# A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing arXiv: https://arxiv.org/abs/2112.05780 ยท HTML: https://mlsanity.com/paper/2112.05780/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 14 of its 56 references that are in the map. - First posted on arXiv 7 Dec 2021. ## Builds on (most cited first, 14 of 14) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019) - [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019) - [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://mlsanity.com/paper/2002.08910/) (2020) - [Lessons from Natural Language Inference in the Clinical Domain](https://mlsanity.com/paper/1808.06752/) (2018) - [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018) - [NegBio: a high-performance tool for negation and uncertainty detection in radiology reports](https://mlsanity.com/paper/1712.05898/) (2017) - [Text-to-SQL Generation for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1908.01839/) (2019) - [Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction](https://mlsanity.com/paper/2004.05438/) (2020) - [Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset](https://mlsanity.com/paper/2005.00574/) (2020) - [CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes](https://mlsanity.com/paper/2106.02524/) (2021) - [A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients](https://mlsanity.com/paper/2003.03044/) (2020) ## Built on by (most cited first, 10 of 10) - [The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs](https://mlsanity.com/paper/2303.12961/) (2023) - [DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing](https://mlsanity.com/paper/2209.14901/) (2022) - [Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes](https://mlsanity.com/paper/2306.05270/) (2023) - [Task formulation for Extracting Social Determinants of Health from Clinical Narratives](https://mlsanity.com/paper/2301.11386/) (2023) - [Progress Note Understanding -- Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 Shared Task](https://mlsanity.com/paper/2303.08038/) (2023) - [A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification](https://mlsanity.com/paper/2401.13887/) (2024) - [ACR: A Benchmark for Automatic Cohort Retrieval](https://mlsanity.com/paper/2406.14780/) (2024) - [Identifying Health Risks from Family History: A Survey of Natural Language Processing Techniques](https://mlsanity.com/paper/2403.09997/) (2024) - [PARHAF, a human-authored corpus of clinical reports for fictitious patients in French](https://mlsanity.com/paper/2603.20494/) (2026) - [Design considerations for a hierarchical semantic compositional framework for medical natural language understanding](https://mlsanity.com/paper/2204.02067/) (2022) ## Abstract (arXiv) Objective: to provide a scoping review of papers on clinical natural language processing (NLP) tasks that use publicly available electronic health record data from a cohort of patients. Materials and Methods: We searched six databases, including biomedical research and computer science literature database. A round of title/abstract screening and full-text screening were conducted by two reviewers. Our method followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Results: A total of 35 papers with 47 clinical NLP tasks met inclusion criteria between 2007 and 2021. We categorized the tasks by the type of NLP problems, including name entity recognition, summarization, and other NLP tasks. Some tasks were introduced with a topic of clinical decision support applications, such as substance abuse, phenotyping, cohort selection for clinical trial. We summarized the tasks by publication and dataset information. Discussion: The breadth of clinical NLP tasks keeps growing as the field of NLP evolves with advancements in language systems. However, gaps exist in divergent interests between general domain NLP community and clinical informatics community, and in generalizability of the data sources. We also identified issues in data selection and preparation including the lack of time-sensitive data, and invalidity of problem size and evaluation. Conclusions: The existing clinical NLP tasks cover a wide range of topics and the field will continue to grow and attract more attention from both general domain NLP and clinical informatics community. We encourage future work to incorporate multi-disciplinary collaboration, reporting transparency, and standardization in data preparation.