Dhruva Biswas, MD, PhD
Associate Research ScientistCards
Contact Info
Cardiovascular Medicine
300 George St
New Haven, Connecticut 06510
United States
Publications Overview
- 48 Publications
- 3,120 Citations
- 8 Yale Co-Authors
Cardiovascular Data Science laboratory
Contact Info
Cardiovascular Medicine
300 George St
New Haven, Connecticut 06510
United States
Publications Overview
- 48 Publications
- 3,120 Citations
- 8 Yale Co-Authors
Cardiovascular Data Science laboratory
Contact Info
Cardiovascular Medicine
300 George St
New Haven, Connecticut 06510
United States
Publications Overview
- 48 Publications
- 3,120 Citations
- 8 Yale Co-Authors
Cardiovascular Data Science laboratory
About
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Titles
Associate Research Scientist
Biography
Dr. Biswas is a Clinician-Data Scientist at the Cardiovascular Data Science (CarDS) Lab, an Associate Research Scientist at the Yale School of Medicine, and a Visiting Professor at King's College London, UK. His research aims to integrate AI-driven insights into clinical practice. This includes developing digital and molecular biomarkers to identify high-risk patients who are currently overlooked, evaluating the clinical contexts in which algorithm performance may be improved, and depicting the underlying pathophysiological mechanisms.
Dr. Biswas has authored more than 30 peer-reviewed publications, including as first author in Nature Medicine '19, Nature Cancer '22, and European Heart Journal Digital Health '25, and as corresponding author in Nature Cancer '25. His work has been featured in media outlets including The Times, the Independent, and Yahoo News. He has been recognized as ‘at the forefront of medical research’ by Nature Medicine.
Dr. Biswas started his medical training at the University of Cambridge, taking a MA in Neuroscience. Next, he joined the MB/PhD programme at University College London, undertaking PhD research between UCL Cancer Institute and the Francis Crick Institute, and graduating with PhD and MBBS degrees. His thesis work was recognized with international prizes, and he was also recognized as a Francis Crick Institute Translation Fellow for acting as a role model in the translational medicine community. Dr. Biswas then secured an Academic Foundation training position in the Cardiology stream at King’s College London. During his two-year medical internship, he also worked as a Postdoctoral Fellow at University College London.
In addition to his research and clinical duties, Dr. Biswas is passionate about mentorship and community engagement. He has supervised several MSc students, all of whom achieved distinction grades, and with three nominated for the Dean's Research Prize for the top project mark in their respective cohorts. He is also committed to patient and public involvement, including being involved with the South London structural heart team looking at equity of access to life-prolonging procedures.
Departments & Organizations
Education & Training
- Postdoctoral Fellow
- University College London
- Translation Fellow
- The Francis Crick Institute
- Academic Foundation Doctor
- King's College London
- PhD
- University College London (2022)
- MD
- University College London (2022)
- PhD
- UCL Cancer Institute & The Francis Crick Institute, Molecular Portraits of Cancer Evolution and Ecology (2020)
- MA
- University of Cambridge, Medical Sciences Tripos (Specialization in Physiology, Development & Neuroscience) (2015)
Research
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Overview
Medical Research Interests
ORCID
0000-0001-9141-5188- View Lab Website
Cardiovascular Data Science laboratory
Research at a Glance
Publications Timeline
Research Interests
Artificial Intelligence
Publications
Featured Publications
A clonal expression biomarker associates with lung cancer mortality
Biswas D, Birkbak N, Rosenthal R, Hiley C, Lim E, Papp K, Boeing S, Krzystanek M, Djureinovic D, La Fleur L, Greco M, Döme B, Fillinger J, Brunnström H, Wu Y, Moore D, Skrzypski M, Abbosh C, Litchfield K, Al Bakir M, Watkins T, Veeriah S, Wilson G, Jamal-Hanjani M, Moldvay J, Botling J, Chinnaiyan A, Micke P, Hackshaw A, Bartek J, Csabai I, Szallasi Z, Herrero J, McGranahan N, Swanton C. A clonal expression biomarker associates with lung cancer mortality. Nature Medicine 2019, 25: 1540-1548. PMID: 31591602, PMCID: PMC6984959, DOI: 10.1038/s41591-019-0595-z.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsNon-small cell lung cancerClinicopathological risk factorsCell lung cancerLung cancer mortalityPrognostic gene expression signaturesCancer cell proliferationGene expression signaturesCancer mortalityLung cancerRisk factorsExpression-based biomarkersCopy number gainsDisease subtypesClinical descriptorsTranscriptomic biomarkersIndividual tumorsCancer typesDiagnostic precisionMolecular biomarkersExpression signaturesCell proliferationDNA copy number gainsBiomarkersPatientsIntratumor heterogeneityA local human Vδ1 T cell population is associated with survival in nonsmall-cell lung cancer
Wu Y, Biswas D, Usaite I, Angelova M, Boeing S, Karasaki T, Veeriah S, Czyzewska-Khan J, Morton C, Joseph M, Hessey S, Reading J, Georgiou A, Al-Bakir M, McGranahan N, Jamal-Hanjani M, Hackshaw A, Quezada S, Hayday A, Swanton C. A local human Vδ1 T cell population is associated with survival in nonsmall-cell lung cancer. Nature Cancer 2022, 3: 696-709. PMID: 35637401, PMCID: PMC9236901, DOI: 10.1038/s43018-022-00376-z.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsT cell populationsT cellsLung tissueLung cancerCD8+ T cellsNonsmall-cell lung cancerNonsmall cell lung cancerEffector memory phenotypeT cell compartmentCell lung cancerAssociated with survivalNonmalignant lung tissuesStem-like featuresNontumor lung tissuesT cell biologyHuman lung tissueImmunotherapeutic strategiesMemory phenotypeNatural killerLung tumorsTissue-residentPost-surgeryResident memoryMurine tissuesTumorProspective validation of ORACLE, a clonal expression biomarker associated with survival of patients with lung adenocarcinoma
Biswas D, Liu Y, Herrero J, Wu Y, Moore D, Karasaki T, Grigoriadis K, Lu W, Veeriah S, Naceur-Lombardelli C, Magno N, Ward S, Frankell A, Hill M, Colliver E, de Carné Trécesson S, East P, Malhi A, Snell D, O’Neill O, Leonce D, Mattsson J, Lindberg A, Micke P, Moldvay J, Megyesfalvi Z, Dome B, Fillinger J, Nicod J, Downward J, Szallasi Z, Hackshaw A, Jamal-Hanjani M, Kanu N, Birkbak N, Swanton C. Prospective validation of ORACLE, a clonal expression biomarker associated with survival of patients with lung adenocarcinoma. Nature Cancer 2025, 6: 86-101. PMID: 39789179, PMCID: PMC11779643, DOI: 10.1038/s43018-024-00883-1.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsLung adenocarcinomaStage I diseaseClinicopathological risk factorsSurvival of patientsResponse to treatmentRNA sequencing dataI diseaseSequence dataMetastatic clonesNeedle biopsyIndividual tumorsLung expressionTranscription signalsPrognostic informationWhole exomeExpressed genesChemotherapy sensitivityProspective validationSurvival associationsTranscriptomic heterogeneityHuman tumorsEvolutionary measuresChromosomal instabilityRisk factorsNatural history
2025
Artificial Intelligence Methods to Detect Heart Failure with Preserved Ejection Fraction (AIM-HFpEF) within Electronic Health Records: An equitable disease detection model
Wu J, Biswas D, Brown S, Ryan M, Bernstein B, To B, Searle T, Rizvi M, Fairhurst N, Kaye G, Baral R, Vijayakumar D, Mehta D, Melikian N, Sado D, Carr-White G, Chowienczyk P, Teo J, Dobson R, Bromage D, Lüscher T, Vazir A, McDonagh T, Webb J, Shah A, O’Gallagher K. Artificial Intelligence Methods to Detect Heart Failure with Preserved Ejection Fraction (AIM-HFpEF) within Electronic Health Records: An equitable disease detection model. European Heart Journal - Digital Health 2025, ztaf107. DOI: 10.1093/ehjdh/ztaf107.Peer-Reviewed Original ResearchCitationsAltmetricConceptsElectronic health recordsPatients of non-White ethnicityNon-white ethnicitySocioeconomic deprivationHealth recordsNatural language processingHigh socioeconomic deprivationNon-white populationsRisk of deathHospital trustsLevels of morbidityDisease detection modelRisk scoreDiagnostic prediction modelHeart failure casesArtificial intelligence methodsMachine learning methodsDetection modelDerivation datasetLanguage processingHeart failureLearning methodsIntelligence methodsEthnicityRiskTransforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges
Biswas D, Aminorroaya A, Croon P, Batinica B, Pedroso A, Khera R. Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges. Current Atherosclerosis Reports 2025, 27: 86. PMID: 40888973, DOI: 10.1007/s11883-025-01337-4.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsAtherosclerotic cardiovascular diseasePopulation health screeningPopulation-level screeningCardiovascular diseaseLow riskHealth screeningStandard risk factorsHospital-basedCardiovascular healthSubclinical coronary artery diseaseWorkflow integrationSingle-lead ECGPersonalized interventionsPatient outcomesDiverse populationsTraditional risk modelsECG interpretationRisk factorsAscertainment biasImplementation challengesAdverse cardiovascular eventsProspective studyLogistical challengesRe-classifying patientsCoronary artery diseasePhenotypic Selectivity of Artificial Intelligence–Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction
Croon P, Dhingra L, Biswas D, Oikonomou E, Khera R. Phenotypic Selectivity of Artificial Intelligence–Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction. Circulation 2025, 152: 1282-1294. PMID: 40888124, PMCID: PMC12573264, DOI: 10.1161/circulationaha.125.076279.Peer-Reviewed Original ResearchCitationsAltmetricConceptsElectronic health recordsNon-cardiovascular conditionsPhenome-wide association studyCross-sectional phenotypingNew-onset cardiovascular diseaseCardiovascular diseaseProspective cohort studyPhenotypic associationsHealth recordsLeft ventricular hypertrophyStructural heart diseaseAI-ECGAssociated with cardiovascular phenotypesPearson correlation coefficientDiagnosis codesCohort studyCardiovascular risk markersLogistic regressionAssociation studiesCardiovascular diagnosisMitral regurgitationAortic stenosisCardiovascular conditionsStudy populationDetection of LVSD2-058 How representative are heart failure clinical trials? A comparative study using natural language processing
Wu J, Biswas D, Brown S, Breeze J, Searle T, Dobson R, Bromage D, McDonagh T, Shah A, O’Gallagher K. 2-058 How representative are heart failure clinical trials? A comparative study using natural language processing. 2025, a95-a96. DOI: 10.1136/heartjnl-2025-bcs.96.Peer-Reviewed Original ResearchCirculating immunoregulatory B cell and autoreactive antibody profiles predict lack of toxicity to anti-PD-1 checkpoint inhibitor treatment in advanced melanoma
Willsmore Z, Booth L, Patel A, Di Meo A, Prassas I, Chauhan J, Wu Y, Fitzpartick A, Stoker K, Kapiris M, Biswas D, Perucha E, Whittaker S, Tsoka S, Diamandis E, Middleton G, Tull T, Papa S, Lacy K, Karagiannis S. Circulating immunoregulatory B cell and autoreactive antibody profiles predict lack of toxicity to anti-PD-1 checkpoint inhibitor treatment in advanced melanoma. Journal For ImmunoTherapy Of Cancer 2025, 13: e011682. PMID: 40449958, PMCID: PMC12142029, DOI: 10.1136/jitc-2025-011682.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsImmune-related adverse eventsAnti-PD-1 therapyStage III/IV melanomaAnti-PD-1Memory B cellsB cellsCheckpoint inhibitorsHigher IgG4Development of immune-related adverse eventsResponse to anti-PD-1 therapyClass-switched memory B cellsClass-switched B cellsAccurate predictive biomarkersDN2 B cellsExtrafollicular B cell responseCheckpoint inhibitor treatmentB cell frequenciesB cell responsesIL-10+Response to therapyIgE serum levelsImmune response profileHumoral immune response profileSerum antibody isotypesAdvanced melanomaFORECASTING DRUG-INDUCED LONG QT SYNDROME FROM PRE-TREATMENT ELECTROCARDIOGRAPHIC IMAGES
Biswas D, Aminorroaya A, Khera R. FORECASTING DRUG-INDUCED LONG QT SYNDROME FROM PRE-TREATMENT ELECTROCARDIOGRAPHIC IMAGES. Journal Of The American College Of Cardiology 2025, 85: 23. DOI: 10.1016/s0735-1097(25)00508-x.Peer-Reviewed Original ResearchA FULLY AUTOMATED ELECTRONIC CLINICAL QUALITY MEASUREMENT FOR HEART FAILURE USING RETRIEVAL AUGMENTED GENERATION AND LARGE LANGUAGE MODELS (LLMS)
Adejumo P, Thangaraj P, Biswas D, Shankar S, Camargos A, Khera R. A FULLY AUTOMATED ELECTRONIC CLINICAL QUALITY MEASUREMENT FOR HEART FAILURE USING RETRIEVAL AUGMENTED GENERATION AND LARGE LANGUAGE MODELS (LLMS). Journal Of The American College Of Cardiology 2025, 85: 1372. DOI: 10.1016/s0735-1097(25)01856-x.Peer-Reviewed Original ResearchCitationsConcepts
Academic Achievements & Community Involvement
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Honors
honor Medical Leadership Award for outstanding contribution
07/01/2024Regional AwardKing’s College HospitalDetailsUnited Kingdomhonor Isaac Schapera Award for research into problems relating to the causes and cures of diseases
05/01/2024Regional AwardUniversity of London medical schoolsDetailsUnited Kingdomhonor Translation Fellow for helping to create an outstanding translation community and acting as a role model for others
12/01/2021Regional AwardThe Francis Crick InstituteDetailsUnited Kingdomhonor Sir David Cooksey Translation Prize Winner for exceptional contribution to the COVID-19 diagnostic testing pipeline
11/01/2020Regional AwardThe Francis Crick InstituteDetailsUnited Kingdomhonor Astor Foundation travel bursary
08/01/2019Regional AwardUniversity College LondonDetailsUnited Kingdom
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Cardiovascular Medicine
300 George St
New Haven, Connecticut 06510
United States
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195 Church Street
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New Haven, CT 06510
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