{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:6AZNQYMDB47GYRBE6VKD7YNDXK","short_pith_number":"pith:6AZNQYMD","schema_version":"1.0","canonical_sha256":"f032d861830f3e6c4424f5543fe1a3ba8bcdd35660b9a6c7b7fe021ac782dc84","source":{"kind":"arxiv","id":"1808.04880","version":2},"attestation_state":"computed","paper":{"title":"A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.AP"],"primary_cat":"stat.ML","authors_text":"Alexander New, Kristin P. Bennett","submitted_at":"2018-08-14T20:08:33Z","abstract_excerpt":"We consider the problem in precision health of grouping people into subpopulations based on their degree of vulnerability to a risk factor. These subpopulations cannot be discovered with traditional clustering techniques because their quality is evaluated with a supervised metric: the ease of modeling a response variable over observations within them. Instead, we apply the supervised cadre model (SCM), which does use this metric. We extend the SCM formalism so that it may be applied to multivariate regression and binary classification problems. We also develop a way to use conditional entropy "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1808.04880","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-08-14T20:08:33Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"ea659013e7edb56d82cb3b00e952d34d798989bb5268ac478a6893426108f8fe","abstract_canon_sha256":"9def02ea2723ae64d15cdbf63a3798f36a7bd5d96a10b3b4e975adff9843d0c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:47.678689Z","signature_b64":"ystGwHrLbzVOAMLq6ls2IeH7fKXUGEA80BVFlkC9VR922GNdamrMN/uDhg+A75qwfxPvO6D2/PtMpeFN3zOoBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f032d861830f3e6c4424f5543fe1a3ba8bcdd35660b9a6c7b7fe021ac782dc84","last_reissued_at":"2026-05-17T23:58:47.678216Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:47.678216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.AP"],"primary_cat":"stat.ML","authors_text":"Alexander New, Kristin P. Bennett","submitted_at":"2018-08-14T20:08:33Z","abstract_excerpt":"We consider the problem in precision health of grouping people into subpopulations based on their degree of vulnerability to a risk factor. These subpopulations cannot be discovered with traditional clustering techniques because their quality is evaluated with a supervised metric: the ease of modeling a response variable over observations within them. Instead, we apply the supervised cadre model (SCM), which does use this metric. We extend the SCM formalism so that it may be applied to multivariate regression and binary classification problems. We also develop a way to use conditional entropy "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04880","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1808.04880","created_at":"2026-05-17T23:58:47.678294+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.04880v2","created_at":"2026-05-17T23:58:47.678294+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04880","created_at":"2026-05-17T23:58:47.678294+00:00"},{"alias_kind":"pith_short_12","alias_value":"6AZNQYMDB47G","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"6AZNQYMDB47GYRBE","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"6AZNQYMD","created_at":"2026-05-18T12:32:08.215937+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6AZNQYMDB47GYRBE6VKD7YNDXK","json":"https://pith.science/pith/6AZNQYMDB47GYRBE6VKD7YNDXK.json","graph_json":"https://pith.science/api/pith-number/6AZNQYMDB47GYRBE6VKD7YNDXK/graph.json","events_json":"https://pith.science/api/pith-number/6AZNQYMDB47GYRBE6VKD7YNDXK/events.json","paper":"https://pith.science/paper/6AZNQYMD"},"agent_actions":{"view_html":"https://pith.science/pith/6AZNQYMDB47GYRBE6VKD7YNDXK","download_json":"https://pith.science/pith/6AZNQYMDB47GYRBE6VKD7YNDXK.json","view_paper":"https://pith.science/paper/6AZNQYMD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.04880&json=true","fetch_graph":"https://pith.science/api/pith-number/6AZNQYMDB47GYRBE6VKD7YNDXK/graph.json","fetch_events":"https://pith.science/api/pith-number/6AZNQYMDB47GYRBE6VKD7YNDXK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6AZNQYMDB47GYRBE6VKD7YNDXK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6AZNQYMDB47GYRBE6VKD7YNDXK/action/storage_attestation","attest_author":"https://pith.science/pith/6AZNQYMDB47GYRBE6VKD7YNDXK/action/author_attestation","sign_citation":"https://pith.science/pith/6AZNQYMDB47GYRBE6VKD7YNDXK/action/citation_signature","submit_replication":"https://pith.science/pith/6AZNQYMDB47GYRBE6VKD7YNDXK/action/replication_record"}},"created_at":"2026-05-17T23:58:47.678294+00:00","updated_at":"2026-05-17T23:58:47.678294+00:00"}