Automated clinical diagnosis: The role of content in various sections of a clinical document

Clinical diagnosis is a critical aspect of patient care that is typically driven by expert medical knowledge and intuition. An automated system for clinical diagnosis could reduce the cognitive burden of clinicians during patient care and medical education. In this paper, we describe a Knowledge Graph (KG)-based clinical diagnosis system that leverages publicly available knowledge sources to infer possible diagnoses from free-text clinical narratives. We experiment with the content in various sections of a clinical document within the electronic health record (EHR) to investigate the contribution of each section to the performance of automated diagnosis systems. Evaluation on MIMIC-III dataset demonstrates that the content of “history of present illness” and “past medical history” sections can play a greater role for clinical diagnosis inference than other sections and all sections combined. Comparison with a state-of-the-art deep learning-based clinical diagnosis system confirms the effectiveness of our system.

[1]  Ian H. Witten,et al.  An open-source toolkit for mining Wikipedia , 2013, Artif. Intell..

[2]  Gang Feng,et al.  Disease Ontology: a backbone for disease semantic integration , 2011, Nucleic Acids Res..

[3]  Daniel S. Weld,et al.  Open Information Extraction Using Wikipedia , 2010, ACL.

[4]  Ellen M. Voorhees,et al.  Overview of the TREC 2014 Clinical Decision Support Track , 2014, TREC.

[5]  Oladimeji Farri,et al.  Clinical Question Answering using Key-Value Memory Networks and Knowledge Graph , 2016, TREC.

[6]  Qin Zhang,et al.  Clinical diagnosis expert system based on dynamic uncertain causality graph , 2014, 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference.

[7]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[8]  Xiaojun Wan,et al.  Graph-Based Multi-Modality Learning for Clinical Decision Support , 2016, CIKM.

[9]  Olivier Bodenreider,et al.  The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..

[10]  Lee Brooks,et al.  Non‐analytical models of clinical reasoning: the role of experience , 2007, Medical education.

[11]  Alexander Kotov,et al.  Optimization Method for Weighting Explicit and Latent Concepts in Clinical Decision Support Queries , 2016, ICTIR.

[12]  Ying Li,et al.  Section classification in clinical notes using supervised hidden markov model , 2010, IHI.

[13]  Walter F. Stewart,et al.  Doctor AI: Predicting Clinical Events via Recurrent Neural Networks , 2015, MLHC.

[14]  Gary C. Borchardt,et al.  External Knowledge Sources for Question Answering , 2005, TREC.

[15]  C. Langlotz RadLex: a new method for indexing online educational materials. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[16]  Siddhartha Jonnalagadda,et al.  Enhancing clinical concept extraction with distributional semantics , 2012, J. Biomed. Informatics.

[17]  Jimeng Sun,et al.  RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism , 2016, NIPS.

[18]  Kent A. Spackman,et al.  SNOMED clinical terms: overview of the development process and project status , 2001, AMIA.

[19]  Peter Szolovits,et al.  MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.

[20]  Erik T. Mueller,et al.  Watson: Beyond Jeopardy! , 2013, Artif. Intell..

[21]  Kent A. Spackman,et al.  SNOMED RT: a reference terminology for health care , 1997, AMIA.

[22]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[23]  Xiaohua Hu,et al.  Integrating extra knowledge into word embedding models for biomedical NLP tasks , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[24]  Yuan Ling,et al.  Improving Clinical Diagnosis Inference through Integration of Structured and Unstructured Knowledge , 2017 .

[25]  Oladimeji Farri,et al.  A Hybrid Approach to Clinical Question Answering , 2014, TREC.

[26]  Siddharth Patwardhan,et al.  WatsonPaths: Scenario-Based Question Answering and Inference over Unstructured Information , 2017, AI Mag..

[27]  Gang Pan,et al.  Semantic Health Knowledge Graph: Semantic Integration of Heterogeneous Medical Knowledge and Services , 2017, BioMed research international.

[28]  Charles Elkan,et al.  Learning to Diagnose with LSTM Recurrent Neural Networks , 2015, ICLR.

[29]  Sanda M. Harabagiu,et al.  Medical Question Answering for Clinical Decision Support , 2016, CIKM.

[30]  Laks V. S. Lakshmanan,et al.  Proceedings of the 2008 ACM SIGMOD international conference on Management of data , 2008, SIGMOD 2008.

[31]  Oladimeji Farri,et al.  Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning , 2017, IJCNLP.

[32]  Yinghui Wu,et al.  SLQ: a user-friendly graph querying system , 2014, SIGMOD Conference.

[33]  Oladimeji Farri,et al.  Using Neural Embeddings for Diagnostic Inferencing in Clinical Question Answering , 2015, TREC.

[34]  Oladimeji Farri,et al.  Condensed Memory Networks for Clinical Diagnostic Inferencing , 2016, AAAI.

[35]  Oladimeji Farri,et al.  Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study , 2017, MLHC.

[36]  Noémie Elhadad,et al.  A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts , 2012, J. Biomed. Informatics.

[37]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.