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- Date
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Gaus, W.; Kaluscha, R.: Maschinelle inhaltliche Erschließung von Arztbriefen und Auswertung von Reha-Entlassungsberichten (2006)
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- Abstract
- Schon Hippokrates empfahl den Ärzten, Krankenakten zu führen. Heute ist die detaillierte Dokumentation eine Standespflicht der Ärzte [Gaus et al 1999]. Diese Dokumentationen medizinischer Behandlungen bergen einen riesigen und wertvollen Erfahrungsschatz. Informationen zu Therapien und Behandlungsergebnissen, die in Studien erst mühsam erhoben werden müssten, sind bereits in der Routinedokumentation wie Operations- und Entlassungsberichten oder Arztbriefen zahlreich vorhanden. Mit der Einführung der elektronischen Datenverarbeitung in der Medizin liegen diese Informationen seit einigen Jahren auch maschinenlesbar vor, so dass ein Haupthemmnis für die Nutzung dieser Dokumentationen, nämlich die mühsame manuelle Aufbereitung der Papierakten, entfällt. Während die formale Erschließung nach Patientenmerkmalen wie Name und Geburtsdatum von den Krankenhaus- bzw. Praxisinformationssystemen heutzutage gut gelöst ist, bleibt die inhaltliche Erschließung dieser Dokumentationen schwierig, da nur wenige Informationen in strukturierter oder intellektuell indexierter Form vorliegen [Leiner et al. 2003]. Auch wenn nach der Einführung der Fallpauschalen (diagnosis related groups, DRG) in den Krankenhäusern die Diagnosen nach ICD-10 verschlüsselt werden, besteht ein Großteil der Informationen weiterhin aus freiem Text, dessen computerbasierte Erschließung aufgrund der Komplexität menschlicher Sprache nicht trivial ist. Zu diesen medizinischen Texten gehören u.a. Gutachten, verbal beschriebene (Differential-) Diagnosen, vielfältige Untersuchungs- und Befundberichte, Visitenblätter, Operationsberichte und der Arztbrief bzw. Entlassungsbericht. Arztbrief und Entlassbericht dienen der Information des einweisenden oder weiterbehandelnden Arztes (z.B. Hausarzt) über das, was mit dem Patienten geschehen ist, und geben Empfehlungen zur Weiterbehandlung. Sie fassen eine (stationäre) Behandlung epikritisch - also nachdem die Krankheit überwunden ist, im Rückblick - zusammen und geben einen Überblick über Anamnese (Vorgeschichte), Beschwerden und Symptome, die eingesetzten diagnostischen Verfahren, die gestellte(n) Diagnose(n), Therapie, Verlauf, Komplikationen und das erzielte Ergebnis. Sie haben somit eine ähnliche Funktion wie das Abstract in der Literaturdokumentation, oft wird eine Kopie in der Krankenakte obenauf abgelegt. Zumindest in Universitätskliniken möchten wissenschaftlich arbeitende Ärzte auch unter inhaltlichen Gesichtspunkten auf die Krankenakten zugreifen können, z.B. die Krankenakten aller Patienten mit einer bestimmten Diagnose einsehen, exzerpieren und die exzerpierten Daten auswerten. Auch bei der Suche nach ähnlichen Fällen oder im Bereich der Aus- und Fortbildung hilft eine inhaltliche Erschließung weiter. So könnte etwa ein Assistenzarzt, der im Rahmen seiner Weiterbildung demnächst Sonografien des Kniegelenkes durchzuführen hat, sich vorhandene Berichte von solchen Sonografien anschauen und sich so über relevante Untersuchungstechniken und Befunde vorab informieren.
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- Source
- Theory of subject analysis: a sourcebook. Ed.: L.M. Chan, et al
-
Needham, R.M.; Sparck Jones, K.: Keywords and clumps (1985)
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- Source
- Theory of subject analysis: a sourcebook. Ed.: L.M. Chan, et al
-
Schneider, C.; Womser-Hacker, C.: Inhaltserschließungssysteme für Patenttexte : Test und Systemvergleich im Projekt PADOK (1986)
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Jones, K.P.: Natural-language processing and automatic indexing : a reply (1990)
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- Footnote
- Erwiderung auf: Korycinski, C. u. A.F. Newell
-
Martins, A.L.; Souza, R.R.; Ribeiro de Mello, H.: ¬The use of noun phrases in information retrieval : proposing a mechanism for automatic classification (2014)
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- Abstract
- This paper presents a research on syntactic structures known as noun phrases (NP) being applied to increase the effectiveness and efficiency of the mechanisms for the document's classification. Our hypothesis is the fact that the NP can be used instead of single words as a semantic aggregator to reduce the number of words that will be used for the classification system without losing its semantic coverage, increasing its efficiency. The experiment divided the documents classification process in three phases: a) NP preprocessing b) system training; and c) classification experiments. In the first step, a corpus of digitalized texts was submitted to a natural language processing platform1 in which the part-of-speech tagging was done, and them PERL scripts pertaining to the PALAVRAS package were used to extract the Noun Phrases. The preprocessing also involved the tasks of a) removing NP low meaning pre-modifiers, as quantifiers; b) identification of synonyms and corresponding substitution for common hyperonyms; and c) stemming of the relevant words contained in the NP, for similitude checking with other NPs. The first tests with the resulting documents have demonstrated its effectiveness. We have compared the structural similarity of the documents before and after the whole pre-processing steps of phase one. The texts maintained the consistency with the original and have kept the readability. The second phase involves submitting the modified documents to a SVM algorithm to identify clusters and classify the documents. The classification rules are to be established using a machine learning approach. Finally, tests will be conducted to check the effectiveness of the whole process.
- Source
- Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
-
Plaunt, C.; Norgard, B.A.: ¬An association-based method for automatic indexing with a controlled vocabulary (1998)
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- Date
- 11. 9.2000 19:53:22
-
Schwarz, C.: Komplexe Nominalgruppen als Indexierungseinheiten am Beispiel des Projekte CONDOR (1982)
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Salton, G.; Allen, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine-readable data (1994)
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