Deep sequence learning with multi-task supervision for scalable population health monitoring.

Imran Ashraf, Inzamam Mashood Nasir, Muhammad Awais, Wided Bouchelligua, Sahar Mansour, Essa Alyounis, Majed Nawaz

Abstract read

In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Imran AshrafComputer Engineering Lab, Quantum and Computer Engineering Department, EEMCS, TU Delft, Delft, Netherlands.

Inzamam Mashood NasirHuman-Environment-Technology (HET) Systems Centre, Mykolas Romeris University, Vilnius, Lithuania.

Muhammad AwaisDepartment of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia.

Wided BouchelliguaApplied College, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Sahar MansourDepartment of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Essa AlyounisDepartment of Health Information Management and Technology, College of Applied Medical Science, King Faisal University, Al Ahsa, Saudi Arabia.

Majed NawazDepartment of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Models designed for artificial intelligence-driven population health monitoring must be able to integrate multiple types of information over time, be heterogeneous in the data they consider, and be deployed at scale. This research proposes an integrated multitask framework for predicting population disease incidence and mortality risk, based on deep learning and using both longitudinal biobank and National Health Survey data in a scalable manner. The framework will learn shared temporal representations from clinical lab-generated/demographically defined survey variables by applying a strict prospective evaluation approach across the framework. On UK Biobank, the proposed model achieves an AUROC of 0.842 and a

Indexed as

Deep LearningPopulation HealthPopulation SurveillanceHealth SurveysHumansUK BiobankUnited Kingdomdeep learningearly diseasehealth analyticslongitudinal healthcare datamulti-task sequence modelingpopulation health surveillancerisk prediction

Identifiers

PMID42445922

DOI10.3389/fpubh.2026.1837769

PMCPMC13357619

What Socratic holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.