特異性敏感性的意義

How do we evaluate how well a machine learning classifier or test model performs? How do we know if a medical test is reliable enough to use in a clinical setting?

^ h流量做我們評估如何以及機器學習分類或測試模型執行？ 我們如何知道醫學測試是否足夠可靠以在臨床環境中使用？

While a highly accurate coronavirus test may be useful where there is a higher incidence, why is it less informative in populations with lower rates of disease? This sounds counterintuitive and confusing, but does have applications for determining the utility of your own binary classifiers!

盡管在發生率較高的地方使用高精度的冠狀病毒檢測可能很有用，但為什么在疾病發生率較低的人群中它的信息量較少？ 這聽起來違反直覺和令人困惑，但是確實有一些應用程序可以確定您自己的二進制分類器的實用程序！

We define the validity of a test by measuring its specificity and sensitivity. Quite simply, we want to know how often the test identifies true positives and true negatives.

我們通過測量其特異性和敏感性來定義測試的有效性。 簡而言之，我們想知道測試多久會發現真實的陽性和陰性的。

Our sensitivity describes how well our test catches all of our positive cases. Sensitivity is calculated by dividing the number of true-positive results by the total number of positives (which include false positives).

我們的敏感性描述了我們的測試對所有陽性病例的捕捉程度。 靈敏度是通過將真實陽性結果的數目除以陽性總數(包括假陽性)而得出的。

Our specificity describes how well our test classifies negative cases as negatives. Specificity is calculated by dividing the number of true-negative results by the total number of negatives (which include false negatives).

我們的特異性描述了我們的測試將陰性案例分類為陰性的效果。 特異性是通過將陰性結果為真陰性結果的總數除以陰性結果總數(包括假陰性結果)而得出的。

FeanDoe / CC BY-SA (FeanDoe / CC BY-SA( https://creativecommons.org/licenses/by-sa/4.0)https://creativecommons.org/licenses/by-sa/4.0 )

The important question is whether a model is meaningful? Simply going by sensitivity and specificity rates won’t cut it! To determine how meaningful or clinically useful a test might be for a population, we need underlying information about the expected incidence or prevalence of a disease. We use Bayes’ Theorem to understand this:

重要的問題是模型是否有意義？ 僅憑敏感性和特異性率就無法解決問題！ 為了確定測試對人群的意義或臨床意義，我們需要有關疾病的預期發病率或患病率的基礎信息。 我們使用貝葉斯定理來理解這一點：

We take a population of 1 000 000 people, where 10% of them have a certain disease. We use a very reliable test with 98% specificity and sensitivity. Here Event A describes the unconditional probability of this disease in the population. P(A) = 0.10.

我們的人口為100萬人，其中10％患有某種疾病。 我們使用具有98％特異性和敏感性的非常可靠的測試。 在這里， 事件A描述了該疾病在人群中的無條件概率。 P(A)= 0.10 。

Event B is the unconditional probability of our test coming up positive. We can calculate P(B) by looking at how many total positives we would get. In this population, we expect 98 000 true positives, calculated by multiplying the disease rate by the total population and sensitivity. For false positives, we take the probability of not having this disease (0.90) and multiple it by the population and (1-Specificity). So we get 18 000 false positives in this scenario. Our P(B) or total positives is then 11.6%.

事件B是我們的測試呈陽性的無條件概率。 我們可以通過查看獲得的總陽性數來計算P(B) 。 在該人群中，我們期望通過將疾病發生率乘以總人群和敏感性得出的98 000個真實陽性。 對于誤報，我們將不患這種疾病的概率設為(0.90) ，然后乘以總體(1-Specificity)并將其乘以。 因此，在這種情況下，我們得到18000個誤報。 那么我們的P(B)或總正值就是11.6％。

Now things get a little bit more complicated. All of these values describe how accurate this test is for this population. But it doesn’t tell us the chances that one person who tests positive has the disease. We need to apply Bayes’ Theorem, using these unconditional values as our prior assumption.

現在事情變得更加復雜了。 所有這些值都說明此測試對該人群的準確性。 但這并沒有告訴我們測試陽性的人患上這種疾病的機會。 我們需要應用貝葉斯定理，將這些無條件值用作我們的先前假設。

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Then if someone takes a test that comes out positive, what is the probability that the disease is present?

然后，如果有人進行了陽性檢測，那么該疾病存在的可能性是多少？

Here we can start to define our variables.

在這里，我們可以開始定義變量。

• P(A) = 0.10

  P(A)= 0.10

• P(B) = 0.116

  P(B)= 0.116

• P(B|A) describes the probability of getting a positive result regardless of whether it’s a true-positive or not, while P(A) is the presence of the disease. Thus P(B|A) is our sensitivity. P(B|A) = 0.98.

  P(B | A)描述獲得陽性結果的可能性，而不管其是否為真陽性，而P(A)是疾病的存在。 因此，P(B | A)是我們的靈敏度。 P(B | A)= 0.98。

• P(A|B) = 0.98 * 0.1 / 0.116 = 84.5%

  P(A | B) = 0.98 * 0.1 / 0.116 = 84.5％

So here we see that even with high sensitivity and specificity, the test may not be as accurate in some populations. Using Bayes’ Theorem, we can calculate this quite easily.

因此，在這里我們看到，即使具有高靈敏度和特異性，該檢測在某些人群中也可能不那么準確。 使用貝葉斯定理，我們可以很容易地計算出這一點。

What would happen though if the disease was less common in our population? Recall that sensitivity and specificity remain at 98%.

但是，如果這種疾病在我們的人群中不那么普遍，會發生什么呢？ 回想一下，敏感性和特異性仍保持在98％。

• P(A) = 0.01

  P(A) = 0.01

• P(B) = (True Positives + True Negatives)/Total Population = (0.01*0.98 + 0.02*0.99)/10000000 = (9800 + 19800)/1000000 = 0.0296

  P(B) =(真陽性+真陰性)/總人口=(0.01 * 0.98 + 0.02 * 0.99)/ 10000000 =(9800 + 19800)/ 1000000 = 0.0296

• P(A|B) = 0.98 * 0.01 / 0.296 = 33.1%

  P(A | B) = 0.98 * 0.01 / 0.296 = 33.1％

Since the disease is now rarer, the posterior probability of receiving a positive test result when you have the disease is lower. As the prevalence of disease decreases within the population, so does our positive predictive value!

由于這種疾病現在比較罕見，因此患這種疾病時獲得陽性檢測結果的后繼可能性較低。 隨著人口中疾病流行率的降低，我們的積極預測價值也將下降 ！

When generating a biomedical test or another model with binary classification, keep in mind when it might be useful. Looking at how well our test works for our specific population because with a low enough prevalence of disease — it might not be very useful!

在生成生物醫學測試或具有二進制分類的其他模型時，請記住可能有用。 看看我們的測試對特定人群的效果如何，因為疾病的患病率很低-可能不是很有用！

It follows that if you’re generating a classifier for something that might be rare in the general population, you need very high sensitivity and specificity for a high positive predictive value!

因此，如果要針對一般人群中罕見的事物生成分類器，則需要非常高的敏感性和特異性才能獲得較高的陽性預測值！

翻譯自: https://towardsdatascience.com/sensitivity-specificity-and-meaningful-classifiers-8326738ec5c2

特異性敏感性的意義

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