activity
20162026
most citedPredicting Pediatric Surgical Durations

7 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

eess.SY2026

Safe Learning of Adaptive Control Policies for Remote Patient Monitoring

Ramanan Tamizholi, Siddharth Chandak, Isha Thapa +2

Remote Patient Monitoring (RPM) enables continuous observation of patients in their daily environments, improving both health outcomes and quality of life. A key challenge in RPM i…

cs.CL2026

An ambiguity taxonomy for evaluating large language model performance on clinical registry abstraction: a multi-site prospective study

James Matheson, Betsy Castillo, Andrew Y. Shin +1

Objective: To evaluate large language model (LLM) performance on unprocessed electronic medical record (EMR) data for clinical registry abstraction. Methods: We evaluated LLM perfo…

eess.SY2025

Optimal Control for Remote Patient Monitoring with Multidimensional Health States

Siddharth Chandak, Isha Thapa, Nicholas Bambos +1

Selecting the right monitoring level in Remote Patient Monitoring (RPM) systems for e-healthcare is crucial for balancing patient outcomes, various resources, and patient's quality…

eess.SY2024

Tiered Service Architecture for Remote Patient Monitoring

Siddharth Chandak, Isha Thapa, Nicholas Bambos +1

We develop a remote patient monitoring (RPM) service architecture, which has two tiers of monitoring: ordinary and intensive. The patient's health state improves or worsens in each…

stat.AP2016★ 7 cited

Predicting Pediatric Surgical Durations

Neal Master, David Scheinker, Nicholas Bambos

Effective management of operating room resources relies on accurate predictions of surgical case durations. This prediction problem is known to be particularly difficult in pediatr…