ICU-Sepsis
基于MIMIC-III构建的脓毒症患者表格MDP环境，用于强化学习算法评估。
基本信息
资源简介
ICU-Sepsis是一个用于评估强化学习算法的基准环境，包含约17,000名脓毒症患者的真实医疗记录（来自MIMIC-III数据库），经过时间离散化和K-means聚类构建为716个状态和25个医疗干预行动的表格MDP，主要用于脓毒症管理中的强化学习算法性能比较。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
Tips: 该数据集 Qianfanghub 可以协助提供有偿下载服务，注意，服务不针对数据相关产权，只是技术服务费。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/icu-sepsis/icu-sepsis.git
curl -L -o repo.zip https://github.com/icu-sepsis/icu-sepsis/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
Python installation and quickstart
ICU-Sepsis can be used with Python 3.10 or later, with gymnasium 0.28.1 or
later, and gym 0.21.0 or later.
Installation with pip
The environment can be installed using the pip command:
pip install icu-sepsis
Installation from source
To install the environment from source, clone the repository and navigate to
the packages directory, and install the icu_sepsis package locally:
git clone https://github.com/icu-sepsis/icu-sepsis.git
cd icu-sepsis/packages/
pip install icu_sepsis/
Uninstalling
To uninstall, use the pip uninstall command:
pip uninstall icu_sepsis -y
Quickstart
The environment can be loaded with the Gym or Gymnasium packages and follows
the standard Gym API. The following code snippet demonstrates how to create
the environment, reset it, and take a step:
import gymnasium as gym
import icu_sepsis
env = gym.make(''Sepsis/ICU-Sepsis-v2'')
state, info = env.reset()
print(''Initial state:'', state)
print(''Extra info:'', info)
next_state, reward, terminated, truncated, info = env.step(0)
print(''\nTaking action 0:'')
print(''Next state:'', next_state)
print(''Reward:'', reward)
print(''Terminated:'', terminated)
print(''Truncated:'', truncated)
You can also run the script examples/quickstart.py to verify that the
installation was successful.
数据加载示例（表格/文本类）
import pandas as pd, glob, os
files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))
精度瓶颈？数据缺失？
当前公开数据无法满足您的算法精度？千方提供针对 脓毒症 的高质量、多模态真实临床数据定制解决方案。
