模块1：医疗数据预处理核心工具链
09:00-10:30
1.1 Pandas医疗数据处理专训
临床数据加载技巧：
import pandas as pd
demo_df = pd.read_excel('患者人口学.xlsx')
lab_df = pd.read_csv('检验报告.csv', parse_dates=['采集时间'])
merged = pd.merge_asof(
demo_df.sort_values('就诊时间'),
lab_df.sort_values('采集时间'),
left_on='就诊时间',
right_on='采集时间',
by='患者ID',
tolerance=pd.Timedelta('2h'))
1.2 NumPy医学计算加速
生物信号处理：
from scipy.signal import butter, filtfilt
def bandpass_filter(data, lowcut, highcut, fs, order=5):
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype='band')
return filtfilt(b, a, data)
模块2：统计分析可视化实战
10:45-12:30
2.1 临床统计核心方法
生存分析全流程：
from sksurv.linear_model import CoxPHSurvivalAnalysis
model = CoxPHSurvivalAnalysis(alpha=0.1)
model.fit(X_train, y_train)
concordance = model.score(X_test, y_test)
2.2 学术级可视化
import matplotlib.pyplot as plt
plt.style.use('seaborn-talk')
fig, ax = plt.subplots(figsize=(8,6))
for group in groups:
kmf.fit(durations[group], events[group], label=group)
kmf.plot_survival_function(ax=ax, ci_show=True)
ax.set(xlabel='Time (months)', ylabel='Survival Probability')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.savefig('KM_curve.tiff', dpi=300, format='tiff')
模块4：全流程综合实战
15:15-18:00
COVID-19临床预测模型案例
clinical_df = pd.read_sas('raw_data.sas7bdat')
clean_df = (clinical_df
.pipe(fill_missing, method='ffill')
.pipe(add_prognostic_scores)
.query('随访时间 > 30'))
X_train, X_test = train_test_split(clean_df, test_size=0.3)
model = RandomSurvivalForest().fit(X_train, y_train)
plot_calibration_curve(model, X_test, y_test)
课后交付：
1. 医疗数据清洗Jupyter Notebook模板
2. 常用生物医学Python库conda环境配置指南
3. SCI图表代码库（含Kaplan-Meier、森林图等）