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Disease Progression Modeling Workbench 360

arXiv 2106.13265 ↗First posted 24 Jun 2021cs.LG cs.HCcited by 2

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Abstract (arXiv)

In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI. DPM360, when fully developed, will manage the entire modeling life cycle, from data analysis (e.g., cohort identification) to machine learning algorithm development and prototyping. DPM360 augments the advantages of data model standardization and tooling (OMOP-CDM, Athena, ATLAS) provided by the widely-adopted OHDSI initiative with a powerful machine learning training framework, and a mechanism for rapid prototyping through automatic deployment of models as containerized services to a cloud environment.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 15 references that are in the map. First posted on arXiv 24 Jun 2021.

Builds on

5 of 15 references.

References · 5

  1. 2017Multitask learning and benchmarking with clinical time series datacited by 287
  2. 2021Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?cited by 37
  3. 2019Reproducibility in Machine Learning for Healthcited by 24
  4. 2016Microservices: yesterday, today, and tomorrowcited by 22
  5. 2021Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Dischargecited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2023Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetescited by 8
  2. 2022Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Databasecited by 0