Building the Model

Authors

He S. Yang, From the Department of Pathology and Laboratory Medicine (Yang, Chadburn), Weill Cornell Medicine, New York, New York.
Daniel D. Rhoads, From the Department of Laboratory Medicine, Cleveland Clinic, Cleveland, Ohio (Rhoads).
Jorge Sepulveda, From the Department of Pathology, School of Medicine and Health Sciences, George Washington University, Washington, District of Columbia (Sepulveda).
Chengxi Zang, From the Department of Population Health Sciences (Zang, Wang), Weill Cornell Medicine, New York, New York.
Amy Chadburn, From the Department of Pathology and Laboratory Medicine (Yang, Chadburn), Weill Cornell Medicine, New York, New York.
Fei Wang, From the Department of Population Health Sciences (Zang, Wang), Weill Cornell Medicine, New York, New York.

Document Type

Journal Article

Publication Date

10-10-2022

Journal

Archives of pathology & laboratory medicine

DOI

10.5858/arpa.2021-0635-RA

Abstract

CONTEXT.—: Machine learning (ML) allows for the analysis of massive quantities of high-dimensional clinical laboratory data, thereby revealing complex patterns and trends. Thus, ML can potentially improve the efficiency of clinical data interpretation and the practice of laboratory medicine. However, the risks of generating biased or unrepresentative models, which can lead to misleading clinical conclusions or overestimation of the model performance, should be recognized. OBJECTIVES.—: To discuss the major components for creating ML models, including data collection, data preprocessing, model development, and model evaluation. We also highlight many of the challenges and pitfalls in developing ML models, which could result in misleading clinical impressions or inaccurate model performance, and provide suggestions and guidance on how to circumvent these challenges. DATA SOURCES.—: The references for this review were identified through searches of the PubMed database, the US Food and Drug Administration white papers and guidelines, conference abstracts, and online preprints. CONCLUSIONS.—: With the growing interest in developing and implementing ML models in clinical practice, laboratorians and clinicians need to be educated in order to collect sufficiently large and high-quality data, properly report the data set characteristics, and combine data from multiple institutions with proper normalization. They will also need to assess the reasons for missing values, determine the inclusion or exclusion of outliers, and evaluate the completeness of a data set. In addition, they require the necessary knowledge to select a suitable ML model for a specific clinical question and accurately evaluate the performance of the ML model, based on objective criteria. Domain-specific knowledge is critical in the entire workflow of developing ML models.

APA Citation

Yang, He S.; Rhoads, Daniel D.; Sepulveda, Jorge; Zang, Chengxi; Chadburn, Amy; and Wang, Fei, "Building the Model" (2022). GW Authored Works. Paper 1793.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/1793

Department

Pathology