Probailizing Pathology: An Investigation of Expert Human    Systems
{'Code': '08080301', 'Directorate': {'Abbreviation': 'BIO', 'LongName': 'Direct For Biological Sciences'}, 'Division': {'Abbreviation': 'DBI', 'LongName': 'Div Of Biological Infrastructure'}}
{'SignBlockName': 'Ronald J. Overmann', 'PO_EMAI': None, 'PO_PHON': None}
Professor Jeffrey, a philosopher of science concerned with the                  foundations of probability and decision theory, in collaboration                with Dr. Michael Hendrickson, a surgical pathologist, is                        undertaking a pilot study of decision making in pathology.  The                 goal under this grant is to detail a theoretical basis in                       probability theory for the proposed applications of probability                 theory to surgical pathology.  Ultimately, they hope                                                                                                                 (1) to model the formation and change of histopathological                 disease concepts with their syndromic basis, coupled                            managerial/scientific (pragmatic/theoretical) aspects, and ways                 in which these change in response to new knowledge;                                                                                                                  (2) to characterize diagnoses that are appropriately handled               probabilistically which entails analyzing the various sources of                uncertainty in histopathological diagnosis, forming a concept of                the value of histopathological information and creating a                       framework for deciding to what extent various uncertainties are                 worth resolving;                                                                                                                                                     (3) to explore strategies for probabilistic reporting of                   diagnoses (as by appropriate ratios of probabilities or logs of                 such ratios, etc.) so as to achieve appropriate resolution when                 probabilities of diagnoses are close to each other or to the                    extremes of the scale; and                                                                                                                                           (4) to explore strategies for combining expert opinions for                use in decision making, e.g. hard ( i.e. credibly transferrable)                probabilistic data from clinician, radiologist and pathologist in               the form of ratios of new to old odds between diagnoses.                                                                                                        They will draw on three major resources to develop, refine and                  validate this model:  the extant taxonomic literature in surgical               pathology, a large consultation practice in academic pathology,                 and the results of several ongoing clinicopathological projects                 of Dr. Hendrickson that include construction of a clinical                      managerial classification of several groups of gynecological                    neoplasms.  This study potentially will fill a troublesome gap in               the histopathology literature while illuminating questions of                   general importance concerning concept formation, decision and                   risk management.
{'FirstName': 'Richard', 'LastName': 'Jeffrey', 'PI_MID_INIT': 'C', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Richard C Jeffrey', 'EmailAddress': None, 'NSF_ID': '000108084', 'StartDate': '03/15/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Princeton University', 'CityName': 'PRINCETON', 'ZipCode': '085442001', 'PhoneNumber': '6092583090', 'StreetAddress': '1 NASSAU HALL', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New Jersey', 'StateCode': 'NJ', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_ORG': 'NJ12', 'ORG_UEI_NUM': 'NJ1YPQXQG7U5', 'ORG_LGL_BUS_NAME': 'THE TRUSTEES OF PRINCETON UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '1353', 'Text': 'Hist & Philosophy of SET'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8821558.xml'}