Proposed clinical interpretation approach which allows a faster evaluation and iteration of ML-derived patient clusters to reduce the time burden on clinical researchers. By introducing the identification (stage 2), the explainability (stage 3), and optimisation stage (stage 5) the time required to evaluate a single set of results will be reduced dramatically in stage 4.

Contexts in source publication

Context 1

... a high-level, these challenges break down into managing large volumes of evaluation results that need to be interpreted by clinicians, facilitating the extraction of insights in studies with large number of observations and support fast iterations of results to increase clinical relevance. The solution we propose (Figure 2) extends the clinical evaluation process with the addition of a key results identification stage (stage 2), an explainability stage (stage 3), and an optimisation loop (stage 5). Note, even though not every tool presented is new, we focus in this publication on how the discovery process using ML methods and large scale EHR datasets can be improved by reducing the burden on clinical domain experts. ...

Context 2

... we suggest that this extension makes the clinical interpretation scalable. Our proposed workflow is shown in Figure 2. Note, the original two stages presented in Figure 1 remain the same (model fitting [stage 1] and clinical evaluation [now stage 4]). ...

Context 3

... have developed methods which can be applied at different stages of the stratification analysis, namely to cohort, feature, or cluster curation, to enhance the quality of the cluster analysis (see Figure 2). Note that this stage is highly dependent on domain expert knowledge. ...