RESULTS: The predictions by others of exon 3 skipping in specific DSPP mutations have been validated and a cryptic splicing donor site has been identified. However, the degree of mutational effect on

pre-mRNA splicing varied considerably depending on the changed nucleotide.

CONCLUSIONS: The predictions of exon 3 skipping in specific DSPP mutations have been validated, and a cryptic splicing donor site has been identified. Our data may provide insight into the contribution of DSPP mutations in the pathogenesis and genotype-phenotype correlations of hereditary dentin defects. Oral Diseases (2011) 17, 690-695″
“Background: In the United States, the Health Insurance Portability and Accountability CP-456773 clinical trial Act (HIPAA) protects the confidentiality check details of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA “”Safe Harbor”" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires

significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here.

Methods: This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers.

Results: The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on

image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification Trichostatin A in vivo systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries.

Conclusions: In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used.