What can machines learn about heart failure? A systematic literature review

Abstract

This paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.

Data-driven versus a domain-led approach to k-means clustering on an open heart failure dataset

“…Skills in the practical application of advanced analytics including artificial intelligence (AI) and machine learning (ML) together with the ability to critically appraise the results will enhance knowledge discovery and provide innovations for the healthcare sector [12]. Education and training in AI and ML will increase the uptake of modern technologies that still suffer from the 'black box' stigma.…”

“…As far back as 2002, Kopnas et al concluded that " in terms of the actors involved in the data mining process, domain experts should be in prominent positions within data analysis, data mining, data warehousing and data processing and should actively participate in and guide the process" [2]. Based on available publications [12] and voices of data science experts from the industry [49], the importance of the first pillar of the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, which is a "Business Understanding", seems to be undervalued. As a learning point from this experiment, we would like to propose a practical checklist to enhance the engagement of domain experts and the application of the domain knowledge in the data mining project related to healthcare data.…”

“…A similar pattern of discrepancies in the goals of data mining performed by technologists versus clinicians has been presented by Jasinska et.al. (2021) in the extensive systematic literature of studies using ML on heart failure data sets [12]. There was observed tendency to overclaim the usefulness and applicability of the predictive models to real-world clinical problems, as well as there seem to be an 'unwritten' race in achieving better than previous authors AUC of the designed model.…”

“…However, one key result of this review showed that a quarter of included papers (22 papers out of 81) were authored exclusively by researchers affiliated with either computer science, IT, business administration, translational research, health informatics, bio-medical informatics, quantitative health sciences, statistics or a related area [12]. Moreover, none of those 22 papers mentioned the incorporation of domain knowledge in the design and execution of the data science project [12]. This is a concerning pattern, illustrating that in the pure data-driven approach to data mining there was very limited (if any) integration of the domain knowledge into the data science process.…”

Time‐domain heart rate variability features for automatic congestive heart failure prediction

“…Machine learning models offer the potential to make significant contributions to early diagnosis by creating models capable of quantifying the complex physiological interactions between HRV and health risks 16 . Machine learning methods have been applied to various heart failure‐related tasks, such as detection of heart failure from patient datasets, prediction of hospital readmissions, mortality prediction, and the classification and clustering of heart failure cohorts into subgroups with distinctive features and responses to heart failure treatments 17 . However, there is a scarcity of studies examining the effectiveness of machine learning applied to HRV for the detection of individuals with heart failure.…”