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.

GPs’ and community pharmacists’ opinions on medication management at transitions of care in Ireland

Objective. The aim of this study was to survey GPs and community pharmacists (CPs) in Ireland regarding current practices of medication management, specifically medication reconciliation, communication between health care providers and medication errors as patients transition in care.
Methods. A national cross-sectional survey was distributed electronically to 2364 GPs, 311 GP Registrars and 2382 CPs. Multivariable associations comparing GPs to CPs were generated and content analysis of free text responses was undertaken.
Results. There was an overall response rate of 17.7% (897 respondents—554 GPs/Registrars and 343 CPs). More than 90% of GPs and CPs were positive about the effects of medication reconciliation on medication safety and adherence. Sixty per cent of GPs reported having no formal system of medication reconciliation. Communication between GPs and CPs was identified as good/very good by >90% of GPs and CPs. The majority (>80%) of both groups could clearly recall prescribing errors, following a transition of care, they had witnessed in the previous 6 months. Free text content analysis corroborated the positive relationship between GPs and CPs, a frustration with secondary care communication, with many examples given of prescribing errors.
Conclusions. While there is enthusiasm for the benefits of medication reconciliation there are limited formal structures in primary care to support it. Challenges in relation to systems that support inter-professional communication and reduce medication errors are features of the primary/secondary care transition. There is a need for an improved medication management system. Future research should focus on the identified barriers in implementing medication reconciliation and systems that can improve it.