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.

Structural and Electrical Investigation of C 60 –Graphene Vertical Heterostructures

Graphene, with its unique electronic and structural qualities, has become an important playground for studying adsorption and assembly of various materials including organic molecules. Moreover, organic/graphene vertical structures assembled by van der Waals interaction have potential for multifunctional device applications. Here, we investigate structural and electrical properties of vertical heterostructures composed of C60 thin film on graphene. The assembled film structure of C60 on graphene is investigated using transmission electron microscopy, which reveals a uniform morphology of C60 film on graphene with a grain size as large as 500 nm. The strong epitaxial relations between C60 crystal and graphene lattice directions are found, and van der Waals ab initio calculations support the observed phenomena. Moreover, using C60-graphene heterostructures, we fabricate vertical graphene transistors incorporating n-type organic semiconducting materials with an on/off ratio above 3 × 10(3). Our work demonstrates that graphene can serve as an excellent substrate for assembly of molecules, and attained organic/graphene heterostructures have great potential for electronics applications.