Matthew M. Engelhard | Scholars@Duke

Matthew M. Engelhard
Assistant Professor of Biostatistics & Bioinformatics

Dr. Engelhard is an Assistant Professor of Biostatistics and Bioinformatics at the Duke University School of Medicine. Prior to that he completed his MD/PhD at the University of Virginia followed by a postdoc at Duke University. His work focuses on the development of novel risk prediction and machine learning methods for large-scale, multi-modal health data, including digital health data streams and electronic health records (EHRs), with applications to autism and ADHD screening, smoking cessation, maternal health, and stroke risk prediction.

Current Appointments & Affiliations

Assistant Professor of Biostatistics & Bioinformatics, Biostatistics & Bioinformatics, Division of Translational Biomedical, Biostatistics & Bioinformatics 2022

Contact Information

2424 Erwin Rd, Durham, NC 27705
m.engelhard@duke.edu (919) 613-3665

Background
Education, Training, & Certifications
- Ph.D., University of Virginia 2016
- M.D., University of Virginia 2014
Previous Appointments & Affiliations
- Assistant Professor of Biostatistics & Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2021 - 2022
Recognition
In the News
- FEB 14, 2023 School of Medicine
  
  Harnessing Electronic Health Record Data for Earlier Autism Detection
- APR 2, 2021 Duke Research Blog
  
  Using Data Science for Early Detection of Autism
Research
Selected Grants
- Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health Record awarded by National Institutes of Health 2022 - 2027
- Improving stroke risk prediction with cohort data and machine learning methods awarded by National Institute of Neurological Disorders and Stroke 2020 - 2025
- Sustainable Habits for Encouraging Even Teen Sleep (SHEETS): A Digital Intervention to Enhance Sleep Regularity and Psychiatric Health in Adolescents awarded by National Institutes of Health 2022 - 2025
- Novel Approaches to Infant Screening for ASD in Pediatric Primary Care awarded by National Institutes of Health 2019 - 2024
- Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKD awarded by National Institutes of Health 2020 - 2024
- Cardiovascular Risk Prediction for Improved Maternal Health awarded by Foundation for Anesthesia Education and Research 2022 - 2023
- Deep learning-based image analysis for assessing real-time smoking risk awarded by National Institutes of Health 2018 - 2021
Publications & Artistic Works
Selected Publications
- Academic Articles
  - Meng, M.-L., Fuller, M., Federspiel, J. J., Engelhard, M., McNeil, A., Ernst, L., … Krishnamoorthy, V. (2023). Maternal Morbidity According to Mode of Delivery Among Pregnant Patients With Pulmonary Hypertension. Anesth Analg. https://doi.org/10.1213/ANE.0000000000006523
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  - Chen, J., Engelhard, M., Henao, R., Berchuck, S., Eichner, B., Perrin, E. M., … Dawson, G. (2023). Enhancing early autism prediction based on electronic records using clinical narratives. J Biomed Inform, 104390. https://doi.org/10.1016/j.jbi.2023.104390
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  - Engelhard, M. M., Henao, R., Berchuck, S. I., Chen, J., Eichner, B., Herkert, D., … Dawson, G. (2023). Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year. Jama Netw Open, 6(2), e2254303. https://doi.org/10.1001/jamanetworkopen.2022.54303
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  - Hong, C., Pencina, M. J., Wojdyla, D. M., Hall, J. L., Judd, S. E., Cary, M., … Henao, R. (2023). Predictive Accuracy of Stroke Risk Prediction Models Across Black and White Race, Sex, and Age Groups. Jama, 329(4), 306–317. https://doi.org/10.1001/jama.2022.24683
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  - Doerstling, S. S., Akrobetu, D., Engelhard, M. M., Chen, F., & Ubel, P. A. (2022). A Disease Identification Algorithm for Medical Crowdfunding Campaigns: Validation Study. J Med Internet Res, 24(6), e32867. https://doi.org/10.2196/32867
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  - Lunsford-Avery, J. R., Wang, K. W., Kollins, S. H., Chung, R. J., Keller, C., & Engelhard, M. M. (2022). Regularity and Timing of Sleep Patterns and Behavioral Health Among Adolescents. J Dev Behav Pediatr, 43(4), 188–196. https://doi.org/10.1097/DBP.0000000000001013
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  - Engelhard, M., & Henao, R. (2022). Disentangling Whether from When in a Neural Mixture Cure Model for Failure Time Data. Proc Mach Learn Res, 151, 9571–9581.
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  - Lunsford-Avery, J. R., Kollins, S. H., Kansagra, S., Wang, K. W., & Engelhard, M. M. (2022). Impact of daily caffeine intake and timing on electroencephalogram-measured sleep in adolescents. J Clin Sleep Med, 18(3), 877–884. https://doi.org/10.5664/jcsm.9736
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  - Lunsford-Avery, J. R., Engelhard, M. M., Navar, A. M., & Kollins, S. H. (2021). Author Correction: Validation of the Sleep Regularity Index in Older Adults and Associations with Cardiometabolic Risk. Sci Rep, 11(1), 24398. https://doi.org/10.1038/s41598-021-03253-4
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  - Engelhard, M. M., D’Arcy, J., Oliver, J. A., Kozink, R., & Joseph McClernon, F. (2021). Prediction of smoking risk from repeated sampling of environmental images: Model validation. Journal of Medical Internet Research, 23(11). https://doi.org/10.2196/27875
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  - Engelhard, M. M., D’Arcy, J., Oliver, J. A., Kozink, R., & McClernon, F. J. (2021). Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation. J Med Internet Res, 23(11), e27875. https://doi.org/10.2196/27875
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  - Engelhard, M., Berchuck, S., Garg, J., Rusincovitch, S., Dawson, G., & Kollins, S. (2021). Patterns of Health Services Use Before Age 1 in Children Later Diagnosed With ADHD. J Atten Disord, 25(12), 1639. https://doi.org/10.1177/1087054720914352
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  - Oliver, J. A., Sweitzer, M. M., Engelhard, M. M., Hallyburton, M. B., Ribisl, K. M., & McClernon, F. J. (2021). Identifying neural signatures of tobacco retail outlet exposure: Preliminary validation of a "community neuroscience" paradigm. Addict Biol, 26(5), e13029. https://doi.org/10.1111/adb.13029
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  - Engelhard, M. M., Navar, A. M., & Pencina, M. J. (2021). Incremental Benefits of Machine Learning-When Do We Need a Better Mousetrap? Jama Cardiol, 6(6), 621–623. https://doi.org/10.1001/jamacardio.2021.0139
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  - Engelhard, M. M., D’Arcy, J., Oliver, J. A., Kozink, R., & McClernon, F. J. (2021). Prediction of Smoking Risk From Repeated Sampling of Environmental Images: Model Validation (Preprint). https://doi.org/10.2196/preprints.27875
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  - Vilardaga, R., Fisher, T., Palenski, P. E., Kumaresan, V., Mannelli, P., Sweitzer, M. M., … Garrison, K. A. (2020). Review of Popularity and Quality Standards of Opioid-Related Smartphone Apps. Curr Addict Rep, 7(4), 486–496. https://doi.org/10.1007/s40429-020-00344-6
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  - Engelhard, M. M., Berchuck, S. I., Garg, J., Henao, R., Olson, A., Rusincovitch, S., … Kollins, S. H. (2020). Health system utilization before age 1 among children later diagnosed with autism or ADHD. Sci Rep, 10(1), 17677. https://doi.org/10.1038/s41598-020-74458-2
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  - Lunsford-Avery, J. R., Keller, C., Kollins, S. H., Krystal, A. D., Jackson, L., & Engelhard, M. M. (2020). Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study. Jmir Mhealth Uhealth, 8(10), e20590. https://doi.org/10.2196/20590
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  - Lunsford-Avery, J. R., Keller, C., Kollins, S. H., Krystal, A. D., Jackson, L., & Engelhard, M. M. (2020). Feasibility and Acceptability of Wearable Sleep Electroencephalogram Device Use in Adolescents: Observational Study (Preprint). https://doi.org/10.2196/preprints.20590
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  - Engelhard, M., Berchuck, S., D’Arcy, J., & Henao, R. (2020). Neural Conditional Event Time Models.
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  - Lunsford-Avery, J. R., Engelhard, M. M., Navar, A. M., & Kollins, S. H. (2020). Author Correction: Validation of the Sleep Regularity Index in Older Adults and Associations with Cardiometabolic Risk. Sci Rep, 10(1), 2993. https://doi.org/10.1038/s41598-020-59762-1
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  - Lunsford-Avery, J. R., Damme, K. S. F., Engelhard, M. M., Kollins, S. H., & Mittal, V. A. (2020). Sleep/Wake Regularity Associated with Default Mode Network Structure among Healthy Adolescents and Young Adults. Sci Rep, 10(1), 509. https://doi.org/10.1038/s41598-019-57024-3
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  - Engelhard, M. M., Oliver, J. A., & McClernon, F. J. (2020). Digital envirotyping: quantifying environmental determinants of health and behavior. Npj Digit Med, 3, 36. https://doi.org/10.1038/s41746-020-0245-3
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  - Engelhard, M. M., & Kollins, S. H. (2019). The Many Channels of Screen Media Technology in ADHD: a Paradigm for Quantifying Distinct Risks and Potential Benefits. Curr Psychiatry Rep, 21(9), 90. https://doi.org/10.1007/s11920-019-1077-1
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  - Engelhard, M. M., Oliver, J. A., Henao, R., Hallyburton, M., Carin, L. E., Conklin, C., & McClernon, F. J. (2019). Identifying Smoking Environments From Images of Daily Life With Deep Learning. Jama Netw Open, 2(8), e197939. https://doi.org/10.1001/jamanetworkopen.2019.7939
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  - Raman, S., Engelhard, M., & Kollins, S. H. (2019). Driving the Point Home: Novel Approaches to Mitigate Crash Risk for Patients With ADHD. Pediatrics. https://doi.org/10.1542/peds.2019-0820
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  - Kuo, C. T., Lu, Y., Kovarik, L., Engelhard, M., & Karim, A. M. (2019). Structure Sensitivity of Acetylene Semi-Hydrogenation on Pt Single Atoms and Subnanometer Clusters. Acs Catalysis, 11030–11041. https://doi.org/10.1021/acscatal.9b02840
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  - Lunsford-Avery, J. R., Engelhard, M. M., Navar, A. M., & Kollins, S. H. (2018). Validation of the Sleep Regularity Index in Older Adults and Associations with Cardiometabolic Risk. Sci Rep, 8(1), 14158. https://doi.org/10.1038/s41598-018-32402-5
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  - Engelhard, M., Xu, H., Carin, L., Oliver, J. A., Hallyburton, M., & McClernon, F. J. (2018). Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model. Proc Mach Learn Res, 85, 312–331.
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  - Dandu, S. R., Engelhard, M. M., Qureshi, A., Gong, J., Lach, J. C., Brandt-Pearce, M., & Goldman, M. D. (2018). Understanding the Physiological Significance of Four Inertial Gait Features in Multiple Sclerosis. Ieee J Biomed Health Inform, 22(1), 40–46. https://doi.org/10.1109/JBHI.2017.2773629
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  - Engelhard, M. M., Patek, S. D., Lach, J. C., & Goldman, M. D. (2018). Real-world walking in multiple sclerosis: Separating capacity from behavior. Gait Posture, 59, 211–216. https://doi.org/10.1016/j.gaitpost.2017.10.015
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  - Engelhard, M., Copley, C., Watson, J., Pillay, Y., Barron, P., & LeFevre, A. E. (2018). Optimising mHealth helpdesk responsiveness in South Africa: towards automated message triage. Bmj Glob Health, 3(Suppl 2), e000567. https://doi.org/10.1136/bmjgh-2017-000567
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  - LeFevre, A. E., Dane, P., Copley, C. J., Pienaar, C., Parsons, A. N., Engelhard, M., … Mohan, D. (2018). Unpacking the performance of a mobile health information messaging program for mothers (MomConnect) in South Africa: evidence on program reach and messaging exposure. Bmj Glob Health, 3(Suppl 2), e000583. https://doi.org/10.1136/bmjgh-2017-000583
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  - Engelhard, M. M., Patek, S. D., Sheridan, K., Lach, J. C., & Goldman, M. D. (2017). Remotely engaged: Lessons from remote monitoring in multiple sclerosis. Int J Med Inform, 100, 26–31. https://doi.org/10.1016/j.ijmedinf.2017.01.006
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  - Engelhard, M. M., Schmidt, K. M., Engel, C. E., Brenton, J. N., Patek, S. D., & Goldman, M. D. (2016). The e-MSWS-12: improving the multiple sclerosis walking scale using item response theory. Qual Life Res, 25(12), 3221–3230. https://doi.org/10.1007/s11136-016-1342-2
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  - Cincotta, M. C., Engelhard, M. M., Stankey, M., & Goldman, M. D. (2016). Fatigue and fluid hydration status in multiple sclerosis: A hypothesis. Mult Scler, 22(11), 1438–1443. https://doi.org/10.1177/1352458516663854
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  - Engelhard, M. M., Dandu, S. R., Patek, S. D., Lach, J. C., & Goldman, M. D. (2016). Quantifying six-minute walk induced gait deterioration with inertial sensors in multiple sclerosis subjects. Gait Posture, 49, 340–345. https://doi.org/10.1016/j.gaitpost.2016.07.184
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  - Ribeiro, S., Shi, X., Engelhard, M., Zhou, Y., Zhang, H., Gervasoni, D., … Nicolelis, M. A. L. (2007). Novel experience induces persistent sleep-dependent plasticity in the cortex but not in the hippocampus. Front Neurosci, 1(1), 43–55. https://doi.org/10.3389/neuro.01.1.1.003.2007
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- Book Sections
  - Bidopia, T., Engelhard, M. M., Kollins, S. H., & Lunsford-Avery, J. R. (2023). Screen media technology and ADHD in children and adolescents: Potential perils and emerging opportunities. In Encyclopedia of Child and Adolescent Health, First Edition (Vol. 3, pp. 260–274). https://doi.org/10.1016/B978-0-12-818872-9.00126-6
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- Conference Papers
  - Glasgow, T. E., Adams, E. L., Ksinan, A., Barsell, D. J., Lunsford-Avery, J., Chen, S., … Fuemmeler, B. F. (2022). Sleep onset, duration, or regularity: which matters most for child adiposity outcomes? In Int J Obes (Lond) (Vol. 46, pp. 1502–1509). England. https://doi.org/10.1038/s41366-022-01140-0
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  - Lunsford-Avery, J., Kansagra, S., Kollins, S., & Engelhard, M. (2022). Leveraging convenient wearable technology to assess adolescent sleep: Physical and psychiatric health correlates of single-channel sleep EEG in a community sample of youth. In Journal of Sleep Research (Vol. 31).
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  - Oliver, J., Engelhard, M., Stevens, B., McQuoid, J., & McClernon, F. J. (2022). Probing Disparities in Exposure to Smoking Contexts Using Computer Vision. In Neuropsychopharmacology (Vol. 47, pp. 439–440).
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  - Engelhard, M., McGough, D., & Kollins, S. (2021). 5.22 Improving the Predictive Value of Self-Report in Adult ADHD Diagnosis. In Journal of the American Academy of Child &Amp; Adolescent Psychiatry (Vol. 60, pp. S157–S157). Elsevier BV. https://doi.org/10.1016/j.jaac.2021.09.070
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  - Subramanian, V., Engelhard, M., Berchuck, S., Chen, L., Henao, R., & Carin, L. (2021). SpanPredict: Extraction of Predictive Document Spans with Neural Attention. In Naacl Hlt 2021 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 5234–5258).
  - Engelhard, M., Berchuck, S., D’Arcy, J., & Henao, R. (2020). Neural Conditional Event Time Models. In F. Doshi-Velez, J. Fackler, K. Jung, D. C. Kale, R. Ranganath, B. Wallace, & J. Wiens (Eds.), Mlhc (Vol. 126, pp. 223–244). PMLR.
  - Lunsford-Avery, J., Kollins, S., Keller, C., & Engelhard, M. (2020). Ambulatory EEG-measured sleep associated with attention-deficit/hyperactivity disorder symptom severity among adolescents. In Journal of Sleep Research (Vol. 29, pp. 162–163).
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  - Lunsford-Avery, J., Wang, K. W., Engelhard, M., Keller, C., Kansagra, S., & Kollins, S. (2020). Impact of Daily Caffeine on Actigraphically-Measured Sleep Duration Among Adolescents With and Without Attention-Deficit/Hyperactivity Disorder. In Neuropsychopharmacology (Vol. 45, pp. 82–83).
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  - Oliver, J., Froeliger, B., Engelhard, M., Conklin, C., & McClernon, F. (2020). Exposure to Smoking Context Potentiates Habitual Motor Response. In Neuropsychopharmacology (Vol. 45, pp. 356–356).
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  - Chen, S., Perera, R., Engelhard, M. M., Lunsford-Avery, J. R., Kollins, S. H., & Fuemmeler, B. F. (2019). A generic algorithm for sleep-wake cycle detection using unlabeled actigraphy data. In 2019 Ieee Embs International Conference on Biomedical and Health Informatics, Bhi 2019 Proceedings. https://doi.org/10.1109/BHI.2019.8834568
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  - Engelhard, M., Xu, H., Carin, L., Oliver, J. A., Hallyburton, M., & McClernon, F. J. (2018). Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model. In F. Doshi-Velez, J. Fackler, K. Jung, D. C. Kale, R. Ranganath, B. C. Wallace, & J. Wiens (Eds.), Mlhc (Vol. 85, pp. 312–331). PMLR.
  - Brenton, J. N., Engelhard, M., Woolbright, E., Koshiya, H., Herod, S. G., Engel, C. E., & Goldman, M. D. (2017). Continuous accelerometry as a measure of physical activity impairment in paediatric-onset multiple sclerosis subjects versus healthy controls. In Multiple Sclerosis Journal (Vol. 23, pp. 107–108). Paris, FRANCE: SAGE PUBLICATIONS LTD.
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  - Qureshi, A., Engelhard, M. M., Brandt-Pearce, M., & Goldman, M. D. (2017). Demonstrating the real-world significance of the mid-swing to heel strike part of the gait cycle using spectral features. In 2017 Ieee 14th International Conference on Wearable and Implantable Body Sensor Networks, Bsn 2017 (pp. 133–136). https://doi.org/10.1109/BSN.2017.7936025
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  - Qureshi, A., Brandt-Pearce, M., Engelhard, M. M., & Goldman, M. D. (2017). Relationship between kernel density function estimates of gait time series and clinical data. In 2017 Ieee Embs International Conference on Biomedical and Health Informatics, Bhi 2017 (pp. 329–332). https://doi.org/10.1109/BHI.2017.7897272
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  - Brenton, J. N., Koshiya, H., Engel, C., Herod, S., Engelhard, M., & Goldman, M. (2017). Utility of Physical Disability Outcome Measures in Pediatric-Onset Multiple Sclerosis. In Neurology (Vol. 88).
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  - Engelhard, M. M., Lach, J. C., Schmidt, K. M., Goldman, M. D., & Patek, S. D. (2016). Adaptive symptom reporting for mobile patient-reported disability assessment. In 2016 Ieee Wireless Health, Wh 2016 (pp. 172–179). https://doi.org/10.1109/WH.2016.7764573
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  - Dandu, S. R., Engelhard, M. M., Goldman, M. D., & Lach, J. (2016). Determining physiological significance of inertial gait features in multiple sclerosis. In Bsn 2016 13th Annual Body Sensor Networks Conference (pp. 266–271). https://doi.org/10.1109/BSN.2016.7516271
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  - Engelhard, M. M., Dandu, S. R., Lach, J. C., Goldman, M. D., & Patek, S. D. (2015). Toward detection and monitoring of gait pathology using inertial sensors under rotation, scale, and offset invariant dynamic time warping. In Bodynets International Conference on Body Area Networks. https://doi.org/10.4108/eai.28-9-2015.2261503
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  - Gong, J., Engelhard, M. M., Goldman, M. D., & Lach, J. (2015). Correlations between inertial body sensor measures and clinical measures in multiple sclerosis. In Bodynets International Conference on Body Area Networks. https://doi.org/10.4108/eai.28-9-2015.2261504
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- Theses and Dissertations
  - Engelhard, M. (n.d.). Adaptive Algorithms for Personalized Health Monitoring.
- Preprints
  - Doerstling, S. S., Akrobetu, D., Engelhard, M. M., Chen, F., & Ubel, P. A. (2021). A Disease Identification Algorithm for Medical Crowdfunding Campaigns: Validation Study (Preprint). JMIR Publications Inc. https://doi.org/10.2196/preprints.32867
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Teaching & Mentoring
Recent Courses
- MMCI 517: Applied Data Science 2023
- CRP 278: Machine Learning For Health 2022
- MMCI 517: Applied Data Science 2022
- CRP 278: Machine Learning For Health 2021
- MMCI 517: Applied Data Science 2021

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Education, Training, & Certifications

Previous Appointments & Affiliations

In the News

Selected Grants

Selected Publications

Academic Articles

Book Sections

Conference Papers

Theses and Dissertations

Preprints

Recent Courses