{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:PY7O5GMCLTOJWKC46HAZ3P7N5R","short_pith_number":"pith:PY7O5GMC","schema_version":"1.0","canonical_sha256":"7e3eee99825cdc9b285cf1c19dbfedec63509f495f1b0109f56e1ac5f3bff926","source":{"kind":"arxiv","id":"1512.03542","version":1},"attestation_state":"computed","paper":{"title":"Distilling Knowledge from Deep Networks with Applications to Healthcare Domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Robinder Khemani, Sanjay Purushotham, Yan Liu, Zhengping Che","submitted_at":"2015-12-11T07:38:12Z","abstract_excerpt":"Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved in decision-making. In this paper, we introduce a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable phenotype features "},"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":"1512.03542","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-12-11T07:38:12Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6c455d012f2c007ae1a9a9c188a9739b313d8b868c1eda50be4d5618517052e5","abstract_canon_sha256":"6647d60ba3a6870ab2d36f3f85667c00c9d04cc8fd38c793f440785f356e026c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:24:32.143279Z","signature_b64":"ifVsBUhq0IN3ySu/rc59Ex0cTR6scOH2mzvu5IYZSgSO6ThzBZGeegDTteXkYQO4pohq6+iUI+lmEbSdyq7SCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e3eee99825cdc9b285cf1c19dbfedec63509f495f1b0109f56e1ac5f3bff926","last_reissued_at":"2026-05-18T01:24:32.142654Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:24:32.142654Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distilling Knowledge from Deep Networks with Applications to Healthcare Domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Robinder Khemani, Sanjay Purushotham, Yan Liu, Zhengping Che","submitted_at":"2015-12-11T07:38:12Z","abstract_excerpt":"Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved in decision-making. In this paper, we introduce a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable phenotype features "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.03542","kind":"arxiv","version":1},"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":"1512.03542","created_at":"2026-05-18T01:24:32.142741+00:00"},{"alias_kind":"arxiv_version","alias_value":"1512.03542v1","created_at":"2026-05-18T01:24:32.142741+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.03542","created_at":"2026-05-18T01:24:32.142741+00:00"},{"alias_kind":"pith_short_12","alias_value":"PY7O5GMCLTOJ","created_at":"2026-05-18T12:29:37.295048+00:00"},{"alias_kind":"pith_short_16","alias_value":"PY7O5GMCLTOJWKC4","created_at":"2026-05-18T12:29:37.295048+00:00"},{"alias_kind":"pith_short_8","alias_value":"PY7O5GMC","created_at":"2026-05-18T12:29:37.295048+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/PY7O5GMCLTOJWKC46HAZ3P7N5R","json":"https://pith.science/pith/PY7O5GMCLTOJWKC46HAZ3P7N5R.json","graph_json":"https://pith.science/api/pith-number/PY7O5GMCLTOJWKC46HAZ3P7N5R/graph.json","events_json":"https://pith.science/api/pith-number/PY7O5GMCLTOJWKC46HAZ3P7N5R/events.json","paper":"https://pith.science/paper/PY7O5GMC"},"agent_actions":{"view_html":"https://pith.science/pith/PY7O5GMCLTOJWKC46HAZ3P7N5R","download_json":"https://pith.science/pith/PY7O5GMCLTOJWKC46HAZ3P7N5R.json","view_paper":"https://pith.science/paper/PY7O5GMC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1512.03542&json=true","fetch_graph":"https://pith.science/api/pith-number/PY7O5GMCLTOJWKC46HAZ3P7N5R/graph.json","fetch_events":"https://pith.science/api/pith-number/PY7O5GMCLTOJWKC46HAZ3P7N5R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PY7O5GMCLTOJWKC46HAZ3P7N5R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PY7O5GMCLTOJWKC46HAZ3P7N5R/action/storage_attestation","attest_author":"https://pith.science/pith/PY7O5GMCLTOJWKC46HAZ3P7N5R/action/author_attestation","sign_citation":"https://pith.science/pith/PY7O5GMCLTOJWKC46HAZ3P7N5R/action/citation_signature","submit_replication":"https://pith.science/pith/PY7O5GMCLTOJWKC46HAZ3P7N5R/action/replication_record"}},"created_at":"2026-05-18T01:24:32.142741+00:00","updated_at":"2026-05-18T01:24:32.142741+00:00"}