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.

Following instructions from working memory: Why does action at encoding and recall help?

Two experiments investigated the consequences of action at encoding and recall on the ability to follow sequences of instructions. Children aged 7–9 years recalled sequences of spoken action commands under presentation and recall conditions that either did or did not involve their physical performance. In both experiments, recall was enhanced by carrying out the instructions as they were being initially presented and also by performing them at recall. In contrast, the accuracy of instruction-following did not improve above spoken presentation alone, either when the instructions were silently read or heard by the child (Experiment 1), or when the child repeated the spoken instructions as they were presented (Experiment 2). These findings suggest that the enactment advantage at presentation does not simply reflect a general benefit of a dual exposure to instructions, and that it is not a result of their self-production at presentation. The benefits of action-based recall were reduced following enactment during presentation, suggesting that the positive effects of action at encoding and recall may have a common origin. It is proposed that the benefits of physical movement arise from the existence of a short-term motor store that maintains the temporal, spatial, and motoric features of either planned or already executed actions.

Shooting for the Moon or Flying Too Near the Sun? Crossing the Value Rubicon in Precision Cancer Care

In his last two State of the Union addresses, President Barack Obama has focused on the need to deliver innovative solutions to improve human health, through the Precision Medicine Initiative in 2015 and the recently announced Cancer Moonshot in 2016. Precision cancer care has delivered clear patient benefit, but even for high-impact medicines such as imatinib mesylate (Glivec) in chronic myeloid leukaemia, the excitement at the success of this practice-changing clinical intervention has been somewhat tempered by the escalating price of this 'poster child' for precision cancer medicine (PCM). Recent studies on the costs of cancer drugs have revealed significant price differentials, which are a major causative factor behind disparities in the access to new generations of immunological and molecularly targeted agents. In this perspective, we will discuss the benefits of PCM to modern cancer control, but also emphasise how increasing costs are rendering the current approaches to integrating the paradigm of PCM unsustainable. Despite the ever increasing pressure on cancer and health care budgets, innovation will and must continue. Value-based frameworks offer one of the most rational approaches for policymakers committed to improving cancer outcomes through a public health approach.

VLT FORS2 comparative transmission spectroscopy: Detection of Na in the atmosphere of WASP-39b from the ground

We present transmission spectroscopy of the warm Saturn-mass exoplanet WASP-39b made with the Very Large Telescope (VLT) FOcal Reducer and Spectrograph (FORS2) across the wavelength range 411-810nm. The transit depth is measured with a typical precision of 240 parts per million (ppm) in wavelength bins of 10nm on a V = 12.1 magnitude star. We detect the sodium absorption feature (3.2-sigma) and find evidence for potassium. The ground-based transmission spectrum is consistent with Hubble Space Telescope (HST) optical spectroscopy, strengthening the interpretation of WASP-39b having a largely clear atmosphere. Our results demonstrate the great potential of the recently upgraded FORS2 spectrograph for optical transmission spectroscopy, obtaining HST-quality light curves from the ground.