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Hong-Jie Dai publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Hong-Jie Dai sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

The last bar groups every scientist with 804 publications or more.

Hong-Jie Dai D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Hong-Jie Dai sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

The last bar groups every scientist with 107 D-Index or more.

Overview

Hong-Jie Dai is affiliated with Southwest University in China and has conducted research primarily in the field of Computer Science.

Their work spans several subfields, including:

  • Artificial Intelligence
  • Molecular Biology
  • Health Information Management
  • Computer Vision and Pattern Recognition
  • Psychiatry and Mental Health

Their research topics cover a range of areas with a focus on applications in healthcare and biomedical data:

  • Machine Learning in Healthcare
  • Topic Modeling
  • Biomedical Text Mining and Ontologies
  • Natural Language Processing Techniques
  • Medical Coding and Health Information
  • Text Readability and Simplification
  • Image Retrieval and Classification Techniques

Recent publications authored or coauthored by Hong-Jie Dai include:

  • Deep Learning-Based Natural Language Processing for Screening Psychiatric Patients, 2021, Frontiers in Psychiatry
  • Cancer Registry Coding via Hybrid Neural Symbolic Systems in the Cross-Hospital Setting, 2021, IEEE Access

Other recent papers within the same research network, although not authored by Dai but relevant to their frequent collaboration groups, include:

  • Vickers Hardness Value Test via Multi-Task Learning Convolutional Neural Networks and Image Augmentation, 2022, Applied Sciences
  • OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study, 2023, Journal of Medical Internet Research
  • Cohort selection for construction of a clinical natural language processing corpus, 2021, Computer Methods and Programs in Biomedicine Update

Hong-Jie Dai frequently collaborates with several researchers, including:

  • Chen-Kai Wang
  • Jitendra Jonnagaddala
  • Chi-Shin Wu
  • Chu-Hsien Su
  • Tatheer Hussain Mir

Their research outputs have been published repeatedly in venues such as:

  • Journal of Medical Internet Research
  • Frontiers in Psychiatry
  • IEEE Access
  • Computer Methods and Programs in Biomedicine Update
  • Applied Sciences

Best Publications

  • Overview of BioCreative II gene mention recognition

    Larry Smith;Lorraine K Tanabe;Rie Johnson nee Ando;Cheng-Ju Kuo

  • Eco-friendly polyvinyl alcohol/carboxymethyl cellulose hydrogels reinforced with graphene oxide and bentonite for enhanced adsorption of methylene blue.

    Hongjie Dai;Yue Huang;Huihua Huang

  • The CHEMDNER corpus of chemicals and drugs and its annotation principles.

    Martin Krallinger;Obdulia Rabal;Florian Leitner;Miguel Vazquez

  • Utilization of pineapple peel for production of nanocellulose and film application

    Hongjie Dai;Shiyi Ou;Yue Huang;Huihua Huang

  • Recent advances on cellulose nanocrystals for Pickering emulsions: Development and challenge

    Hongjie Dai;Jihong Wu;Huan Zhang;Yuan Chen

  • Enhanced Swelling and Responsive Properties of Pineapple Peel Carboxymethyl Cellulose-g-poly(acrylic acid-co-acrylamide) Superabsorbent Hydrogel by the Introduction of Carclazyte.

    Hongjie Dai;Huihua Huang

  • Extraction and comparison of cellulose nanocrystals from lemon (Citrus limon) seeds using sulfuric acid hydrolysis and oxidation methods.

    Huan Zhang;Yuan Chen;Shanshan Wang;Liang Ma

  • The gene normalization task in BioCreative III

    Zhiyong Lu;Hung-Yu Kao;Chih-Hsuan Wei;Minlie Huang

  • NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition

    Richard Tzong Han Tsai;Cheng Lung Sung;Hong Jie Dai;Hsieh Chuan Hung

  • Pineapple peel carboxymethyl cellulose/polyvinyl alcohol/mesoporous silica SBA-15 hydrogel composites for papain immobilization.

    Hongjie Dai;Shiyi Ou;Zhijun Liu;Huihua Huang

  • Green pH/magnetic sensitive hydrogels based on pineapple peel cellulose and polyvinyl alcohol: synthesis, characterization and naringin prolonged release

    Hongjie Dai;Huan Zhang;Liang Ma;Hongyuan Zhou

  • Modified pineapple peel cellulose hydrogels embedded with sepia ink for effective removal of methylene blue

    Hongjie Dai;Huihua Huang

  • Synthesis and response of pineapple peel carboxymethyl cellulose-g-poly (acrylic acid-co-acrylamide)/graphene oxide hydrogels

    Hongjie Dai;Yuhao Zhang;Liang Ma;Huan Zhang

  • Properties of Pickering emulsion stabilized by food-grade gelatin nanoparticles: influence of the nanoparticles concentration.

    Xin Feng;Hongjie Dai;Liang Ma;Yu Fu

  • Overview of the interactive task in BioCreative V

    Qinghua Wang;Shabbir S. Abdul;Lara Almeida;Sophia Ananiadou

  • Using text mining to extract depressive symptoms and to validate the diagnosis of major depressive disorder from electronic health records.

    Chi-Shin Wu;Chian-Jue Kuo;Chu-Hsien Su;Shi‐Heng Wang

  • Coronary artery disease risk assessment from unstructured electronic health records using text mining

    Jitendra Jonnagaddala;Siaw-Teng Liaw;Pradeep Ray;Manish Kumar

  • Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization.

    Hong Jie Dai;Po Ting Lai;Yung Chun Chang;Yung Chun Chang;Richard Tzong Han Tsai

  • New Challenges for Biological Text-Mining in the Next Decade

    Hong Jie Dai;Hong Jie Dai;Yen Ching Chang;Richard Tzong-Han Tsai;Wen Lian Hsu;Wen Lian Hsu

  • Direct fabrication of hierarchically processed pineapple peel hydrogels for efficient Congo red adsorption.

    Hongjie Dai;Yue Huang;Huan Zhang;Liang Ma

Frequent Co-Authors

Pradeep Ray
Pradeep Ray Shanghai Jiao Tong University
Andrew Chatr-aryamontri
Andrew Chatr-aryamontri University of Montreal
Cathy H. Wu
Cathy H. Wu University of Delaware
Patrick Ruch
Patrick Ruch Swiss Institute of Bioinformatics
Yu-Ju Chen
Yu-Ju Chen Academia Sinica
Lars Juhl Jensen
Lars Juhl Jensen University of Copenhagen
Mike Tyers
Mike Tyers University of Montreal
Tim Rocktäschel
Tim Rocktäschel University College London
Suresh Subramani
Suresh Subramani University of California, San Diego
Matthew Mort
Matthew Mort Cardiff University

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