ABSTRACT
BackgroundSocial determinants of health are non-medical factors that influence health outcomes (SDOH). There is a wealth of SDOH information available via electronic health records, clinical reports, and social media, usually in free texts format, which poses a significant challenge and necessitates the use of natural language processing (NLP) techniques to extract key information. ObjectiveThe objective of this research is to advance the automatic extraction of SDOH from clinical texts. Setting and DataThe case reports of COVID-19 patients from the published literature are curated to create a corpus. A portion of the data is annotated by experts to create gold labels, and active learning is used for corpus re-annotation. MethodsA named entity recognition (NER) framework is developed and tested to extract SDOH along with a few prominent clinical entities (diseases, treatments, diagnosis) from the free texts. The proposed model consists of three deep neural networks - A Transformer-based model, a BiLSTM model and a CRF module. ResultsThe proposed NER implementation achieves an accuracy (F1-score) of 92.98% on our test set and generalizes well on benchmark data. A careful analysis of case examples demonstrates the superiority of the proposed approach in correctly classifying the named entities. ConclusionsNLP can be used to extract key information, such as SDOH from free texts. A more accurate understanding of SDOH is needed to further improve healthcare outcomes.
ABSTRACT
BackgroundThere remains significant uncertainty in the definition of the long COVID disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. ObjectiveWe aim to determine the validity and effectiveness of advanced NLP approaches built to derive insight into Long COVID-related patient-reported health outcomes from social media platforms. MethodologyWe use Transformer-based BERT models to extract and normalize long COVID Symptoms and Conditions (SyCo) from English posts on Twitter and Reddit. Furthermore, we estimate the occurrence and co-occurrence of SyCo terms at any point or across time and locations. Finally, we compare the extracted health outcomes with human annotations and highly utilized clinical outcomes grounded in the medical literature. ResultBased on our findings, the top three most commonly occurring groups of long COVID symptoms are systemic (such as "fatigue"), neuropsychiatric (such as "anxiety" and "brain fog"), and respiratory (such as "shortness of breath"). Regarding the co-occurring symptoms, the pair of fatigue & headaches is most common. In addition, we show that other conditions, such as infection, hair loss, and weight loss, as well as mentions of other diseases, such as flu, cancer, or Lyme disease, are among the top reported terms by social media users. ConclusionThe outcome of our social media-derived pipeline is comparable with the outcomes of peer-reviewed articles relevant to long COVID symptoms. Overall, this study provides unique insights into patient-reported health outcomes from long COVID and valuable information about the patients journey that can help healthcare providers anticipate future needs.
