Naoki Nakashima
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Pediatrics, Perinatology and Child Health
- Epidemiology
- Materials Chemistry
- Co-authors
- Yasunobu NoharaKoutarou MatsumotoHidehisa SoejimaNorio InuiKōzō MochijiKousuke MoritaniFumihiko YokotaKimiyo Kikuchi
- Topics
- Mobile Health and mHealth Applications (3 papers)Biomedical Text Mining and Ontologies (2 papers)Explainable Artificial Intelligence (XAI) (2 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEScientific Reports
- Partner nations
- JapanSouth KoreaMalaysia
In The Last Decade
Naoki Nakashima
15 papers receiving 627 citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Artificial Intelligence 104
- Radiology, Nuclear Medicine and Imaging 69
- Pediatrics, Perinatology and Child Health 58
- Epidemiology 55
- Materials Chemistry 51
Countries citing papers authored by Naoki Nakashima
This map shows the geographic impact of Naoki Nakashima's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Naoki Nakashima with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Naoki Nakashima more than expected).
Fields of papers citing papers by Naoki Nakashima
This network shows the impact of papers produced by Naoki Nakashima. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Naoki Nakashima. The network helps show where Naoki Nakashima may publish in the future.
Co-authorship network of co-authors of Naoki Nakashima
This figure shows the co-authorship network connecting the top 25 collaborators of Naoki Nakashima. A scholar is included among the top collaborators of Naoki Nakashima based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Naoki Nakashima. Naoki Nakashima is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 1 | |
| 4 | 5 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 4 | |
| 8 | 1 | |
| 9 | Explanation of machine learning models using shapley additive explanation and application for real data in hospitalbreakdown → | 449 |
| 10 | 74 | |
| 11 | 54 | |
| 12 | 1 | |
| 13 | Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records | 1 |
| 14 | 2 | |
| 15 | 0 | |
| 16 | 7 | |
| 17 | 26 | |
| 18 | A remote educational system in medicine using digital video. | 13 |
About Naoki Nakashima
Naoki Nakashima is a scholar working on Health Information Management, Periodontics and Management Science and Operations Research, having authored 18 papers that have together received 642 indexed citations. Recurring topics across this work include Mobile Health and mHealth Applications (3 papers), Biomedical Text Mining and Ontologies (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). The work is most often cited by research in Health Informatics (16 citations), Health Information Management (36 citations) and Finance (35 citations). Naoki Nakashima has collaborated with scholars based in Japan, South Korea and Malaysia. Frequent co-authors include Yasunobu Nohara, Koutarou Matsumoto, Hidehisa Soejima, Norio Inui, Kōzō Mochiji, Kousuke Moritani, Fumihiko Yokota, Kimiyo Kikuchi, Ashir Ahmed and Junko Yasuoka. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.