Raphael Cohen, Michael Elhadad Noemie Elhadad

1. If it has to do with human readable (more or less) text – it NLP!

2. Search engines.3. Information extraction.4. Helping the government read your emails.5. Topic Models.6. Movie reviews aggregators.7. Spell chekers.8. …

Detecting collocations: " כאב ראש, “קפה עלית" “

Dunning 1994 – Word occurrences, Chi-Square / Maximum Likelyhood

Topic Modeling: “לידה / הריון“ vs "טפיל"Blei et al. 2003 – Mixed generative model acquired using Gibbs sampling over word occurrences in document.

Hospital data is becoming digital. Textual part of EHR is important. In our

Hebrew collection of 900 neurology notes – only 12 prescriptions are indexed.

This data is used for a variety of purposes: Discovering drug side effects (Saadon and Shahar), discovering adverse drug relations, creating summaries for physicians in hospitals, studying diseases and more.

Observation:Physicians like to copy/paste previous visits to save time (couldn’t do it with paper notes).

Wrenn et al. showed up to 74% redundancy. It occurs in the same patient notes (Thank god…), usually within the same form but not always.

No fear, other interesting datasets are also redundant:News reports (try Google News)Movie reviewsProduct reviewsTalkbacks in Ynet…

Also, we call ourselves Medical-Informatics, and have our own conferences.

On average 52% identity, but we can see two document populations.

Conventional wisdom – the more data the better performance of statistical algorithms.

This usually works for huge corpora (the internet).

To solve domain specific problems we have to use smaller corpora (For example, translating CS literature from English to Chinese)

However, redundancy creates false occurrence counts. With some patients having hundreds of redundant notes, this might create a bias in smaller corpora.

22,564 patient notes of patients with kidney problems.

6,131,879 tokens. The physician tells us that the most

important notes are those from the “primary-health-care-provider” table in the database.

There are 504 patients with such notes, and 1,618 “primary-provider” notes.

Effect on word counts

Medical concepts are detected using Health-Term-Finder, an NLP program based on the OpenNLP suite and UMLS (Unified Medical Language System) a medical concept repository.

These concepts include drugs, findings, symptoms…

Hey, you said no bio… - annotations are used with names of actors (movie reviews / gossip), corporations (news) and terrorists (online forums and chats).

Effect on UMLS concept counts

Effect on co-occurrence in UMLS concepts

Build a corpus with controlled amount of redundancy.

Reminiscent of Non-Redundant protein/DNA databases built in the beginning of the last decade [Holes and Sanders (1998)].

Our easy and naïve approach:We have the patients’ ids. Let’s sample a small number of notes from each patient (The “Last” dataset in the graphs we saw).

Drawbacks:a) Annonimized data-sets are the future (our Soroka collection is on example)- they ain’t got ids.b) Are we throwing out some good data along with the redundant stuff?

Align all pairs of sequences (Nimrod showed us how to do that last week) and kick out the redundant ones.

Problem: Alignment costs ~O(n²), this will take a while.

Solution: BLAST / FASTA algorithms use short identical finger prints (substrings) to only compare sequences likely to be similar and to cut down O(n²) to ~O(n) in most cases.

*Experts say that using borrowed algorithm from another discipline gets you into journals

The Bioinfo algorithms are optimized for 4/20 (now 21) alphabets, and the sequences are shorter (usually less than 5K characters).

Texts are easier than DNA, the have defined end of lines and only one reading frame.

Fingerprinting methods for texts already exist in order to find plagiarism.

Sort documents by size.For each document: Find finger prints by lines (For each line,

break into substrings of length F) Add to the corpus if there is no document

sharing more than Max_redundancy substrings in the corpus

How long does it take?5 minutes for our 20K documents.20 minutes for our 400k documents.

Is it better than the “Last note” naïve approach?