scholarly journals PatientMatcher: a customizable Python-based open-source tool for matching undiagnosed rare disease patients via the MatchMaker Exchange network

2021 ◽  
Author(s):  
Chiara Rasi ◽  
Daniel Nilsson ◽  
Måns Magnusson ◽  
Nicole Lesko ◽  
Kristina Lagerstedt-Robinson ◽  
...  
Keyword(s):  
Open Source ◽  
Rare Disease ◽  
Genomic Medicine ◽  
Similarity Score ◽  
Patient Data ◽  
University Hospital ◽  
Phenotypic Characteristics ◽  
Clinical Genomics ◽  
Rest Api ◽  
Exchange Network

The amount of data available from genomic medicine has revolutionized the approach to identify the determinants underlying many rare diseases. The task of confirming a genotype-phenotype causality for a patient affected with a rare genetic disease is often challenging. In this context, the establishment of the MatchMaker Exchange (MME) network has assumed a pivotal role in bridging heterogeneous patient information stored on different medical and research servers. MME has made it possible to solve rare disease cases by “matching” the genotypic and phenotypic characteristics of a patient of interest with patient data available at other clinical facilities participating in the network. Here, we present PatientMatcher (https://github.com/Clinical-Genomics/patientMatcher), an open-source Python and MongoDB-based software solution developed by Clinical Genomics facility at the Science for Life Laboratory in Stockholm. PatientMatcher is designed as a standalone MME server, but can easily communicate via REST API with external applications managing genetic analyses and patient data. The MME node is being implemented in clinical production in collaboration with the Genomic Medicine Center Karolinska at the Karolinska University Hospital. PatientMatcher is written to implement the MME API and provides several customizable settings, including a custom-fit similarity score algorithm and adjustable matching results notifications.

Clinical genomics in the 21st century: The fine balance between ethics and science

2021 ◽  
pp. 147775092110366
Author(s):  
Terence YS Liew ◽  
Chun Y Khoo
Keyword(s):  
Clinical Practice ◽  
Informed Consent ◽  
21St Century ◽  
Preventive Medicine ◽  
Genomic Medicine ◽  
Traditional Practices ◽  
Current Clinical Practice ◽  
Common Goal ◽  
Clinical Genomics ◽  
Ethical Concerns

The 21st century has been revolutionary for the field of clinical genomics, with major advancements and breakthroughs over the years. It is now considered an instrumental tool in clinical and preventive medicine and has been used on a day-to-day basis to complement current clinical practice. However, with advancements in genomics comes greater bioethical concerns, which becomes increasingly complex with more cutting-edge technology. Some of the major ethical concerns include obtaining informed consent, possibility for genetic enhancements and eugenics, genomic equity and potential discrimination and cloning. It is imperative that we appreciate the benefits of genomic medicine in complementing traditional practices, identify and address the ethical concerns with relation to the practice of genomic medicine, and to ensure a common goal of improving human lives. With these in mind, the practice of genomics can have maximum impact in the collective health of the population, with greater benefit to all.

scholarly journals Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics

2015 ◽  
Vol 55 ◽  
pp. 174-187 ◽  
Author(s):  
Christina Pahl ◽  
Mojtaba Zare ◽  
Mehrbakhsh Nilashi ◽  
Marco Aurélio de Faria Borges ◽  
Daniel Weingaertner ◽  
...  
Keyword(s):  
Open Source ◽  
Patient Data ◽  
Regional Modelling

scholarly journals The case for open science: rare diseases

JAMIA Open ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 472-486
Author(s):  
Yaffa R Rubinstein ◽  
Peter N Robinson ◽  
William A Gahl ◽  
Paul Avillach ◽  
Gareth Baynam ◽  
...  
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Medical Management ◽  
Open Science ◽  
Patient Data ◽  
Diagnosis And Treatment ◽  
Community Research ◽  
The Core ◽  
Delays In Diagnosis

Abstract The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally.

scholarly journals Doclass: open-source software to support document labeling and classification

2020 ◽  
Author(s):  
Marcelo Inuzuka ◽  
Hugo Do Nascimento ◽  
Fernando Almeida ◽  
Bruno Barros ◽  
Walid Jradi
Keyword(s):  
Active Learning ◽  
Open Source ◽  
Open Source Software ◽  
Design Science ◽  
Text Processing ◽  
Science Research ◽  
Development Stage ◽  
Large Sets ◽  
Rest Api ◽  
Future Work

This article introduces Doclass, a free and open-source software for the Web that aims to assist in labeling and classifying large sets of documents. The research involved a design science research methodology, guided by the real demands of a legal text processing company. The architecture, several design decisions and the current development stage of the software are presented. Preliminary user experiments for evaluating interactive document labeling are described. As a result, the first version of a system with an architecture composed of a mobile frontend that communicates with a backend through a REST API was published, with satisfactory performance evaluation by the applicant. Other results involve the use of active learning techniques to reduce human effort when performing the classification of documents, as well as the Uncertainty strategy to choose the document to be labeled. The effectiveness of the stop criterion for the active learning technique based on confidence level was tested and proved unsatisfactory, remaining as a future work.

GeneBreaker - Variant simulation to improve the diagnosis of Mendelian rare genetic diseases

2020 ◽  
Author(s):  
Phillip A. Richmond ◽  
Tamar V. Av-Shalom ◽  
Oriol Fornes ◽  
Bhavi Modi ◽  
Alison M. Elliott ◽  
...  
Keyword(s):  
Rare Disease ◽  
Genetic Diseases ◽  
Genomic Medicine ◽  
Real Life ◽  
Variant Calling ◽  
Pathogenic Variants ◽  
Rare Genetic Diseases ◽  
Diagnosis Rate ◽  
Effective Use ◽  
Potential Pathogenicity

AbstractMendelian rare genetic diseases affect 5-10% of the population, and with over 5,300 genes responsible for ~7,000 different diseases, they are challenging to diagnose. The use of whole genome sequencing (WGS) has bolstered the diagnosis rate significantly. Effective use of WGS relies upon the ability to identify the disrupted gene responsible for disease phenotypes. This process involves genomic variant calling and prioritization, and is the beneficiary of improvements to sequencing technology, variant calling approaches, and increased capacity to prioritize genomic variants with potential pathogenicity. As analysis pipelines continue to improve, careful testing of their efficacy is paramount. However, real-life cases typically emerge anecdotally, and utilization of clinically sensitive and identifiable data for testing pipeline improvements is regulated and limiting. We identified the need for a gene-based variant simulation framework which can create mock rare disease scenarios, utilizing known pathogenic variants or through the creation of novel gene-disrupting variants. To fill this need, we present GeneBreaker, a tool which creates synthetic rare disease cases with utility for benchmarking variant calling approaches, testing the efficacy of variant prioritization, and as an educational mechanism for training diagnostic practitioners in the expanding field of genomic medicine. GeneBreaker is freely available at http://GeneBreaker.cmmt.ubc.ca.

scholarly journals A Comprehensive Approach to Assessing the Value of Prophylactic Therapy for the Ultra Rare Disease Hereditary Angioedema Using Real World Patient Data

2019 ◽  
Vol 143 (2) ◽  
pp. AB426 ◽  
Author(s):  
Anthony J. Castaldo ◽  
Christian Jervelund ◽  
Andreas R. Kirk ◽  
Deborah Corcoran ◽  
Henrik Balle Boysen ◽  
...  
Keyword(s):  
Rare Disease ◽  
Real World ◽  
Hereditary Angioedema ◽  
Patient Data ◽  
Comprehensive Approach ◽  
Prophylactic Therapy

Creating champions for open source rare disease drug discovery with an app

2014 ◽  
Vol 111 (2) ◽  
pp. S40
Author(s):  
Sean Ekins ◽  
Jill Wood ◽  
Lori Sames ◽  
Allison Moore ◽  
Alex M. Clark
Keyword(s):  
Drug Discovery ◽  
Open Source ◽  
Rare Disease

scholarly journals Security of patient data when decommissioning ultrasound systems

Ultrasound ◽  
2017 ◽  
Vol 25 (1) ◽  
pp. 16-24 ◽  
Author(s):  
James Moggridge
Keyword(s):  
Open Source ◽  
Communication Systems ◽  
Hard Disk ◽  
Local System ◽  
Patient Data ◽  
Healthcare Organisation ◽  
File Allocation ◽  
Picture Archiving And Communication ◽  
Recovery Tools ◽  
Identifiable Data

Background Although ultrasound systems generally archive to Picture Archiving and Communication Systems (PACS), their archiving workflow typically involves storage to an internal hard disk before data are transferred onwards. Deleting records from the local system will delete entries in the database and from the file allocation table or equivalent but, as with a PC, files can be recovered. Great care is taken with disposal of media from a healthcare organisation to prevent data breaches, but ultrasound systems are routinely returned to lease companies, sold on or donated to third parties without such controls. Methods In this project, five methods of hard disk erasure were tested on nine ultrasound systems being decommissioned: the system’s own delete function; full reinstallation of system software; the manufacturer’s own disk wiping service; open source disk wiping software for full and just blank space erasure. Attempts were then made to recover data using open source recovery tools. Results All methods deleted patient data as viewable from the ultrasound system and from browsing the disk from a PC. However, patient identifiable data (PID) could be recovered following the system’s own deletion and the reinstallation methods. No PID could be recovered after using the manufacturer’s wiping service or the open source wiping software. Conclusion The typical method of reinstalling an ultrasound system’s software may not prevent PID from being recovered. When transferring ownership, care should be taken that an ultrasound system’s hard disk has been wiped to a sufficient level, particularly if the scanner is to be returned with approved parts and in a fully working state.

scholarly journals The Implementation Science for Genomic Health Translation (INSIGHT) Study in Epilepsy: Protocol for a Learning Health Care System

10.2196/25576 ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. e25576
Author(s):  
Elena Valeryevna Feofanova ◽  
Guo-Qiang Zhang ◽  
Samden Lhatoo ◽  
Ginger A Metcalf ◽  
Eric Boerwinkle ◽  
...  
Keyword(s):  
Patient Care ◽  
Implementation Science ◽  
Health Informatics ◽  
Domain Knowledge ◽  
Genomic Medicine ◽  
Science Research ◽  
Health Science ◽  
Science Center ◽  
Clinical Genomics ◽  
Genomic Knowledge

Background Genomic medicine is poised to improve care for common complex diseases such as epilepsy, but additional clinical informatics and implementation science research is needed for it to become a part of the standard of care. Epilepsy is an exemplary complex neurological disorder for which DNA diagnostics have shown to be advantageous for patient care. Objective We designed the Implementation Science for Genomic Health Translation (INSIGHT) study to leverage the fact that both the clinic and testing laboratory control the development and customization of their respective electronic health records and clinical reporting platforms. Through INSIGHT, we can rapidly prototype and benchmark novel approaches to incorporating clinical genomics into patient care. Of particular interest are clinical decision support tools that take advantage of domain knowledge from clinical genomics and can be rapidly adjusted based on feedback from clinicians. Methods Building on previously developed evidence and infrastructure components, our model includes the following: establishment of an intervention-ready genomic knowledge base for patient care, creation of a health informatics platform and linking it to a clinical genomics reporting system, and scaling and evaluation of INSIGHT following established implementation science principles. Results INSIGHT was approved by the Institutional Review Board at the University of Texas Health Science Center at Houston on May 15, 2020, and is designed as a 2-year proof-of-concept study beginning in December 2021. By design, 120 patients from the Texas Comprehensive Epilepsy Program are to be enrolled to test the INSIGHT workflow. Initial results are expected in the first half of 2023. Conclusions INSIGHT’s domain-specific, practical but generalizable approach may help catalyze a pathway to accelerate translation of genomic knowledge into impactful interventions in patient care. International Registered Report Identifier (IRRID) PRR1-10.2196/25576

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