{"doi":"10.3390/diagnostics15080956","title":"Deep Learning Approaches to Forecast Physical and Mental Deterioration During Chemotherapy in Patients with Cancer","abstract":"Background/Objectives: Predicting symptom escalation during chemotherapy is crucial for timely interventions and improved patient outcomes. This study employs deep learning models to predict the deterioration of 12 self-reported symptoms, categorized into physical (e.g., nausea, fatigue, pain) and mental (e.g., feeling blue, trouble thinking) groups. Methods: The analytical dataset comprises daily self-reported symptom logs from individuals undergoing chemotherapy. To address class imbalance—where 84% of cases showed no escalation—symptoms were grouped into intervals of 3 to 7 days. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models were trained on 80% of the data and evaluated on the remaining 20%. Results: Results showed that 3-day intervals yielded the best predictive performance. CNNs excelled in predicting physical symptoms, achieving 79.2% accuracy, 84.1% precision, 78.8% recall, and an F1 score of 81.4%. For mental symptoms, GRU outperformed other models, with an accuracy of 77.2%, precision of 71.6%, recall of 62.2%, and an F1 score of 66.6%. Performance declined for longer intervals due to reduced temporal resolution and fewer training samples, though CNNs and GRU remained relatively stable. Conclusions: The findings emphasize the advantage of categorizing symptoms for more tailored predictions and demonstrate the potential of deep learning in forecasting symptom escalation. Integrating these predictive models into clinical workflows could facilitate proactive symptom management, allowing timely interventions and enhanced patient care during chemotherapy.","journal":"Diagnostics","year":2025,"id":523063,"datarank":0.25946815573686305,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.018052468871747964,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.018052468871747964,"corpus_percentile":40.914365281967974,"corpus_rank":7639,"citation_count":4,"citer_count":4,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5029,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":4.1667,"fair_percentile":4.891470498318557,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":990866,"name":"Aref Smiley","orcid":"0000-0002-1077-2229","position":1,"is_corresponding":false},{"id":986125,"name":"Christina Echeverria","orcid":null,"position":2,"is_corresponding":false},{"id":613765,"name":"Kathi Mooney","orcid":"0000-0003-3371-5646","position":3,"is_corresponding":false},{"id":240827,"name":"Joseph Finkelstein","orcid":"0000-0002-8084-7441","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T02:49:58.707747Z","pmid":"40310358","pmcid":"PMC12025769","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":22.2222,"fair_a":18.75,"fair_i":0.0,"fair_r":16.6667,"fair_zscore":-1.1986,"fair_rationale":{"fair_score":4.17,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":22.22,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No persistent identifier string is given for the study's own data.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"The data presented in this study are available on request from the corresponding author.","grounded":true,"rationale":"The holder is the corresponding author, a natural person, not a repository.","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"The data presented in this study are available on request from the corresponding author.","grounded":true,"rationale":"The statement points to the corresponding author, which is Colavizza category 1, classified as partial.","anchors":["Colavizza, Hrynaszkiewicz, Staden, Whitaker & McGillivray (2020), 'The citation advantage of li","Springer Nature research data policy — Data Availability Statements: standard statement templat","RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes"],"scored":false,"signal":null},{"key":"f_discovery_metadata","label":"Description of the dataset as an object","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"The analytical dataset comprises daily self-reported symptom logs from individuals undergoing chemotherapy.","grounded":true,"rationale":"The dataset's content is described in running prose, not in an itemised inventory. 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