Dengue FeverDiGiNCT07432061
Prediction of Infectious Diseases in LMICs Using Electronic Health Record Data
An observational study of Dengue Fever and Electronic Health Records, run by Mahidol University at 1 site in Thailand.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Dengue Fever, Electronic Health Records, Prediction
- How many people
- 1,000 participants
- Where
- 1 siteThailand
- Study dates
- 14 Nov 2024to 15 Sep 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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)
Trial locations and contacts
1 site in 1 country.
Thailand1 site
Hospital for Tropical Diseases, Faculty of Tropical Medicine
Bangkok, 10400
About this study
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).
Read more
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.
- Conditions
- Dengue Fever, Electronic Health Records, Prediction
- Being tested
- No intervention
- Medical subject headings
- Dengue
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
No intervention
| Study group | Role | What they receive |
|---|---|---|
| Records of patients diagnosed with dengue and non-dengue infections | Medical record between 1January 2016 to 30 September 2024 Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Dfferentiate dengue from unspecified causes of acute febrile illness
To create AI models able to differentiate dengue from unspecified causes of acute febrile illness in terms of clinical diagnosis and characteristics
Measured: At baseline (time of initial clinical presentation)
Prediction of severe dengue
To predict the development of severe dengue using routinely available clinical data
Measured: At baseline (time of initial clinical presentation)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Feb 2026
- Study start14 Nov 2024
- Primary completion15 Sep 2025
- Study completion15 Sep 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Dengue FeverStudies of the same condition, open and under way.
- More trials run by Mahidol UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Mahidol University
- Sponsor type
- University, hospital or other
- Collaborators
- National Hospital for Tropical Diseases, Hanoi, Vietnam, Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07432061
- Sponsor's protocol ID
- MUTM 2024-084-01
- Acronym
- DiGi
- Status last verified
- Feb 2026
- First submitted
- 15 Nov 2025
- Last updated
- 25 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Yang J, Dung NT, Thach PN, Phong NT, Phu VD, Phu KD, Yen LM, Thy DBX, Soltan AAS, Thwaites L, Clifton DA. Generalizability assessment of AI models across hospitals in a low-middle and high income country. Nat Commun. 2024 Sep 27;15(1):8270. doi: 10.1038/s41467-024-52618-6.
PubMed 39333515Yang J, Clifton L, Dung NT, Phong NT, Yen LM, Thy DBX, Soltan AAS, Thwaites L, Clifton DA. Mitigating machine learning bias between high income and low-middle income countries for enhanced model fairness and generalizability. Sci Rep. 2024 Jun 10;14(1):13318. doi: 10.1038/s41598-024-64210-5.
PubMed 38858466Soltan AAS, Yang J, Pattanshetty R, Novak A, Yang Y, Rohanian O, Beer S, Soltan MA, Thickett DR, Fairhead R, Zhu T, Eyre DW, Clifton DA; CURIAL Translational Collaborative. Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening. Lancet Digit Health. 2022 Apr;4(4):e266-e278. doi: 10.1016/S2589-7500(21)00272-7. Epub 2022 Mar 9.
PubMed 35279399Yang J, Soltan AAS, Clifton DA. Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening. NPJ Digit Med. 2022 Jun 7;5(1):69. doi: 10.1038/s41746-022-00614-9.
PubMed 35672368McBride A, Vuong NL, Van Hao N, Huy NQ, Chanh HQ, Chau NTX, Nguyet NM, Ming DK, Ngoc NT, Nhat PTH, Phong NT, Tai LTH, Tho PV, Trung DT, Tam DTH, Trieu HT, Geskus RB, Llewelyn MJ, Thwaites CL, Yacoub S. A modified Sequential Organ Failure Assessment score for dengue: development, evaluation and proposal for use in clinical trials. BMC Infect Dis. 2022 Sep 3;22(1):722. doi: 10.1186/s12879-022-07705-8.
PubMed 36057771Luvira V, Silachamroon U, Piyaphanee W, Lawpoolsri S, Chierakul W, Leaungwutiwong P, Thawornkuno C, Wattanagoon Y. Etiologies of Acute Undifferentiated Febrile Illness in Bangkok, Thailand. Am J Trop Med Hyg. 2019 Mar;100(3):622-629. doi: 10.4269/ajtmh.18-0407.
PubMed 30628565Sa-Ngamuang C, Haddawy P, Luvira V, Piyaphanee W, Iamsirithaworn S, Lawpoolsri S. Accuracy of dengue clinical diagnosis with and without NS1 antigen rapid test: Comparison between human and Bayesian network model decision. PLoS Negl Trop Dis. 2018 Jun 18;12(6):e0006573. doi: 10.1371/journal.pntd.0006573. eCollection 2018 Jun.
PubMed 29912875
Study at a glance
From the registry record, last updated by the sponsor on 25 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Infectious Diseases in LMICs Using Electronic Health Record Data
- ClinicalTrials.gov ID
- NCT07432061
- Acronym / sponsor ID
- DiGi / MUTM 2024-084-01
- Conditions
- Dengue Fever, Electronic Health Records, Prediction
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,000 participants (actual)
- Lead sponsor
- Mahidol UniversityUniversity, hospital or other
- Collaborators
- National Hospital for Tropical Diseases, Hanoi, Vietnam, Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam
- Study start
- 14 Nov 2024 (actual)
- Primary completion
- 15 Sep 2025 (actual)
- Study completion
- 15 Sep 2025 (actual)
- First posted
- 25 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Records of patients diagnosed with dengue and non-dengue infections | – | Other: No interventionMedical record between 1January 2016 to 30 September 2024 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- 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
Key exclusion criteria (2 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionNo intervention | Other | Records of patients diagnosed with dengue and non-dengue infections |
Primary outcome
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
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Feb 2026.
- Results publication: PubMed 39333515Yang J, Dung NT, Thach PN, Phong NT, Phu VD, Phu KD, Yen LM, Thy DBX, Soltan AAS, Thwaites L, Clifton DA. Generalizability assessment of AI models across hospitals in a low-middle and high income country. Nat Commun. 2024 Sep 27;15(1):8270. doi: 10.1038/s41467-024-52618-6.
- Results publication: PubMed 38858466Yang J, Clifton L, Dung NT, Phong NT, Yen LM, Thy DBX, Soltan AAS, Thwaites L, Clifton DA. Mitigating machine learning bias between high income and low-middle income countries for enhanced model fairness and generalizability. Sci Rep. 2024 Jun 10;14(1):13318. doi: 10.1038/s41598-024-64210-5.
- Results publication: PubMed 35279399Soltan AAS, Yang J, Pattanshetty R, Novak A, Yang Y, Rohanian O, Beer S, Soltan MA, Thickett DR, Fairhead R, Zhu T, Eyre DW, Clifton DA; CURIAL Translational Collaborative. Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening. Lancet Digit Health. 2022 Apr;4(4):e266-e278. doi: 10.1016/S2589-7500(21)00272-7. Epub 2022 Mar 9.
- Results publication: PubMed 35672368Yang J, Soltan AAS, Clifton DA. Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening. NPJ Digit Med. 2022 Jun 7;5(1):69. doi: 10.1038/s41746-022-00614-9.
- Results publication: PubMed 36057771McBride A, Vuong NL, Van Hao N, Huy NQ, Chanh HQ, Chau NTX, Nguyet NM, Ming DK, Ngoc NT, Nhat PTH, Phong NT, Tai LTH, Tho PV, Trung DT, Tam DTH, Trieu HT, Geskus RB, Llewelyn MJ, Thwaites CL, Yacoub S. A modified Sequential Organ Failure Assessment score for dengue: development, evaluation and proposal for use in clinical trials. BMC Infect Dis. 2022 Sep 3;22(1):722. doi: 10.1186/s12879-022-07705-8.
- Results publication: PubMed 30628565Luvira V, Silachamroon U, Piyaphanee W, Lawpoolsri S, Chierakul W, Leaungwutiwong P, Thawornkuno C, Wattanagoon Y. Etiologies of Acute Undifferentiated Febrile Illness in Bangkok, Thailand. Am J Trop Med Hyg. 2019 Mar;100(3):622-629. doi: 10.4269/ajtmh.18-0407.
- Results publication: PubMed 29912875Sa-Ngamuang C, Haddawy P, Luvira V, Piyaphanee W, Iamsirithaworn S, Lawpoolsri S. Accuracy of dengue clinical diagnosis with and without NS1 antigen rapid test: Comparison between human and Bayesian network model decision. PLoS Negl Trop Dis. 2018 Jun 18;12(6):e0006573. doi: 10.1371/journal.pntd.0006573. eCollection 2018 Jun.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Dengue Fever and Electronic Health Records, run by Mahidol University at 1 site in Thailand.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 25 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Thailand. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Dfferentiate dengue from unspecified causes of acute febrile illness (time frame: At baseline (time of initial clinical presentation)). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07432061, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Feb 2026.