Discovering the Context of People With Disabilities: Semantic Categorization Test and Environmental Factors Mapping of Word Embeddings from Reddit

Measuring Implicit Bias in ICU Notes Using Word-Embedding Neural Network Models

BACKGROUND

Language in nonmedical data sets is known to transmit human-like biases when used in natural language processing (NLP) algorithms that can reinforce disparities. It is unclear if NLP algorithms of medical notes could lead to similar transmissions of biases.

RESEARCH QUESTION

Can we identify implicit bias in clinical notes, and are biases stable across time and geography?

STUDY DESIGN AND METHODS

To determine whether different racial and ethnic descriptors are similar contextually to stigmatizing language in ICU notes and whether these relationships are stable across time and geography, we identified notes on critically ill adults admitted to the University of California, San Francisco (UCSF), from 2012 through 2022 and to Beth Israel Deaconess Hospital (BIDMC) from 2001 through 2012. Because word meaning is derived largely from context, we trained unsupervised word-embedding algorithms to measure the similarity (cosine similarity) quantitatively of the context between a racial or ethnic descriptor (eg, African-American) and a stigmatizing target word (eg, nonco-operative) or group of words (violence, passivity, noncompliance, nonadherence).

RESULTS

In UCSF notes, Black descriptors were less likely to be similar contextually to violent words compared with White descriptors. Contrastingly, in BIDMC notes, Black descriptors were more likely to be similar contextually to violent words compared with White descriptors. The UCSF data set also showed that Black descriptors were more similar contextually to passivity and noncompliance words compared with Latinx descriptors.

INTERPRETATION

Implicit bias is identifiable in ICU notes. Racial and ethnic group descriptors carry different contextual relationships to stigmatizing words, depending on when and where notes were written. Because NLP models seem able to transmit implicit bias from training data, use of NLP algorithms in clinical prediction could reinforce disparities. Active debiasing strategies may be necessary to achieve algorithmic fairness when using language models in clinical research.

Health Communication through Positive and Solidarity Messages Amid the COVID-19 Pandemic: Automated Content Analysis of Facebook Uses

The COVID-19 outbreak has caused significant stress in our lives, which potentially increases frustration, fear, and resentful emotions. Managing stress is complex, but helps to alleviate negative psychological effects. In order to understand how the public coped with stress during the COVID-19 pandemic, we used Macao as a case study and collected 104,827 COVID-19 related posts from Facebook through data mining, from 1 January to 31 December 2020. Divominer, a big-data analysis tool supported by computational algorithm, was employed to identify themes and facilitate machine coding and analysis. A total of 60,875 positive messages were identified, with 24,790 covering positive psychological themes, such as "anti-epidemic", "solidarity", "hope", "gratitude", "optimism", and "grit". Messages that mentioned "anti-epidemic", "solidarity", and "hope" were the most prevalent, while different crisis stages, key themes and media elements had various impacts on public involvement. To the best of our knowledge, this is the first-ever study in the Chinese context that uses social media to clarify the awareness of solidarity. Positive messages are needed to empower social media users to shoulder their shared responsibility to tackle the crisis. The findings provide insights into users' needs for improving their subjective well-being to mitigate the negative psychological impact of the pandemic.