Scott Fleming

Ph.D. Student in Biomedical Informatics
M.S. Student in Computer Science (Artificial Intelligence)

Hi there! I'm a third-year Ph.D. student in the Biomedical Informatics (BMI) Graduate Program at Stanford University (housed in the Department of Biomedical Data Science in Stanford's School of Medicine). I am co-advised by Emma Brunskill (Computer Science) and Nigam Shah (Biomedical Informatics). My research focuses on off-policy reinforcement learning for healthcare, using historical data from the clinical record to develop translational tools for sequential decision making under uncertainty. I am supported by a Stanford Graduate Fellowship and a National Defense Science and Engineering Graduate (NDSEG) Fellowship. In my spare time, I am an outdoor enthusiast and amateur photographer (the banner above is a photo I took on a recent trip to Yosemite).


Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine
Lin Lawrence Guo, Stephen Pfohl, Jason Fries, Jose Posada, Scott Fleming, Catherine Aftandilian, Nigam Shah, Lillian Sung.
Published in Applied Clinical Informatics, 2021.

Mapping Neural Circuit Biotypes to Symptoms and Behavioral Dimensions of Depression and Anxiety
Andrea Goldstein-Piekarski*, Tali Ball*, Zoe Samara, Brooke Staveland, Arielle Keller, Scott Fleming, Katherine Grisanzio, Bailey Holt-Gosselin, Patrick Stetz, Jun Ma, Leanne Williams.
Published in Biological Psychiatry, 2021.

Ontology-driven weak supervision for clinical entity classification in electronic health records.
Jason Fries, Ethan Steinberg, Saelig Khattar, Scott Fleming, Jose Posada, Alison Callahan, Nigam Shah.
Published in Nature Communications, 2021.

Intrinsic reward circuit connectivity profiles underlying symptom and quality of life outcomes following antidepressant medication: a report from the iSPOT-D trial.
Adina Fischer*, Bailey Holt-Gosselin*, Scott Fleming*, Laura Hack, Tali Ball, Alan Schatzberg, Leanne Williams.
Published in Neuropsychopharmacology, 2021.

Test-retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices.
Leonardo Tozzi, Scott Fleming, Zachary Taylor, Cooper Raterink, Leanne Williams.
Published in Network Neuroscience, 2020.

Assessing the accuracy of automatic speech recognition for psychotherapy.
Adam Miner*, Albert Haque*, Jason Fries, Scott Fleming, Denise Wilfley, Terence Wilson, Arnold Milstein, Dan Jurafsky, Bruce Arnow, Stewart Agras, Li Fei-Fei, Nigam Shah.
Published in NPJ Digital Medicine, 2020.

Automated Classification of Knee X-rays Using Deep Neural Networks Outperforms Radiologist.
Kevin Thomas, Lukasz Kidzinski, Eni Halilaj, Scott Fleming, Guhan Venkataraman, Edwin Oei, Garry Gold, Scott Delp.
Published in Radiology: Artificial Intelligence, 2019.

Detecting Developmental Delay and Autism Through Machine Learning Models UsingHome Videos of Bangladeshi Children: Development and Validation Study.
Qandeel Tariq*, Scott Fleming*, Jessey Schwartz, Kaitlyn Dunlap, Conor Corbin, Peter Washington, Haik Kalantarian, Naila Khan, Gary Darmstadt, Dennis Wall.
Spotlight Oral (top 20%), Maternal and Child Health Research Institute Symposium, Stanford, CA. 2018.
3rd place for most exciting application, Stanford Biomedical Informatics Retreat, Asilomar, CA. 2018.
Published in Journal of Medical Internet Research (JMIR), 2019.
[Paper] [Video] [Poster]

Workshops, Posters, and Presentations

Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare.
Scott Fleming, Kuhan Jeyapragasan, Tony Duan, Daisy Ding, Saurabh Gombar, Nigam Shah, Emma Brunskill.
NeurIPS ML for Health (ML4H) Workshop, 2019. (Acceptance Rate: 68/198 = 34%)
[Paper] [Poster]

Symptom Profile Subtypes Predict Treatment Response to 5 Hz rTMS in MDD and Co-Morbid PTSD.
Yosef Berlow, Katherine Grisanzio, Scott Fleming, Abdullah Rashed Ahmed, Emily Aiken, Linda Carpenter, Noah Philip
Poster, American College of Neuropsychopharmacology (ACNP) 57th Annual Meeting. 2018.

Interpretable Machine Learning Models for Precision Psychiatry.
Scott Fleming, Adina Fischer, Bailey Holt-Gosselin, Leanne Williams
Oral Presentation, Poster, the Stanford Data Science Initiative Fall Retreat, Stanford, CA. 2018.

A Data-Driven Characterization of Neuropsychiatric Disorders using Measures of Attention, Working Memory, and Response Inhibition.
Scott Fleming, John Leikauf, Matthew Sacchet, Russell Poldrack
Poster, the Big Data in Precision Health Conference, Stanford, CA. 2018.


See here.