Deep sequence learning with multi-task supervision for scalable population health monitoring.
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| Title: | Deep sequence learning with multi-task supervision for scalable population health monitoring. |
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| Authors: | Ashraf I; Computer Engineering Lab, Quantum and Computer Engineering Department, EEMCS, TU Delft, Delft, Netherlands., Nasir IM; Human-Environment-Technology (HET) Systems Centre, Mykolas Romeris University, Vilnius, Lithuania., Awais M; Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia., Bouchelligua W; Applied College, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia., Mansour S; Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia., Alyounis E; Department of Health Information Management and Technology, College of Applied Medical Science, King Faisal University, Al Ahsa, Saudi Arabia., Nawaz M; Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia. |
| Source: | Frontiers in public health [Front Public Health] 2026 Jun 29; Vol. 14, pp. 1837769. Date of Electronic Publication: 2026 Jun 29 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Editorial Office Country of Publication: Switzerland NLM ID: 101616579 Publication Model: eCollection Cited Medium: Internet ISSN: 2296-2565 (Electronic) Linking ISSN: 22962565 NLM ISO Abbreviation: Front Public Health Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2296-2565 |
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| DOI: | 10.3389/fpubh.2026.1837769 |
