BC HEALTH

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Santiago
@svpino 4 years, 5 months ago 967 views

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You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through it!

https://t.co/jGt006Vlh5

Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021

You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.

👇

Here is what we know:

- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative

Assuming 1 million people take the test (including you):

- 100 of them are sick
- 999,900 of them are healthy

👇

Let's now test both groups, starting with the 100 people sick:

▫️ 99 of them will be diagnosed (correctly) as sick (99%)

▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)

👇

Let's now test the group of 999,900 healthy individuals:

▫️ 989,901 of them will be diagnosed (correctly) as healthy (99%)

▫️ 9,999 of them will be diagnosed (incorrectly) as sick (1%)

👇

Since your test came back positive, it means that you belong to either one of the groups that had a positive result:

1. 99 people that are truly sick, or
2. 9,999 people that are actually healthy (but were diagnosed as sick.)

👇

Basically, out of 10,098, only 99 are truly sick.

That'll give you a 0.98% chance of being sick!

So no, most likely, you are fine!

👇

Here is something important: this is true as long as our only priors are that 1 in 10,000 people have the disease.

For example, if you were showing symptoms, then your chance of being sick after receiving a positive test will be higher.

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Santiago
@svpino

Machine learning education is broken.

If you are preparing for a research position, you are good. If you are looking to get out there and start solving problems, not even close.

Here are some thoughts so you can get ahead.

↓

Most classes, courses, and books cover the same road.

They start with a dataset. They finish with a working model. The focus is always on everything that happens in between.

Dataset → Model.

This is great, but not enough.

Real-life situations rarely start with a dataset, and they never end after you finish building your model.

Applying machine learning successfully is hard.

Here are a few examples that you should keep in mind.

First challenge: Properly framing up the problem.

If you don't understand the problem, you can't determine what data you need. If you don't understand the data, you can't build a good model.

I've never seen a company that had their data ready to go.

In fact, most of them don't even have data at all and need you to determine what exactly they should start collecting.

You usually have to go Problem → Potential Solution → Data.

Santiago
@svpino

11 short programming problems to stretch your imagination and make sure you are staying on your toes.

(Starting with the simple ones, they get more fun as you move towards the end.)

🧵👇

1. Write a function that reverses an array in place.

In other words, the function should not use an auxiliary array to do the work.

2. Write a function that finds the missing number in an unsorted array containing every one of the other 99 numbers ranging from 1 to 100.

3. Write a function that finds the duplicate number in an unsorted array containing every number from 1 to 100.

Only one of the numbers will appear twice.

4. Write a function that removes every duplicate value in an array.

There could be more than one value duplicated. You should remove all of them leaving a single copy of the value.

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Santiago
@svpino

12 machine learning YouTube videos.

On libraries, algorithms, tools, and theory.

↓

1. Jupyter Notebooks: https://t.co/HqE9yt8TkB

2. Pandas: https://t.co/aOLh0dcGF5

3. Matplotlib: https://t.co/tKADpmihkh

4. Seaborn: https://t.co/s8EUxh6x1f

5. Numpy: https://t.co/pJoc0Lfjwm

6. Decision Trees: https://t.co/tKtUpO1K3l

7. Neural Networks: https://t.co/bc7emyjc9q

8. Scikit-Learn: https://t.co/LrKG7cMxRq

9. TensorFlow: https://t.co/fhO6T9sblU

10. PyTorch: https://t.co/5w9mJxijdd

11. Essense of Linear Algebra: https://t.co/o3kOnxl90i

12. Essense of Calculus: https://t.co/rfo7v0cpR4

Santiago
@svpino

I'll blow your mind with a technique you aren't using yet.

Sometimes, you want your system to do exactly the opposite of what your machine learning model thinks you should do.

Let me convince you. ↓

I'm going to start with a nice problem:

Imagine a model that looks at a picture of an electrical transformer and predicts whether it's about to break or not.

Don't worry about how the model does this. We are going to focus on the results instead.

There are 4 possible results for this model:

1. It predicts a bad unit as bad.
2. It predicts a bad unit as good.
3. It predicts a good unit as bad.
4. It predicts a good unit as good.

#2 and #3 are the mistakes the model makes.

Assuming we run 100 units through the model, we can organize the results in a matrix:

• The rows represent the "actual" condition of the transformer.

• The columns represent the "prediction" of the model.

We call this a "Confusion Matrix."

This is how we can read this confusion matrix:

• 60 bad units were predicted as bad.
• 3 bad units were predicted as good.
• 7 good units were predicted as bad.
• 30 good units were predicted as good.

Santiago
@svpino

10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)

🧵👇

1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

https://t.co/HqE9yt8TkB

2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

https://t.co/aOLh0dcGF5

3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

https://t.co/QxjsODI1HB

4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.

https://t.co/eAU5NBucbm

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didn't appear in today drops, while several old ones have been posted again. I would say that all December 22 drops regarding facial expression mean and clarify "surprise". 2/*

And more. So, they didn't point to a negative event, just to one that generates surprise. 4/*

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$Nick Hudson \U0001f642$

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tracy the business goo...
@brokeymcpoverty

breaking my social media fast, which may or may not exist anymore, to share something i found really really helpful from today's therapy sesh. i figured out why i (we?) have so much trouble celebrating our wins

so ive been in therapy for 100 years. still anxious, but im pretty sure i was born that way so i'll stay that way. still depressed too, which i thought id be cured of by now. aa few sessions ago we talked about what i think a return to health 'should' look like

initially i figured itd look like being the person i was before i got depressed. energetic, not needing pep talks to do shit like go outside, cooking everyday, working out at least 30 mins at least 3 days a week, calling my mom everyday, etc.

that session i realized that im a completely different person than i was before i got depressed. also the world has changed a lot & things are just done differently now. not allowing for those things had me reaching for shit that was impossible.

so i make some concessions but say that i *still* want to be able to do some things that i dont want to do. like i cant just sit on the couch and never move in the name of accepting who i am, which is A Bitch Who Don't Like To Move. i still want to be able to eve if i dont want

ConquerMindsetMoney | ...
@TheConquerMM

8 superfoods that will 10x your body mass:

1. Red Meat

Red meat is an excellent source of protein that aids in weight gain and muscular development.

It has leucine and creatine, which are substances important for increasing muscle building.

Protein and fat found in steak and other red meats contribute to weight gain.

2. Starchy Foods

Potatoes, sweet potatoes, corn, whole grain bread, oatmeal, whole wheat pasta, and brown rice are high-carbohydrate foods.

Carbohydrates, which have a protein-sparing effect, are essential for muscle growth along with proteins.

3. Eggs

Eggs are a powerhouse of protein, zinc, vitamins A, D, B 12, phosphorus, and folate.

Egg protein has a high biological value, which makes it the ideal post-workout snack.

There are 90 calories, 6.7 grams of fat, and 6.1 grams of protein in one large scrambled egg.

4. Fatty Fish

Essential fatty acids and proteins are abundant in fatty fish like salmon.

They aid in weight gain and immune boosting.

The finest source of omega-3 which is important for weight gain is found in these fish.

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#OTD 1901, Queen Victoria's funeral took place. Here's a little medical history THREAD (1/9) about one of Britain's longest reigning monarchs. 👇

"Queen Victorian on her death bed" by Emil Fuchs.

(2/9) On 7 April 1853, Queen Victoria became the first monarch to use chloroform to ease the pains of childbirth. Prince Leopold was born within 53 minutes of administration of the drug, which Victoria described as "delightful beyond measure.”

(3/9) The anaesthetic powers of chloroform was first discovered in 1847 by the Scottish physician James Young Simpson. He and his two friends experimented with it on the evening of November 4th. At first, they felt very cheerful and talkative. After a short time, they passed out.

(4/9) Impressed with the drug’s potency, Simpson began using chloroform as an anaesthetic. In December 1847, he delivered the first baby using it. Simpson nicknamed the girl “Saint Anaesthesia.” Her real name was Wilhelmina Carstairs, pictured here.

(5/9) During this time, Queen Victorian was pregnant with her sixth child. She heard of Simpson’s discovery and was keen to benefit from it, but royal doctors cautioned against it, claiming that painful contractions during labour were ordained by God.

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