# Presentation: 215436

## Text Categorization

- Lexical Systems Group
- National Library of Medicine
- National Institutes of Health

## Table of Contents

-  Introduction
-  TC Tools
-  Journal Descriptor Indexing (JDI)
-  Semantic Type Indexing (STI)
-  Demo – TC Web Tools
-  Applications
-  JDI – Text Categorization on MEDLINE
-  JDI – Identify text in a domain of interest
-  STI - Word Sense Disambiguation (WSD)
-  Demo – Text Categorization Application Tool
-  Future Plan

## Introduction

## Introduction:

_**Text Categorization Tools**_

**TC**
**Tools**

- A set of tools for
- Document classification
- Identify text in a domain of interest
- Word sense disambiguation
- Indexing & retrieval
- etc..

**Notes:**
TC tools allows users to categorize text to a domain disciplines. 

## Introduction - TC Tools

**Input**

**TC**
**Tools**

- A set of tools takes the given input
- Free text: word, phrase, sentence, paragraph, etc..

_**Introduction - TC Tools**_

## Introduction - TC Tools

**Input**

**Output**

**TC**
**Tools**

- A set of tools that generates ranked JD (biomedical disciplines) or ST (semantic categories) lists with scores to categorize the input text

_**Introduction - TC Tools**_

**Notes:**
 Change  this to generic biomedical disciplines, and output

## What are JD & ST?

** ****Journal Descriptors (JDs): **
-  A set of 122 descriptors from MeSH (Medical Subject
-    Headings) used for indexing MEDLINE journals per se
-  For example, Journal of “*Pediatric Surgery*” is indexed and
-    listed under both [Pediatrics] and [Surgery]
-  **Semantic Types (STs):**
-  A set of 135 Semantic Types in the Semantic Network in
-   NLM’s UMLS (Unified Medical Language System)
-  Concepts in the UMLS Metathesaurus are assigned one or
-    more STs which semantically characterize those concepts
-  For example, concept ***Aspirin*** is assigned the STs
-    [Pharmacologic Substance] and [Organic Chemical]

## Introduction - Example

** ****Inputs:** heart valve
** ****Outputs:**
- --- JD scores and rank based on document count for word ---
- JD018|Cardiology
- 1|0.123606|JD018|Cardiology
- 2|0.086522|JD099|Pulmonary Disease (Specialty)
- 3|0.062557|JD124|Vascular Diseases
- 4|0.045034|JD115|Surgery
- 5|0.024740|JD120|Transplantation
- 6|0.024412|JD005|Anesthesiology
- 7|0.023319|JD030|Diagnostic Imaging
- 8|0.016154|JD092|Physiology
- 9|0.012300|JD055|Internal Medicine
- 10|0.012124|JD086|Pediatrics
- ...

**Notes:**
Pulmonary: The reason why this is on the 2nd rank is because lots of journals are included with heart and lung together.

Pul-mo-nary

## Introduction - Tool & Types

- Tools:
- JDI (Journal Descriptor Indexing)
- STI (Semantic Type Indexing)
- STRI (Semantic Type Real-Time Indexing)
- MLT (MEDLINE Tokenizer)
- Tool Types
- Command line tools
- _[Web tools](http://lexsrv3.nlm.nih.gov:8100/WebTc.2007/jsp/jdi/vJdi.jsp?type=Jdi)_
- _[Java APIs](http://lexsrv3.nlm.nih.gov/LexSysGroup/Projects/tc/2007/docs/apiDoc/index.html)_

## Introduction - Facts

-  Free distributed with open source code
-  100% in Java
-  Run on different platforms
-  One complete package
-  Documents & support
-  Provides Java APIs, command line tools,
-   and Web tools
-  First release, TC 2007

**Notes:**
* Embedded database (HyperSonic Database) and trie in the package

## TC Tools

## JDI Methodology

-  JDI is the core methodology (Humphrey, 1998)
-  JDI categorizes text according to a set of JDs
-  Journal Descriptors (JDs):
-  A set of 122 descriptors from MeSH (Medical
-    Subject Headings) is used for indexing MEDLINE
-    journals per se
-  For example, Journal of “*Pediatric Surgery*” is
-    indexed and listed under both [Pediatrics] and
-    [Surgery]

## Tools – Journal Descriptor Indexing

-  Based on statistical word-JD associations from a
-   training set:
-  3 years MEDLINE (2002 ~ 2004)
-  4093 journals
-  1.38 M MEDLINE citations
-  Word-Jd score table:
-  Word count
-  Document count

## JDI: Word-Jd Table

** ****Word Count Score:**
-  Titles and abstracts in MEDLINE citations (training set)
-  Get total word count for all words from titles and abstracts
-  Get word count for all words co-occurs with all JDs
-  Calculate word score to generate Word-Jd table (WC):

- TI & AB

- Total word count

- Word-Jd count

- Word-Jd table

**Notes:**
Show an examples of JD table

## JDI: Word-Jd Table

** ****Document count score:**
-  Titles and abstracts in MEDLINE citations (training set)
-  Get total document count for all words from titles and abstracts
-  Get document count for all words co-occurs with all JDs
-  Calculate document score to generate Word-Jd table (DC):

- TI & AB

- Total doc count

- Word-Jd count

- Word-Jd table

**Notes:**
Show an examples of JD table

## JDI: Word-Jd Table

- TI & AB

- Total word count

- Word-Jd count

- Word-Jd Table

- PMID- 961031
- OWN - NLM
- STAT- MEDLINE
- DA  - 19761020
- DCOM- 19761020
- LR  - 20041117
- PUBM- Print
- IS  - 0042-2835 (Print)
- VI  - 10
- IP  - 1
- DP  - 1976 Jan-Feb
- TI  - Postoperative arrhythmias in open-heart surgery, A study on fifty cases.
- PG  - 30-7
- AB  - 50 consecutive patients undergone open heart surgery were analyzed
-       regarding postoperative arrhythmias in the first postoperative 3 days.
-       Disturbances of rhythm occurred in each case of our group, serious or not
-       serious (100%). Ventricular premature beats were the most frequent type of
-       arrhythmia in the first and second postoperative days (80%). Two cases
-       expired postoperatively. In one of them complete atrioventricular block
-       developed after double valvular replacements (mitral and tricuspid). The
-       other died of low cardiac output syndrome. Etiology of the arrhythmias
- ...

**Notes:**
Show an examples of JD table

## JDI: Word-Jd Table

- TI & AB

- Total word count

- Word-Jd count

- Word-Jd Table

- PMID- 961031
- OWN - NLM
- STAT- MEDLINE
- ...
- DP  - 1976 Jan-Feb
- TI  - Postoperative arrhythmias in open-heart surgery, A study on fifty cases.
- PG  - 30-7
- AB  - 50 consecutive patients undergone open heart surgery were analyzed
-       regarding postoperative arrhythmias in the first postoperative 3 days.
-       Disturbances of rhythm occurred in each case of our group, ...
- ...
- JT  - Vascular surgery
- JID - 0103277

**Notes:**
Show an examples of JD table

## JDI: Word-Jd Table

_**JDI: Word-Jd Table**_

- TI & AB

- Total word count

- Word-Jd count

- Word-Jd Table

- PMID- 961031
- OWN - NLM
- STAT- MEDLINE
- ...
- DP  - 1976 Jan-Feb
- TI  - Postoperative arrhythmias in open-heart surgery, A study on fifty cases.
- PG  - 30-7
- AB  - 50 consecutive patients undergone open heart surgery were analyzed
-       regarding postoperative arrhythmias in the first postoperative 3 days.
-       Disturbances of rhythm occurred in each case of our group, ...
- ...
- JT  - Vascular surgery
- JID - 0103277

**Notes:**
Arrhythmias: Irregular heart beat
Ar-rhyth-mias

## JDI: Word-Jd Table

_**JDI: Word-Jd Table**_

- TI & AB

- Total word count

- Word-Jd count

- Word-Jd Table

- PMID- 961031
- OWN - NLM
- STAT- MEDLINE
- ...
- DP  - 1976 Jan-Feb
- TI  - Postoperative arrhythmias in open-heart surgery, A study on fifty cases.
- PG  - 30-7
- AB  - 50 consecutive patients undergone open heart surgery were analyzed
-       regarding postoperative arrhythmias in the first postoperative 3 days.
-       Disturbances of rhythm occurred in each case of our group, ...
- ...
- JT  - Vascular surgery
- JID - 0103277

**Notes:**
Show an examples of JD table

## JDI: Word-Jd Table

- TI & AB

- Total word count

- Word-Jd count

- Word-Jd Table

| Word | Count |
| --- | --- |
| postoperative | 3 |
| arrhythmias | 2 |
| in | 3 |
| open | 3 |
| heart | 2 |
| surgery | 2 |
| a | 1 |
| ... | ... |

- TI  - Postoperative arrhythmias in open-heart surgery, A study on fifty cases.
- AB  - 50 consecutive patients undergone open heart surgery were analyzed
-       regarding postoperative arrhythmias in the first postoperative 3 days.
-       Disturbances of rhythm occurred in each case of our group, ...

**Notes:**
Show an examples of JD table

## JDI: Word-Jd Table

_**JDI: Word-Jd Table**_

- TI & AB

- Total word count

- Word-Jd count

- Word-Jd Table

- Cardiology|JD018
- Surgery|JD115

- PMID- 961031
- OWN - NLM
- STAT- MEDLINE
- ...
- DP  - 1976 Jan-Feb
- TI  - Postoperative arrhythmias in open-heart surgery, A study on fifty cases.
- PG  - 30-7
- AB  - 50 consecutive patients undergone open heart surgery were analyzed
-       regarding postoperative arrhythmias in the first postoperative 3 days.
-       Disturbances of rhythm occurred in each case of our group, ...
- ...
- JT  - Vascular surgery
- JID - 0103277

**Notes:**
We use JID, Journal unique identifier because some different journals have same titles (not unique) with different JID.
For example:
Physiol Genomics|100894125
Physiol Genomics|9815683

## JDI: Word-Jd Table

- TI & AB

- Total word count

- Word-Jd count

- Word-Jd Table

-  Cardiology|JD018

-  Surgery|JD115

| Word | Count |
| --- | --- |
| study | 412 |
| rhythm | 304 |
| ... | ... |
| heart | 4248 |
| ... | ... |

- TI  - Postoperative arrhythmias in open-heart surgery, A study on fifty cases.
- AB  - 50 consecutive patients undergone open heart surgery were analyzed
-       regarding postoperative arrhythmias in the first postoperative 3 days.
-       Disturbances of rhythm occurred in each case of our group, ...
- JDs – Cardiology|Surgery

| Word | Count |
| --- | --- |
| group | 128 |
| surgery | 5043 |
| ... | ... |
| heart | 605 |
| ... | ... |

**Notes:**
Show an examples of JD table

## JDI: Word-Jd Table

| Word | JDID | WC score | DC score |
| --- | --- | --- | --- |
| a00 | JD036 | 0.5608018 | 0.3280480 |
| ... | ... | ... | ... |
| heart | JD017 | 0.00180937 | 0.00524491 |
| heart | JD018 | 0.04665726 | 0.09366356 |
| heart | ... | ... | ... |
| heart | JD114 | 0.00075555 | 0.00223503 |
| heart | JD115 | 0.00665273 | 0.01562358 |
| ... | ... | ... | ... |

_**JDI: Word-Jd Table**_

- TI & AB

- Total word count

- Word-Jd count

- Word-Jd Table

-  Cardiology|JD018

-  Surgery|JD115

- WC/DC Scores

| Word | Count |
| --- | --- |
| study | 412 |
| rhythm | 304 |
| ... | ... |
| heart | 4248 |
| ... | ... |

| Word | Count |
| --- | --- |
| group | 128 |
| surgery | 5043 |
| ... | ... |
| heart | 605 |
| ... | ... |

## TC Tools - JDI

**Input:**
** ****Text**

**Output**

- final
- words

- Results

- Display

- Input Filter

- Output Filter

## TC Tools - JDI

**Input:**
** ****Text**

**Output**

**JDI**

**Java Classes:**
-  JdiApi
-  JournalDescriptors(122)
-  InputFilter
-  Subheadings
-  OutputFilter
-  Legal Words
-  WordExtractionFilter
-  etc..

**DB Tables:**
-  Word-Jd

- final
- words

- Results

- Display

- Input Filter

- Output Filter

## TC Tools - JDI

-  Input filter
-  Tokenize and filter out words for processing
-  Word extraction filter (filter out irrelevant words)
-  Unique word filter (filter out duplicated words)
-  Legal word filter: stopwords filter, restrictwords filter,
-    word length, word count, document count, etc.
-  Process:
-  Get JD scores from DB for each final word
-  Calculate average JD scores for the input
-  Output filter
-  Ranked JD list with scores (0 ~ 1)
-  Cluster display
-  Display number
-  Candidate only display
-  etc..

**Notes:**
Please note that the input filter and output filter are adjustable. We can turn it on or off if we want to change from the default setup. Also, we can adjust the value of the range.

## JDI - Example

_**JDI - Example**_

- JDI

-   Text

- JD score and rank

** ****Inputs:** The Heart Valve
** ****Outputs:**
- --- JD scores and rank based on document count for word ---
- JD018|Cardiology
- 1|0.123606|JD018|Cardiology
- 2|0.086522|JD099|Pulmonary Disease (Specialty)
- 3|0.062557|JD124|Vascular Diseases
- 4|0.045034|JD115|Surgery
- 5|0.024740|JD120|Transplantation
- 6|0.024412|JD005|Anesthesiology
- 7|0.023319|JD030|Diagnostic Imaging
- 8|0.016154|JD092|Physiology
- 9|0.012300|JD055|Internal Medicine
- 10|0.012124|JD086|Pediatrics

- In

- Out

**Notes:**
Pul-mo-nary

## JDI - Example

_**JDI - Example**_

- JDI

-   Text

- JD score and rank

- In

- Out

## JDI - Example

_**JDI - Example**_

- JDI

-   Text

- JD score and rank

- In

- Out

- Input: **The**** ****Heart Valve**

- Filtered out (stop word)

- Algorithm: Java Program

## JDI - Example

_**JDI - Example**_

- JDI

-   Text

- JD score and rank

- In

- Out

- Input: **The**** ****Heart**** Valve**

| Heart |  |
| --- | --- |
| JD001 | 0.0001787949 |
| JD002 | 0.0015644556 |
| ..... | ..... |
| JD018 | 0.09365937 |
| ... | ... |

- Filtered out (stop word)

- DB: Word-JD table

- Algorithm: Java Program

**Notes:**
Cardiology, is the top rank. Thus, we use it as example.

## JDI - Example

_**JDI - Example**_

- JDI

-   Text

- JD score and rank

- In

- Out

- Input: **The Heart ****Valve**

| Heart |  |
| --- | --- |
| JD001 | 0.0001787949 |
| JD002 | 0.0015644556 |
| ..... | ..... |
| JD018 | 0.09365937 |
| ... | ... |

| Valve |  |
| --- | --- |
| JD001 | 0 |
| JD002 | 0.0008151288 |
| ..... | ..... |
| JD018 | 0.15355311 |
| ... | ... |

- Filtered out (stop word)

- DB: Word-JD table

- Algorithm: Java Program

## JDI - Example

_**JDI - Example**_

- JDI

-   Text

- JD score and rank

- In

- Out

- Input: **The**** Heart Valve**

| Heart |  |
| --- | --- |
| JD001 | 0.0001787949 |
| JD002 | 0.0015644556 |
| ..... | ..... |
| JD018 | 0.09365937 |
| ... | ... |

| Valve |  |
| --- | --- |
| JD001 | 0 |
| JD002 | 0.0008151288 |
| ..... | ..... |
| JD018 | 0.15355311 |
| ... | ... |

- Filtered out (stop word)

**Calculate average scores:**
**(0.09365937 + 0.15355311)/2 = 0.1236062**

- DB: Word-JD table

- Algorithm: Java Program

- Cardiology

**Notes:**
JD018 is Cardiology

## JDI - Example

_**JDI - Example**_

- JDI

-   Text

- JD score and rank

- In

- Out

- Input: **The ****heart valve**

| Heart |  |
| --- | --- |
| JD001 | 0.0001787949 |
| JD002 | 0.0015644556 |
| ..... | ..... |
| JD018 | 0.09365937 |
| ... | ... |

| Valve |  |
| --- | --- |
| JD001 | 0 |
| JD002 | 0.0008151288 |
| ..... | ..... |
| JD018 | 0.15355311 |
| ... | ... |

- Filtered out (stop word)

| Heart Valve |  |
| --- | --- |
| JD001 | 0.0000894 |
| JD002 | 0.0011898 |
| ..... | ..... |
| JD018 | 0.1236062 |
| ... | ... |

**Calculate average scores:**
**(0.09365937 + 0.15355311)/2 = 0.1236062**

- DB: Word-JD table

- Algorithm: Java Program

- Output

## JDI - Example

_**JDI - Example**_

- JDI

-   Text

- JD score and rank

** ****Inputs:** The Heart Valve
** ****Outputs:**
- --- JD scores and rank based on document count for word ---
- JD018|Cardiology
- 1|0.123606|JD018|Cardiology
- 2|0.086522|JD099|Pulmonary Disease (Specialty)
- 3|0.062557|JD124|Vascular Diseases
- 4|0.045034|JD115|Surgery
- 5|0.024740|JD120|Transplantation
- 6|0.024412|JD005|Anesthesiology
- 7|0.023319|JD030|Diagnostic Imaging
- 8|0.016154|JD092|Physiology
- 9|0.012300|JD055|Internal Medicine
- 10|0.012124|JD086|Pediatrics

- In

- Out

**Notes:**
Use this same slide as example.

## JDI - Summary

- JDI tool
- JDI methodology
- Training set (citations & descriptors)
- Word-Jd table
- What if training set is not available?

## Tools – Semantic Type Indexing

** ****Semantic Types:**
-  A set of 135 Semantic Types in the Semantic Network in NLM’s
-   UMLS (Unified Medical Language System) is used for STI.
-  Concepts in the UMLS Metathesaurus are assigned one or
-    more STs which semantically characterize those concepts.
-  For example, concept ***Aspirin*** is assigned the STs
-    [Pharmacologic Substance] and [Organic Chemical].
** ****STI Tool:**
-  Use JDI methodology as basis
-  Calculate the average ST scores for input text from Word-St table
-  Print out ranked ST list with scores

**Notes:**
 Phar-maco-logic

## STI: Word-St Table

-  Generate ST “documents” (all words associated with ST).
-  Apply JDI on ST “documents” to generate St-Jd table
-  Calculate similarity (cosine coefficient) on JDI of ST “documents”
-   (St-Jd) and JDI on individual training set words (Word-Jd) to
-   generate Word-St table

- ST “documents”

- St-Jd table

- Word-St table

- JDI

-   Similarity
- On Word-Jd

**Notes:**
 ST “document” – is one word UMLS terms belong to a particular Semantic type. We use double quote on document because it is a list of words.
 Example: antb – antibiotics  
 ST-Document: from
 UMLS Metathesaurus MRCON (string)
 MRSTY (Semantic types) 
 by associating with CUI
 Use one word String in the MRCON because multiple words usually represent the same thing. For example: “African Horsesickness Virus” is a “virus”

## STI: Word-St Table

_**STI: Word-St Table**_

- ST “documents”

- St-Jd table

- Word-St table

- JDI

-   Similarity on Word-Jd

- ...
- C0018786|SPA|P|L0352821|PF|S0461126|TESTS AUDITIVOS|3|
- C0018786|SPA|S|L2710424|PF|S3184484|Prueba de audicion|3|
- C0018787|DUT|P|L2060782|PF|S2399004|Hart|3|
- C0018787|ENG|P|L0018787|PF|S0047194|Heart|0|
- C0018787|ENG|P|L0018787|VC|S0375948|HEART|0|
- C0018787|ENG|P|L0018787|VC|S0419735|heart|0|
- ...

** ****MRCON**

** ****MRSTY**

- ...
- C0018786|T060|Diagnostic Procedure|
- C0018787|T023|Body Part, Organ, or Organ Component|
- C0018789|T047|Disease or Syndrome|
- ...

- BPOC

**Notes:**
 ST “document” – is one word UMLS terms belong to a particular Semantic type. We use double quote on document because it is a list of words.
 Example: antb – antibiotics  
ST-Document: from
 UMLS Metathesaurus MRCON (string)
 MRSTY (Semantic types) 
 by associating with CUI
 Use one word String in the MRCON because multiple words usually represent the same thing. For example: “African Horsesickness Virus” is a “virus”

## STI: Word-St Table

- ST “documents”

- St-Jd table

- Word-St table

- JDI

-   Similarity on Word-Jd

** ****ST “documents”**

| ST | Words |
| --- | --- |
| aapp | fetuins, ache, actin, maltase, aminoacid, diaminoacid, ... |
| acab | stump, ankylosis, scar, corn, hernia, keloid, kyphosis, ... |
| acty | activities, bioterrorism, burial, interment, civilization, ... |
| ... | ... |
| antb | calcimycin, topicycline, albicdin, ansamycins, menomycin, ... |
| ... | ... |
| bpoc | liver, jaw, skin, kidney, liver, heart, hand, finger, stomach, ... |
| ... | ... |

**Notes:**
 T195antb|Antibiotic
 T023|bpoc| body part, organ, or Organ Component

 T052|acty|Activity
 T116|aapp|Amino Acid, Peptide, or Protein
 T020|acab|Acquired Abnormality

## STI: Word-St Table

- ST “documents”

- St-Jd table

- Word-St table

- JDI

-   Similarity on Word-Jd

| ST | JDID | JD | WC | DC |
| --- | --- | --- | --- | --- |
| ... | ... | ... | ... | ... |
| bpoc | JD001 | Acquired Immunodeficiency Syndrome | 0.002833920 | 0.003035284 |
| bpoc | JD002 | Aerospace Medicine | 0.007579821 | 0.005971246 |
| bpoc | JD003 | Allergy and Immunology | 0.012128671 | 0.012012365 |
| ... | ... | ... | ... | ... |
| bpoc | JD134 | Women’s Health | 0.03777063 | 0.048345257 |
| ... | ... | ... | ... | ... |

** ****ST-Jd table**

**Notes:**
 ST “document” – is one word UMLS terms belong to a particular Semantic type. We use double quote on document because it is a list of words.
 Example: antb – antibiotics  
ST-Document: from
 UMLS Metathesaurus MRCON (string)
 MRSTY (Semantic types) 
 by associating with CUI
 Use one word String in the MRCON because multiple words usually represent the same thing. For example: “African Horsesickness Virus” is a “virus”

## STI: Word-St Table

- ST “documents”

- St-Jd table

- Word-St table

- JDI

-   **Similarity on Word-Jd**

| ST | JDID | WC | DC |
| --- | --- | --- | --- |
| ... | ... | ... | ... |
| bpoc | JD001 | 0.002833920 | 0.003035284 |
| bpoc | JD002 | 0.002833920 | 0.003035284 |
| ... | ... | ... | ... |
| bpoc | JD134 | 0.03777063 | 0.048345257 |
| ... | ... | ... | ... |

** ****ST-Jd table**

| Word | JDID | WC | DC |
| --- | --- | --- | --- |
| ... | ... | ... | ... |
| heart | JD001 | 0.00004663 | 0.00017879 |
| heart | JD002 | 0.00004664 | 0.00015644 |
| ... | ... | ... | ... |
| heart | JD134 | 0.00020986 | 0.00040228 |
| ... | ... | ... | ... |

** ****Word-Jd table (JDI)**

**Notes:**
 ST “document” – is one word UMLS terms belong to a particular Semantic type. We use double quote on document because it is a list of words.
 Example: antb – antibiotics  
ST-Document: from
 UMLS Metathesaurus MRCON (string)
 MRSTY (Semantic types) 
 by associating with CUI
 Use one word String in the MRCON because multiple words usually represent the same thing. For example: “African Horsesickness Virus” is a “virus”

## STI: Cosine Coefficient

**Notes:**
Ortho-gonal: cos(90) = 0, no similarity

## STI: Word-St Table

- ST “documents”

- St-Jd table

- Word-St table

- JDI

-   Similarity on Word-Jd

| ST | JDID | WC | DC |
| --- | --- | --- | --- |
| ... | ... | ... | ... |
| bpoc | JD001 | 0.002833920 | 0.003035284 |
| bpoc | JD002 | 0.002833920 | 0.003035284 |
| ... | ... | ... | ... |
| bpoc | JD134 | 0.03777063 | 0.048345257 |
| ... | ... | ... | ... |

** ****ST-Jd table**

| Word | JDID | WC | DC |
| --- | --- | --- | --- |
| ... | ... | ... | ... |
| heart | JD001 | 0.00004663 | 0.00017879 |
| heart | JD002 | 0.00004664 | 0.00015644 |
| ... | ... | ... | ... |
| heart | JD134 | 0.00020986 | 0.00040228 |
| ... | ... | ... | ... |

** ****Word-Jd table (JDI)**

| Word | St | WC | DC |
| --- | --- | --- | --- |
| ... | ... | ... | ... |
| heart | blor | 0.3981 | 0.4469 |
| heart | bmod | 0.3299 | 0.3868 |
| heart | bodm | 0.1714 | 0.2251 |
| heart | bpoc | 0.3400 | 0.3903 |
| ... | ... | ... | ... |

**Notes:**
 ST “document” – is one word UMLS terms belong to a particular Semantic type. We use double quote on document because it is a list of words.
 Example: antb – antibiotics  
ST-Document: from
 UMLS Metathesaurus MRCON (string)
 MRSTY (Semantic types) 
 by associating with CUI
 Use one word String in the MRCON because multiple words usually represent the same thing. For example: “African Horsesickness Virus” is a “virus”

## Tools - STI

- STI - Text

-   Text

- ST score and rank

- In

- Out

** ****Input filter:**
-  Tokenize and filter out words for processing
-  Word extraction filter (filter out irrelevant words)
-  Unique word filter (filter out duplicated words)
-  Legal word filter: stopwords filter, restrictwords filter,
-    word length, word count, document count, etc.
** ****Process:**
-  Get ST scores from DB for each final word
-  Calculate average ST scores for the input
-  **Output filter**
-  Ranked ST list with scores (0 ~ 1)
-  Cluster display
-  Display number
-  Candidate only display
-  etc..

## Tools – STI Inputs

- STI - Text

-   Text

- ST score and rank

- In

- Out

-  Text
-  word
-  phrase
-  sentence
-  paragraph
-  page
-  document
-  etc..

## Tools - STI Outputs

- STI - Text

-   Text

- ST score and rank

- In

- Out

-  Ranked ST list
-  ST Score based on document count
-  ST Score based on word frequency
-  Input filter details
-  Output filter details
-  etc..

## STI - Example

- STI - Text

-   Text

- ST score and rank

**Inputs:** Race, ethnicity, culture, and disparities in health care
**Outputs:**
- --- ST scores and rank based on document count for word ---
- popg|Population Group
- 1|0.8373|popg|Population Group
- 2|0.7722|socb|Social Behavior
- 3|0.7591|aggp|Age Group
- 4|0.7385|idcn|Idea or Concept
- 5|0.7385|shro|Self-help or Relief Organization
- 6|0.7272|famg|Family Group
- 7|0.7232|orgt|Organization
- 8|0.7086|inbe|Individual Behavior
- 9|0.6965|gora|Governmental or Regulatory Activity
- 10|0.6905|edac|Educational Activity

- In

- Out

**Notes:**
 eth-ni-city
 dis-pa-ri-ties

## STI - Summary

- STI tool
- For different disciplines
- Word-St table

## TC Web Tools

-  Web based tool
-  Uses HTML forms as front end GUI
-  Uses TC Java APIs as back end algorithm
-  Same functions as command line tool
-  Includes following tools:
-  JDI
-  STI
-  STRI
-  MLT
-  _[Demo](http://lexsrv3.nlm.nih.gov:8100/WebTc.2007/jsp/jdi/vJdi.jsp?type=Jdi)_

**Notes:**
Stopwords, use different option

## Applications

## Application:JDI for Text Categorization

-  JDI to index and categorize MEDLINE
-  Inputs:
-  Title
-  Abstract
-  Title & abstract
-  Outputs:
-  A ranked JD list with scores

## Application: JDI for TC

-  Select the MEDLINE citation
-  Filtered text
- - Identify fields of interest (TI, AB, TI & AB)
- - Filter out irrelevant characters and words
-  Apply JDI
-  Get results

- MEDLINE
- Citation

- Filtered text

- A ranked JD list

_**Application: JDI for TC**_

## Application: JDI for TC

-  MEDLINE Citation:

**PMID- 15547873**
**OWN - NLM**
**STAT- MEDLINE**
**DA  - 20041119**
**DCOM- 20051108**
**PUBM- Print**
**IS  - 1531-5037 (Electronic)**
**VI  - 39**
**IP  - 11**
**DP  - 2004 Nov**
**TI  - Outcome and complications after resection of hepatoblastoma.**
**PG  - 1744-5; author reply 1745**
**FAU - Pritchard, Jon**
**AU  - Pritchard J**
**FAU - Stringer, Mark**
**AU  - Stringer M**
**LA  - eng**
**...**
**SO  - J Pediatr Surg. 2004 Nov;39(11):1744-5; author reply 1745.**

**MEDLINE**
**Citation**

**Filtered text**

**A ranked JD list**

**Notes:**
 he-pato-blas-toma

## Application: JDI for TC

_**Application: JDI for TC**_

-  MEDLINE Citation:

- PMID- 15547873
- OWN - NLM
- STAT- MEDLINE
- ...
- TI  - Outcome and complications after resection of hepatoblastoma.
- ...
- SO  - J Pediatr Surg. 2004 Nov;39(11):1744-5; author reply 1745.

**MEDLINE**
**Citation**

**Filtered text**

**A ranked JD list**

## Application: JDI for TC

_**Application: JDI for TC**_

-  MEDLINE Citation:

- PMID- 15547873
- OWN - NLM
- STAT- MEDLINE
- ...
- TI  - Outcome and complications after resection of hepatoblastoma.
- ...
- SO  - J Pediatr Surg. 2004 Nov;39(11):1744-5; author reply 1745.

**MEDLINE**
**Citation**

**Filtered text**

**A ranked JD list**

## Application: JDI for TC

-  MEDLINE Tokenizer (MLT) with tag TI:
-  Outcome and complications after resection of hepatoblastoma.

**MEDLINE**
**Citation**

**Filtered text**

**A ranked JD list**

**Notes:**
He-pa-to-blas-toma

## Application: JDI for TC

-  Input Text (title):
-  Outcome and complications after resection of hepatoblastoma.
-  Input Filter:
-  Word Extraction Filter:
-  outcome and complications after resection hepatoblastoma
-  Legal words Filter:
-  resection hepatoblastoma
-  Unique words Filter
-  resection hepatoblastoma
-  Final words
-  resection hepatoblastoma
-  Get JD scores for both words from DB and calculate the
-    average JD scores

**MEDLINE**
**Citation**

**Filtered text**

**A ranked JD list**

## Application: JDI for TC

**MEDLINE**
**Citation**

**Filtered text**

**A ranked JD list**

- Input: Outcome and complications after resection of hepatoblastoma.
- --- JD scores and rank based on word frequency ---
- JD115|Surgery
- 1|0.031578|JD115|Surgery
- 2|0.028905|JD086|Pediatrics
- 3|0.024402|JD129|Neoplasms
- 4|0.023639|JD041|Gastroenterology
- 5|0.013307|JD045|Genetics, Medical
- --- JD scores and rank based on document count for word ---
- JD115|Surgery
- 1|0.060629|JD115|Surgery
- 2|0.050550|JD041|Gastroenterology
- 3|0.048146|JD129|Neoplasms
- 4|0.044012|JD086|Pediatrics
- 5|0.029093|JD123|Urology

**Notes:**
He-pato-blas-toma:
Hepatoblastoma is a very rare cancerous tumor that originates in the liver. 
This disease primarily affects children from infancy to about 5 years of age. Most cases appear during the first 18 months of life. 

We search MedLine, hepatoblastoma happen mostly in children. That is why Pediatrics.

## Application:JDI to identify text in a domain of interest

**MEDLINE**
**Citation**

**Filtered text**

**A ranked Candidate list**

**Output Filter**

- Example: to identify text in molecular biology domain
-  Run text through JDI
-  Apply candidates output filter option
-  Check if either [Biochemistry] or [Molecular Biology]
-    is in the top 15 rank JDs

**Notes:**
I use JDI to identify text in molecular biology, with the following rule: Consider text to be in the molecular biology domain if either of the following JD’s occur in the top 15 JD’s returned by JDI: Biochemistry, Molecular Biology. The motivation for using JDI is to enhance the accuracy of semantic processing for molecular genetics.

Some citations that are kept with this rule:
12928051
12928053
12946561
Candidate only option display JD of interests.

## Application: JDI to IT

_**Application: JDI to IT**_

**MEDLINE**
**Citation**

**Filtered text**

**A ranked Candidate list**

**Output Filter**

**PMID: 12928053**
**TI: Methylenetetrahydrofolate reductase and angiotensin converting enzyme gene **
**      ****polymorphisms in two genetically and diagnostically distinct cohort of Alzheimer **
**      ****patients.**
**AB: The role of methylenetetrahydrofolate reductase (MTHFR) and angiotensin**
**      ****converting enzyme (ACE) gene polymorphisms as risk factors for the**
**      ****occurrence of Alzheimer's disease (AD) is still controversial. In this ....**
**JDI Outputs (with candidate filter option):**
- --- JD scores and rank based on word frequency ---
- 10|0.008049|JD067|Molecular Biology
- 17|0.006159|JD012|Biochemistry
- --- JD scores and rank based on document count for word ---
-   8|0.018241|JD067|Molecular Biology
- 16|0.012244|JD012|Biochemistry

**Notes:**
I use JDI to identify text in molecular biology, with the following rule: Consider text to be in the molecular biology domain if either of the following JD’s occur in the top 15 JD’s returned by JDI: Biochemistry, Molecular Biology. The motivation for using JDI is to enhance the accuracy of semantic processing for molecular genetics.

Some citations that are kept with this rule:
12928051
12928053
12946561

## Application:

_**STI for Word Sense Disambiguation**_

- NLP applications use MetaMap to map arbitrary text to concepts in the UMLS Metathesaurus

**Free Text**

**Metathesaurus**
**Concept**

**MetaMap**
**(MMTX)**

**Notes:**
MetaMap (MMTX): maps free text to UMLS Metathesaurus concepts, It tokenlizes the input text into word or term (phrase). Each word/term are then map into UMLS concept.

## Application: STI for WSD

**Free Text**

**Concept n**

**Concept 2**

**Concept 1**

**MetaMap**
**(MMTX)**

_**Application: STI for WSD**_

**Notes:**
 MetaMap (MMTX): maps free text to UMLS Metathesaurus concepts
 If the text maps to more than one Metathesaurus concept at the same high confidence scores. MetaMap has no way of knowing which concept is the correct mapping. It cause ambiguity problem.
 Example, “follow up” is the editor rule in Urology.
 Thus is why we put “follow up” is in stopwords.

## Application: STI for WSD

**Free Text**

**Concept n**

**Concept 2**

**Concept 1**

**MetaMap**
**(MMTX)**

**STI**

**Best**
**Concept**

_**Application: STI for WSD**_

**Notes:**
MetaMap (MMTX): maps free text to UMLS Metathesaurus concepts
If the text maps to more than one Metathesaurus concept at the same high confidence score. MetaMap has no way of knowing which concept is the correct mapping.
JDI, based on statistical associations between words in a training set of MEDLINE citations and small set of JDs.
JDI select the best meaning assigned to ambiguous concepts in the Metathesaurus

## Application: STI for WSD

** ****Example:**
-  Input: Race, ethnicity, culture, and disparities in health care
-  Where “culture” has two UMLS concepts/Semantic Types
-    mapping from MetaMap/UMLSKS with same score:
- - Anthropological Culture - Idea or Concept
- - Laboratory Culture - Laboratory Procedure
-  Multiple concept mapping can cause ambiguity

**Notes:**
 An-thro-po-logical

## Application: STI for WSD

**MetaMap**
**(MMTX)**

_**Application: STI for WSD**_

**Notes:**
MetaMap (MMTX): maps free text to UMLS Metathesaurus concepts
If the text maps to more than one Metathesaurus concept at the same high confidence score. MetaMap has no way of knowing which concept is the correct mapping.
JDI, based on statistical associations between words in a training set of MEDLINE citations and small set of JDs.
JDI select the best meaning assigned to ambiguous concepts in the Metathesaurus

## Application: STI for WSD

**Anthropological**** Culture**** - ****Idea or Concept**

**Laboratory Culture - Laboratory Procedure**

**MetaMap**
**(MMTX)**

_**Application: STI for WSD**_

**Notes:**
MetaMap (MMTX): maps free text to UMLS Metathesaurus concepts
If the text maps to more than one Metathesaurus concept at the same high confidence score. MetaMap has no way of knowing which concept is the correct mapping.
JDI, based on statistical associations between words in a training set of MEDLINE citations and small set of JDs.
JDI select the best meaning assigned to ambiguous concepts in the Metathesaurus

## Application: STI for WSD

**Race, ethnicity, culture, and disparities in health care**

**Anthropological**** Culture**** - ****Idea or Concept**

**Laboratory Culture - Laboratory Procedure**

**MetaMap**
**(MMTX)**

**STI**

_**Application: STI for WSD**_

- --- ST scores and rank based on word frequency ---
-   6|0.7184|idcn|Idea or Concept
- 47|0.3718|lbpr|Laboratory Procedure
- --- ST scores and rank based on document count for word ---
-   4|0.7385|idcn|Idea or Concept
- 50|0.3749|lbpr|Laboratory Procedure

- Input
- Text

- St Candidate Option

**Notes:**
MetaMap (MMTX): maps free text to UMLS Metathesaurus concepts
If the text maps to more than one Metathesaurus concept at the same high confidence score. MetaMap has no way of knowing which concept is the correct mapping.
JDI, based on statistical associations between words in a training set of MEDLINE citations and small set of JDs.
JDI select the best meaning assigned to ambiguous concepts in the Metathesaurus

## TCAT

-  Text Categorization Application Tools
-  A showcase of applying TC package on our
-   research projects
-  Use HTML as front end GUI
-  Use TC Java APIs as back end algorithm
-  Design to ease the processes of our existing research
-  _[Demo](http://lexsrv3.nlm.nih.gov:8100/WebTcat.2007/jsp/tools/vJdiText.jsp)_
-  Apply JDI for TC on MEDLINE
- - PMID: 15547873
-  Apply STI for WSD
-  Input: Race, ethnicity, culture, and disparities in health care
-  ST candidate: idcn, lbpr

## Future Plan

-  Tool package
-  Annual release with updated training set
-  Automated training set generation
-  Research:
-  Use JD to index and retrieve MEDLINE
-  Apply TC tools on more medical databases
-  Apply TC tools on more WSD applications
-  Use WSD for automatic indexing
-  Automatic stopwords determination
-  Enhance training set
-  Quality control
-  General categorization of specialized medical collection
-  Apply JDI methodology to different set of descriptor
-    (Library of Congress, class number)

**Notes:**
 Other Medical Journals:
 New England Journal of Medicine
 JAMA
 Journal of Pediatric
 Lancet
 British Medical Journal

 Vitamin, food, Example.

## Thank You!

- _[http://](http://umlslex.nlm.nih.gov/)__[umlslex.nlm.nih.gov](http://umlslex.nlm.nih.gov/)_
- _[http://](http://umlslex.nlm.nih.gov/tc)__[umlslex.nlm.nih.gov/tc](http://umlslex.nlm.nih.gov/tc)_
- jdi@nlm.nih.gov