项目作者: SimonBussy

项目描述 :
Biological and vital parameters trajectories visualization tool
高级语言: Jupyter Notebook
项目地址: git://github.com/SimonBussy/redcvo.git
创建时间: 2018-05-21T10:03:42Z
项目社区:https://github.com/SimonBussy/redcvo

开源协议:MIT License

下载


Biological and vital parameters trajectories visualization tool for a sickle-cell disease cohort

Sickle-cell disease is the one of the most common monogenic disorders, resulting from a range of recessive mutations. The inherited mutated variants lead to a defective beta-hemoglobin sub-unit, which predisposes the sickling of erythrocytes. Consequently, these misshaped and rigid red blood cells will, under certain condition, obstruct capillaries, thus inducing acute ischemia to downstream organs and tissues. Such episodes, called vaso-occlusive crisis (VOC), are responsible for acute pain syndromes and ultimately result in increased morbidity and mortality.

The main objective for this study is to describe the behavior of biomarkers and vital parameters throughout a non-complicated VOC hospital stay.
The secondary objective is to identify which biomarker(s) and/or vital sign(s) should be monitored in the days following a hospital admission for VOC in order to help identifying stays with high risk of early readmissions after hospital discharge.

duration_1647803674841.pdf
gender_1647803674876.pdf
pts_cloud_1647803674936.pdf
GHM_1647803675018.pdf
T_1647803675067.pdf
age_1647803675117.pdf
average_1647803675160.pdf
averageBySex_1647803675175.pdf
averageByT15_1647803675236.pdf
averageByT30_1647803675261.pdf
duration_1647803675279.pdf
gender_1647803675305.pdf
pts_cloud_1647803675341.pdf
GHM_1647803675381.pdf
T_1647803675435.pdf
age_1647803675448.pdf
average_1647803675499.pdf
averageBySex_1647803675536.pdf
averageByT15_1647803675590.pdf
averageByT30_1647803675609.pdf
duration_1647803675668.pdf
gender_1647803675693.pdf
pts_cloud_1647803675712.pdf
GHM_1647803675748.pdf
T_1647803675764.pdf
age_1647803675935.pdf
average_1647803675961.pdf
averageBySex_1647803675979.pdf
averageByT15_1647803675993.pdf
averageByT30_1647803676014.pdf
duration_1647803676027.pdf
gender_1647803676054.pdf
pts_cloud_1647803676093.pdf
GHM_1647803676121.pdf
T_1647803676136.pdf
age_1647803676239.pdf
average_1647803676270.pdf
averageBySex_1647803676288.pdf
averageByT15_1647803676310.pdf
averageByT30_1647803676325.pdf
duration_1647803676339.pdf
gender_1647803676372.pdf
pts_cloud_1647803676386.pdf
threshold_above_135_1647803676417.pdf
GHM_1647803676433.pdf
T_1647803676447.pdf
age_1647803676472.pdf
average_1647803676515.pdf
averageBySex_1647803676583.pdf
averageByT15_1647803676611.pdf
averageByT30_1647803676650.pdf
duration_1647803676693.pdf
gender_1647803676722.pdf
pts_cloud_1647803676763.pdf
GHM_1647803676796.pdf
T_1647803676880.pdf
age_1647803676927.pdf
average_1647803676960.pdf
averageBySex_1647803676995.pdf
averageByT15_1647803677021.pdf
averageByT30_1647803677045.pdf
duration_1647803677067.pdf
gender_1647803677082.pdf
pts_cloud_1647803677106.pdf
threshold_below_37.5_1647803677164.pdf
threshold_below_38.5_1647803677195.pdf
threshold_below_38_1647803677238.pdf
threshold_below_39_1647803677257.pdf
GHM_1647803677297.pdf
T_1647803677331.pdf
age_1647803677371.pdf
average_1647803677400.pdf
averageBySex_1647803677426.pdf
averageByT15_1647803677439.pdf
averageByT30_1647803677469.pdf
duration_1647803677491.pdf
gender_1647803677496.pdf
pts_cloud_1647803677513.pdf
threshold_above_17_1647803677528.pdf
threshold_above_20_1647803677550.pdf
threshold_above_25_1647803677619.pdf
threshold_above_30_1647803677698.pdf
GHM_1647803677716.pdf
T_1647803677740.pdf
age_1647803677761.pdf
average_1647803677777.pdf
averageBySex_1647803677799.pdf
averageByT15_1647803677815.pdf
averageByT30_1647803677828.pdf
duration_1647803677833.pdf
gender_1647803677863.pdf
pts_cloud_1647803677891.pdf
GHM_1647803677916.pdf
T_1647803677984.pdf
age_1647803678030.pdf
average_1647803678061.pdf
averageBySex_1647803678102.pdf
averageByT15_1647803678182.pdf
averageByT30_1647803678256.pdf
duration_1647803678296.pdf