Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

Asymmetric electrocyclic reactions

This critical review offers an overview of asymmetric electrocyclic processes, where diastereo- or enantioselectivity is a consequence of the influence of a chiral component (be it substrate or catalyst) on the electrocyclic bond-forming process (195 references). © 2011 The Royal Society of Chemistry.

Regulator of G-protein signaling 4 (RGS4) gene is associated with schizophrenia in Irish high density families

The regulator of the G-protein signaling 4 (RGS4) gene was shown to have a different expression pattern in schizophrenia patients in a microarray study. A family-based study subsequently implicated the association of this gene with schizophrenia. We replicated the study with our sample from the Irish Study of High Density Schizophrenia Families (ISHDSF). Single marker transmission disequilibrium tests (TDT) for the four core SNPs showed modest association for SNP 18 (using a narrow diagnostic approach with FBAT P = 0.044; with PDT P = 0.0073) and a trend for SNP 4 (with FBAT P = 0.1098; with PDT P = 0.0249). For SNP 1 and 7, alleles overtransmitted to affected subjects were the same as previously reported. Haplotype analyses suggested that haplotype G-G-G for SNP1-4-18, which is the most abundant haplotype (42.3%) in the Irish families, was associated with the disease (narrow diagnosis, FBAT P = 0.0061, PDT P = 0.0498). This was the same haplotype implicated in the original study. While P values were not corrected for multiple testing because of the clear prior hypothesis, these results could be interpreted as supporting evidence for the association between RGS4 and schizophrenia.