Early fortification of enteral feedings for infants <1250 grams birth weight receiving a human milk diet including human milk based fortifier.

Background: An exclusive human milk diet (EHM) including fortification with a human milk-based fortifier has been shown to decrease the occurrence of necrotizing enterocolitis (NEC) but growth velocity may be less for infants receiving EHM compared to a bovine diet.

Objective: The objective of this study was to determine if growth is improved by earlier fortification of breast milk for preterm infants supported with a human milk based fortifier.

Study Design: A multi-center retrospective cohort study of the outcomes of infants of 500- 1250 g birth weight whose breast milk feedings were fortified at >60 mL/kg/day (late) versus <60 mL/kg/day (early) of enteral feeding volume.

Medical language processing applied to extract clinical information from Dutch medical documents.

In this paper, we want to show how an existing morpho-syntactic analyser for Dutch (Dutch Medical Language Processor--DMLP) has been extended in order to produce output that is compatible with the language independent modules of the LSP-MLP system (Linguistic String Project--Medical Language Processor) of the New York University. The former can focus on idiosyncrasies for Dutch and take advantage of the language independent developments of the latter. This general strategy will be illustrated by a practical application, namely the extraction of clinical information from Dutch patient discharge summaries.

Medical language processing with SGML display.

The paper demonstrates several ways that medical language processing can be combined with emerging display technologies to facilitate the extraction of data from free-text patient documents. The techniques allow rapid review via highlighting of the results of processing. Coupling of text markup with further procedures is envisioned.

Medical language processing: applications to patient data representation and automatic encoding.

A linguistic approach is presented to develop a representation of patient data. Semantic categories developed for computer processing of narrative clinical reports are shown to be similar to the Medical Concepts used manually to extract data from narrative in Exercises of the Computer-based Patient Record Institute. Clinical statement types composed of these categories are used in the Linguistic String Project (LSP) medical language processing (MLP) system to convert narrative information into relational database tables of patient information.

Natural language processing and the representation of clinical data.

Objective: Develop a representation of clinical observations and actions and a method of processing free-text patient documents to facilitate applications such as quality assurance.

Design: The Linguistic String Project (LSP) system of New York University utilizes syntactic analysis, augmented by a sublanguage grammar and an information structure that are specific to the clinical narrative, to map free-text documents into a database for querying.

Measurements: Information precision (I-P) and information recall (I-R) were measured for queries for the presence of 13 asthma-health-care quality assurance criteria in a database generated from 59 discharge letters.