Self-Service Data Science for Adverse Event Prediction in Electronic Healthcare Records

Spruit, Marco; Vries, Niels de

doi:10.1007/978-3-030-62066-0_39

Cited by 2 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concept of self-service data science was first described in [4] and has been defined as "the engineering discipline in which analytic systems are designed and evaluated to empower domain professionals to perform their own data analyses on their own data sources without coding in a reliable, usable and transparent manner within their own daily practices" [5]. Figure 1 visualises self-service data science research in the context of adjacent data science research disciplines.…”

Section: Introductionmentioning

confidence: 99%

Self-Service Data Science in Healthcare with Automated Machine Learning

Ooms

Spruit

2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

(1) Background: This work investigates whether and how researcher-physicians can be supported in their knowledge discovery process by employing Automated Machine Learning (AutoML). (2) Methods: We take a design science research approach and select the Tree-based Pipeline Optimization Tool (TPOT) as the AutoML method based on a benchmark test and requirements from researcher-physicians. We then integrate TPOT into two artefacts: a web application and a notebook. We evaluate these artefacts with researcher-physicians to examine which approach suits researcher-physicians best. Both artefacts have a similar workflow, but different user interfaces because of a conflict in requirements. (3) Results: Artefact A, a web application, was perceived as better for uploading a dataset and comparing results. Artefact B, a Jupyter notebook, was perceived as better regarding the workflow and being in control of model construction. (4) Conclusions: Thus, a hybrid artefact would be best for researcher-physicians. However, both artefacts missed model explainability and an explanation of variable importance for their created models. Hence, deployment of AutoML technologies in healthcare remains currently limited to the exploratory data analysis phase.

show abstract

Section: Introductionmentioning

confidence: 99%