Difference between revisions of "Template:Article of the week"

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(Updated article of the week text.)
(Updated article of the week text.)
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Eyal-Altman BMCBioinformatics2017 18.gif|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab2 Alsaffar JMIRMedicalInfo2017 5-1.png|240px]]</div>
'''"[[Journal:PCM-SABRE: A platform for benchmarking and comparing outcome prediction methods in precision cancer medicine|PCM-SABRE: A platform for benchmarking and comparing outcome prediction methods in precision cancer medicine]]"'''
'''"[[Journal:The state of open-source electronic health record projects: A software anthropology study|The state of open-source electronic health record projects: A software anthropology study]]"'''
[[Electronic health record]]s (EHR) are a key tool in managing and storing patients’ [[information]]. Currently, there are over 50 open-source EHR systems available. Functionality and usability are important factors for determining the success of any system. These factors are often a direct reflection of the domain knowledge and developers’ motivations. However, few published studies have focused on the characteristics of [[free and open-source software]] (F/OSS) EHR systems, and none to date have discussed the motivation, knowledge background, and demographic characteristics of the developers involved in open-source EHR projects.


Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models.
This study analyzed the characteristics of prevailing F/OSS EHR systems and aimed to provide an understanding of the motivation, knowledge background, and characteristics of the developers. ('''[[The state of open-source electronic health record projects: A software anthropology study|Full article...]]''')<br />
 
We developed the PCM-SABRE platform, which supports the entire knowledge discovery process for cancer outcome analysis. PCM-SABRE was developed using [[KNIME]]. By using PCM-SABRE to reproduce the results of previously published works on breast cancer survival, we define a baseline for evaluating future attempts to predict cancer outcome with machine learning. ('''[[PCM-SABRE: A platform for benchmarking and comparing outcome prediction methods in precision cancer medicine|Full article...]]''')<br />
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Revision as of 17:14, 30 May 2017

Tab2 Alsaffar JMIRMedicalInfo2017 5-1.png

"The state of open-source electronic health record projects: A software anthropology study" Electronic health records (EHR) are a key tool in managing and storing patients’ information. Currently, there are over 50 open-source EHR systems available. Functionality and usability are important factors for determining the success of any system. These factors are often a direct reflection of the domain knowledge and developers’ motivations. However, few published studies have focused on the characteristics of free and open-source software (F/OSS) EHR systems, and none to date have discussed the motivation, knowledge background, and demographic characteristics of the developers involved in open-source EHR projects.

This study analyzed the characteristics of prevailing F/OSS EHR systems and aimed to provide an understanding of the motivation, knowledge background, and characteristics of the developers. (Full article...)

Recently featured:

▪ PCM-SABRE: A platform for benchmarking and comparing outcome prediction methods in precision cancer medicine
▪ Ten simple rules for cultivating open science and collaborative R&D
▪ Ten simple rules to enable multi-site collaborations through data sharing
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