Artificial intelligence based predictive models trained on the clinical notes of patients can be demographically biased, often influenced by the demographic distribution of the training data. This could lead to adverse healthcare disparities in predicting outcomes like length of stay of the patients. To avoid such possibilities, it is necessary to mitigate the demographic biases within these models so that the model predicts outcomes for individual patients in a fair manner. We proposed an implicit in-processing debiasing method to combat disparate treatment which occurs when the machine learning model predict different outcomes for individuals based on the sensitive attributes like gender, ethnicity, race, and likewise. For this purpose, we used clinical notes of heart failure patients and used diagnostic codes, procedure reports and physiological vitals of the patients. We used Clinical Bidirectional Encoder Representations from Transformers (Clinical BERT) to obtain feature embeddings within the diagnostic codes and procedure reports, and Long Short-Term Memory (LSTM) autoencoders to obtain feature embeddings within the physiological vitals. Then, we trained two separate deep learning contrastive learning frameworks, one for gender and the other for ethnicity to obtain debiased representations within those demographic traits. We called this debiasing framework as Debias-CLR. We leveraged clinical phenotypes of the patients identified in the diagnostic codes and procedure reports in the previous study to measure the fairness statistically. We found that Debias-CLR was able to reduce the Single-Category Word Embedding Association Test (SC-WEAT) effect size score when debiasing for gender from 0.8 to 0.3 and from 0.4 to 0.2 while using clinical phenotypes in the diagnostic codes and procedure reports respectively as targets. Similarly, after debiasing for ethnicity, the SC-WEAT effect size score reduced from 1 to 0.5 and from -1 to 0.3 in an opposite bias direction while using clinical phenotypes in the diagnostic codes and procedure reports respectively as targets. We further found that in order to obtain fair representations in the embedding space using Debias-CLR, the accuracy of the predictive models on downstream tasks like predicting length of stay of the patients did not get reduced as compared to using the un-debiased counterparts for training the predictive models. Hence, we conclude that our proposed approach, Debias-CLR is fair and representative in mitigating demographic biases and can reduce health disparities by making fair predictions for the underrepresented populations.

Understanding public outlook in healthcare management is important in the study of the various diseases. With respect to vaccinations, which play a major role in combating vaccine-preventable diseases, the study on their acceptance or rejection by the public becomes useful. In particular to the influenza vaccine, studies on the public opinion and views is ongoing. Social media platforms like Twitter help us to leverage thoughts and attitudes related to the flu vaccine. The data set used for our analysis contained tweets related to vaccines which were collected using vaccine-related keywords over a period of twelve months from February, 2018 to January, 2019. Out of these tweets, we filtered out the tweets specific to the flu vaccine and generated our corpus for further study. By using Latent Dirichlet Allocation (LDA), we identified eighteen topics comprising six major themes which best represented our corpus. In this paper, we discuss these six themes and subsequently analyze the trend observed in these themes over a period of twelve months. The themes identified covered various aspects related to the flu vaccine. Among the six major themes, four showed a distinctive temporal trend with respect to the annual flu season.

The recent global outbreak of the coronavirus disease (COVID-19) has spread to all corners of the globe, introducing numerous social challenges. Twitter platforms have been used to identify public opinion about events at the local and global scale. In this study, we constructed a system to identify the relevant tweets related to the COVID-19 pandemic throughout January 1st, 2020 to April 30th, 2020 and explored topic modeling to identify the most discussed topics and themes during this period. Additionally, we analyzed the temporal changes in the topics with respect to the events that occurred. We found eight topics were sufficient to identify the themes in our corpus. The dominant topics were found to vary over time and align with the events related to the COVID-19 pandemic.

Educational programs about vaccination tend to target vaccine acceptance and reduction of hesitancy. Social media provides a promising platform for studying public perception regarding vaccination. In this study, we harvested tweets over a year related to vaccines from February 2018 to January 2019. We present a two-stage classifier to: (1) classify the tweets as relevant or non-relevant and (2) categorize them in terms of pro-vaccination, anti-vaccination, or neutral outlooks. We found that the classifier was able to distinguish clearly between antivaccination and pro-vaccination tweets, but also misclassified many of these as neutral. Using Latent Dirichlet Allocation, we found that two topics were sufficient to describe the corpus of tweets. These dealt with: (1) consequences of vaccination/non- vaccination, and (2) promotion of vaccination/non-vaccination. Finally, using the NRC emotion lexicon, we found practically significant differences in emotions expressed about vaccination between vaccine outlooks, but no practically significant temporal differences by month across a year.