Enabling scalable clinical interpretation of ML-based phenotypes using real world data

by   Owen Parsons, et al.

The availability of large and deep electronic healthcare records (EHR) datasets has the potential to enable a better understanding of real-world patient journeys, and to identify novel subgroups of patients. ML-based aggregation of EHR data is mostly tool-driven, i.e., building on available or newly developed methods. However, these methods, their input requirements, and, importantly, resulting output are frequently difficult to interpret, especially without in-depth data science or statistical training. This endangers the final step of analysis where an actionable and clinically meaningful interpretation is needed.This study investigates approaches to perform patient stratification analysis at scale using large EHR datasets and multiple clustering methods for clinical research. We have developed several tools to facilitate the clinical evaluation and interpretation of unsupervised patient stratification results, namely pattern screening, meta clustering, surrogate modeling, and curation. These tools can be used at different stages within the analysis. As compared to a standard analysis approach, we demonstrate the ability to condense results and optimize analysis time. In the case of meta clustering, we demonstrate that the number of patient clusters can be reduced from 72 to 3 in one example. In another stratification result, by using surrogate models, we could quickly identify that heart failure patients were stratified if blood sodium measurements were available. As this is a routine measurement performed for all patients with heart failure, this indicated a data bias. By using further cohort and feature curation, these patients and other irrelevant features could be removed to increase the clinical meaningfulness. These examples show the effectiveness of the proposed methods and we hope to encourage further research in this field.


page 7

page 11

page 14

page 17


Patient Clustering via Integrated Profiling of Clinical and Digital Data

We introduce a novel profile-based patient clustering model designed for...

Deep Semi-Supervised Embedded Clustering (DSEC) for Stratification of Heart Failure Patients

Determining phenotypes of diseases can have considerable benefits for in...

An Information Extraction Approach to Prescreen Heart Failure Patients for Clinical Trials

To reduce the large amount of time spent screening, identifying, and rec...

Surrogate-assisted performance tuning of knowledge discovery algorithms: application to clinical pathway evolutionary modeling

The paper proposes an approach for surrogate-assisted tuning of knowledg...

Clustering Patients with Tensor Decomposition

In this paper we present a method for the unsupervised clustering of hig...

Trace Clustering on Very Large Event Data in Healthcare Using Frequent Sequence Patterns

Trace clustering has increasingly been applied to find homogenous proces...

Please sign up or login with your details

Forgot password? Click here to reset