Hello students, today we are about to take an exciting detour from the traditional Python approach that we have used throughout this course. Instead, we will harness the power of the ChatGPT beta code interpreter to analyze the OB GYN claims data set and predict childbirth deliveries, just as in the previous lectures.
This session is not just about showcasing a new method, but demonstrating how the world of data science is evolving and adapting with advancements in artificial intelligence. By the end of this lecture, you will see just how intuitive and powerful ChatGPT can be in transforming complex data sets into insightful predictions.
Are you ready to dive in? Let’s explore the future of data science together!
Alright, this is my ChatGPT interface and I need to make sure that I’m using Chachi pt4, which is the paid version of JGPT. Next, I will go to settings and click on beta, and here I click on code interpreter. Now, in my chat interface, I will select code interpreter.
As you can see, it now has an extra button where I can upload a file. I am uploading my file called OB GYN and clicking on enter. I will give it this command: ‘Please provide a list of the columns included in this data set.’
Let’s click over here. We can see that it has created the Python code for us, so we can always review this code and even make adaptations if you want to.
Now, the command is as follows: ‘Please transform this data set into a tidy data set using the following parameters: each person ID is a unique observer row, the names in the column category, according to domain expert, should become the names of the individual columns, and the values in these columns should be the sum of the amount.’
Let’s click enter.
Okay, now I want to convert this column over here in such a way that there are only ones and zeros because we’re using logistic regression. So, the command now is: ‘Please change the values in the column delivery to binary values. Anything that is more than the value 0 should be labeled as one, anything else including empty values should be labeled as zero.’
Again, in both cases, we can check out the Python code.
Let’s add another command. The command reads as follows: ‘Please conduct a logistic regression using the other columns to predict deliveries. Provide an evaluation matrix as well.’
If you are interested in learning more in-depth information about healthcare claims data, then you should definitely check out my online course about data science for healthcare claims data. This course contains hours of lectures covering both theoretical knowledge as well as hands-on practical activities. Click on the link in the description of this video to access this course.
Let’s check it out.
Again, let’s just check out the code. In this case, I don’t really like the way that this matrix looks. You see, it is missing headers over here and also headers over here. So, I’m going to add another command. The command reads as follows: ‘Please provide the confusion matrix with headers so I can read what the values represent.’
There we have it, now the confusion matrix is a lot easier to read.
Now it’s time to create some visualizations, so I will put in this command: ‘Using this information, please recommend and provide three relevant visualizations.’
There we have it, some really cool and interesting visualizations.
Now, I will add just one more command to create a memo based on these findings. The command reads: ‘Write a memo as a data scientist explaining this research, the method, and the findings. Max 750 words and two graphs.’
Well, there you have it, guys, a complete memo based on our data set.
Well, I don’t know about you guys, but I am super impressed by this feature in ChatGPT, and it is really changing the way that I’m approaching my healthcare claims data analysis.
If you’re interested in this particular chat, you can find the link to this chat in the resource section of this video.
Alright, guys, I hope this video has given you some fresh new ideas about how to approach a healthcare claims data set. Thanks for watching and see you in the next lecture.
If you’re interested in learning more in-depth information about healthcare claims data, then you should definitely check out my online course about data science for healthcare claims data. This course contains hours of lectures covering both theoretical knowledge as well as hands-on practical activities. Click on the link in the description of this video to access this course.
