Lin Lawrence Guo
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Explore the profile of Lin Lawrence Guo including associated specialties, affiliations and a list of published articles.
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17
Citations
62
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Recent Articles
1.
Yan A, Guo L, Inoue J, Arciniegas S, Vettese E, Wolochacz A, et al.
Front Digit Health
. 2025 Feb;
7:1462751.
PMID: 39906065
Background: The adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its...
2.
Hassan H, Chen Y, Lu A, Arciniegas S, Yan A, Guo L, et al.
Pediatr Blood Cancer
. 2024 Nov;
72(2):e31459.
PMID: 39587410
Purpose: This systematic review aimed to identify and synthesize evidence on hospital readmissions among pediatric oncology patients, focusing on the indications, risk factors, and proposed strategies to prevent readmissions. Method:...
3.
Guo L, Niemeier M
J Neurosci
. 2024 Jul;
44(33).
PMID: 39019614
The simple act of viewing and grasping an object involves complex sensorimotor control mechanisms that have been shown to vary as a function of multiple object and other task features...
4.
Guo L, Fries J, Steinberg E, Fleming S, Morse K, Aftandilian C, et al.
NPJ Digit Med
. 2024 Jun;
7(1):171.
PMID: 38937550
Foundation models are transforming artificial intelligence (AI) in healthcare by providing modular components adaptable for various downstream tasks, making AI development more scalable and cost-effective. Foundation models for structured electronic...
5.
Lee N, Guo L, Nestor A, Niemeier M
J Neurosci
. 2024 May;
44(29.
PMID: 38789263
The intention to act influences the computations of various task-relevant features. However, little is known about the time course of these computations. Furthermore, it is commonly held that these computations...
6.
Guo L, Morse K, Aftandilian C, Steinberg E, Fries J, Posada J, et al.
BMC Med Inform Decis Mak
. 2024 Feb;
24(1):51.
PMID: 38355486
Background: Diagnostic codes are commonly used as inputs for clinical prediction models, to create labels for prediction tasks, and to identify cohorts for multicenter network studies. However, the coverage rates...
7.
Guo L, Calligan M, Vettese E, Cook S, Gagnidze G, Han O, et al.
Heliyon
. 2023 Nov;
9(11):e21586.
PMID: 38027579
Objectives: To describe the processes developed by The Hospital for Sick Children (SickKids) to enable utilization of electronic health record (EHR) data by creating sequentially transformed schemas for use across...
8.
Lemmon J, Guo L, Steinberg E, Morse K, Fleming S, Aftandilian C, et al.
J Am Med Inform Assoc
. 2023 Aug;
30(12):2004-2011.
PMID: 37639620
Objective: Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to...
9.
Guo L, Steinberg E, Fleming S, Posada J, Lemmon J, Pfohl S, et al.
Sci Rep
. 2023 Mar;
13(1):3767.
PMID: 36882576
Temporal distribution shift negatively impacts the performance of clinical prediction models over time. Pretraining foundation models using self-supervised learning on electronic health records (EHR) may be effective in acquiring informative...
10.
Lemmon J, Guo L, Posada J, Pfohl S, Fries J, Fleming S, et al.
Methods Inf Med
. 2023 Feb;
62(1-02):60-70.
PMID: 36812932
Background: Temporal dataset shift can cause degradation in model performance as discrepancies between training and deployment data grow over time. The primary objective was to determine whether parsimonious models produced...