Is it possible to identify patient's sex when reading blinded illness narratives? An experimental study about gender bias

<p>Abstract</p> <p>Background</p> <p>In many diseases men and women, for no apparent medical reason, are not offered the same investigations and treatment in health care. This may be due to staff's stereotypical preconceptions about men and women, i.e., gender bias. In the clinical situation it is difficult to know whether gender differences in management reflect physicians' gender bias or male and female patients' different needs or different ways of expressing their needs. To shed some light on these possibilities this study investigated to what extent it was possible to identify patients' sex when reading their blinded illness narratives, i.e., do male and female patients express themselves differently enough to be recognised as men and women without being categorised on beforehand?</p> <p>Methods</p> <p>Eighty-one authentic letters about being diseased by cancer were blinded regarding sex and read by 130 students of medicine and psychology. For each letter the participants were asked to give the author's sex and to explain their choice. The success rates were analysed statistically. To illuminate the participants' reasoning the explanations of four letters were analysed qualitatively.</p> <p>Results</p> <p>The patient's sex was correctly identified in 62% of the cases, with significantly higher rates in male narratives. There were no differences between male and female participants. In the qualitative analysis the choice of a male writer was explained by: a short letter; formal language; a focus on facts and a lack of emotions. In contrast the reasons for the choice of a woman were: a long letter; vivid language; mention of emotions and interpersonal relationships. Furthermore, the same expressions were interpreted differently depending on whether the participant believed the writer to be male or female.</p> <p>Conclusion</p> <p>It was possible to detect gender differences in the blinded illness narratives. The students' explanations for their choice of sex agreed with common gender stereotypes implying that such stereotypes correspond, at least on a group level, to differences in male and female patients' illness descriptions. However, it was also obvious that preconceptions about gender obstructed and biased the interpretations, a finding with implications for the understanding of gender bias in clinical practice.</p

Recognition and normalization of temporal expressions in Serbian medical narratives

The temporal dimension emerges as one of the essential concepts in the field of medicine, providing a basis for the proper interpretation and understanding of medically relevant information, often recorded only in unstructured texts. Automatic processing of temporal expressions involves their identification and formalization in a language understandable to computers. This paper aims to apply the existing system for automatic processing of temporal expressions in Serbian natural language texts to medical narrative texts, to evaluate the system’s efficiency in recognition and normalization of temporal expressions and to determine the degree of necessary adaptation according to the characteristics and requirements of the medical domain

The impact of pretrained language models on negation and speculation detection in cross-lingual medical text: Comparative study

Background: Negation and speculation are critical elements in natural language processing (NLP)-related tasks, such as information extraction, as these phenomena change the truth value of a proposition. In the clinical narrative that is informal, these linguistic facts are used extensively with the objective of indicating hypotheses, impressions, or negative findings. Previous state-of-the-art approaches addressed negation and speculation detection tasks using rule-based methods, but in the last few years, models based on machine learning and deep learning exploiting morphological, syntactic, and semantic features represented as spare and dense vectors have emerged. However, although such methods of named entity recognition (NER) employ a broad set of features, they are limited to existing pretrained models for a specific domain or language. Objective: As a fundamental subsystem of any information extraction pipeline, a system for cross-lingual and domain-independent negation and speculation detection was introduced with special focus on the biomedical scientific literature and clinical narrative. In this work, detection of negation and speculation was considered as a sequence-labeling task where cues and the scopes of both phenomena are recognized as a sequence of nested labels recognized in a single step. Methods: We proposed the following two approaches for negation and speculation detection: (1) bidirectional long short-term memory (Bi-LSTM) and conditional random field using character, word, and sense embeddings to deal with the extraction of semantic, syntactic, and contextual patterns and (2) bidirectional encoder representations for transformers (BERT) with fine tuning for NER. Results: The approach was evaluated for English and Spanish languages on biomedical and review text, particularly with the BioScope corpus, IULA corpus, and SFU Spanish Review corpus, with F-measures of 86.6%, 85.0%, and 88.1%, respectively, for NeuroNER and 86.4%, 80.8%, and 91.7%, respectively, for BERT. Conclusions: These results show that these architectures perform considerably better than the previous rule-based and conventional machine learning-based systems. Moreover, our analysis results show that pretrained word embedding and particularly contextualized embedding for biomedical corpora help to understand complexities inherent to biomedical text.This work was supported by the Research Program of the Ministry of Economy and Competitiveness, Government of Spain (DeepEMR Project TIN2017-87548-C2-1-R)

Proceedings

Proceedings of the 3rd Nordic Symposium on Multimodal Communication. Editors: Patrizia Paggio, Elisabeth Ahlsén, Jens Allwood, Kristiina Jokinen, Costanza Navarretta. NEALT Proceedings Series, Vol. 15 (2011), vi+87 pp. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/22532

Socio-emotional Processing in Children, Adolescents and Young Adults with Traumatic Brain Injury

Objective: Research has demonstrated deficits in socio-emotional processing following childhood traumatic brain injury (TBI; Tonks et al., 2009a). However, it is not known whether a link exists between socio-emotional processing, TBI and offending. Drawing on Ochsner’s (2008) socio-emotional processing model, the current study aimed to investigate facial emotion recognition accuracy and bias in young offenders with TBI. Setting: Research was conducted across three youth offender services. Participants: Thirty seven participants completed the study. Thirteen participants reported a high dosage of TBI. Design: The study had a cross sectional within and between subjects design. Main Measures: Penton-Voak and Munafo’s (2012) emotional recognition task was completed. Results: The results indicated that young offenders with a TBI were not significantly worse at facial emotion recognition compared to those with no TBI. Both groups showed a bias towards positive emotions. No between group differences were found for emotion bias. Conclusion: The findings did not support the use of Ochsner’s (2008) socio-emotional processing model for this population. Due to the small sample size, inadequate power and lack of non-offender control groups, it is not possible to draw any firm conclusions from the results of this study. Future research should aim to investigate whether there are any links between TBI, socio-emotional processing and offending