Condensed Memory Networks for Clinical Diagnostic Inferencing

By Aaditya (Adi) Prakash, Siyuan Zhao, Sadid A. Hasan, Vivek Datla, Kathy Lee, Ashequl Qadir, Joey Liu, Oladimeji Farri. 2016

108 citations recorded by the source corpus.

Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly consider multivariate observational data (e.g. physiological signals, lab tests etc.). In contrast, we explore the problem using free-text medical notes recorded in an electronic health record (EHR). Complex tasks like these can benefit from structured knowledge bases, but those are not scalable. We instead exploit raw text from Wikipedia as a knowledge source. Memory networks have been demonstrated to be effective in tasks which require comprehension of free-form text. They use the final iteration of the learned representation to predict probable classes. We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory. Experiments on the MIMIC-III dataset show that the proposed model outperforms other variants of memory networks to predict the most probable diagnoses given a complex clinical scenario.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Joint Embedding of Words and Labels for Text Classification (2018, methodology): “We also compare with three recent methods for multi-label classification of clinical text, including Condensed Memory Networks (C-MemNN) (Prakash et al., 2017), Attentive LSTM (Shi et al.”
Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study (2017, background): “Existing works on diagnostic inferencing mostly propose supervised classification models using various neural network architectures (Lipton et al., 2015; Choi et al., 2015; Prakash et al., 2017).”
Deep learning in clinical natural language processing: a methodical review (2019, methodology): “During the process, we noticed that specific DL models mentioned in the title were probably missed based on the current search and can be considered as keywords in the future—for instance, condensed memory networks, 39 graph-based models, BERT 41 , etc.”
Graph Classification using Structural Attention (2018, background): “In a sense, this is conceptually similar to the memory networks of [22, 27].”
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning (2017, methodology): “Existing approaches for diagnosis inferencing mostly propose supervised classiﬁcation models using various neural network architectures (Lipton et al., 2015; Choi et al., 2015; Prakash et al., 2017).”