| dc.date.accessioned | 2008-04-10T13:30:36Z | |
| dc.date.accessioned | 2018-11-24T10:26:37Z | |
| dc.date.available | 2008-04-10T13:30:36Z | |
| dc.date.available | 2018-11-24T10:26:37Z | |
| dc.date.issued | 1974-03 | |
| dc.identifier.uri | http://hdl.handle.net/1721.1/41100 | |
| dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/41100 | |
| dc.description | Work reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under Contract Number N00014-70-A-0362-0005. | en |
| dc.description.abstract | The differential diagnosis of hematuria, blood in the urine, is studied from the point of view of identifying crucial structures and processes in medical diagnosis. The thesis attempts to fit the problem of medical diagnosis into the framework of other A.I. problems and paradigms and in particular explores the notions of pure search vs. heuristic methods, linearity and interaction, plausibility and the structure of hypotheses within the world of kidney disease. | en |
| dc.language.iso | en_US | en |
| dc.publisher | MIT Artificial Intelligence Laboratory | en |
| dc.title | Artificial Intelligence Approaches to Medical Diagnosis | en |
| dc.type | Working Paper | en |
