Attentive Multiview Text Representation for Differential Diagnosis (2021)

Abstract: We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes. We apply our model to the problem of differential diagnosis, which aims to find the most probable diseases that match with clinical descriptions of patients, using data from the Undiagnosed Diseases Network. Our model outperforms several ranking approaches (including a commercially-supported system) by effectively prioritizing and combining representations obtained from traditional and recent text representation techniques. We elaborate on several aspects of our model and shed light on its improved performance.

## introduction electronic health records (ehrs) @xcite contain a wealth of documented information and insights about patients health and well-being. however, it is difficult to effectively process such data due to complex terminology, missing information, and imprecise clinical descriptions @xcite . in addition, an especially challenging class of diseases are orphan or rare diseases @xcite , which are diverse in symptoms and affect a smaller percentage of the population. in this paper, we investigate how well natural language processing (nlp) algorithms could reproduce the performance of clinical experts in the task of differential diagnosis-the process of distinguishing a particular disease from others that present similar clinical features, given medical histories (descriptions) of individual patients. we formulate this task as a ranking problem where the aim is to find the most probable diseases given medical histories of patients @xcite . we develop a novel pairwise ranking algorithm that combines different views of patient and disease descriptions, and prioritizes effective views through an attentive multiview neural model (amnm). we research this problem using data from the undiagnosed diseases network (udn) @xcite foot_0 , which includes concise medical history of patients and their corresponding diseases in the online mendelian inheritance in man (omim) dataset @xcite . 2 all diagnoses-mappings between each patient and corresponding diseases-are provided by a team of expert clinicians from the udn. the contributions of this paper are as follows: our attentive multiview neural model employs traditional and recent representation learning techniques and outperforms current pairwise neural ranking approaches through effective data fusion and attention strategies. we conduct several experiments to illustrate the utility of different fusion techniques for combining patient (query) and disease (document) representations. 3 ## method in many domains, entities can be represented from multiple views. for example, a patient can be represented by demographic data, medical history, diagnosis codes, radiology images, etc. we propose a neural model to effectively prioritize important views and combine them for ranking purposes. figure 1 shows our model, which comprises of three major components: (a): an attention network that estimates and weights the contribution of each view in the ranking process, (b): a fusion network the utilizes intra-view feature interactions to effectively combine query-document representations, and (c): a softmax layer at the end that estimates the query-document relevance scores given their combined representations. we first formulate the problem and then explain these components. ## related work the national institutes of health established the undiagnosed diseases network (udn) @xcite to facilitate research on undiagnosed and rare diseases. the udn is a network of 12 clinical sites, and application to the udn is open to all individuals who complete the application form and submit a referral letter from a health care professional @xcite . a committee of experts in a review session reviews each udn application and makes admission decisions. @xcite investigated major factors that may determine application outcomes of the udn, which has been found effective in developing computational models for predicting admission outcomes @xcite . in @xcite , authors developed a search engine for rare diseases, named findzebra foot_7 , which was based on information retrieval techniques available in indri search engine @xcite . in addition, previous work developed experimental setup to evaluate and compare search engines such as google or bing in predicting relevant diseases to given phenotypes @xcite , employed medical anthologies and information content techniques @xcite , leveraged collaborative filtering @xcite and ensemble techniques @xcite for this purpose. our work departs from previous research by investigating a multiview approach to undiagnosed patients, where we show effective attention and fusion techniques lead to better pairwise ranking for differential diagnosis. ## conclusion and future work given electronic health records of patients, we develop an attentive multiview text representation model to assist clinical experts by ranking the most probable and relevant diseases. accurate and timely diagnosis is especially important for critically ill patients as it assists specialists to distinguish, prioritize, and accelerate treatment for such patients. our work can be improved by (a): enriching the feature space through patient-and diseasespecific information such patient demographic information and clinical synopsis of diseases, (b): improving model's attention mechanism, and (c): tackling differences in word distributions across patients (queries) and diseases (documents).