Fairchild, K. D. | Search bioRxiv

bioRxiv2021

Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis

ObjectiveTo seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU), we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. DesignWe collected 0.5Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4988 algorithmic operations from 11 mathematical families to random daily ten-minute segments. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. SettingNeonatal ICU Patients5957 NICU infants; 205 died. Measurements and main results3555 operations were usable; 20 cluster medoids held more than 81% of the information. A multivariable model had AUC 0.83. Five algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. ConclusionsHighly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.

bioinformatics↗

bioRxiv2020

FAIRSCAPE: A Framework for FAIR and Reproducible Biomedical Analytics

Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the results metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

bioinformatics↗