Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. The current scenario of assigning codes is a manual process which is very expensive, time-consuming and error prone. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) and Deep Learning (DL) methods and techniques to resolve the problem of manual coding of clinical narratives and to assist human coders to assign clinical codes more accurately and efficiently. This systematic literature review provides a comprehensive overview of automated clinical coding systems that utilises appropriate NLP, ML and DL methods and techniques to assign ICD codes to discharge summaries. We have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines and conducted a comprehensive search of publications from January, 2010 to December 2020 in four academic databases- PubMed, ScienceDirect, Association for Computing Machinery(ACM) Digital Library, and the Association for Computational Linguistics(ACL) Anthology. We reviewed 7,556 publications; 38 met the inclusion criteria. This review identified: datasets having discharge summaries; NLP techniques along with some other data extraction processes, different feature extraction and embedding techniques. To measure the performance of classification methods, different evaluation metrics are used. Lastly, future research directions are provided to scholars who are interested in automated ICD code assignment. Efforts are still required to improve ICD code prediction accuracy, availability of large-scale de-identified clinical corpora with the latest version of the classification system. This can be a platform to guide and share knowledge with the less experienced coders and researchers.
MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries
Miner School of Computer & Information Sciences, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校 Miner 计算机与信息科学学院）；Institute for Medical Engineering and Science, Massachusetts Institute of Technology（麻省理工学院医学工程与科学研究院）
Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding
University of New South Wales（新南威尔士大学）；The Australian e-Health Research Centre, CSIRO（澳大利亚联邦科学与工业研究组织澳大利亚电子健康研究中心）；Australian Institute of Health and Welfare（澳大利亚卫生与福利研究所）
Discharge Summary Hospital Course Summarisation of In Patient Electronic Health Record Text with Clinical Concept Guided Deep Pre-Trained Transformer Models
King’s College London（伦敦国王学院）；King’s College Hospital NHS Foundation Trust（国王学院医院NHS基金会信托）；University College London（伦敦大学学院）
Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital
Department of Health Information Office, Data & Digitalization, University Medical Center（健康信息办公室、数据与数字化化大学医院）；Department of Trauma Surgery / Orthopedics, University Medical Center（创伤外科/骨科大学医院）；Department of Orthopaedic Surgery, Massachusetts General Hospital（骨科手术科马萨诸塞总医院）；Harvard Medical School Orthopaedic Trauma Initiative（哈佛医学院骨创伤倡议）；Department of Orthopaedic Surgery and Sports Medicine, Amsterdam UMC location University of Amsterdam（骨科手术与运动医学部门，阿姆斯特丹UMC地点大学）；Department of Orthopaedic Trauma, Flinders University Medical Centre（骨创伤部门，弗林德斯大学医疗中心）；Department of Otolaryngology – Head and Neck Surgery, University Medical Center（耳鼻喉科-头颈外科大学医院）；Department of Pediatrics, Pediatrics Nephrology, Beatrix Children’s Hospital, University Medical Center（儿科、儿科肾病学，比西特儿童医院，大学医院）
De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models
University of New South Wales（新南威尔士大学）；The Australian e-Health Research Centre, CSIRO（澳大利亚联邦科学与工业研究组织澳大利亚电子健康研究中心）；Australian Institute of Health and Welfare（澳大利亚卫生与福利研究所）
EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries
KAIST（韩国科学技术院）；Seoul National University（首尔大学）；Seoul National University Bundang Hospital（首尔大学盆唐医院）；SAIHST, Sungkyunkwan University（成均馆大学）；Yonsei University College of Medicine（延世大学医学院）；Gangnam Severance Hospital（江南塞弗伦斯医院）；Severance Hospital（塞弗伦斯医院）；Seoul Medical Center（首尔医疗中心）；Seoul National University Hospital（首尔大学医院）；National Cancer Center（国立癌症中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Samsung Medical Center（三星医疗中心）
GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries
School of Computer Science, The University of Manchester（曼彻斯特大学计算机学院）；Health eResearch Center, The University of Manchester（曼彻斯特大学健康电子研究中心）；The Alan Turing Institute（艾伦·图灵研究所）
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification
Institute for Language, Cognition and Computation, University of Edinburgh（爱丁堡大学语言、认知与计算研究所）；Usher Institute, University of Edinburgh（爱丁堡大学乌谢尔研究所）；School of Literatures, Languages and Cultures, University of Edinburgh（爱丁堡大学文学、语言与文化学院）
WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV
University of Applied Sciences and Arts Dortmund（多特蒙德应用科学与艺术大学）；University Hospital Essen（埃森大学医院）；Bielefeld University of Applied Sciences and Arts（比勒费尔德应用科学与艺术大学）
Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan
Tokyo Metropolitan University（东京都立大学）；RIKEN（理化学研究所）；National Hospital Organization（国立医院机构）；Kitami Institute of Technology（北见工业大学）
MedFactEval and MedAgentBrief: A Framework and Workflow for Generating and Evaluating Factual Clinical Summaries
Center for Biomedical Informatics Research, Stanford University（生物医学信息学研究所在斯坦福大学）；Department of Biomedical Data Science, Stanford University（生物医学数据科学系，斯坦福大学）；Stanford Health Care（斯坦福健康中心）；Department of Anesthesiology and Pain Medicine, Stanford Medicine（麻醉与疼痛医学系，斯坦福医学院）；Department of Radiology, Stanford University（放射科，斯坦福大学）；Stanford Clinical Excellence Research Center, Stanford University（斯坦福临床卓越研究中心，斯坦福大学）；Department of Medicine, Stanford University（医学系，斯坦福大学）
