title: Transformers for multi-label classification of medical text: an empirical comparison

authors: Yogarajan, Vithya

subjects: computer science

abstract: Recent advancements in machine learning-based multi-label medical text classification techniques have been used to help enhance healthcare and aid better patient care. This research is motivated by transformers’ success in natural language processing tasks, and the opportunity to further improve performance for medical-domain specific tasks by exploiting models pre-trained on health data. We consider transfer learning involving fine-tuning of pre-trained models for predicting medical codes, formulated as a multi-label problem. We find that domain-specific transformers outperform state-of-the-art results for multi-label problems with the number of labels ranging from 18 to 158, for a fixed sequence length. Additionally, we find that, for longer documents and/or number of labels greater than 300, traditional neural networks still have an edge over transformers. These findings are obtained by performing extensive experiments on the semi-structured eICU data and the free-form MIMIC III data, and applying various transformers including BERT, RoBERTa, and Longformer variations. The electronic health record data used in this research exhibits a high level of label imbalance. Considering individual label accuracy, we find that for eICU data medical-domain specific RoBERTa models achieve improvements for more frequent labels. For infrequent labels, in both datasets, traditional neural networks still perform better.

text: Transformers for multi-label classification of medical text: an empirical comparison Recent advancements in machine learning-based multi-label medical text classification techniques have been used to help enhance healthcare and aid better patient care. This research is motivated by transformers’ success in natural language processing tasks, and the opportunity to further improve performance for medical-domain specific tasks by exploiting models pre-trained on health data. We consider transfer learning involving fine-tuning of pre-trained models for predicting medical codes, formulated as a multi-label problem. We find that domain-specific transformers outperform state-of-the-art results for multi-label problems with the number of labels ranging from 18 to 158, for a fixed sequence length. Additionally, we find that, for longer documents and/or number of labels greater than 300, traditional neural networks still have an edge over transformers. These findings are obtained by performing extensive experiments on the semi-structured eICU data and the free-form MIMIC III data, and applying various transformers including BERT, RoBERTa, and Longformer variations. The electronic health record data used in this research exhibits a high level of label imbalance. Considering individual label accuracy, we find that for eICU data medical-domain specific RoBERTa models achieve improvements for more frequent labels. For infrequent labels, in both datasets, traditional neural networks still perform better.

year: 2021

title: Part-time Employees Were Not Created Equal: Exploring How Part-time Groups Differ on Measures That Can Predict Employee Turnover Intention

authors: Whistler, Hamish Tomas

subjects: part-time

abstract: This study built upon research by Martin and Sinclair (2007) who found the part-time employee group is made up of distinct groups that differ in their responses across a range of attitudinal variables (e.g. measures of job satisfaction and commitment). Part-time employees typically have higher employee turnover rates than full-time employees and this study investigated this issue by collecting data on attitudinal variables known to predict employee turnover intentions. 
The 311 participants of this study were from different branches across a large retail organisation in New Zealand. The results show that the high-school and tertiary student part-time groups often responded to scales similar to each other, while the 'PT (other)' and full-time employee groups were more similar in their responses. These results show a pattern of responses for the part-time group called 'PT (other)' that suggests they are less likely to quit compared to the two student part-time groups. Work status (part-time or full-time) was found to moderate the relationship between fulfilment of psychological contract with both turnover intentions (job search behaviours) and overall satisfaction. This study also developed and tested two new measures expected to relate to employee turnover. The first was pre-planned intention to quit, which successfully predicted differences between groups and had correlations with other scales. The second measure was social network strength, but this did not predict differences between groups or have correlations with other variables. 
	The main implication was that retail organisations should be aware that the student part-time groups are more likely to have a higher turnover rate than other groups. This information might be useful when recruiting for positions where an employee quit has significant negative effect on the operation of the organisation.

text: Part-time Employees Were Not Created Equal: Exploring How Part-time Groups Differ on Measures That Can Predict Employee Turnover Intention This study built upon research by Martin and Sinclair (2007) who found the part-time employee group is made up of distinct groups that differ in their responses across a range of attitudinal variables (e.g. measures of job satisfaction and commitment). Part-time employees typically have higher employee turnover rates than full-time employees and this study investigated this issue by collecting data on attitudinal variables known to predict employee turnover intentions. 
The 311 participants of this study were from different branches across a large retail organisation in New Zealand. The results show that the high-school and tertiary student part-time groups often responded to scales similar to each other, while the 'PT (other)' and full-time employee groups were more similar in their responses. These results show a pattern of responses for the part-time group called 'PT (other)' that suggests they are less likely to quit compared to the two student part-time groups. Work status (part-time or full-time) was found to moderate the relationship between fulfilment of psychological contract with both turnover intentions (job search behaviours) and overall satisfaction. This study also developed and tested two new measures expected to relate to employee turnover. The first was pre-planned intention to quit, which successfully predicted differences between groups and had correlations with other scales. The second measure was social network strength, but this did not predict differences between groups or have correlations with other variables. 
	The main implication was that retail organisations should be aware that the student part-time groups are more likely to have a higher turnover rate than other groups. This information might be useful when recruiting for positions where an employee quit has significant negative effect on the operation of the organisation.

year: 2009

title: Invading the Waikato: A postcolonial re-view

authors: Dench, Simon

subjects: New Zealand

abstract: The article discusses images of the Waikato Region of New Zealand from the 1860s, examining them within the context of British colonization and invasion. It comments on photographs by surveyor Daniel Manders Beere and British Army Royal Artillery Regiment Assistant Surgeon William Temple. The author also comments on a map of New Zealand's North Island made by surveyor Charles Heaphy and a map of Newcastle, New Zealand, depicting its earlier names of Ngāruawāhia and Queenstown. Other topics include spatial history, postcolonialism, and the 1863 to 1864 British invasion of Waikato.

text: Invading the Waikato: A postcolonial re-view The article discusses images of the Waikato Region of New Zealand from the 1860s, examining them within the context of British colonization and invasion. It comments on photographs by surveyor Daniel Manders Beere and British Army Royal Artillery Regiment Assistant Surgeon William Temple. The author also comments on a map of New Zealand's North Island made by surveyor Charles Heaphy and a map of Newcastle, New Zealand, depicting its earlier names of Ngāruawāhia and Queenstown. Other topics include spatial history, postcolonialism, and the 1863 to 1864 British invasion of Waikato.

year: 2011