Lifelong learning:Reflections of a junior doctor

Purpose - The aim of this paper is investigate the importance of lifelong learning in the area of medicine. Design/methodology/approach - The paper examines changes in methods of learning and challenges to educators today. Findings - The paper finds that today there is a pressure for the formalized accounting of education with a few high profile cases of negligent medical care which have perhaps created the belief that recording learning for all may catch errant doctors in the future. It is suggested that learning risks becoming an exercise in meeting demands, but inspiring the desire to learn is also crucial. Originality/value - The paper provides a useful opinion on lifelong learning for the medical profession. © Emerald Group Publishing Limited.

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.

A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings

Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at this http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York.

Additivity Pretraining and cue competition effects: Developmental evidence for a reasoning account of causal learning

The effect of additivity pretraining on blocking has been taken as evidence for a reasoning account of human and animal causal learning. If inferential reasoning underpins this effect, then developmental differences in the magnitude of this effect in children would be expected. Experiment 1 examined cue competition effects in children's (4- to 5-year-olds and 6- to 7-year-olds) causal learning using a new paradigm analogous to the food allergy task used in studies of human adult causal learning. Blocking was stronger in the older than the younger children, and additivity pretraining only affected blocking in the older group. Unovershadowing was not affected by age or by pretraining. In experiment 2, levels of blocking were found to be correlated with the ability to answer questions that required children to reason about additivity. Our results support an inferential reasoning explanation of cue competition effects. (c) 2012 APA, all rights reserved.

Risky decision making in younger and older adults:The role of learning.

Are older adults risk seeking or risk averse? Answering this question might depend on both the task used and the analysis performed. By modeling responses to the Balloon Analogue Risk Task (BART), our results illustrate the value of modeling as compared to relying on common analysis techniques. While analysis of overall measures suggested initially that older and younger adults do not differ in their risky decisions, our modeling results indicated that younger adults were at first more willing to take greater risks. Furthermore, older adults may be more cautious when their decision making is based on initial perceptions of risk, rather than learning following some experience with a task.

Evaluating the quality of learning in Computer Supported Co-operative Learning

Can learning quality be maintained in the face of increasing class size by the use of Computer Supported Co-operative Learning (CSCL) technologies? In particular, can Computer-Mediated Communication promote critical thinking in addition to surface information transfer? We compared face-to-face seminars with asynchronous computer conferencing in the same Information Management class. From Garrison's theory of critical thinking and Henri's critical reasoning skills, we developed two ways of evaluating critical thinking: a student questionnaire and a content analysis technique. We found evidence for critical thinking in both situations, with some subtle differences in learning style. This paper provides an overview of this work.

A defeasible reasoning model of inductive concept learning from examples and communication

This paper introduces a logical model of inductive generalization, and specifically of the machine learning task of inductive concept learning (ICL). We argue that some inductive processes, like ICL, can be seen as a form of defeasible reasoning. We define a consequence relation characterizing which hypotheses can be induced from given sets of examples, and study its properties, showing they correspond to a rather well-behaved non-monotonic logic. We will also show that with the addition of a preference relation on inductive theories we can characterize the inductive bias of ICL algorithms. The second part of the paper shows how this logical characterization of inductive generalization can be integrated with another form of non-monotonic reasoning (argumentation), to define a model of multiagent ICL. This integration allows two or more agents to learn, in a consistent way, both from induction and from arguments used in the communication between them. We show that the inductive theories achieved by multiagent induction plus argumentation are sound, i.e. they are precisely the same as the inductive theories built by a single agent with all data. © 2012 Elsevier B.V.

Categorizations of physical gesture in piano teaching: A preliminary enquiry

The significance of the “physicality” involved in learning to play a musical instrument and the essential role of teachers are areas in need of research. This article explores the role of gesture within teacher–student communicative interaction in one-to-one piano lessons. Three teachers were required to teach a pre-selected repertoire of two contrasting pieces to three students studying piano grade 1. The data was collected by video recordings of piano lessons and analysis based on the type and frequency of gestures employed by teachers in association to teaching behaviours specifying where gestures fit under (or evade) predefined classifications. Spontaneous co-musical gestures were observed in the process of piano tuition emerging with similar general communicative purposes as spontaneous co-verbal gestures and were essential for the process of musical communication between teachers and students. Observed frequencies of categorized gestures varied significantly between different teaching behaviours and between the three teachers. Parallels established between co-verbal and co-musical spontaneous gestures lead to an argument for extension of McNeill’s (2005) ideas of imagery–language–dialectic to imagery–music–dialectic with relevant implications for piano pedagogy and fields of study invested in musical communication.