Risk management and healthcare policy
Semantic Mapping of Public Health Legislation and Emergency Legal Responses Using Natural Language Processing Techniques
By Wang D, Guan Z and Wu B.
Abstract
Background
Public-health legislation is heterogeneous across jurisdictions, creating challenges for reproducible policy surveillance.
Methods
Six publicly available policy-surveillance datasets from the LawAtlas Policy Surveillance Portal were consolidated and analyzed. After excluding records coded as "No bills" and collapsing repeated longitudinal snapshots within source domains, the data comprised 4328 snapshot rows, 2126 jurisdiction-by-bill source-domain records, and 1534 unique bills. Observed legal coverage extended from 6 August 2020 to 20 May 2022. Semantic mapping used TF-IDF, truncated singular-value decomposition, and K-Means clustering. Records containing at least 10 alphabetic tokens formed the semantic-mapping sample (n = 1875). Cluster number was selected using the maximum cosine silhouette coefficient across k = 2-12. Independent validation used the six curated LawAtlas source-domain memberships as multilabel targets for 821 unique bills; unsupervised cluster assignments were not used as supervised ground truth.
