A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning

被引：18

作者：

Schmidt, R ^{[1
]}

Gierl, L ^{[1
]}

机构：

[1] Univ Rostock, Inst Med Informat & Biometrie, D-18055 Rostock, Germany

来源：

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS | 2005年 / 74卷 / 2-4期

关键词：

kidney; influenza; forecast; prognosis; artificial intelligence; case-based reasoning;

D O I：

10.1016/j.ijmedinf.2004.03.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Since clinical management of patients and clinical research are essentially time-oriented endeavours, reasoning about time has become a hot topic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with case-based reasoning. It is useful for application domains where neither well-known standards, nor known periodicity, nor a complete domain theory exist. We have used our method in two prognostic applications. The first one deals with prognosis of the kidney function for intensive care patients. The idea is to elicit impairments on time, especially to warn against threatening kidney failures. Our second application deals with a completely different domain, namely geographical medicine. Its intention is to compute early warnings against approaching infectious diseases, which are characterised by irregular cyclic occurrences. So far, we have applied our program on influenza and bronchitis. In this paper, we focus on influenza forecast and show first experimental results. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

引用

页码：307 / 315

页数：9

共 28 条

[21] Cased-Based Reasoning for medical knowledge-based systems [J].

Schmidt, R ;

Montani, S ;

Bellazzi, R ;

Portinale, L ;

Gierl, L .

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2001, 64 (2-3) :355-367

[22] Case-based reasoning for antibiotics therapy advice: an investigation of retrieval algorithms and prototypes [J].

Schmidt, R ;

Gierl, L .

ARTIFICIAL INTELLIGENCE IN MEDICINE, 2001, 23 (02) :171-186

[23]

Schmidt R, 1999, LECT NOTES ARTIF INT, V1620, P124

[24] A framework for knowledge-based temporal abstraction [J].

Shahar, Y .

ARTIFICIAL INTELLIGENCE, 1997, 90 (1-2) :79-133

[25]

SHINDO N, 2001, DISTRIBUTION INFLUEN

[26] Adaptation-guided retrieval: questioning the similarity assumption in reasoning [J].

Smyth, B ;

Keane, MT .

ARTIFICIAL INTELLIGENCE, 1998, 102 (02) :249-293

[27]

TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/h0026750

[28]

Wenkebach U, 1992, Proc Annu Symp Comput Appl Med Care, P18

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