The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

Abstract Introduction: Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into digital biomarkers that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking. Methods: In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development: The Digital Biomarker Discovery Pipeline (DBDP). Results: Here, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases. Conclusions: The clear need for such a platform will accelerate the DBDP’s adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.

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Integrated multi-modality image-guided navigation for neurosurgery: open-source software platform using state-of-the-art clinical hardware

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02374-5 ◽

2021 ◽

Author(s):

Jonathan Shapey ◽

Thomas Dowrick ◽

Rémi Delaunay ◽

Eleanor C. Mackle ◽

Stephen Thompson ◽

...

Keyword(s):

Skull Base ◽

Open Source ◽

Open Source Software ◽

Skull Base Surgery ◽

Patient Specific ◽

Software Platform ◽

Image Guided ◽

Phantom Model ◽

Technical Requirements ◽

Navigated Ultrasound

Abstract Purpose Image-guided surgery (IGS) is an integral part of modern neuro-oncology surgery. Navigated ultrasound provides the surgeon with reconstructed views of ultrasound data, but no commercial system presently permits its integration with other essential non-imaging-based intraoperative monitoring modalities such as intraoperative neuromonitoring. Such a system would be particularly useful in skull base neurosurgery. Methods We established functional and technical requirements of an integrated multi-modality IGS system tailored for skull base surgery with the ability to incorporate: (1) preoperative MRI data and associated 3D volume reconstructions, (2) real-time intraoperative neurophysiological data and (3) live reconstructed 3D ultrasound. We created an open-source software platform to integrate with readily available commercial hardware. We tested the accuracy of the system’s ultrasound navigation and reconstruction using a polyvinyl alcohol phantom model and simulated the use of the complete navigation system in a clinical operating room using a patient-specific phantom model. Results Experimental validation of the system’s navigated ultrasound component demonstrated accuracy of $$<4.5\,\hbox {mm}$$ < 4.5 mm and a frame rate of 25 frames per second. Clinical simulation confirmed that system assembly was straightforward, could be achieved in a clinically acceptable time of $$<15\,\hbox {min}$$ < 15 min and performed with a clinically acceptable level of accuracy. Conclusion We present an integrated open-source research platform for multi-modality IGS. The present prototype system was tailored for neurosurgery and met all minimum design requirements focused on skull base surgery. Future work aims to optimise the system further by addressing the remaining target requirements.

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OptFlux: an open-source software platform for in silico metabolic engineering

BMC Systems Biology ◽

10.1186/1752-0509-4-45 ◽

2010 ◽

Vol 4 (1) ◽

Cited By ~ 236

Author(s):

Isabel Rocha ◽

Paulo Maia ◽

Pedro Evangelista ◽

Paulo Vilaça ◽

Simão Soares ◽

...

Keyword(s):

Metabolic Engineering ◽

Open Source ◽

Open Source Software ◽

In Silico ◽

Software Platform

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Pulse Physiology Engine: an Open-Source Software Platform for Computational Modeling of Human Medical Simulation

SN Comprehensive Clinical Medicine ◽

10.1007/s42399-019-00053-w ◽

2019 ◽

Vol 1 (5) ◽

pp. 362-377 ◽

Cited By ~ 4

Author(s):

Aaron Bray ◽

Jeffrey B. Webb ◽

Andinet Enquobahrie ◽

Jared Vicory ◽

Jerry Heneghan ◽

...

Keyword(s):

Computational Modeling ◽

Open Source ◽

Open Source Software ◽

Medical Simulation ◽

Software Platform

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USE OF THE OPEN SOURCE SOFTWARE PLATFORM OPENSIMULATOR FOR THE VIRTUAL RECONSTRUCTION OF HISTORICALLY SIGNIFICANT INDUSTRIAL SITES

19th International Multidisciplinary Scientific GeoConference SGEM2019, Informatics, Geoinformatics and Remote Sensing ◽

10.5593/sgem2019/2.1/s07.092 ◽

2019 ◽

Author(s):

Vladimir Nemtinov

Keyword(s):

Open Source ◽

Open Source Software ◽

Software Platform ◽

Industrial Sites ◽

Virtual Reconstruction

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Patroon: Open Source Software Platform for Environmental Mass Spectrometry Based Non-target Screening

10.21203/rs.3.rs-36675/v1 ◽

2020 ◽

Cited By ~ 3

Author(s):

Rick Helmus ◽

Thomas Ter laak ◽

Pim de Voogt ◽

Annemarie van Wezel ◽

Emma Schymanski

Keyword(s):

Mass Spectrometry ◽

Open Source ◽

Open Source Software ◽

Current Data ◽

Environmental Data ◽

Software Platform ◽

Environmental Sciences ◽

Target Analysis ◽

Usage Evaluation ◽

Target Screening

Abstract Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon, a new R based open-source software platform, which provides comprehensive, fully tailoredand straightforwardnon-target analysis workflows. This platform makes the usage, evaluation and mixing of well-tested algorithms seamless by harmonizing various commonly (primarily open) software tools under a consistent interface. In addition, patRoonoffersvarious functionality and strategies tosimplify and perform automated processing of complex (environmental) data effectively.patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform a straightforward and effective non-target analysis was demonstrated with real-world environmental samples, showing thatpatRoonmakes comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers.

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Open-source software platform for medical image segmentation applications

13th International Conference on Medical Information Processing and Analysis ◽

10.1117/12.2283487 ◽

2017 ◽

Author(s):

Juan P. D'Amato ◽

Rafael Namias ◽

Mariana Del Fresno

Keyword(s):

Image Segmentation ◽

Open Source ◽

Open Source Software ◽

Medical Image ◽

Medical Image Segmentation ◽

Software Platform

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Harmonization of clinical data across Gen3 data commons.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e18094 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e18094-e18094 ◽

Cited By ~ 1

Author(s):

LaRon Hughes ◽

Robert L. Grossman ◽

Zachary Flamig ◽

Andrew Prokhorenkov ◽

Michael Lukowski ◽

...

Keyword(s):

Open Source ◽

Clinical Data ◽

Open Source Software ◽

Data Model ◽

Reference Standards ◽

Data Dictionary ◽

Software Platform ◽

Operating Data ◽

External Reference ◽

Data Commons

e18094 Background: Gen3 is an open source software platform for developing and operating data commons. Gen3 systems are now used by a variety of institutions and agencies to share and analyze large biomedical datasets including clinical and genomic data. One of the challenges of working with these datasets is disparate clinical data standards used by researchers across different studies and fields. We have worked to address these hurdles in a variety of ways. Methods: Gen3 is an open source software platform for developing and operating data commons. Detailed specification and features can be found at https://gen3.org/ with code located on GitHub ( https://github.com/UC-cdis ). Results: The Gen3 data model is a graphical representation of the different nodes or classes of data that have been collected. Examples include diagnosis, demographic, exposure, and family history. The properties and values on each node are controlled by the data dictionary specified by the data commons creator. While each commons may have a unique data model and dictionary, specifying external standards allows for easier submission of new data and assists data consumers with interpretation of results. A variety of external references can be supported, but here we demonstrate the use of the National Cancer Institute Thesaurus (NCIt). NCIt provides reference terminologies and biomedical standards that contain a rich set of terms, codes, definitions, and concepts. Using the same reference standards across commons allows for the export of clinical data between commons. The Portable Format for Biomedical Data (PFB) was created to facilitate data export and to allow the data dictionary schema as well as the raw data to be compressed and exported. This new file format, which utilizes an Avro serialization, is small, fast, easy to modify, and enables simple data export and import. PFB also has the ability to house entire external reference ontologies and it is easy to update the PFB references as changes are introduced. Conclusions: We have shown here how the Gen3 data model, use of external reference standards for clinical data, and the export/import format of PFB enable the harmonization of clinical data across different data commons.

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