TY - JOUR
T1 - Weak Supervision for Clinical Phenotyping Under Ambiguity: A Scalable Labeling Theory for Noisy Electronic Health Records
AU - Lucas Meyer
AU - Anna Schmid
AU - Stefan Braun
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2021
VL - 1
IS - 1
SP - 4
N2 - Electronic health records (EHRs) are central to modern healthcare analytics but are often characterized by noise, ambiguity, and missing information, making reliable clinical phenotyping difficult. Clinical phenotypes—observable characteristics derived from patient data—are essential for diagnosis, prognosis, and treatment planning. Yet, traditional supervised machine learning methods depend on large volumes of high-quality annotated data that are difficult to obtain at scale.This review examines the role of weak supervision in enabling scalable clinical phenotyping from noisy and heterogeneous EHR data. Weak supervision frameworks generate labels using heuristic rules, knowledge-based signals, or programmatic labeling functions, allowing models to learn from large datasets without extensive expert annotation. These approaches help address challenges such as inconsistent terminology, missing values, and temporal irregularities commonly found in clinical records.We synthesize recent developments in scalable phenotyping systems that integrate machine learning architectures, probabilistic labeling strategies, and multimodal data representations to extract meaningful patterns from imperfect clinical data. The review also outlines a systems-level perspective on healthcare analytics pipelines, covering data ingestion, model training under label uncertainty, deployment in clinical environments, and governance considerations for responsible AI integration.Overall, weak supervision emerges as a practical strategy for transforming noisy EHR data into usable clinical intelligence, enabling more scalable and trustworthy analytics for healthcare decision support.
UR - https://cirpublications.com/o262985102
ER -