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sensitivity 敏感性：患病且检测阳性概率
specificity特异性：健康且检测阴性概率
ppv（positive predict value）阳性预测值:指机器检查后，患病概率多高，这和疾病流行程度有关
npv（negative predict value）阴性预测值:指机器检查后，非患病概率多高，这和疾病流行程度有关
但现实中机器会把很多正常人也检查出阳性，所以没病的人也可能检查出阳性
所以阳性预测值和阴性预测值可以很好预测你检查结果后，患病概率
阳性还和疾病流行程度有关，疾病是罕见病，即使机器检测阳性，概率也不会很高
阳性预测率：当你被检查出阳性时，你想知道你真的患病概率。
阳性预测率=患病且检查出阳性数量/（患病且被诊断阳性数量+不患病且被诊断阳性数量）
阴性预测率：检查阴性时，健康概率
阴性预测率=不患病且检查出阴性数量/（患病且被诊断阴性数量+不患病且被诊断阴性数量）
案例中，总人口1000人（A+B+C+D），90%患病率，机器敏感度低50%，但阳性预测值高90%
如果疾病流行度很低，10%感染率，虽然敏感度50%，但阳性预测率只有10%
阴性时，用于排除疾病
用于前期筛选工具
敏感度和疾病流行程度无关
For example, pregnancy tests have a high sensitivity: when a woman is pregnant,
the probability that the test is positive is very high.
In contrast, an indicator for an attack with atomic weapons on the White House
should have a very high specificity: if there is no attack, the probability that the
indicator is positive should be very, very small.
While sensitivity and specificity characterize a test and are independent of
prevalence, they do not indicate what portion of patients with abnormal test results
are truly abnormal. This information is provided by the positive/negative predictive
value (PPV/NPV). These are the values relevant for a doctor diagnosing a patient:
when a patient has a positive test result, how likely is it that the patient is in fact sick?
Unfortunately, as Fig. 7.8 indicates, these values are affected by the prevalence of the
disease. The prevalence of a disease indicates how many out of 100,000 people are
affected by it; in contrast, the incidence gives the number of newly diagnosed cases
per 100,000 people. In summary, we need to know the prevalence of the disease as
well as the PPV/NPV of a test to provide a sensible interpretation of medical test
results.
Take for example a test where a positive test results implies a 50% chance
of having a certain medical condition. If half the population has this condition,
a positive test result tells the doctor nothing. But if the condition is very rare, a
positive test result indicates that the patient has a fifty–fifty chance of having this
rare condition—a piece of information that is very valuable.
Figure 7.8 shows how the prevalence of a disease affects the interpretation
of diagnostic results, with a test with a given specificity and sensitivity: a high
prevalence of the disease increases the PPV of the test, but decreases the NPV;
and a low prevalence does exactly the opposite. Figure 7.9 gives a worked example.
疾病流行率与阳性预测值关系密切
即使敏感度很高，但ppv也可能低，因为机器预测阳性错误的数量太多。所以敏感度也不是唯一判断金指标
python风控评分卡建模和风控常识
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