Harrison's Internal Medicine Chapter 19. Fever of Unknown Origin
Definition and Classification Fever of unknown origin (FUO) was defined by Petersdorf and Beeson in 1961 as (1) temperatures of 38.3°C (101°F) on several occasions; (2) a duration of fever of 3 weeks; and (3) failure to reach a diagnosis despite 1 week of inpatient investigation. While this classification has stood for more than 30 years, Durack and Street have proposed a new system for classification of FUO: (1) classic FUO; (2) nosocomial FUO; (3) neutropenic FUO; and (4) FUO associated with HIV infection.
Harrison's Internal Medicine Chapter 95. Carcinoma of Unknown Primary
Carcinoma of Unknown Primary: Introduction Carcinoma of unknown primary (CUP) is a biopsy-proven (mainly epithelial) malignancy for which the anatomic site of origin remains unidentified after an intensive search. CUP is one of the 10 most frequently diagnosed cancers worldwide, accounting for approximately 3–5% of all cancer cases.
We present a statistical model of Japanese unknown words consisting of a set of length and spelling models classified by the character types that constitute a word. The point is quite simple: different character sets should be treated differently and the changes between character types are very important because Japanese script has both ideograms like Chinese (kanji) and phonograms like English (katakana). Both word segmentation accuracy and part of speech tagging accuracy are improved by the proposed model. ...
Sheldon M. Wolff, MD, now deceased, was an author of a previous version of this chapter. It is to his memory that the chapter is dedicated. The substantial contributions of Charles A. Dinarello, MD, to this chapter in previous editions are gratefully acknowledged.
Bleeker-Rovers CP et al: A prospective multicenter study on fever of unknown origin: The yield of a structured diagnostic protocol. Medicine 86:26, 2007 [PMID: 17220753]
de Kleijn EM et al: Fever of unknown origin (FUO): I.
Source: From a study of 347 patients referred to the National Institutes of Health from 1961 to 1977 with a presumptive diagnosis of FUO of 6 months' duration (R Aduan et al. Prolonged fever of unknown origin. Clin Res 26:558A, 1978).
More than 200 conditions may be considered in the differential diagnosis of classic FUO in adults; the most common of these are listed in Table 19-3. This list applies strictly to the United States. Geographic considerations are paramount.
This paper addresses the task of handling unknown terms in SMT. We propose using source-language monolingual models and resources to paraphrase the source text prior to translation. We further present a conceptual extension to prior work by allowing translations of entailed texts rather than paraphrases only. A method for performing this process efﬁciently is presented and applied to some 2500 sentences with unknown terms. Our experiments show that the proposed approach substantially increases the number of properly translated texts. ...
This paper examines the feasibility of using statistical methods to train a part-of-speech predictor for unknown words. By using statistical methods, without incorporating hand-crafted linguistic information, the predictor could be used with any language for which there is a large tagged training corpus. Encouraging results have been obtained by testing the predictor on unknown words from the Brown corpus. The relative value of information sources such as affixes and context is discussed.
This paper presents a decision-tree approach to the problems of part-ofspeech disambiguation and unknown word guessing as they appear in Modem Greek, a highly inflectional language. The learning procedure is tag-set independent and reflects the linguistic reasoning on the specific problems. The decision trees induced are combined with a highcoverage lexicon to form a tagger that achieves 93,5% overall disambiguation accuracy.
In this paper, we present a method for guessing POS tags of unknown words using local and global information. Although many existing methods use only local information (i.e. limited window size or intra-sentential features), global information (extra-sentential features) provides valuable clues for predicting POS tags of unknown words. We propose a probabilistic model for POS guessing of unknown words using global information as well as local information, and estimate its parameters using Gibbs sampling.
We propose a collaborative framework for collecting Thai unknown words found on Web pages over the Internet. Our main goal is to design and construct a Webbased system which allows a group of interested users to participate in constructing a Thai unknown-word open dictionary. The proposed framework provides supporting algorithms and tools for automatically identifying and extracting unknown words from Web pages of given URLs. The system yields the result of unknownword candidates which are presented to the users for veriﬁcation. ...
The limited coverage of lexical-semantic resources is a signiﬁcant problem for NLP systems which can be alleviated by automatically classifying the unknown words. Supersense tagging assigns unknown nouns one of 26 broad semantic categories used by lexicographers to organise their manual insertion into W ORD N ET. Ciaramita and Johnson (2003) present a tagger which uses synonym set glosses as annotated training examples. We describe an unsupervised approach, based on vector-space similarity, which does not require annotated examples but signiﬁcantly outperforms their tagger. ...
This paper describes a hybrid model that combines a rule-based model with two statistical models for the task of POS guessing of Chinese unknown words. The rule-based model is sensitive to the type, length, and internal structure of unknown words, and the two statistical models utilize contextual information and the likelihood for a character to appear in a particular position of words of a particular length and POS category.
Morphological disambiguation proceeds in 2 stages: (1) an analyzer provides all possible analyses for a given token and (2) a stochastic disambiguation module picks the most likely analysis in context. When the analyzer does not recognize a given token, we hit the problem of unknowns. In large scale corpora, unknowns appear at a rate of 5 to 10% (depending on the genre and the maturity of the lexicon). We address the task of computing the distribution p(t|w) for unknown words for full morphological disambiguation in Hebrew. ...
This paper presents an approach to text categorization that i) uses no machine learning and ii) reacts on-the-ﬂy to unknown words. These features are important for categorizing Blog articles, which are updated on a daily basis and ﬁlled with newly coined words. We categorize 600 Blog articles into 12 domains. As a result, our categorization method achieved an accuracy of 94.0% (564/600).
Many events in news articles don’t include time arguments. This paper describes two methods, one based on rules and the other based on statistical learning, to predict the unknown time argument for an event by the propagation from its related events. The results are promising – the rule based approach was able to correctly predict 74% of the unknown event time arguments with 70% precision.
This paper describes a classifier that assigns semantic thesaurus categories to unknown Chinese words (words not already in the CiLin thesaurus and the Chinese Electronic Dictionary, but in the Sinica Corpus). The focus of the paper differs in two ways from previous research in this particular area. Prior research in Chinese unknown words mostly focused on proper nouns (Lee 1993, Lee, Lee and Chen 1994, Huang, Hong and Chen 1994, Chen and Chen 2000). This paper does not address proper nouns, focusing rather on common nouns, adjectives, and verbs. ...
Since written Chinese has no space to delimit words, segmenting Chinese texts becomes an essential task. During this task, the problem of unknown word occurs. It is impossible to register all words in a dictionary as new words can always be created by combining characters. We propose a uniﬁed solution to detect unknown words in Chinese texts. First, a morphological analysis is done to obtain initial segmentation and POS tags and then a chunker is used to detect unknown words.
Humans know a great deal about relationships among words. This paper discusses relationships among word pronunciations. We describe a computer system which models human judgement of rhyme by assigning specific roles to the location of primary stress, the similarity of phonetic segments, and other factors. By using the model as an experimental tool, we expect to improve our understanding of rhyme. A related computer model will attempt to generate pronunciations for unknown words by analogy with those for known words. ...
This paper describes a method of analysing words through morphological decomposition when the lexicon is incomplete. The method is used within a text-to-speech system to help generate pronunciations of unknown words. The method is achieved within a general morphological analyser system using Koskenniemi twolevel rules.
After completing this chapter, students will be able to: Explain the basic procedures used to solve equations for the unknown; list the five rules and the mechanical steps used to solve for the unknown in seven situations; know how to check the answers; list the steps for solving word problems; complete blueprint aids to solve word problems; check the solutions.