Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., University of Virginia 2016
  • M.D., University of Virginia 2014
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  Previous Appointments & Affiliations

  • Assistant Professor of Biostatistics & Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2021 - 2022
• Recognition
• 

  In the News

  • 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
  • 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
• 

  External Relationships

  • Salutary, Inc
• Publications & Artistic Works
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  Selected Publications

  • Academic Articles

    • 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/e27875
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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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    • 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., 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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    • 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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  • 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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    • 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
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  Recent Courses

  • 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
  • MMCI 517: Applied Data Science 2020

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