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% The Failure of Bayesian Filtering For Automated Screening of Med School Applications % somis, intelligence, programming, upenn % 2008-09-18

OK, I’ll admit it. I failed. I failed to apply a simple Bayes net to a simple problem, which humans can do with a little bit of reading and some coffee.

At my place of employment, we have a problem. Our solutions to this problem, right now, relies too much on time consuming human interaction and, certainly, too much on the knowledge gained from history. The problem is that of a multitude of pre-med students hoping to be granted the opportunity to come here for an interview regarding admission into the world renouned medical school.

It’d be both impractical and impossible to interview everyone, so there exists a rigorous screening process before interview decisions are made. We have some tools that automatically look through an application and parameterise it for easier searching. We also have some tools that automatically weed out applications that have extremely low MCATs and GPAs. By and large, these processes get the number of applications the screening team must thoroughly inspect down to a manageable number of very competitive candidates, but the number is still too large.

Now, immediately you must be thinking, Why can’t they just change the parameters to eliminate more people? And the answer is that we could, but what the screeners look for isn’t just numeric. The screeners are looking at many different aspects of the candidates past. Many of the students accepted to the school weren’t the top students in their class. They certainly weren’t horrible students academically, but they had diverse experiences in school, or otherwise, that the professors of the school felt qualified them as being an outstanding addition to this premier institution.

As a programmmer, I see this as a fun and interesting challenge. A challenge where I can apply some “computer sciency” things to add just a touch of intelligence to the automated process and filter out those candidates that maybe are not as “rock star” as they appear in numbers. My first though was to use a [Naive Bayes classifier][1].

A medical school applicant writes a lot. AMCAS asks of students to submit information on past experiences (e.g. research, personal, travel, professional, community service), and for information about publications they’ve (co)authored, and a personal statement. In addition, applicants submit some other free form text to us directly. This data seems like a natural fit for training a classifier.

So, I go with it; I mention to my project manager that it might be possible to probabalistically predict the scores generated from the manual, time consuming screening process, using historic applicant entered data as a training corpus. As his eyes lit up, I explained the basic idea of a Naive Bayes classifier and said, it’s the basis of many SPAM filters. This was enough to sell it to him, and to our immediate client, the Admissions office. I now had permission to play around and see how well I could predict the past.

From the database, I pulled out as much data as I possible could and build two buckets; successful and unsuccessful. Successful candidates had the property that they were all likely invited for interviews, and well unsuccessful weren’t. I tried to make it roughly even, taking as many of the least successful candidates as I could to match as closely as possible to the number of successful candidates. I proceeded to write a basic Naive Bayes classifier, made it general enough that I could add and subtract some ideas easily, and setup a simple cross validation. I was set.

I couldn’t have been more excited to see things pass the screen like:

Run 0 -------------

Good: S 57 / F 17 = 0.773333

Bad: S 22 / F 49 = 0.319444

Run 1 -------------

Good: S 71 / F 3 = 0.960000

Bad: S 8 / F 63 = 0.125000

Run 2 -------------

Good: S 70 / F 4 = 0.946667

Bad: S 13 / F 58 = 0.194444

Run 3 -------------

Good: S 61 / F 13 = 0.826667

Bad: S 12 / F 59 = 0.180556

But the results didn’t look good to me. The first line “Good: … “ states that of the 74 attempts at classifying a known “successful” candidate only 57 were predicted correctly. Even worse, only 22 of the “unsuccessful” candidates were predicted successfully. Surely, this could be fixed, I thought. It’s simply a matter of doing better smoothing, and eliminating the common words with high frequencies.

Still nothing. Stem the words? Worse. Eliminate more words? Worse. Smoothing? No better. Discouragement sets in.

I look into it a bit further and discover something interesting. All these essays, all these experiences sound exactly the same! They all talk about community service and late nights in the research labs. They all discuss the fact that for as long as they can remember they’ve wanted to become physicians. It’s as if all the candidates are the same person.

They aren’t. Unlike a note from my wife and an offer for viagra or cialis, their goals are the same, and their language is in the same domain.

Seems like I need another idea.

[1]: (Naive Bayes on WikiPedia)

*[GPA]: Grade Point Average

*[MCAT]: Medical College Admission Test