中医：Rust Python 智能辨证新实践-EW帮帮网

中医辨证法在Python中的应用

中医辨证法在Python中的应用通常涉及数据分析、自然语言处理（NLP）或规则引擎的实现。以下是常见辨证方法的实例方向和代码片段示例，可用于构建更复杂的系统。

八纲辨证示例（寒热虚实）

通过症状输入判断寒热属性：

def cold_hot_pattern(symptoms):
    cold_signs = ["畏寒", "四肢冷", "喜热饮"]
    hot_signs = ["发热", "口渴", "喜冷饮"]
    cold_score = sum(1 for s in symptoms if s in cold_signs)
    hot_score = sum(1 for s in symptoms if s in hot_signs)
    return "寒证" if cold_score > hot_score else "热证" if hot_score > cold_score else "寒热错杂"

脏腑辨证示例（心气虚）

使用字典匹配脏腑症状组合：

organ_patterns = {
    "心气虚": ["心悸", "气短", "自汗"],
    "肝郁气滞": ["胁痛", "抑郁", "脉弦"]
}

def diagnose_organ(symptoms):
    for pattern, signs in organ_patterns.items():
        if all(s in symptoms for s in signs[:2]):
            return pattern
    return "未明确辨证"

六经辨证（伤寒论）

基于规则判断太阳病：

def taiyang_disease(symptoms):
    required = ["恶寒", "发热", "脉浮"]
    optional = ["头痛", "项强"]
    return "太阳病" if all(x in symptoms for x in required) and any(x in symptoms for x in optional)

气血津液辨证

气虚判断逻辑：

qi_deficiency = {
    "主要症状": ["乏力", "气短"],
    "次要症状": ["自汗", "舌淡"]
}

def check_qi_deficiency(symptoms):
    main_match = sum(1 for s in symptoms if s in qi_deficiency["主要症状"])
    minor_match = sum(1 for s in symptoms if s in qi_deficiency["次要症状"])
    return main_match >= 1 and minor_match >= 2

数据驱动辨证实现

使用Pandas分析症状数据集：

import pandas as pd

# 假设df包含症状和辨证结果列
df = pd.read_csv("tcm_data.csv")

def pattern_by_statistics(symptoms):
    matched = df[df['symptoms'].apply(lambda x: all(s in x.split(',') for s in symptoms))]
    return matched['pattern'].mode()[0] if not matched.empty else "未知"

NLP辨证系统框架

构建简单的症状关键词分类器：

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC

# 示例训练数据
texts = ["畏寒 腹泻 舌白", "面红 发热 口渴"]
labels = ["寒证", "热证"]

vectorizer = TfidfVectorizer(tokenizer=lambda x: x.split())
X = vectorizer.fit_transform(texts)
clf = LinearSVC().fit(X, labels)

def predict_pattern(text_input):
    return clf.predict(vectorizer.transform([text_input]))[0]

注意事项

实际中医辨证需结合脉诊、舌诊等综合信息
复杂辨证建议使用知识图谱技术
临床验证的辨证规则比纯数据驱动更可靠

完整项目通常需要结合：

症状权重计算（如主症/次症）
辨证规则树（决策树或专家系统）
中医古籍文献的结构化处理

自学中医学与Rust编程实例

以下是通过Rust编程语言实现中医学相关功能的示例，涵盖数据处理、算法应用和知识管理等方面。

中医基础理论模型

Rust结构体表示中医基础概念：

struct TCMTheory {
    yin_yang: String,
    five_elements: Vec<String>,
    meridians: HashMap<String, Vec<String>>,
}
impl TCMTheory {
    fn new() -> Self {
        Self {
            yin_yang: "阴阳平衡".to_string(),
            five_elements: vec!["木","火","土","金","水"].into_iter().map(|s| s.to_string()).collect(),
            meridians: HashMap::from([
                ("肝经".to_string(), vec!["太冲".to_string(),"期门".to_string()]),
                ("肺经".to_string(), vec!["中府".to_string(),"尺泽".to_string()])
            ])
        }
    }
}

穴位数据库系统

使用Rust构建穴位查询系统：

#[derive(Debug)]
struct Acupoint {
    name: String,
    meridian: String,
    location: String,
    functions: Vec<String>
}

fn load_acupoints() -> Vec<Acupoint> {
    vec![
        Acupoint {
            name: "足三里".to_string(),
            meridian: "胃经".to_string(),
            location: "小腿外侧".to_string(),
            functions: vec!["健脾","和胃".to_string()]
        },
        Acupoint {
            name: "合谷".to_string(),
            meridian: "大肠经".to_string(),
            location: "手背".to_string(),
            functions: vec!["止痛","解表".to_string()]
        }
    ]
}

中医：Rust Python 智能辨证新实践