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CATEGORIES:PhD Defense
DESCRIPTION:Title: Digital Phenoptyping and Early Disease Detection using C
 onsumer Wearable Devices\n\nAbstract: Consumer wearable devices\, including
  smartwatches\, rings\, and activity bands\, continuously capture physiolog
 ical and behavioral data from millions of individuals. These data streams e
 nable estimation of individual baselines\, detection of deviations\, and ri
 sk stratification—the central promise of precision medicine. However\, the 
 field currently lacks standardized methods to rigorously analyze these high
 -dimensional\, longitudinal datasets. In my defense\, I will provide a prac
 tical blueprint for how high-dimensional wearable data can be rigorously an
 alyzed and translated into clinical insights\, by using interpretable\, uns
 upervised\, and state-aware modeling approaches. To demonstrate the richnes
 s of wearable data\, I first introduce a retrospective clinical case report
  that integrates five years of multi-wearable data with clinical records of
  a 76-year-old patient\, who had structural heart disease and experienced s
 udden cardiac death immediately after intense workout. This case report hig
 hlights the potential role of integrating continuous digital physiology wit
 h traditional cardiology for timely evaluation and intervention. Next\, I p
 resent an interpretable framework for cohort-level phenotyping that leverag
 es timestamped wearable metrics\, clinically relevant risk strata\, and uns
 upervised machine learning methods. Applied across two independent real-wor
 ld cohorts\, this method uncovers latent cohort structure\, clinically mean
 ingful outliers\, and distributions of physiological risk that are otherwis
 e obscured by standard reporting methods. Finally\, by applying an ensemble
  of dimensionality reduction and unsupervised learning techniques on a long
 itudinal wearable and self-reported stress dataset\, I reveal two reproduci
 ble modes of stress-physiology association that reflects either sympathetic
  ("fight-or-flight") or parasympathetic ("rest-and-digest") dominance that 
 individuals transition between over time. My contributions establish a scal
 able\, reproducible\, and personalized inference from real-world wearable m
 easurements\, supporting future applications in precision medicine.\n\nZoom
 : Please contact Leyre Caracuel for the zoom link.
DTEND:20260130T190000Z
DTSTAMP:20260307T114057Z
DTSTART:20260130T180000Z
GEO:37.42989;-122.174837
LOCATION:ALLEN BUILDING ANNEX\, 101X
SEQUENCE:0
SUMMARY:PhD Dissertation Defense: Ziv Lautman
UID:tag:localist.com\,2008:EventInstance_51880284670196
URL:https://events.stanford.edu/event/copy-of-phd-dissertation-defense-Ziv-
 Lautman
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