Natural Language Processing for Smart Healthcare

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 focus on feature extraction and modelling for various NLP tasks encountered in smart healthcare from a technical point of view. In the context of smart healthcare applications employing NLP techniques, the elaboration largely attends to representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss the limitations of current works and identify the directions for future works.

[1]  Manuel Palomar,et al.  A knowledge based method for the medical question answering problem , 2007, Comput. Biol. Medicine.

[2]  Bo Liu,et al.  Medical Visual Question Answering via Conditional Reasoning , 2020, ACM Multimedia.

[3]  Dan Roth,et al.  Extraction of events and temporal expressions from clinical narratives , 2013, J. Biomed. Informatics.

[4]  Arzucan Özgür,et al.  Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery , 2020, Drug discovery today.

[5]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[6]  Weida Tong,et al.  InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance , 2021, Frontiers in Artificial Intelligence.

[7]  Emiel Krahmer,et al.  Making effective use of healthcare data using data-to-text technology , 2018, Data Science for Healthcare.

[8]  Zhiyong Lu,et al.  A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval , 2018, WWW.

[9]  Wenge Rong,et al.  External features enriched model for biomedical question answering , 2021, BMC Bioinform..

[10]  Navdeep Jaitly,et al.  Speech recognition for medical conversations , 2017, INTERSPEECH.

[11]  Ankush Mittal,et al.  CLINIQA: A Machine Intelligence Based Clinical Question Answering System , 2018, ArXiv.

[12]  Martin H. Fischer,et al.  The Human Takes It All: Humanlike Synthesized Voices Are Perceived as Less Eerie and More Likable. Evidence From a Subjective Ratings Study , 2020, Frontiers in Neurorobotics.

[13]  Joyce A. Mitchell,et al.  Evidence-based retrieval in evidence-based medicine. , 2004, Journal of the Medical Library Association : JMLA.

[14]  Dejan Dinevski,et al.  Biomedical question answering using semantic relations , 2015, BMC Bioinformatics.

[15]  Lendie Follett,et al.  Latent Dirichlet Allocation in predicting clinical trial terminations , 2019, BMC Medical Informatics and Decision Making.

[16]  Jie Tang,et al.  Self-Supervised Learning: Generative or Contrastive , 2020, IEEE Transactions on Knowledge and Data Engineering.

[17]  H. Akhlaghi,et al.  Advanced natural language processing technique to predict patient disposition based on emergency triage notes , 2020, Emergency medicine Australasia : EMA.

[18]  Long Chen,et al.  Clinical trial cohort selection based on multi-level rule-based natural language processing system , 2019, J. Am. Medical Informatics Assoc..

[19]  Krzysztof Wołk,et al.  Translation of Medical Texts using Neural Networks , 2016, Deep Learning and Neural Networks.

[20]  D Feil-Seifer,et al.  Socially Assistive Robotics , 2011, IEEE Robotics & Automation Magazine.

[21]  Savita Choudhary,et al.  Multilingual Medical Question Answering and Information Retrieval for Rural Health Intelligence Access , 2021, ArXiv.

[22]  Tianxi Cai,et al.  Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data , 2018, PSB.

[23]  Diana Trandabat,et al.  Medi-Test: GENERATING Tests from Medical Reference Texts , 2018 .

[24]  Lei Xie,et al.  A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing , 2021, Nature Machine Intelligence.

[25]  Erik M. van Mulligen,et al.  Using rule-based natural language processing to improve disease normalization in biomedical text , 2012, J. Am. Medical Informatics Assoc..

[26]  Xipeng Qiu,et al.  Pre-trained models for natural language processing: A survey , 2020, Science China Technological Sciences.

[27]  Kai Wang,et al.  On the Generation of Medical Question-Answer Pairs , 2018, AAAI.

[28]  Qingyu Chen,et al.  BioWordVec, improving biomedical word embeddings with subword information and MeSH , 2019, Scientific Data.

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

[30]  Bahar Khalighinejad,et al.  Towards reconstructing intelligible speech from the human auditory cortex , 2019, Scientific Reports.

[31]  Francesca Lake.  Artificial intelligence in drug discovery: what is new, and what is next? , 2019 .

[32]  Abeer Alwan,et al.  Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop , 2020, Comput. Speech Lang..

[33]  Hao Wu,et al.  Augmented LSTM Framework to Construct Medical Self-Diagnosis Android , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[34]  Dujiang Yang,et al.  Early Prediction of Organ Failures in Patients with Acute Pancreatitis Using Text Mining , 2021, Sci. Program..

[35]  Emerson Cabrera Paraiso,et al.  A GPT-2 Language Model for Biomedical Texts in Portuguese , 2021, 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS).

[36]  Meera Gandhi,et al.  IntelliDoctor - AI based Medical Assistant , 2019, 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM).

[37]  Tianyong Hao,et al.  T-Know: a Knowledge Graph-based Question Answering and Infor-mation Retrieval System for Traditional Chinese Medicine , 2018, COLING.

[38]  Gorka Labaka,et al.  Neural machine translation of clinical texts between long distance languages , 2019, J. Am. Medical Informatics Assoc..

[39]  Junichi Hoshino,et al.  Lifestyle Agent: The Chat-Oriented Dialogue System for Lifestyle Management , 2017, ICEC.

[40]  Bruce MacDonald,et al.  Towards Expressive Speech Synthesis in English on a Robotic Platform , 2006 .

[41]  Ali Montazeralghaem,et al.  Relevance Ranking Based on Query-Aware Context Analysis , 2020, ECIR.

[42]  Yujia Li,et al.  Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer , 2020, AAAI.

[43]  Wendy W. Chapman,et al.  Developing a natural language processing application for measuring the quality of colonoscopy procedures , 2011, J. Am. Medical Informatics Assoc..

[44]  Wei Huang,et al.  Smart healthcare: making medical care more intelligent , 2019, Global Health Journal.

[45]  Hong-Jun Yoon,et al.  Information Extraction from Cancer Pathology Reports with Graph Convolution Networks for Natural Language Texts , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[46]  Evangelos C. Papakitsos,et al.  Exploring natural language understanding in robotic interfaces , 2017 .

[47]  Pingkun Yan,et al.  Reinforced Transformer for Medical Image Captioning , 2019, MLMI@MICCAI.

[48]  Ziqian Xie,et al.  Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction , 2020, npj Digital Medicine.

[49]  Jimmy J. Lin,et al.  Answering Clinical Questions with Knowledge-Based and Statistical Techniques , 2007, CL.

[50]  Antal van den Bosch,et al.  Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records , 2019, BMC Medical Informatics and Decision Making.

[51]  Timothy Baldwin,et al.  Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration , 2020, NLP4COVID@EMNLP.

[52]  Yijia Zhang,et al.  Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation , 2020, JMIR medical informatics.

[53]  Elke A. Rundensteiner,et al.  Time-Aware Transformer-based Network for Clinical Notes Series Prediction , 2020, MLHC.

[54]  Annette ten Teije,et al.  Ten years of knowledge representation for health care (2009-2018): Topics, trends, and challenges , 2019, Artif. Intell. Medicine.

[55]  Hongzhi Yin,et al.  Disease Prediction via Graph Neural Networks , 2020, IEEE Journal of Biomedical and Health Informatics.

[56]  Haipeng Shen,et al.  Artificial intelligence in healthcare: past, present and future , 2017, Stroke and Vascular Neurology.

[57]  Xiaoyan Wang,et al.  Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. , 2009, Journal of the American Medical Informatics Association : JAMIA.

[58]  Haolin Wang,et al.  Semantically Enhanced Medical Information Retrieval System: A Tensor Factorization Based Approach , 2017, IEEE Access.

[59]  Yung-Chun Chang,et al.  Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization , 2015, Journal of Cheminformatics.

[60]  Edward F. Chang,et al.  Speech synthesis from neural decoding of spoken sentences , 2019, Nature.

[61]  H. Timothy Bunnell,et al.  VocaliD: personalizing text-to-speech synthesis for individuals with severe speech impairment , 2009, Assets '09.

[62]  Ping Wang,et al.  Text-to-SQL Generation for Question Answering on Electronic Medical Records , 2020, WWW.

[63]  Spyros Kotoulas,et al.  Medical Text Classification using Convolutional Neural Networks , 2017, Studies in health technology and informatics.

[64]  Curtis P. Langlotz,et al.  Improving language models for radiology speech recognition , 2009, J. Biomed. Informatics.

[65]  Jane Holland,et al.  Service Robots in the Healthcare Sector , 2021, Robotics.

[66]  Kenneth Jung,et al.  Effective Representations of Clinical Notes , 2017 .

[67]  Kshitij Saxena,et al.  Provider Adoption of Speech Recognition and its Impact on Satisfaction, Documentation Quality, Efficiency, and Cost in an Inpatient EHR , 2018, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[68]  Zhongfei Zhang,et al.  Local–Global Memory Neural Network for Medication Prediction , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[69]  Marko Bajec,et al.  Sieve-based relation extraction of gene regulatory networks from biological literature , 2015, BMC Bioinformatics.

[70]  P. Clamp,et al.  Smartphone speech-to-text applications for communication with profoundly deaf patients , 2015, The Journal of Laryngology & Otology.

[71]  R. Dobson,et al.  Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project , 2017, BMJ Open.

[72]  Tong Wu,et al.  Leveraging graph-based hierarchical medical entity embedding for healthcare applications , 2021, Scientific reports.

[73]  Chengying Chi,et al.  Research on Medical Question Answering System Based on Knowledge Graph , 2021, IEEE Access.

[74]  Anita Burgun-Parenthoine,et al.  Natural language understanding for task oriented dialog in the biomedical domain in a low resources context , 2018, ArXiv.

[75]  Serena Villata,et al.  Transformer-Based Argument Mining for Healthcare Applications , 2020, ECAI.

[76]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[77]  K. Pottie,et al.  Using machine translation in clinical practice. , 2013, Canadian family physician Medecin de famille canadien.

[78]  Khalid Nawab,et al.  Natural Language Processing to Extract Meaningful Information from Patient Experience Feedback , 2020, Applied Clinical Informatics.

[79]  Gang Chen,et al.  A New Remote Health-Care System Based on Moving Robot Intended for the Elderly at Home , 2018, Journal of healthcare engineering.

[80]  Daniel R. Luna,et al.  A Machine Translation Approach for Medical Terms , 2018, HEALTHINF.

[81]  Patrick Ruch,et al.  Deep Question Answering for protein annotation , 2015, Database J. Biol. Databases Curation.

[82]  Matthias Dehmer,et al.  Named Entity Recognition and Relation Detection for Biomedical Information Extraction , 2020, Frontiers in Cell and Developmental Biology.

[83]  David Suendermann-Oeft,et al.  Medical Speech Recognition: Reaching Parity with Humans , 2017, SPECOM.

[84]  Hongfang Liu,et al.  A clinical text classification paradigm using weak supervision and deep representation , 2019, BMC Medical Informatics and Decision Making.

[85]  Bruce A. MacDonald,et al.  Expressive facial speech synthesis on a robotic platform , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[86]  Yingying Qu,et al.  A Mobile-Based Question-Answering and Early Warning System for Assisting Diabetes Management , 2018, Wirel. Commun. Mob. Comput..

[87]  Sarah E. Wallace,et al.  Effect of Text-to-Speech Rate on Reading Comprehension by Adults With Aphasia. , 2020, American journal of speech-language pathology.

[88]  Yunxin Zhao,et al.  Speech-recognition technology in health care and special-needs assistance [Life Sciences] , 2009, IEEE Signal Processing Magazine.

[89]  Sanda M. Harabagiu,et al.  Automatic Generation of a Qualified Medical Knowledge Graph and Its Usage for Retrieving Patient Cohorts from Electronic Medical Records , 2013, 2013 IEEE Seventh International Conference on Semantic Computing.

[90]  Pam Peters,et al.  Translating medical terminology and bilingual terminography , 2018 .

[91]  Olga V. Patterson,et al.  Transparent Reporting on Research Using Unstructured Electronic Health Record Data to Generate ‘Real World’ Evidence of Comparative Effectiveness and Safety , 2019, Drug Safety.

[92]  David Suendermann-Oeft,et al.  Semi-Supervised Acoustic Model Retraining for Medical ASR , 2018, SPECOM.

[93]  Jean-Louis Reymond,et al.  SMIfp (SMILES fingerprint) Chemical Space for Virtual Screening and Visualization of Large Databases of Organic Molecules , 2013, J. Chem. Inf. Model..

[94]  Matthew S. Tata,et al.  Speech Interaction to Control a Hands-Free Delivery Robot for High-Risk Health Care Scenarios , 2021, Frontiers in Robotics and AI.

[95]  Suzanne V. Blackley,et al.  A clinician survey of using speech recognition for clinical documentation in the electronic health record , 2019, Int. J. Medical Informatics.

[96]  Elke A. Rundensteiner,et al.  Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding , 2019, Drug Safety.

[97]  Aly Fahmy,et al.  Automated radiology report generation using conditioned transformers , 2021 .

[98]  Kazem Rahimi,et al.  BEHRT: Transformer for Electronic Health Records , 2019, Scientific Reports.

[99]  Jun Yan,et al.  An CNN-LSTM Attention Approach to Understanding User Query Intent from Online Health Communities , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[100]  Nicholas W. Sterling,et al.  Prediction of emergency department patient disposition based on natural language processing of triage notes , 2019, Int. J. Medical Informatics.

[101]  Krzysztof Marasek,et al.  Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts , 2015, CENTERIS/ProjMAN/HCist.

[102]  Karin M. Verspoor,et al.  BioLemmatizer: a lemmatization tool for morphological processing of biomedical text , 2012, J. Biomed. Semant..

[103]  Huilong Duan,et al.  Using neural attention networks to detect adverse medical events from electronic health records , 2018, J. Biomed. Informatics.

[104]  Aidarus M. Ibrahim,et al.  ONTOLOGY -DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : A CASE STUDY , 2013 .

[105]  Frederick Reiss,et al.  Rule-Based Information Extraction is Dead! Long Live Rule-Based Information Extraction Systems! , 2013, EMNLP.

[106]  Hong Yu,et al.  Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records , 2019, Drug Safety.

[107]  Hegler Tissot,et al.  Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial , 2019, IEEE Journal of Biomedical and Health Informatics.

[108]  Dahdouh Yousra,et al.  A new visual question answering system for medical images characterization , 2019 .

[109]  Sarah E. Wallace,et al.  Comprehension of synthetic speech and digitized natural speech by adults with aphasia. , 2017, Journal of communication disorders.

[110]  Paul Jasmin Rani,et al.  Voice controlled home automation system using Natural Language Processing (NLP) and Internet of Things (IoT) , 2017, 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM).

[111]  Fei Zhang,et al.  Prediction of adverse drug reactions based on knowledge graph embedding , 2021, BMC Medical Informatics and Decision Making.

[112]  Information retrieval as a part of evidence-based practice: Retrieval skills, behavior and needs among nurses at a large university hospital , 2019, Nordic Journal of Nursing Research.

[113]  David Sontag,et al.  Learning a Health Knowledge Graph from Electronic Medical Records , 2017, Scientific Reports.

[114]  Krishnaprasad Thirunarayan,et al.  Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study , 2020, JMIR mental health.

[115]  Tony Russell-Rose,et al.  Extracting sentiment from healthcare survey data: An evaluation of sentiment analysis tools , 2015, 2015 Science and Information Conference (SAI).

[116]  Sumithra Velupillai,et al.  Exploring Transformer Text Generation for Medical Dataset Augmentation , 2020, LREC.

[117]  Ophir Frieder,et al.  Passage relevance models for genomics search , 2008, DTMBIO '08.

[118]  Tianyong Hao,et al.  Automatic Question Generation for Learning Evaluation in Medicine , 2007, ICWL.

[119]  Diego Reforgiato Recupero,et al.  TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial Study , 2021, SmartPhil@IUI.

[120]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[121]  Chunxiao Xing,et al.  A Knowledge-Based Health Question Answering System , 2017, ICSH.

[122]  Reza Safdari,et al.  Words prediction based on N-gram model for free-text entry in electronic health records , 2019, Health Information Science and Systems.

[123]  Fusheng Wang,et al.  Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study , 2018, JMIR medical informatics.

[124]  Daniel L. Rubin,et al.  Automatic information extraction from unstructured mammography reports using distributed semantics , 2018, J. Biomed. Informatics.

[125]  Remle Newton-Dame,et al.  The state of population health surveillance using electronic health records: a narrative review. , 2015, Population health management.

[126]  A. Skuse,et al.  Pregnant women's use of information and communications technologies to access pregnancy-related health information in South Australia. , 2013, Australian journal of primary health.

[127]  Vasiliki Kougia,et al.  A Survey on Biomedical Image Captioning , 2019, Proceedings of the Second Workshop on Shortcomings in Vision and Language.

[128]  Dongsup Kim,et al.  FP2VEC: a new molecular featurizer for learning molecular properties , 2019, Bioinform..

[129]  Rajesh Ranganath,et al.  ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission , 2019, ArXiv.

[130]  Jessica López Espejel.  Automatic summarization of medical conversations, a review , 2019, JEPTALNRECITAL.

[131]  Abbas Akkasi,et al.  ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition , 2016, BioMed research international.

[132]  Jeffrey Dean,et al.  Scalable and accurate deep learning with electronic health records , 2018, npj Digital Medicine.

[133]  William W. Cohen,et al.  Probing Biomedical Embeddings from Language Models , 2019, Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for.

[134]  A. Darzi,et al.  Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review , 2021, BMJ Health & Care Informatics.

[135]  Wei Zhao,et al.  DOP-Tacotron: a Fast Chinese TTS System with Local-based Attention , 2020, 2020 Chinese Control And Decision Conference (CCDC).

[136]  Mike Conway,et al.  Understanding patient satisfaction with received healthcare services: A natural language processing approach , 2016, AMIA.

[137]  Bruce A. MacDonald,et al.  Empathetic Speech Synthesis and Testing for Healthcare Robots , 2020, International Journal of Social Robotics.

[138]  Xingyi Yang,et al.  On the Generation of Medical Dialogues for COVID-19 , 2020, medRxiv.

[139]  Hsin-Min Lu,et al.  Modeling healthcare data using multiple-channel latent Dirichlet allocation , 2016, J. Biomed. Informatics.

[140]  Julie Cattiau,et al.  Automatic Speech Recognition of Disordered Speech: Personalized Models Outperforming Human Listeners on Short Phrases , 2021, Interspeech.

[141]  Haoran Xie,et al.  Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis , 2020, Applied Sciences.

[142]  C. L. Ventola.  Big Data and Pharmacovigilance: Data Mining for Adverse Drug Events and Interactions. , 2018, P & T : a peer-reviewed journal for formulary management.

[143]  Roger B. Davis,et al.  Significant and Distinctive n-Grams in Oncology Notes: A Text-Mining Method to Analyze the Effect of OpenNotes on Clinical Documentation , 2019, JCO clinical cancer informatics.

[144]  Kira Radinsky,et al.  Building Causal Graphs from Medical Literature and Electronic Medical Records , 2019, AAAI.

[145]  Dean J. Krusienski,et al.  Generating Natural, Intelligible Speech From Brain Activity in Motor, Premotor, and Inferior Frontal Cortices , 2019, Front. Neurosci..

[146]  Jun Yan,et al.  Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF , 2019, BMC Medical Informatics and Decision Making.

[147]  Kun Li,et al.  Leveraging text skeleton for de-identification of electronic medical records , 2018, BMC Medical Informatics and Decision Making.

[148]  Chiori Hori,et al.  Non-monologue HMM-based speech synthesis for service robots: A cloud robotics approach , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[149]  Ricardo da Silva Torres,et al.  KGen: a knowledge graph generator from biomedical scientific literature , 2020, BMC Medical Informatics and Decision Making.

[150]  Liang Lin,et al.  End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis , 2019, AAAI.

[151]  R. Cardinal,et al.  Generation and evaluation of artificial mental health records for Natural Language Processing , 2020, npj Digital Medicine.

[152]  Jianjun Ni,et al.  A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches , 2021, Applied Sciences.

[153]  Simon M. Lin,et al.  Readiness for voice assistants to support healthcare delivery during a health crisis and pandemic , 2020, npj Digital Medicine.

[154]  Hagit Shatkay,et al.  Identifying patterns of associated-conditions through topic models of Electronic Medical Records , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[155]  Yuenan Liu,et al.  Information Extraction from Electronic Medical Records Using Multitask Recurrent Neural Network with Contextual Word Embedding , 2019, Applied Sciences.

[156]  Bart Vanrumste,et al.  Building blocks of a task-oriented dialogue system in the healthcare domain , 2021, NLPMC.

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

[158]  Kavishwar B. Wagholikar,et al.  Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach , 2017, BMC Medical Informatics and Decision Making.

[159]  Joanna H. Lowenstein,et al.  Speech Recognition in Noise by Children with and without Dyslexia: How is it Related to Reading? , 2018, Research in developmental disabilities.

[160]  Nikolaos Mavridis,et al.  A review of verbal and non-verbal human-robot interactive communication , 2014, Robotics Auton. Syst..

[161]  G. Kurdi,et al.  Ontology-Based Generation of Medical, Multi-term MCQs , 2019, International Journal of Artificial Intelligence in Education.

[162]  Hong Yu,et al.  Bidirectional RNN for Medical Event Detection in Electronic Health Records , 2016, NAACL.

[163]  Peter Stone,et al.  Improving Grounded Natural Language Understanding through Human-Robot Dialog , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[164]  Riky Tri Yunardi,et al.  Voice recognition system for controlling electrical appliances in smart hospital room , 2019, TELKOMNIKA (Telecommunication Computing Electronics and Control).

[165]  Anthony N. Nguyen,et al.  The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction , 2016, ALTA.

[166]  Vassilina Nikoulina,et al.  A Multilingual Neural Machine Translation Model for Biomedical Data , 2020, NLP4COVID@EMNLP.

[167]  Philip S. Yu,et al.  Bringing semantic structures to user intent detection in online medical queries , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[168]  Alexander H. Waibel,et al.  Natural human-robot interaction using speech, head pose and gestures , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[169]  Jianying Hu,et al.  Artificial Intelligence for Clinical Trial Design. , 2019, Trends in pharmacological sciences.

[170]  Björn Stenger,et al.  Expressive visual text-to-speech as an assistive technology for individuals with autism spectrum conditions , 2016, Comput. Vis. Image Underst..

[171]  Huixin He,et al.  Intelligent diagnosis with Chinese electronic medical records based on convolutional neural networks , 2019, BMC Bioinformatics.

[172]  Jiamou Liu,et al.  HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention , 2020, ACSW.

[173]  Rodica Potolea,et al.  Intent Detection and Slot Filling with Capsule Net Architectures for a Romanian Home Assistant , 2021, Sensors.

[174]  Ross D. Shachter,et al.  Uncovering interpretable potential confounders in electronic medical records , 2021, Nature Communications.

[175]  Theodosia Togia,et al.  Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture , 2019, EMNLP.

[176]  Pengtao Xie,et al.  Towards Visual Question Answering on Pathology Images , 2021, ACL.

[177]  Anouar Boucheham,et al.  A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure–activity relationship modeling , 2020, Chemical biology & drug design.

[178]  A. Naidech,et al.  Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing , 2021, Frontiers in Neurology.

[179]  Fei Wang,et al.  Explicit-Blurred Memory Network for Analyzing Patient Electronic Health Records , 2019, ArXiv.

[180]  J. Betka,et al.  Text-to-speech synthesis as an alternative communication means after total laryngectomy. , 2020, Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia.

[181]  Dave Miller,et al.  Information retrieval for evidence-based decision making , 1999, J. Documentation.

[182]  Ibrahim El-Henawy,et al.  Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping , 2020, Sustainability.

[183]  Amittai Axelrod,et al.  Application of statistical machine translation to public health information: a feasibility study , 2011, J. Am. Medical Informatics Assoc..

[184]  Xin Liu,et al.  MedWriter: Knowledge-Aware Medical Text Generation , 2020, COLING.

[185]  Namit Katariya,et al.  Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization , 2021, NLPMC.

[186]  Katrin Kirchhoff,et al.  Development of machine translation technology for assisting health communication: A systematic review , 2018, J. Biomed. Informatics.

[187]  Beng Chin Ooi,et al.  Medical Concept Embedding with Time-Aware Attention , 2018, IJCAI.

[188]  Pan Zhou,et al.  Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation , 2020, AAAI.

[189]  Son Doan,et al.  Extracting health-related causality from twitter messages using natural language processing , 2019, BMC Medical Informatics and Decision Making.

[190]  Jaewoo Kang,et al.  BioBERT: a pre-trained biomedical language representation model for biomedical text mining , 2019, Bioinform..

[191]  Dominik Heider,et al.  The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetes , 2019, Artif. Intell. Medicine.

[192]  Umit Topaloglu,et al.  Concept Discovery for Pathology Reports using an N-gram Model , 2010, Summit on translational bioinformatics.

[193]  Buyue Qian,et al.  Graph Neural Network-Based Diagnosis Prediction , 2020, Big Data.

[194]  Muhammad Afzal,et al.  Clinical Context–Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation , 2020, Journal of medical Internet research.

[195]  Qingcai Chen,et al.  Family history information extraction via deep joint learning , 2019, BMC Medical Informatics and Decision Making.

[196]  M. Ghassemi,et al.  Predicting early psychiatric readmission with natural language processing of narrative discharge summaries , 2016, Translational psychiatry.

[197]  Animesh Mukherjee,et al.  Deep Learning for Social Media Health Text Classification , 2018, EMNLP 2018.

[198]  Liang Yang,et al.  Improve Biomedical Information Retrieval Using Modified Learning to Rank Methods , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[199]  Alaa A. Kharbouch,et al.  Three models for the description of language , 1956, IRE Trans. Inf. Theory.

[200]  Katherine A. Keith,et al.  Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond , 2021, ArXiv.

[201]  Miao Fan,et al.  Semi-Supervised Variational Reasoning for Medical Dialogue Generation , 2021, SIGIR.

[202]  Xin Sun,et al.  Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence , 2019, Nature Medicine.

[203]  Xuanjing Huang,et al.  Task-oriented Dialogue System for Automatic Diagnosis , 2018, ACL.

[204]  Mayank Agarwal,et al.  Machine Translation: A Literature Review , 2018, ArXiv.

[205]  Ozan Ozyegen,et al.  Word-level Text Highlighting of Medical Texts forTelehealth Services , 2021, Artif. Intell. Medicine.

[206]  Simon Lin,et al.  Pre-training transformer-based framework on large-scale pediatric claims data for downstream population-specific tasks , 2021, ArXiv.

[207]  Henk Harkema,et al.  Applying a natural language processing tool to electronic health records to assess performance on colonoscopy quality measures. , 2012, Gastrointestinal endoscopy.