Philippe Lenca

62 papers receiving 1.2k citations

Philippe Lenca's Hit Papers

Machine Learning and Natural Language Processing in Mental Health: Systematic Review 2020 · 347 citations
3470+2+4Years since publication100200300

Peers

Philippe Lenca
Comparison fields: 5 of 129
  • Health Informatics 44
  • Applied Psychology 102
  • Computational Theory and Mathematics 344
  • Information Systems 474
  • Signal Processing 217
Replace Sandra Bringay with:
Sandra Bringay France
Sebastian Tschiatschek Austria
Erik Linstead United States
Shahla Nemati Iran
Will Bridewell United States
Andreas Prinz Norway
Manuel Campos Spain
Kevin Small United States
Philippe Lenca relative to Sandra Bringay France Sandra Bringay's profile →
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Countries citing papers authored by Philippe Lenca

Since Specialization
Citations

This map shows the geographic impact of Philippe Lenca'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 Philippe Lenca with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philippe Lenca more than expected).

Fields of papers citing papers by Philippe Lenca

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philippe Lenca. 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 Philippe Lenca. The network helps show where Philippe Lenca may publish in the future.

Co-authors

The 25 scholars most cited alongside Philippe Lenca, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Philippe Lenca Line = papers co-authored together Philippe Lenca links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 65 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Machine Learning and Natural Language Processing in Mental Health: Systematic Review
Hit paper breakdown →
2020347
2 2007148
3 200966
4 200759
5 200456
6 201046
7 201745
8 200827
9 201826
10 201524
11 201423
12 201620
13 201519
14 202118
15 201715
16 201815
17 200713
18 201113
19 201812
20 200912

About Philippe Lenca

Philippe Lenca is a scholar working on Information Systems, Computational Theory and Mathematics, Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition, having authored 65 papers that have together received 1.2k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (28 papers), Rough Sets and Fuzzy Logic (23 papers), Data Management and Algorithms (14 papers), Imbalanced Data Classification Techniques (9 papers), Anomaly Detection Techniques and Applications (4 papers), AI-based Problem Solving and Planning (4 papers), Time Series Analysis and Forecasting (4 papers) and Context-Aware Activity Recognition Systems (4 papers). The work is most often cited by research in Health Informatics (44 citations), Applied Psychology (102 citations), Computational Theory and Mathematics (344 citations), Information Systems (474 citations) and Signal Processing (217 citations). Philippe Lenca has collaborated with scholars based in France, Thailand and United States. Frequent co-authors include Stéphane Lallich, Benoît Vaillant, Patrick Meyer, Sofian Berrouiguet, Michel Walter, Romain Billot, Christophe Lemey, Jordan DeVylder, Deok-Hee Kim-Dufor and Aziliz Le Glaz. Their work appears in journals such as Journal of Medical Internet Research, Expert Systems with Applications, European Journal of Operational Research, Lecture notes in computer science and Frontiers in Psychiatry.

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.

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