Prediction of Infectious Diseases in LMICs Using Electronic Health Record Data (DiGi)

February 24, 2026 updated by: Mahidol University

Dengue is a rapidly emerging infectious disease in South and Southeast Asia. Definitive diagnosis requires laboratory testing (PCR or antigen testing) which are often unavailable in settings with highest incidence. Correctly identifying patients who have dengue, and the small number of patients with dengue who will progress to severe disease is important to ensure prompt institution of appropriate treatments.

Existing models use a combination of clinical and laboratory features. A model developed and tested on data from 397 patients admitted to the Hospital for Tropical Diseases in Bangkok in 2013 - 2014 used Bayesian modelling of variables (liver and full blood count) and clinical symptoms (including fever, petechiae, bleeding) to distinguish dengue from other febrile illness. The resultant model performed had an AUC of 0.75 which improved to 0.8 when NS1 was included. The Sequential Organ Failure (SOFA) scores, or modified versions use vital sign and blood test (liver, renal and haematology) data and are good indicators of those likely to die. However, they function less well in moderately severe diseases (e.g. predicting need for ICU admission).

These approaches are promising, but are limited by limited generalizability, use of multiple blood tests and clinical symptoms. A low-cost easy tool able to rapidly diagnose dengue and predict disease severity would be of great value in the region. With modern machine learning methods, this is now feasible and previously identified barriers such as the requirement for large amounts of training data can now be overcome. For example, models can be created from large datasets, but then optimized for smaller different datasets (data either from other locations/conditions, or with less input data).

We've previously shown that data-driven machine learning algorithms could generalize across multiple United Kingdom (UK) National Health Service (NHS) Trusts (for predicting COVID-19). Whilst initially trained on data from over 77,000 patients, we created a model requiring only vital sign data and bedside blood count able to predict COVID-19 diagnosis in patients presenting at UK hospitals. We have demonstrated ability to adapt this model for a lower middle-income country (LMIC) setting using data from two Vietnamese hospitals. The adapted models achieved AUROCs around 0.75 and AUPRCs around 0.89 (similar to UK sites where much larger amounts of data were available). Performing "transfer learning," whereby a small subset of UK data was used to support model development in Vietnam, improved performances between 5-10%. We also found that using statistical methods for addressing missing values can further improve predictive performance by 2-5%. This machine learning model can also function as a 'baseline model' and be adapted for a new task i.e. dengue.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Thailand
      • Bangkok, Thailand, 10400
        • Hospital for Tropical Diseases, Faculty of Tropical Medicine

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

All anonymized medical records of inpatients and out-patients adults aged ≥18 years visiting the Hospital for Tropical Diseases in Bangkok between January 1, 2016, and September 30, 2024, will be included in this study.

Description

Inclusion Criteria:

  • Dengue-related ICD codes: A90-94, A910, A911, A919, A970-972, A979
  • Non-dengue ICD codes: R78.81, A79.9, A27, B34.9, A49.9

Exclusion Criteria:

  • Medical records with significant missing values, as determined by the Principal Investigators (PIs) and co-investigators.
  • Records of patients diagnosed with mixed infections (causative agents ≥ 2)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Records of patients diagnosed with dengue and non-dengue infections
Medical record between 1January 2016 to 30 September 2024
Other: No intervention
No intervention

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Dfferentiate dengue from unspecified causes of acute febrile illness
Time Frame: At baseline (time of initial clinical presentation)
To create AI models able to differentiate dengue from unspecified causes of acute febrile illness in terms of clinical diagnosis and characteristics
At baseline (time of initial clinical presentation)
Prediction of severe dengue
Time Frame: At baseline (time of initial clinical presentation)
To predict the development of severe dengue using routinely available clinical data
At baseline (time of initial clinical presentation)

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Mahidol University

Collaborators

National Hospital for Tropical Diseases, Hanoi, Vietnam
Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 14, 2024

Primary Completion (Actual)

September 15, 2025

Study Completion (Actual)

September 15, 2025

Study Registration Dates

First Submitted

November 15, 2025

First Submitted That Met QC Criteria

February 24, 2026

First Posted (Actual)

February 25, 2026

Study Record Updates

Last Update Posted (Actual)

February 25, 2026

Last Update Submitted That Met QC Criteria

February 24, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • MUTM 2024-084-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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