Christopher Rhodes

1
Clinical Free Processing

• Christopher Rhodes
• Uhd-reu
• 17 July 2009

2
Clinical Free Text

• Primary data about patients
• As opposed to journal articles
• Problems posed to Natural Language Processing
• Documents need to be edited for confidentiality
  reasons which takes time and money
• These texts do not follow strictly-edited format,
  which means the texts could contain various
  sub-language characterisitics (fragmented
  sentences, abbreviations, doctor-dependant notes,
  etc)

3
ICD-9-CM

• "The International Classification of Diseases,
  9th Revision, Clinical Modification" (ICD-9-CM)

ICD-9-CM Examples ICD-9-CM Examples
Astham 493.xx
Diabetes 250.xx
Hyperlipidemia 272.0 - 272.4
Arthritis 714.0 715.9
Hypertension 401.1 401.9
Ischemic heart disease 410.0 414.9x
Depression/dysthymia 296.2, 296.3, 296.82, 296.9, 300.4, 309.0, 309.1, 311
x refers to any possible number in that subset
4
The Research

• Automated System for assigning ICD-9-CM codes
  using Natural Language Processing
• The types of files Radiology Reports
• Contains Majority ICD-9-CM code
• Contain Clinical History and Impressions
• Set of Training and Testing Data

5
The Research

• Testing File -gt Parsed Testing File (miniPar)

6
Where We Are

• Beginning State
• Complete program ran with 50.9 accuracy
• Merged Training files to specific codes
• Possible advances that didnt work
• Changing from total summed score from all
  training sentences in a training document to the
  highest individual sentence score of all the
  training sentences (i.e. Ideally the best
  sentence match)
• Manipulating the way the score/weight is
  calculated for the above method
• Current state
• Reverted to original programs algorithm for
  finding the score
• Normalized the merged training files (Score /
  total sentences)
• Complete program now ran with 60.5 accuracy

7
Future Hopes

• Word Importance
• Medical words should receive higher priority
  (score) than non-medical words that are matched
  between sentences. We will use the UMLS medical
  database for active word searching and comparing.
• Negation
• Search for words like no, hardly, none,
  doesnt, not and accurately deals with
  certain Training documents
• For instance No pneumonia should not match to a
  training document with the code for pneumonia,
  but it will if negation is not taken into
  consideration

8
Goal

• Our goal when adding the previous two attributes
  to our program is to have about 80 accuracy or
  more.
• The top accuracy for this type of program was
  submitted by Szeged at 89.08 accuracy.

9
References

• A Shared Task Involving Multi-label
  Classification of Clinical Free Text - John P.
  Pestian, Christopher Brew, Pawe Matykiewicz, DJ
  Hovermale, Neil Johnson, K. Bretonnel Cohen, W
  lodzis law Duch4