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Electronic health records (EHR) aggregate extensive data critical for advancing patient care and refining intervention strategies. EHR data is essential for epidemiological study, more commonly referred to as cohort study, where patients with shared characteristics or similar diseases are analyzed over time. Unfortunately, existing studies on cohort modeling are limited, struggling to derive fine-grained cohorts or effectively utilize cohort information, which hinders their ability to uncover intrinsic relationships between cohorts. To this end, we propose NeuralCohort, a cohort-aware neural representation learning method that precisely segments patients into finer-grained cohorts via an innovative cohort contextualization mechanism and captures both intra- and inter-cohort information using a Biscale Cohort Learning Module. Designed as a plug-in, NeuralCohort integrates seamlessly with existing backbone models, enhancing their cohort analysis capabilities by infusing deep cohort insights into the representation learning processes. The effectiveness and generalizability of NeuralCohort are validated across extensive real-world EHR datasets. Experimental results demonstrate that NeuralCohort consistently improves the performance of various backbone models, achieving up to an 8.1% increase in AUROC.