Abstract
BackgroundBio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to “understand” biomedical data.ResultsThis retrospective study included 723 cancer patients from the SEER-MHOS dataset. Two ML methods were applied to create predictive models for ADL disabilities for the first year after a patient’s cancer diagnosis. The first method is a standard rule learning algorithm; the second is that same algorithm additionally equipped with methods for reasoning with ontologies. The models showed that a patient’s race, ethnicity, smoking preference, treatment plan and tumor characteristics including histology, staging, cancer site, and morphology were predictors for ADL performance levels one year after cancer diagnosis. The ontology-guided ML method was more accurate at predicting ADL performance levels (P < 0.1) than methods without ontologies.ConclusionsThis study demonstrated that bio-ontologies can be harnessed to provide medical knowledge for ML algorithms. The presented method demonstrates that encoding specific types of hierarchical relationships to guide rule learning is possible, and can be extended to other types of semantic relationships present in biomedical ontologies. The ontology-guided ML method achieved better performance than the method without ontologies. The presented method can also be used to promote the effectiveness and efficiency of ML in healthcare, in which use of background knowledge and consistency with existing clinical expertise is critical.
Highlights
Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields
AQ21 rule learning AQ21 is a multi-task ML and data mining system for attributional rule learning and rule testing that can be applied to a wide range of classification problems [17]
One major advantage is that AQ21-OG can optimize attributional rules with the assistance of medical knowledge from the Unified Medical Language System (UMLS), for the purposes of rule generalization based on the hierarchical relationships
Summary
Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. Precision medicine is an emerging approach for disease prevention and treatment that takes into account individualized patient information including genomics, environment, and lifestyle [1]. This new era in medicine and health requires advanced methodologies for analyzing, synthesizing, and disseminating heterogeneous data, as well as the ability to harness existing knowledge in order to discover relationships and create computational models for improving care and quality of life. The focus on big data analysis in the biomedical field creates an even greater need for advanced computational methodologies that can translate data into computer-interpretable knowledge and produce comprehensible models that can be used to advance patient-centric healthcare. Very few ML algorithms are capable of interpreting data beyond the mechanical fitting of input data/matrix of numbers into a given model
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