PubMed · 14335316
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J P DEMARCO. 1965. AUTOMATIC NURSING'S PAPER WORK.. https://pubmed.ncbi.nlm.nih.gov/14335316/
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OBJECTIVES: We present in this article experiments on multi-language information extraction and access in the medical domain. For such applications, multilingual terminology plays a crucial role when working on specialized languages and specific domains. MATERIAL AND METHODS: We propose firstly a method for enriching multilingual thesauri which extracts new terms from parallel corpora, and secondly, a new approach for bilingual lexicon extraction from comparable corpora, which uses a bilingual thesaurus as a pivot. We illustrate their use in multi-language information retrieval (English/German) in the medical domains. RESULTS: Our experiments show that these automatically extracted bilingual lexicons are accurate enough (85% precision for term extraction) for semi-automatically enriching mono- or bi-lingual thesauri such as the universal medical language system, and that their use in cross-language information retrieval significantly improves the retrieval performance (from 22 to 40% average precision) and clearly outperforms existing bilingual lexicon resources (both general lexicons and specialized ones). CONCLUSION: We show in this paper first that bilingual lexicon extraction from parallel corpora in the medical domain could lead to accurate, specialized lexicons, which can be used to help enrich existing thesauri and second that bilingual lexicons extracted from comparable corpora outperform general bilingual resources for cross-language information retrieval.
A detailed approach is described for the vibrational spectroscopic encoding of polystyrene-based resin beads by converting the infrared absorption peaks suitable for encoding (encoding peaks) into barcodes. Based on combining the FT-IR measurements and the quantum-chemical computations, the vibrational characteristics of p-tert-butylstyrene monomer, polystyrene and poly(p-tert-butylstyrene) resin beads are analyzed, which are helpful for the selection of encoding peaks. The vibrational spectroscopic encoding of polystyrene-based resin beads could be obtained by converting the wavenumber, intensity and full width at half maximum (FWHM) of the encoding peaks into barcodes automatically through a computer program designed in our laboratory.
Extraction of relevant information from free-text clinical notes is becoming increasingly important in healthcare to provide personalized care to patients. The purpose of this dictionary-based NLP study was to determine the effects of using varying drug lexicons to automatically extract medication information from electronic medical records. A convenience training sample of 52 documents, each containing at least one medication, and a randomized test sample of 100 documents were used in this study. The training and test set documents contained a total of 681 and 641 medications respectively. Three sets of drug lexicons were used as sources for medication extraction: first, containing drug name and generic name; second with drug, generic and short names; third with drug, generic and short names followed by filtering techniques. Extraction with the first drug lexicon resulted in 83.7% sensitivity and 96.2% specificity for the training set and 85.2% sensitivity and 96.9% specificity for the test set. Adding the list of short names used for drugs resulted in increasing sensitivity to 95.0%, but decreased the specificity to 79.2% for the training set. Similar results of increased sensitivity of 96.4% and 80.1% specificity were obtained for the test set. Combination of a set of filtering techniques with data from the second lexicon increased the specificity to 98.5% and 98.8% for the training and test sets respectively while slightly decreasing the sensitivity to 94.1% (training) and 95.8% (test). Overall, the lexicon with filtering resulted in the highest precision, i.e., extracted the highest number of medications while keeping the number of extracted non-medications low.