific Reports (2020).
4. HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms. EBioMedicine (2023).