{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:21:46Z","timestamp":1778167306478,"version":"3.51.4"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T00:00:00Z","timestamp":1748390400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T00:00:00Z","timestamp":1748390400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,28]]},"DOI":"10.1109\/memea65319.2025.11068057","type":"proceedings-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T17:45:25Z","timestamp":1752169525000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning for Forecasting Patient Visits: A Comparative Study of CNNs, xLSTM, and Transformers"],"prefix":"10.1109","author":[{"given":"Nicolas","family":"Haxaire","sequence":"first","affiliation":[{"name":"OPTACARE, Rosières-Près-Troyes,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farah","family":"Mourad-Chehade","sequence":"additional","affiliation":[{"name":"LIST3N, University of Technology of Troyes,Troyes,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alice","family":"Yalaoui","sequence":"additional","affiliation":[{"name":"LIST3N, University of Technology of Troyes,Troyes,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Nader","sequence":"additional","affiliation":[{"name":"OPTACARE, Rosières-Près-Troyes,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Axelle","family":"Torelli","sequence":"additional","affiliation":[{"name":"OPTACARE, Rosières-Près-Troyes,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zakaria Ait","family":"Gana","sequence":"additional","affiliation":[{"name":"OPTACARE, Rosières-Près-Troyes,France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hicham","family":"Chehade","sequence":"additional","affiliation":[{"name":"OPTACARE, Rosières-Près-Troyes,France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1097\/00005650-197109000-00005"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0527-0"},{"key":"ref3","first-page":"100200","volume":"7","author":"Harrou","journal-title":"Effective forecasting of key features in hospital emergency department: Hybrid deep learning-driven methods"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1186\/s12245-021-00357-6"},{"key":"ref5","first-page":"112777","volume":"165","author":"Zhao","journal-title":"A deep learning architecture for forecasting daily emergency department visits with acuity levels"},{"key":"ref6","volume-title":"Attention is All you Need.","author":"Vaswani"},{"key":"ref7","volume-title":"Gpt-4 technical report","year":"2024"},{"key":"ref8","volume-title":"Llama: Open and efficient foundation language models","year":"2023"},{"key":"ref9","doi-asserted-by":"crossref","volume-title":"Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting","author":"Lim","DOI":"10.1016\/j.ijforecast.2021.03.012"},{"key":"ref10","volume-title":"Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting","author":"Wu"},{"issue":"12","key":"ref11","first-page":"11106","volume":"35","author":"Zhou","journal-title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting"},{"key":"ref12","volume-title":"FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting","author":"Zhou"},{"key":"ref13","volume-title":"A TIME SERIES IS WORTH 64 WORDS: LONG-TERM FORECASTING WITH TRANSFORMERS","author":"Nie"},{"key":"ref14","volume-title":"Deep Time Series Models: A Comprehensive Survey and Benchmark","author":"Wang"},{"key":"ref15","volume-title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","author":"Bai","year":"2018"},{"key":"ref16","volume-title":"TIMESNET: TEMPORAL 2D-VARIATION MODELING FOR GENERAL TIME SERIES ANALYSIS","author":"Wu"},{"key":"ref17","volume-title":"xLSTM: Extended Long Short-Term Memory","author":"Beck"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/ai5030071"},{"key":"ref19","volume-title":"xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories","author":"Kraus"},{"key":"ref20","doi-asserted-by":"crossref","volume-title":"Forecasting at scale","author":"Taylor","DOI":"10.1080\/00031305.2017.1380080"},{"key":"ref21","volume-title":"Sequence to Sequence Learning with Neural Networks","author":"Sutskever"},{"key":"ref22","volume-title":"RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks","author":"Hou"}],"event":{"name":"2025 IEEE Medical Measurements & Applications (MeMeA)","location":"Chania, Greece","start":{"date-parts":[[2025,5,28]]},"end":{"date-parts":[[2025,5,30]]}},"container-title":["2025 IEEE Medical Measurements & Applications (MeMeA)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11067912\/11066288\/11068057.pdf?arnumber=11068057","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T05:22:20Z","timestamp":1752211340000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11068057\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,28]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/memea65319.2025.11068057","relation":{},"subject":[],"published":{"date-parts":[[2025,5,28]]}}}