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Andrew Beam, Ph.D.

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Overview
Andrew Beam, PhD is an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health, with secondary appointments in the Department of Biomedical Informatics at Harvard Medical School and the Department of Newborn Medicine at Brigham and Women's Hospital. His research develops and applies machine-learning methods to extract meaningful insights from clinical and biological datasets, and he is the recipient of a Pioneer Award from the Robert Wood Johnson Foundation for his work on medical artificial intelligence.

Previously he was a Senior Fellow at Flagship Pioneering and the founding head of machine learning at VL56, a Flagship-backed venture that seeks to use machine learning to improve our ability to engineer proteins.

He earned his PhD in 2014 from N.C. State University for work on Bayesian neural networks, and he holds degrees in computer science (BS), computer engineering (BS), electrical engineering (BS), and statistics (MS), also from N.C. State. He completed a postdoctoral fellowship in Biomedical Informatics at Harvard Medical School and then served as a junior faculty member.

Dr. Beam's group is principally concerned with improving, stream-lining, and automating decision-making in healthcare through the use of quantitative, data-driven methods. He does this through rigorous methodological research coupled with deep partnerships with physicians and other members of the healthcare workforce. As part of this vision, he works to see these ideas translated into decision-making tools that doctors can use to better care for their patients.

For more information, please see his group's website at beamlab.org



INSTRUMENTING THE NICU WITH MODERN PREDICTIVE TOOLS

We are developing deep learning models to equip neonatologists with modern predictive tools to help them better understand and care for their patients. Infants born prematurely (before 37 weeks of gestation) experience very high levels of morbidity and are among the most expensive patients in all of pediatrics. NICU infants generate a tremendous amount of high-signal, multimodal data as part of their clinical care, but this data is currently under-utilized to inform decision-making.

These modalities are ones where deep learning has had tremendous success to date (e.g. imaging, text), thus there is an opportunity to create highly accurate predictive models for proactive decision-making. Specifically, we are interested in developing models in the following areas:

Convolutional models for NICU imaging data including x-rays, ROP screens, and ultrasounds.
Recurrent and transformer models for admission, progress, and discharge notes.
Recurrent and transformer models of real-time monitoring data.
Longitudinal disease trajectories built using large administrative databases.
We are extremely interested in developing new techniques that combine two or more of the above modalities to enable "pan-diagnostic" capabilities for NICU patients. Beyond model development, we are very committed to translational research to better understand how these models can be implemented in clinical work flows in a natural, easy-to-use manner.

AUTOMATIC DIAGNOSIS AND MEDICAL REASONING WITH NLP/NLU

A large portion of the world's medical knowledge is in unstructured sources such as textbooks, websites, and biomedical journal articles. We are developing a large-scale natural language processing (NLP) and natural language understanding (NLU) system capable of extracting general medical and diagnostic principles from unstructured medical text. For this project, we have created a unique collection of biomedical texts containing of 4.3 million articles, 50,000 pages of reference material, 15,000 flash cards, dozens of medical text books, and 20,000 multiple choice medical questions.

Using this data, we are creating models that can perform a broad range of medical reasoning tasks such as providing a differential diagnosis on the basis of a short textual description and answering complex medical questions posed in natural language. This work starts with current state of the art NLP/NLU/QA models based on deep learning, but seeks to extend them with explicit forms of symbolic reasoning and other less traditional computational models that are not currently in vogue.

METHODS DEVELOPMENT TO MOVE BEYOND DEEP LEARNING

Deep learning has had tremendous success in medicine. However, despite these successes deep learning models are in fact brittle and there are classes of problems that are not solvable by deep learning, even in principle. Moreover, at least in its current framing, nearly all of modern machine learning techniques are designed to give predictions, but what doctors often want are decisions. This necessitates moving beyond simple correlation-based models towards ones with richer understanding of the world, and are capable of understanding the effects of interventions.

In collaboration with our colleagues in causal inference group at HSPH, we are exploring the interface of machine learning and causal inference methods. This is a new, but very active, area of research and we are excited what new questions can be answered as machine learning models are imbued with a causal understanding of the world.

Research
The research activities and funding listed below are automatically derived from NIH ExPORTER and other sources, which might result in incorrect or missing items. Faculty can login to make corrections and additions.
  1. K01HL141771 (BEAM, ANDREW L.) Jul 1, 2019 - Jun 30, 2024
    NIH
    Predicting Pulmonary and Cardiac Morbidity in Preterm Infants with Deep Learning
    Role: Principal Investigator

Bibliographic
Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
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PMC Citations indicate the number of times the publication was cited by articles in PubMed Central, and the Altmetric score represents citations in news articles and social media. (Note that publications are often cited in additional ways that are not shown here.) Fields are based on how the National Library of Medicine (NLM) classifies the publication's journal and might not represent the specific topic of the publication. Translation tags are based on the publication type and the MeSH terms NLM assigns to the publication. Some publications (especially newer ones and publications not in PubMed) might not yet be assigned Field or Translation tags.) Click a Field or Translation tag to filter the publications.
  1. Hakim JB, Zhou A, Hernandez-Diaz S, Hart JM, Wylie BJ, Beam AL. Effectiveness of 17-OHP for Prevention of Recurrent Preterm Birth: A Retrospective Cohort Study. Am J Perinatol. 2021 Dec 31. PMID: 34972229.
    Citations:    Fields:    
  2. Kompa B, Snoek J, Beam AL. Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. Entropy (Basel). 2021 Nov 30; 23(12). PMID: 34945914.
    Citations:    
  3. Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021 11; 3(11):e745-e750. PMID: 34711379.
    Citations: 4     Fields:    Translation:Humans
  4. Beaulieu-Jones BK, Yuan W, Brat GA, Beam AL, Weber G, Ruffin M, Kohane IS. Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians? NPJ Digit Med. 2021 Mar 30; 4(1):62. PMID: 33785839.
    Citations: 10     
  5. Beam KS, Lee M, Hirst K, Beam A, Parad RB. Specificity of International Classification of Diseases codes for bronchopulmonary dysplasia: an investigation using electronic health record data and a large insurance database. J Perinatol. 2021 04; 41(4):764-771. PMID: 33649436.
    Citations:    Fields:    Translation:Humans
  6. Levin JC, Beam AL, Fox KP, Mandl KD. Medication utilization in children born preterm in the first two years of life. J Perinatol. 2021 07; 41(7):1732-1738. PMID: 33547407.
    Citations:    Fields:    Translation:Humans
  7. Kompa B, Snoek J, Beam AL. Second opinion needed: communicating uncertainty in medical machine learning. NPJ Digit Med. 2021 Jan 05; 4(1):4. PMID: 33402680.
    Citations: 9     
  8. Singh K, Beam AL, Nallamothu BK. Machine Learning in Clinical Journals: Moving From Inscrutable to Informative. Circ Cardiovasc Qual Outcomes. 2020 10; 13(10):e007491. PMID: 33079583.
    Citations: 1     Fields:    Translation:Humans
  9. Wilkinson J, Arnold KF, Murray EJ, van Smeden M, Carr K, Sippy R, de Kamps M, Beam A, Konigorski S, Lippert C, Gilthorpe MS, Tennant PWG. Time to reality check the promises of machine learning-powered precision medicine. Lancet Digit Health. 2020 12; 2(12):e677-e680. PMID: 33328030.
    Citations: 14     Fields:    Translation:Humans
  10. Schmaltz A, Beam AL. Sharpening the resolution on data matters: a brief roadmap for understanding deep learning for medical data. Spine J. 2021 10; 21(10):1606-1609. PMID: 32858170.
    Citations:    Fields:    Translation:Humans
  11. Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. A Review of Challenges and Opportunities in Machine Learning for Health. AMIA Jt Summits Transl Sci Proc. 2020; 2020:191-200. PMID: 32477638.
    Citations: 20     
  12. Challa AP, Beam AL, Shen M, Peryea T, Lavieri RR, Lippmann ES, Aronoff DM. Machine learning on drug-specific data to predict small molecule teratogenicity. Reprod Toxicol. 2020 08; 95:148-158. PMID: 32428651.
    Citations: 3     Fields:    Translation:Humans
  13. Beam AL, Fried I, Palmer N, Agniel D, Brat G, Fox K, Kohane I, Sinaiko A, Zupancic JAF, Armstrong J. Estimates of healthcare spending for preterm and low-birthweight infants in a commercially insured population: 2008-2016. J Perinatol. 2020 07; 40(7):1091-1099. PMID: 32103158.
    Citations: 14     Fields:    Translation:Humans
  14. Beam AL, Manrai AK, Ghassemi M. Challenges to the Reproducibility of Machine Learning Models in Health Care. JAMA. 2020 01 28; 323(4):305-306. PMID: 31904799.
    Citations: 39     Fields:    Translation:Humans
  15. Beam AL, Kompa B, Schmaltz A, Fried I, Weber G, Palmer N, Shi X, Cai T, Kohane IS. Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data. Pac Symp Biocomput. 2020; 25:295-306. PMID: 31797605.
    Citations: 25     Fields:    Translation:Humans
  16. Zhang L, Zhang Y, Cai T, Ahuja Y, He Z, Ho YL, Beam A, Cho K, Carroll R, Denny J, Kohane I, Liao K, Cai T. Automated grouping of medical codes via multiview banded spectral clustering. J Biomed Inform. 2019 12; 100:103322. PMID: 31672532.
    Citations: 1     Fields:    Translation:Humans
  17. Palmer NP, Silvester JA, Lee JJ, Beam AL, Fried I, Valtchinov VI, Rahimov F, Kong SW, Ghodoussipour S, Hood HC, Bousvaros A, Grand RJ, Kunkel LM, Kohane IS. Concordance between gene expression in peripheral whole blood and colonic tissue in children with inflammatory bowel disease. PLoS One. 2019; 14(10):e0222952. PMID: 31618209.
    Citations: 8     Fields:    Translation:Humans
  18. Beaulieu-Jones B, Finlayson SG, Chivers C, Chen I, McDermott M, Kandola J, Dalca AV, Beam A, Fiterau M, Naumann T. Trends and Focus of Machine Learning Applications for Health Research. JAMA Netw Open. 2019 10 02; 2(10):e1914051. PMID: 31651969.
    Citations: 14     Fields:    Translation:Humans
  19. Kartoun U, Iglay K, Shankar RR, Beam A, Radican L, Chatterjee A, Pai JK, Shaw S. Factors associated with clinical inertia in type 2 diabetes mellitus patients treated with metformin monotherapy. Curr Med Res Opin. 2019 12; 35(12):2063-2070. PMID: 31337263.
    Citations: 1     Fields:    Translation:Humans
  20. Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. Practical guidance on artificial intelligence for health-care data. Lancet Digit Health. 2019 08; 1(4):e157-e159. PMID: 33323184.
    Citations: 13     Fields:    Translation:Humans
  21. Chen ML, Doddi A, Royer J, Freschi L, Schito M, Ezewudo M, Kohane IS, Beam A, Farhat M. Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction. EBioMedicine. 2019 May; 43:356-369. PMID: 31047860.
    Citations: 12     Fields:    Translation:HumansCells
  22. Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS. Adversarial attacks on medical machine learning. Science. 2019 03 22; 363(6433):1287-1289. PMID: 30898923.
    Citations: 64     Fields:    Translation:Humans
  23. Ning W, Chan S, Beam A, Yu M, Geva A, Liao K, Mullen M, Mandl KD, Kohane I, Cai T, Yu S. Feature extraction for phenotyping from semantic and knowledge resources. J Biomed Inform. 2019 03; 91:103122. PMID: 30738949.
    Citations: 6     Fields:    Translation:Humans
  24. Beaulieu-Jones BK, Kohane IS, Beam AL. Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes. Pac Symp Biocomput. 2019; 24:8-17. PMID: 30864306.
    Citations: 4     Fields:    Translation:Humans
  25. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018 10; 2(10):719-731. PMID: 31015651.
    Citations: 214     Fields:    Translation:Humans
  26. Oakden-Rayner L, Beam AL, Palmer LJ. Medical journals should embrace preprints to address the reproducibility crisis. Int J Epidemiol. 2018 10 01; 47(5):1363-1365. PMID: 29868762.
    Citations: 3     Fields:    Translation:Humans
  27. Kartoun U, Aggarwal R, Beam AL, Pai JK, Chatterjee AK, Fitzgerald TP, Kohane IS, Shaw SY. Development of an Algorithm to Identify Patients with Physician-Documented Insomnia. Sci Rep. 2018 05 18; 8(1):7862. PMID: 29777125.
    Citations: 4     Fields:    Translation:Humans
  28. Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA. 2018 Apr 03; 319(13):1317-1318. PMID: 29532063.
    Citations: 260     Fields:    Translation:Humans
  29. Brat GA, Agniel D, Beam A, Yorkgitis B, Bicket M, Homer M, Fox KP, Knecht DB, McMahill-Walraven CN, Palmer N, Kohane I. Postsurgical prescriptions for opioid naive patients and association with overdose and misuse: retrospective cohort study. BMJ. 2018 01 17; 360:j5790. PMID: 29343479.
    Citations: 116     Fields:    Translation:Humans
  30. Fried I, Beam AL, Kohane IS, Palmer NP. Utilization, Cost, and Outcome of Branded vs Compounded 17-Alpha Hydroxyprogesterone Caproate in Prevention of Preterm Birth. JAMA Intern Med. 2017 11 01; 177(11):1689-1690. PMID: 28973537.
    Citations:    Fields:    Translation:Humans
  31. Palmer N, Beam A, Agniel D, Eran A, Manrai A, Spettell C, Steinberg G, Mandl K, Fox K, Nelson SF, Kohane I. Association of Sex With Recurrence of Autism Spectrum Disorder Among Siblings. JAMA Pediatr. 2017 11 01; 171(11):1107-1112. PMID: 28973142.
    Citations: 21     Fields:    Translation:Humans
  32. Miron O, Beam AL, Kohane IS. Auditory brainstem response in infants and children with autism spectrum disorder: A meta-analysis of wave V. Autism Res. 2018 02; 11(2):355-363. PMID: 29087045.
    Citations: 18     Fields:    Translation:Humans
  33. Nitzschke S, Offodile AC, Cauley RP, Frankel JE, Beam A, Elias KM, Gibbons FK, Salim A, Christopher KB. Long term mortality in critically ill burn survivors. Burns. 2017 Sep; 43(6):1155-1162. PMID: 28606748.
    Citations: 5     Fields:    Translation:Humans
  34. Beam AL, Kartoun U, Pai JK, Chatterjee AK, Fitzgerald TP, Shaw SY, Kohane IS. Predictive Modeling of Physician-Patient Dynamics That Influence Sleep Medication Prescriptions and Clinical Decision-Making. Sci Rep. 2017 02 09; 7:42282. PMID: 28181568.
    Citations: 8     Fields:    Translation:Humans
  35. Beam AL, Kohane IS. Translating Artificial Intelligence Into Clinical Care. JAMA. 2016 12 13; 316(22):2368-2369. PMID: 27898974.
    Citations: 40     Fields:    Translation:Humans
  36. Beam AL, Motsinger-Reif AA, Doyle J. An investigation of gene-gene interactions in dose-response studies with Bayesian nonparametrics. BioData Min. 2015; 8:6. PMID: 25691918.
    Citations:    
  37. Beam AL, Motsinger-Reif A, Doyle J. Bayesian neural networks for detecting epistasis in genetic association studies. BMC Bioinformatics. 2014 Nov 21; 15:368. PMID: 25413600.
    Citations: 8     Fields:    Translation:HumansCells
  38. Beam A, Motsinger-Reif A. Beyond IC50s: Towards Robust Statistical Methods for in vitro Association Studies. J Pharmacogenomics Pharmacoproteomics. 2014 Mar 01; 5(1):1000121. PMID: 25110614.
    Citations: 9     
  39. Beam A, Garber L, Sakugawa J, Kopral C. Salmonella awareness and related management practices in U.S. urban backyard chicken flocks. Prev Vet Med. 2013 Jul 01; 110(3-4):481-8. PMID: 23290129.
    Citations: 15     Fields:    Translation:HumansAnimalsCells
  40. Padilla S, Corum D, Padnos B, Hunter DL, Beam A, Houck KA, Sipes N, Kleinstreuer N, Knudsen T, Dix DJ, Reif DM. Zebrafish developmental screening of the ToxCast™ Phase I chemical library. Reprod Toxicol. 2012 Apr; 33(2):174-87. PMID: 22182468.
    Citations: 99     Fields:    Translation:Animals
  41. Motsinger-Reif A, Brown C, Havener T, Hardison N, Peters E, Beam A, Everrit L, McLeod H. Ex-Vivo Modeling for Heritability Assessment and Genetic Mapping in Pharmacogenomics. Proc Am Stat Assoc. 2011 Jul-Aug; 2011:306-318. PMID: 30627054.
    Citations: 1     
  42. Beam AL, Motsinger-Reif AA. Optimization of nonlinear dose- and concentration-response models utilizing evolutionary computation. Dose Response. 2011; 9(3):387-409. PMID: 22013401.
    Citations: 8     
  43. Rotroff DM, Beam AL, Dix DJ, Farmer A, Freeman KM, Houck KA, Judson RS, LeCluyse EL, Martin MT, Reif DM, Ferguson SS. Xenobiotic-metabolizing enzyme and transporter gene expression in primary cultures of human hepatocytes modulated by ToxCast chemicals. J Toxicol Environ Health B Crit Rev. 2010 Feb; 13(2-4):329-46. PMID: 20574906.
    Citations: 21     Fields:    Translation:HumansAnimalsCells
  44. Garber L, Forde-Folle K, Beam A, Hill G. Survey of small-enterprise chicken operations in the United States. Prev Vet Med. 2009 Aug 01; 90(3-4):204-10. PMID: 19501925.
    Citations: 1     Fields:    Translation:Animals
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Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.