Peter John Haug
Physician in Salt Lake City, UT
License number
Utah 161594-8905
Issued Date
Jan 9, 1978
Expiration Date
Jan 31, 2018
Category
Physician
Type
Physician/Surgeon CS (Schedule 2-5)
Address
Address
Salt Lake City, UT
Personal information
See more information about Peter John Haug at radaris.comName
Address
Phone
2420 E Stringham Ave, Salt Lake City, UT 84109
2420 Stringham Ave, Salt Lake Cty, UT 84109
2420 Stringham Ave, Salt Lake Cty, UT 84109
2420 Sheridan Rd, Salt Lake City, UT 84108
Salt Lake City, UT
Professional information
Medical Informaticist At Intermountain Healthcare
Position:
Medical Informaticist at Intermountain Healthcare, Professor at University of Utah
Location:
Greater Salt Lake City Area
Industry:
Health, Wellness and Fitness
Work:
Intermountain Healthcare
-
Medical Informaticist
University of Utah
since 2002
-
Professor
Education:
University of Wisconsin-Madison 1968 - 1976
Dr. Peter J Haug - MD (Doctor of Medicine)
Hospitals:
5171 S Cottonwood St SUITE 220, Murray 84107
5171 S Cottonwood St SUITE 220, Murray 84107
5171 S Cottonwood St SUITE 220, Murray 84107
Education:
Medical Schools
University of Wisconsin / Madison
Graduated: 1976
University of Wisconsin / Madison
Graduated: 1976
Peter John Haug, Murray UT
Specialties:
Internist
Address:
5171 S Cottonwood St, Murray, UT 84107
Education:
University of Wisconsin, School of Medicine and Public Health - Doctor of Medicine
Board certifications:
American Board of Internal Medicine Certification in Internal Medicine
Probabilistic System For Natural Language Processing
US Patent:
6556964, Apr 29, 2003
Filed:
Jul 23, 2001
Appl. No.:
09/911976
Inventors:
Peter J. Haug - Salt Lake City UT
Spencer B. Koehler - Orem UT
Lee M. Christensen - Salt Lake City UT
Michael L. Gundersen - West Jordan UT
Rudy E. Van Bree - Kaysville UT
Spencer B. Koehler - Orem UT
Lee M. Christensen - Salt Lake City UT
Michael L. Gundersen - West Jordan UT
Rudy E. Van Bree - Kaysville UT
Assignee:
IHC Health Services - Salt Lake City UT
International Classification:
G06F 1727
US Classification:
704 9, 707533
Abstract:
A natural language understanding system is described to provide generation of concept codes from free-text medical data. A probabilistic model of lexical semantics, is implemented by means of a Bayesian network, and is used to determine the most probable concept or meaning associated with a sentence or phrase. The inventive method and system includes the steps of checking for synonyms, checking spelling, performing syntactic parsing, transforming text to its âdeepâ or semantic form, and performing a semantic analysis based on a probabilistic model of lexical semantics.
Systems And Methods For Exploiting Missing Clinical Data
US Patent:
2008013, Jun 5, 2008
Filed:
Nov 27, 2007
Appl. No.:
11/945933
Inventors:
Peter J. Haug - Salt Lake City UT, US
Jau-Huei Lin - Sandy UT, US
Jau-Huei Lin - Sandy UT, US
Assignee:
IHC Intellectual Asset Management, LLC - Salt Lake City UT
International Classification:
G06Q 50/00
US Classification:
705 3
Abstract:
A method for providing information to a clinician regarding a patient's medical problems based upon a combination of the information recorded in the medical record and information omitted from the medical record is described. A patient's medical record is obtained. The medical record may include information regarding the medical conditions experienced by the patient, information from a clinician's observations of treating or testing the patient, and results from tests or therapies administered to the patient. A computer system having a decision support system is used. The decision support system comprises a prediction engine. The decision support system is used to predict conditions or problems omitted from the patient's medical record. These predictions are then provided to the clinician for recording into the medical record.
Probabilistic Method For Natural Language Processing And For Encoding Free-Text Data Into A Medical Database By Utilizing A Bayesian Network To Perform Spell Checking Of Words
US Patent:
6292771, Sep 18, 2001
Filed:
Sep 30, 1998
Appl. No.:
9/164048
Inventors:
Peter J. Haug - Salt Lake City UT
Spencer B. Koehler - Salt Lake City UT
Lee M. Christensen - Salt Lake City UT
Michael L. Gundersen - Holladay UT
Rudy E. Van Bree - Kaysville UT
Spencer B. Koehler - Salt Lake City UT
Lee M. Christensen - Salt Lake City UT
Michael L. Gundersen - Holladay UT
Rudy E. Van Bree - Kaysville UT
Assignee:
IHC Health Services, Inc. - Salt Lake City UT
International Classification:
G06F 1727, G06F 1721, G06F 1740
US Classification:
704 9
Abstract:
A natural language understanding system is described which provides for the generation of concept codes from free-text medical data. A probabilistic model of lexical semantics, in the preferred embodiment of the invention implemented by means of a Bayesian network, is used to determine the most probable concept or meaning associated with a sentence or phrase. The inventive method and system includes the steps of checking for synonyms, checking spelling, performing syntactic parsing, transforming text to its "deep" or semantic form, and performing a semantic analysis based on a probabilistic model of lexical semantics. In the preferred embodiment of the invention, spell checking and transformational processing as well as semantic analysis make use of semantic probabilistic determinations.
Probabilistic Natural Language Processing Using A Likelihood Vector
US Patent:
8639493, Jan 28, 2014
Filed:
Dec 18, 2009
Appl. No.:
12/642675
Inventors:
Peter J. Haug - Salt Lake City UT, US
Assignee:
Intermountain Invention Management, LLC - Salt Lake City UT
International Classification:
G06F 17/28
US Classification:
704 9, 704 1, 704 10
Abstract:
A method for natural language processing on a computing device is described. The computing device receives a free text document. The computing device parses the free text document for gross structure. The gross structure includes sections, paragraphs and sentences. The computing device determines an application of at least one knowledge base. The free text document is parsed for fine structure on the computing device. The fine structure includes sub-sentences. The computing device applies the parsed document and at least one likelihood vector to a Bayesian network. The computing device outputs meanings and probabilities.