# Natural Language Processing for Smart Healthcare

arXiv: https://arxiv.org/abs/2110.15803 · HTML: https://mlsanity.com/paper/2110.15803/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 248 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 30 of 49)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer](https://mlsanity.com/paper/1906.04716/) (2019)
- [Text-to-SQL Generation for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1908.01839/) (2019)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis](https://mlsanity.com/paper/1901.10623/) (2019)
- [Understanding the Behaviors of BERT in Ranking](https://mlsanity.com/paper/1904.07531/) (2019)
- [Probing Biomedical Embeddings from Language Models](https://mlsanity.com/paper/1904.02181/) (2019)
- [Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data](https://mlsanity.com/paper/1804.01486/) (2018)
- [A Survey on Biomedical Image Captioning](https://mlsanity.com/paper/1905.13302/) (2019)
- [Semi-Supervised Variational Reasoning for Medical Dialogue Generation](https://mlsanity.com/paper/2105.06071/) (2021)
- [Improving Grounded Natural Language Understanding through Human-Robot Dialog](https://mlsanity.com/paper/1903.00122/) (2019)
- [Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery](https://mlsanity.com/paper/2002.06053/) (2020)
- [Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation](https://mlsanity.com/paper/2012.11988/) (2020)
- [Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records](https://mlsanity.com/paper/1606.07953/) (2016)
- [Medical Text Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1704.06841/) (2017)
- [Speech recognition for medical conversations](https://mlsanity.com/paper/1711.07274/) (2017)
- [A Review of Verbal and Non-Verbal Human-Robot Interactive Communication](https://mlsanity.com/paper/1401.4994/) (2014)
- [Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop](https://mlsanity.com/paper/2011.05533/) (2020)
- [On the Generation of Medical Dialogues for COVID-19](https://mlsanity.com/paper/2005.05442/) (2020)
- [Medical Concept Embedding with Time-Aware Attention](https://mlsanity.com/paper/1806.02873/) (2018)
- [Making effective use of healthcare data using data-to-text technology](https://mlsanity.com/paper/1808.03507/) (2018)

## Built on by (most cited first, 14 of 14)

- [Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?](https://mlsanity.com/paper/2301.09017/) (2023)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages](https://mlsanity.com/paper/2302.09685/) (2023)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Text-Transport: Toward Learning Causal Effects of Natural Language](https://mlsanity.com/paper/2310.20697/) (2023)
- [Text2MDT: Extracting Medical Decision Trees from Medical Texts](https://mlsanity.com/paper/2401.02034/) (2024)
- [Intent Detection and Entity Extraction from BioMedical Literature](https://mlsanity.com/paper/2404.03598/) (2024)
- [mhGPT: A Lightweight Generative Pre-Trained Transformer for Mental Health Text Analysis](https://mlsanity.com/paper/2408.08261/) (2024)
- [Evaluating ChatGPT on Medical Information Extraction Tasks: Performance, Explainability and Beyond](https://mlsanity.com/paper/2601.21767/) (2026)
- [Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data](https://mlsanity.com/paper/2411.15586/) (2024)
- [The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP](https://mlsanity.com/paper/2606.03250/) (2026)
- [Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation](https://mlsanity.com/paper/2606.05510/) (2026)
- [What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approaches](https://mlsanity.com/paper/2607.29090/) (2026)
- [Clinical Domain Classification from Medical Transcriptions](https://mlsanity.com/paper/2609.22734/) (2026)

## Abstract (arXiv)

Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work, we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We first elaborate on different NLP approaches and the NLP pipeline for smart healthcare from the technical point of view. Then, in the context of smart healthcare applications employing NLP techniques, we introduce representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss two specific medical issues, i.e., the coronavirus disease 2019 (COVID-19) pandemic and mental health, in which NLP-driven smart healthcare plays an important role. Finally, we discuss the limitations of current works and identify the directions for future works.