%global _empty_manifest_terminate_build 0
Name: python-akiFlagger
Version: 1.0.10
Release: 1
Summary: Flag patients with acute kidney injury as per the KDIGO guidelines.
License: MIT License
URL: https://github.com/isaranwrap/StandardizingAKI
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f1/14/151dd6fe2bc6823157d3151fdbbc09b6745f424c5214bb34f7dced58a317/akiFlagger-1.0.10.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-pandas
%description
# AKIFlagger
## Introduction
Acute Kidney Injury (AKI) is a sudden onset of kidney failure and damage marked by an increase in the serum creatinine levels (amongst other biomarkers) of the patient. Kidney Disease Improving Global Outcomes (KDIGO) has a set of guidelines and standard definitions of AKI:
* *Stage 1*: 50% increase in creatinine in < 7 days or 0.3 increase in creatinine in < 48 hours
* *Stage 2*: 100% increase in (or doubling of) creatinine in < 48 hours
* *Stage 3*: 200% increase in (or tripling of) creatinine in < 48 hours
This package contains a flagger to determine if a patient has developed AKI based on longitudinal data of serum creatinine measurements. More information about the specific data input format can be found in the documentation under the *Getting Started* section.
## Installation
You can install the flagger with ``pip``. Simply type the following into command line and the
package should install properly.
```python
pip install akiFlagger
```
To ensure that it is working properly, you can open a Python session and test it with.
```python
import akiFlagger
print(akiFlagger.__version__)
>> '1.0.0'
```
Alternatively, you can download the source and wheel files to build manually from https://pypi.org/project/akiFlagger/.
## Getting started
There is a [walk-through notebook](https://colab.research.google.com/github/isaranwrap/StandardizingAKI/blob/master/GettingStarted.ipynb) available on Github to introduce the necessary components and parameters of the flagger. The notebook can be accessed via Google Colab notebooks. The notebook has also been adapted in the [documentation](https://akiflagger.readthedocs.io/en/latest/).
## Change Log
**Version 0.1.x** - Function-based implementation of flagger.
**Version 0.2.x** - Switched to class-based implementation (OOP approach).
**Version 0.3.x** - Switched to single-column output for AKI column.
**Version 0.4.x** - Removed encounter and admission as optional columns.
%package -n python3-akiFlagger
Summary: Flag patients with acute kidney injury as per the KDIGO guidelines.
Provides: python-akiFlagger
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-akiFlagger
# AKIFlagger
## Introduction
Acute Kidney Injury (AKI) is a sudden onset of kidney failure and damage marked by an increase in the serum creatinine levels (amongst other biomarkers) of the patient. Kidney Disease Improving Global Outcomes (KDIGO) has a set of guidelines and standard definitions of AKI:
* *Stage 1*: 50% increase in creatinine in < 7 days or 0.3 increase in creatinine in < 48 hours
* *Stage 2*: 100% increase in (or doubling of) creatinine in < 48 hours
* *Stage 3*: 200% increase in (or tripling of) creatinine in < 48 hours
This package contains a flagger to determine if a patient has developed AKI based on longitudinal data of serum creatinine measurements. More information about the specific data input format can be found in the documentation under the *Getting Started* section.
## Installation
You can install the flagger with ``pip``. Simply type the following into command line and the
package should install properly.
```python
pip install akiFlagger
```
To ensure that it is working properly, you can open a Python session and test it with.
```python
import akiFlagger
print(akiFlagger.__version__)
>> '1.0.0'
```
Alternatively, you can download the source and wheel files to build manually from https://pypi.org/project/akiFlagger/.
## Getting started
There is a [walk-through notebook](https://colab.research.google.com/github/isaranwrap/StandardizingAKI/blob/master/GettingStarted.ipynb) available on Github to introduce the necessary components and parameters of the flagger. The notebook can be accessed via Google Colab notebooks. The notebook has also been adapted in the [documentation](https://akiflagger.readthedocs.io/en/latest/).
## Change Log
**Version 0.1.x** - Function-based implementation of flagger.
**Version 0.2.x** - Switched to class-based implementation (OOP approach).
**Version 0.3.x** - Switched to single-column output for AKI column.
**Version 0.4.x** - Removed encounter and admission as optional columns.
%package help
Summary: Development documents and examples for akiFlagger
Provides: python3-akiFlagger-doc
%description help
# AKIFlagger
## Introduction
Acute Kidney Injury (AKI) is a sudden onset of kidney failure and damage marked by an increase in the serum creatinine levels (amongst other biomarkers) of the patient. Kidney Disease Improving Global Outcomes (KDIGO) has a set of guidelines and standard definitions of AKI:
* *Stage 1*: 50% increase in creatinine in < 7 days or 0.3 increase in creatinine in < 48 hours
* *Stage 2*: 100% increase in (or doubling of) creatinine in < 48 hours
* *Stage 3*: 200% increase in (or tripling of) creatinine in < 48 hours
This package contains a flagger to determine if a patient has developed AKI based on longitudinal data of serum creatinine measurements. More information about the specific data input format can be found in the documentation under the *Getting Started* section.
## Installation
You can install the flagger with ``pip``. Simply type the following into command line and the
package should install properly.
```python
pip install akiFlagger
```
To ensure that it is working properly, you can open a Python session and test it with.
```python
import akiFlagger
print(akiFlagger.__version__)
>> '1.0.0'
```
Alternatively, you can download the source and wheel files to build manually from https://pypi.org/project/akiFlagger/.
## Getting started
There is a [walk-through notebook](https://colab.research.google.com/github/isaranwrap/StandardizingAKI/blob/master/GettingStarted.ipynb) available on Github to introduce the necessary components and parameters of the flagger. The notebook can be accessed via Google Colab notebooks. The notebook has also been adapted in the [documentation](https://akiflagger.readthedocs.io/en/latest/).
## Change Log
**Version 0.1.x** - Function-based implementation of flagger.
**Version 0.2.x** - Switched to class-based implementation (OOP approach).
**Version 0.3.x** - Switched to single-column output for AKI column.
**Version 0.4.x** - Removed encounter and admission as optional columns.
%prep
%autosetup -n akiFlagger-1.0.10
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-akiFlagger -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.10-1
- Package Spec generated