Dr.Ghaith Habboub,Spine fellow,Cleveland Clinic, discussed challenges with scaling and generalizing artificial intelligence(AI) applications in healthcare.
He pointed out that most of the effort currently goes into pre-processing and processing of data.
Machine learning applications are being created but integration into current workflow to make them actionable remains another challenge. Dr.Habboub presented some solutions to integrate both applications developed within EHR(electronic health record) and from outside of EHR.
Containerized API(application programming interface) appear to have certain advantages for embedding and scaling AI applications.
Presentation slides available via link below:
Embedding and Scaling AI Models in Healthcare Applications.11_3_2018
Follow his work on GitHb repository via link below:
https://github.com/rocketheat/Kubeadm_Rocketheat
Journal club on “Scalable and accurate deep learning with electronic health records”,generated interesting discussion on processing of the data using FHIR(Fast Healthcare Interoperability Resource).Use of multimodal data and automated feature engineering seems to be a strength of this study.
https://www.nature.com/articles/s41746-018-0029-1
