{"doi":"10.1109/tkde.2020.2989405","title":"CHEER: Rich Model Helps Poor Model via Knowledge Infusion","abstract":"There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">rich-data</i> environments (e.g., intensive care units). However, in many other practical situations, we can only access data with much fewer feature channels in a <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">poor-data</i> environments (e.g., at home), which often results in predictive models with poor performance. How can we boost the performance of models learned from such <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">poor-data</i> environment by leveraging knowledge extracted from existing models trained using <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">rich data</i> in a related environment? To address this question, we develop a knowledge infusion framework named <monospace xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">CHEER</monospace> that can succinctly summarize such <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">rich model</i> into transferable representations, which can be incorporated into the <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">poor model</i> to improve its performance. The infused model is analyzed theoretically and evaluated empirically on several datasets. Our empirical results showed that <monospace xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">CHEER</monospace> outperformed baselines by 5.60 to 46.80 percent in terms of the macro-F1 score on multiple physiological datasets.","journal":"IEEE Transactions on Knowledge and Data Engineering","year":2020,"id":115612,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9574,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":541252,"name":"Trong Nghia Hoang","orcid":"0000-0002-9175-6246","position":1,"is_corresponding":false},{"id":228150,"name":"Shenda Hong","orcid":"0000-0001-7521-5127","position":2,"is_corresponding":false},{"id":471376,"name":"Tengfei Ma","orcid":"0000-0002-1086-529X","position":3,"is_corresponding":false},{"id":227378,"name":"Jimeng Sun","orcid":"0000-0003-1512-6426","position":4,"is_corresponding":false},{"id":227377,"name":"Cao Xiao","orcid":"0000-0002-3869-6942","position":0,"is_corresponding":true}],"reference_count":61,"raw_metadata":null,"created_at":"2026-07-18T23:13:36.820928Z","pmid":"36712193","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}