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This study examined the impact of irrelevant dimensional variation on rule-based category learning in patients with Parkinson's disease (PD), older controls (OC), and younger controls (YC). Participants were presented with 4-dimensional, binary-valued stimuli and were asked to categorize each into 1 of 2 categories. Category membership was based on the value of a single dimension. Four experimental conditions were administered in which there were zero, 1, 2, or 3 randomly varying irrelevant dimensions. Results indicated that patients with PD were impacted to a greater extent than both the OC and YC participants when the number of randomly varying irrelevant dimensions increased. These results suggest that the degree of working memory and selective attention requirements of a categorization task will impact whether PD patients are impaired in rule-based category learning, and help to clarify recent discrepancies in the literature.
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http://dx.doi.org/10.1017/S1355617705050617 | DOI Listing |
Int J Med Inform
September 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address:
Background: Identifying patient-specific barriers to statin therapy, such as intolerance or deferral, from clinical notes is a major challenge for improving cardiovascular care. Automating this process could enable targeted interventions and improve clinical decision support (CDS).
Objective: To develop and evaluate a novel hybrid artificial intelligence (AI) framework for accurately and efficiently extracting information on statin therapy barriers from large volumes of clinical notes.
JMIR Med Inform
August 2025
School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.
Background: Bleeding adverse drug events (ADEs), particularly among older inpatients receiving antithrombotic therapy, represent a major safety concern in hospitals. These events are often underdetected by conventional rule-based systems relying on structured electronic medical record data, such as the ICD-10 (International Statistical Classification of Diseases and Related Health Problems 10th Revision) codes, which lack the granularity to capture nuanced clinical narratives.
Objective: This study aimed to develop and evaluate a natural language processing (NLP) model to detect and categorize bleeding ADEs in discharge summaries of older adults.
Proc Natl Acad Sci U S A
September 2025
Department of Cognitive and Psychological Sciences, Brown University, Providence, RI 02912.
Human learning embodies a striking duality: Sometimes, we can rapidly infer and compose logical rules, benefiting from structured curricula (e.g., in formal education), while other times, we rely on an incremental approach or trial-and-error, learning better from curricula that are randomly interleaved.
View Article and Find Full Text PDFMem Cognit
August 2025
School of Psychology, The University of Sydney, Sydney, NSW, 2006, Australia.
Evidence for two qualitatively different learning strategies has emerged from the function- and category-learning literatures: a rule-based and an exemplar-based strategy. With a rule-based strategy, learners abstract some common principle from training items, which allows extrapolation to novel instances. With an exemplar-based strategy, learners encode training items without abstraction, which facilitates generalisation based on surface similarity to trained items.
View Article and Find Full Text PDFOphthalmol Sci
June 2025
Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon.
Purpose: To develop a computable phenotype for normal tension glaucoma (NTG) to enhance disease identification from electronic health records (EHRs).
Design: Retrospective cohort study.
Subjects: Deidentified EHR data from an academic medical center identified 1851 patients aged ≥40 years, with glaucoma and available clinical notes.