Thanks to visit codestin.com
Credit goes to github.com

Skip to content

This project applies deep learning to analyze patients' laboratory test results to determine the next treatment step—whether they remain in care or are discharged. I first used TensorFlow and then implemented the same approach with PyTorch. A comparison of the results is documented in a report included in the project files.

Notifications You must be signed in to change notification settings

amisamyra99/Deeplearning_patient_classification

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 

Repository files navigation

Deeplearning_patient_classification

Data Set Information:

The dataset is Electronic Health Record Predicting collected from a private Hospital in Indonesia. It contains the patients laboratory test results used to determine next patient treatment whether in care or out care patient. The task embedded to the dataset is classification prediction. Attribute Information: Given is the attribute name, attribute type, the measurement unit and a brief description. The number of rings is the value to predict: either as a continuous value or as a classification problem.

Name / Data Type / Value Sample/ Description


HAEMATOCRIT /Continuous /35.1 / Patient laboratory test result of haematocrit

HAEMOGLOBINS/Continuous/11.8 / Patient laboratory test result of haemoglobins

ERYTHROCYTE/Continuous/4.65 / Patient laboratory test result of erythrocyte

LEUCOCYTE /Continuous /6.3 / Patient laboratory test result of leucocyte

THROMBOCYTE/Continuous/310/ Patient laboratory test result of thrombocyte

MCH/Continuous /25.4/ Patient laboratory test result of MCH

MCHC/Continuous/33.6/ Patient laboratory test result of MCHC

MCV/Continuous /75.5/ Patient laboratory test result of MCV

AGE/Continuous/12/ Patient age

SEX/Nominal – Binary/F/ Patient gender

SOURCE/Nominal/ {in,out}/The class target in.= in care patient, out = out care patient

About

This project applies deep learning to analyze patients' laboratory test results to determine the next treatment step—whether they remain in care or are discharged. I first used TensorFlow and then implemented the same approach with PyTorch. A comparison of the results is documented in a report included in the project files.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published