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Understanding causality is a longstanding goal across many different domains. Different articles, such as those published in medical journals, publish newly discovered knowledge, often causal. In this paper, we use this intuition to build a model that leverages causal relations to unearth factors related to Sjögren’s syndrome. Sjögren’s syndrome is an autoimmune disease affecting up to 3.1 million Americans. The uncommon nature of the disease, coupled with common symptoms of other autoimmune conditions such as rheumatoid arthritis, it is difficult for clinicians to timely diagnose the disease. This is further worsened by suboptimal communication between dentists, and physicians, including rheumatologists and ophthalmologists, because clinical manifestations of this disease require the patients to visit physicians with different specialties. A centralized information system with easy access to common and uncommon factors related to Sjögren’s syndrome may alleviate the problem. We use automatically extracted causal relationships from text related to Sjögren’s syndrome collected from the medical literature to identify a set of factors, such as “signs and symptoms” and “associated conditions”, related to this disease. We show that our approach is capable of retrieving such factors with high precision and recall values. Comparative experiments show that this approach leads to 25% improvement in retrieval F1-score compared to several state-of-the-art biomedical models, including BioBERT and Gram-CNN. 

Research articles published in medical journals often present findings from causal experiments. In this paper, we use this intuition to build a model that leverages causal relations expressed in text to unearth factors related to Sjögren’s syndrome. Sjögren’s syndrome is an auto-immune disease affecting up to 3.1 million Americans. The uncommon nature of the disease, coupled with common symptoms with other autoimmune conditions make the timely diagnosis of this disease very hard. A centralized information system with easy access to common and uncommon factors related to Sjögren’s syndrome may alleviate the problem. We use automatically extracted causal relationships from text related to Sjögren’s syndrome collected from the medical literature to identify a set of factors, such as “signs and symptoms” and “associated conditions”, related to this disease. We show that our approach is capable of retrieving such factors with a high precision and recall values. Comparative experiments show that this approach leads to 25% improvement in retrieval F1-score compared to several state-of-the-art biomedical models, including BioBERT and Gram-CNN.