Automated coding of diagnoses - three methods compared

1. Automated coding of diagnoses -three methods compared Presenter : Shao-Wei Cheng Authors : Pius Franz, Albrecht Zaiss, Stefan Schulz, Udo Hahn, Rüdiger Klar AMIA 2000

2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Personal Comments

3. Motivation • In Germany, new legal requirements have raised the importance of the accurate encoding of admission and discharge diseases for in- and outpatients. ？ 3

4. Objectives • In response to emerging needs for computer-supported tools, this paper examined three methods for automated coding of German-language free-text diagnosis phrases. √ ？

5. Methodology • SNOMED encoding ( MSVS and MSMS ) • Preprocessing • Morphological Segmentation • SNOMED Indexing • MedSearch retrieval • Ranking of Retrieval Terms • Exploitation of the SNOMED Hierarchy • Retrieval Algorithm Stems, like gastr, hepat, diaphys, Prefixes, like a, de, in, ent, ver, anti, Infixes (e.g., o in gastr-o-intestinal) Derivational suffixes, such as io, ion, ung, Inflectional suffixes, like e, en, s, idis, ae, oris, Eponyms, digits and acronyms like AIDS, ECG, Stems  ad Prefixes  dec ( 十的意思 ) Inflectional suffixes  e  Decad ( 十個構成一組 )  Decade ( 十年 ) 5

6. Experiments 6

7. Conclusion • A satisfactory quality of automated encoding of free-text diagnoses into ICD is not yet reached. • From these results we deduce the following requirements for further work • With a comprehensive Thesaurus of Diagnoses, a better support of the clinical jargon will be given. • A mapping to synonymous expressions can already be done at the level of lexical morphemes. • The latter, provided a formal reconstruction of the ICD, would allow for substituting ICD disease encoding by SNOMED disease encoding.

8. Personal Comments • Advantage • … • Drawback • … • Application • Information retrieval.