Biomedical subjects

Tapio Salakoski

Publications and source records attributed to Tapio Salakoski.

6 recordsLinked to original sources

PubMed2006

Lexical adaptation of link grammar to the biomedical sublanguage: a comparative evaluation of three approaches.

BACKGROUND: We study the adaptation of Link Grammar Parser to the biomedical sublanguage with a focus on domain terms not found in a general parser lexicon. Using two biomedical corpora, we implement and evaluate three approaches to addressing unknown words: automatic lexicon expansion, the use of morphological clues, and disambiguation using a part-of-speech tagger. We evaluate each approach separately for its effect on parsing performance and consider combinations of these approaches. RESULTS: In addition to a 45% increase in parsing efficiency, we find that the best approach, incorporating information from a domain part-of-speech tagger, offers a statistically significant 10% relative decrease in error. CONCLUSION: When available, a high-quality domain part-of-speech tagger is the best solution to unknown word issues in the domain adaptation of a general parser. In the absence of such a resource, surface clues can provide remarkably good coverage and performance when tuned to the domain. The adapted parser is available under an open-source license.

Abstracting and Indexing↗

PubMed2006

Theoretical considerations of ethics in text mining of nursing documents.

This paper discusses theoretical considerations of ethics in building and using a text mining application in nursing documentation. Nursing documentation is based on the process of gathering information from the patient, setting goals for care, documenting nursing interventions and evaluating delivered nursing care. Privacy-sensitive health care documentation brings specific ethical concerns and difficulties that one needs to be aware of and conform to when developing and using text mining tools in electronic patient records. We discuss how patient confidentiality can be ensured in this domain and how text mining might support nurses to give better and more efficient care for their patients. Our conclusion is that text mining of nursing documents holds the promise of great benefits when the potential risks are taken into consideration.

Confidentiality↗

PubMed2006

Towards automated classification of intensive care nursing narratives.

Nursing narratives are an important part of patient documentation, but the possibilities to utilize them in the direct care process are limited due to the lack of proper tools. One solution to facilitate the utilization of narrative data could be to classify them according to their content. In this paper, we addressed two issues related to designing an automated classifier: domain experts' agreement on the content of the classes into which the data are to be classified, and the ability of the machine-learning algorithm to perform the classification on an acceptable level. The data we used were a set of Finnish intensive care nursing narratives. By using Cohen's kappa, we assessed the agreement of three nurses on the content of the classes Breathing, Blood Circulation and Pain, and by using the area under ROC curve (AUC), we measured the ability of the Least Squares Support Vector Machine (LS-SVM) algorithm to learn the classification patterns of the nurses. On average, the values of kappa were around 0.8. The agreement was highest in the class Blood Circulation, and lowest in the class Breathing. The LS-SVM algorithm was able to learn the classification patterns of the three nurses on an acceptable level; the values of AUC were generally around 0.85. Our results indicate that one way to develop electronic patient records could be tools that handle the free text in nursing documentation.

Algorithms↗

PubMed2005

Evaluation of two dependency parsers on biomedical corpus targeted at protein-protein interactions.

We present an evaluation of Link Grammar and Connexor Machinese Syntax, two major broad-coverage dependency parsers, on a custom hand-annotated corpus consisting of sentences regarding protein-protein interactions. In the evaluation, we apply the notion of an interaction subgraph, which is the subgraph of a dependency graph expressing a protein-protein interaction. We measure the performance of the parsers for recovery of individual dependencies, fully correct parses, and interaction subgraphs. For Link Grammar, an open system that can be inspected in detail, we further perform a comprehensive failure analysis, report specific causes of error, and suggest potential modifications to the grammar. We find that both parsers perform worse on biomedical English than previously reported on general English. While Connexor Machinese Syntax significantly outperforms Link Grammar, the failure analysis suggests specific ways in which the latter could be modified for better performance in the domain.

Abstracting and Indexing↗

PubMed2005

Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation.

BACKGROUND: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very efficient in general data mining tasks. We explore their capability for the gene versus protein name disambiguation task. RESULTS: We incorporated into the conventional SVM a weighting scheme based on distances of context words from the word to be disambiguated. This weighting scheme increased the performance of SVMs by five percentage points giving performance better than 85% as measured by the area under ROC curve and outperformed the Weighted Additive Classifier, which also incorporates the weighting, and the Naive Bayes classifier. CONCLUSION: We show that the performance of SVMs can be improved by the proposed weighting scheme. Furthermore, our results suggest that in this study the increase of the classification performance due to the weighting is greater than that obtained by selecting the underlying classifier or the kernel part of the SVM.

Algorithms↗

PubMed2002

Mobile access to biological databases on the Internet.

We have developed a new way of accessing biological databases and bioinformatics applications on the Internet. This new service, bioinformatics wireless application protocol (BioWAP) service, which is accessible by mobile devices makes it possible to access bioinformatics services, where normal PC or personal digital assistant (PDA) connections are not feasible. The BioWAP service includes major biological databases and applications demonstrating a simple method of implementing WAP interfaces to uncompliant applications, i.e. the applications that are not WAP or Internet based. The BioWAP service can be browsed with any WAP terminal.

Computational Biology↗