%0 Journal Article %1 colubri2016transforming %A Colubri, Andres %A Silver, Tom %A Fradet, Terrence %A Retzepi, Kalliroi %A Fry, Ben %A Sabeti, Pardis %D 2016 %I Public Library of Science %J PLoS Negl Trop Dis %K clinical_data data_sharing ebola machine_learning mobile_apps outbreak %N 3 %P 1-17 %R 10.1371/journal.pntd.0004549 %T Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients %U http://dx.doi.org/10.1371%2Fjournal.pntd.0004549 %V 10 %X <title>Author Summary</title> <p>We introduce a machine-learning framework and field-deployable app to predict outcome of Ebola patients from their initial clinical symptoms. Recent work from other authors also points out to the clinical factors that can be used to better understand patient prognosis, but there is currently no predictive model that can be deployed in the field to assist health care workers. Mobile apps for clinical diagnosis and prognosis allow using more complex models than the scoring protocols that have been traditionally favored by clinicians, such as Apgar and MTS. Furthermore, the WHO Ebola Interim Assessment Panel has recently concluded that innovative tools for data collection, reporting, and monitoring are needed for better response in future outbreaks. However, incomplete clinical data will continue to be a serious problem until more robust and standardized data collection systems are in place. Our app demonstrates how systematic data collection could lead to actionable knowledge, which in turn would trigger more and better collection, further improving the prognosis models and the app, essentially creating a virtuous cycle.</p>

@article{colubri2016transforming, abstract = {<title>Author Summary</title> <p>We introduce a machine-learning framework and field-deployable app to predict outcome of Ebola patients from their initial clinical symptoms. Recent work from other authors also points out to the clinical factors that can be used to better understand patient prognosis, but there is currently no predictive model that can be deployed in the field to assist health care workers. Mobile apps for clinical diagnosis and prognosis allow using more complex models than the scoring protocols that have been traditionally favored by clinicians, such as Apgar and MTS. Furthermore, the WHO Ebola Interim Assessment Panel has recently concluded that innovative tools for data collection, reporting, and monitoring are needed for better response in future outbreaks. However, incomplete clinical data will continue to be a serious problem until more robust and standardized data collection systems are in place. Our app demonstrates how systematic data collection could lead to actionable knowledge, which in turn would trigger more and better collection, further improving the prognosis models and the app, essentially creating a virtuous cycle.</p>}, added-at = {2016-06-07T05:06:12.000+0200}, author = {Colubri, Andres and Silver, Tom and Fradet, Terrence and Retzepi, Kalliroi and Fry, Ben and Sabeti, Pardis}, biburl = {https://www.bibsonomy.org/bibtex/2e5664bb1d3acce870b5e2c415de8d014/isaric1}, doi = {10.1371/journal.pntd.0004549}, interhash = {3271db7eb32fab8eaf4a439c49cc83e4}, intrahash = {e5664bb1d3acce870b5e2c415de8d014}, journal = {PLoS Negl Trop Dis}, keywords = {clinical_data data_sharing ebola machine_learning mobile_apps outbreak}, month = {03}, number = 3, pages = {1-17}, publisher = {Public Library of Science}, timestamp = {2016-06-08T03:25:37.000+0200}, title = {Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients}, url = {http://dx.doi.org/10.1371%2Fjournal.pntd.0004549}, volume = 10, year = 2016 }