{"doi":"10.1101/2021.08.26.21262548","title":"Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS","abstract":"Abstract Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developing late-AMD, the most severe form of AMD. Our study hypothesizes that 1) incorporating historical data improves predictive strength of developing late-AMD and 2) state-of-the-art deep-learning techniques extract more predictive image features than clinicians do. We incorporate longitudinal data from the Age-Related Eye Disease Studies and deep-learning extracted image features in survival settings to predict development of late-AMD. To extract image features, we used multi-task learning frameworks to train convolutional neural networks. Our findings show 1) incorporating longitudinal data improves prediction of late-AMD for clinical standard features, but only the current visit is informative when using complex features and 2) “deep-features” are more informative than clinician derived features. We make codes publicly available at https://github.com/bionlplab/AMD_prognosis_amia2021 .","journal":"medRxiv","year":2021,"id":214620,"datarank":0.4369461230749259,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.09155835912581899,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.09155835912581899,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":5,"citers_with_citation_signal":5,"citers_with_endowment":5,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9458,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":284895,"name":"Matthew Brendel","orcid":"0000-0003-3417-4597","position":1,"is_corresponding":false},{"id":678125,"name":"Mingquan Lin","orcid":"0000-0003-0862-6588","position":2,"is_corresponding":false},{"id":236881,"name":"Qingyu Chen","orcid":"0000-0002-6036-1516","position":3,"is_corresponding":false},{"id":254976,"name":"Tiarnán D L Keenan","orcid":"0000-0002-2253-1772","position":4,"is_corresponding":false},{"id":258199,"name":"Kun Chen","orcid":"0000-0003-3579-5467","position":5,"is_corresponding":false},{"id":54567,"name":"Emily Y. Chew","orcid":"0000-0003-0999-9802","position":6,"is_corresponding":false},{"id":45332,"name":"ZHIYONG LU","orcid":"0000-0001-9998-916X","position":7,"is_corresponding":false},{"id":85506,"name":"Yifan Peng","orcid":"0000-0001-9309-8331","position":8,"is_corresponding":false},{"id":240709,"name":"Fei Wang","orcid":"0000-0001-9459-9461","position":9,"is_corresponding":false},{"id":808181,"name":"Gregory Ghahramani","orcid":"0000-0002-7338-6819","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:52:46.604690Z","pmid":null,"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":[]}