One of the problems in medical autocoding are the existence of polysemous disease names (disease names that correspond to different diseases depending on the context in which they appear).
Here are some examples:
Cervical carcinoma. Is this a carcinoma involving the neck (the structure between your head and your shoulder), or does this refer to a tumor of the uterine cervix?
Medullary carcinoma. This term can refer to medullary carcinoma of breast, or medullary carcinoma of the thyroid gland or medullary carcinoma of the adrenal medulla. All these neoplasms are distinctive, and different tumors.
Paget's disease. This term can refer to a non-neoplastic disease of bones or a neoplastic process that most often involves the nipple overlying a breast cancer.
Because a disease name can refer to different biological diseases, the task of automatically mapping a disease name to a single disease concept is unlikely to be something that can be done with 100% accuracy.
The oddities of medical nomenclatures, and the impact on data mining, are discussed at length in my book, Biomedical Informatics .
In June, 2014, my book, entitled Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases was published by Elsevier. The book builds the argument that our best chance of curing the common diseases will come from studying and curing the rare diseases.
I urge you to read more about my book. There's a generous preview of the book at the Google Books site. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.
- Jules J. Berman, Ph.D., M.D.
tags: common disease, orphan disease, orphan drugs, rare disease, subsets of disease, disease genetics, genetics of complex disease,
genetics of common diseases, medical nomenclatures, homonyms, medical autocoding, text retrieval, data mining