{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T17:07:28Z","timestamp":1778951248908,"version":"3.51.4"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"vor","delay-in-days":135,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100007065","name":"Universit\u00e0 degli Studi di Salerno","doi-asserted-by":"publisher","award":["CRUI-CARE"],"award-info":[{"award-number":["CRUI-CARE"]}],"id":[{"id":"10.13039\/501100007065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pain Research and Management"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Although telehealth strategies can be effectively adopted to manage cancer pain, identifying the optimal care pathway for tailoring interventions and allocating resources remains difficult. Artificial intelligence and machine learning (ML) may help clinicians develop more accurate strategies for predicting whether patients need remote consultations or in\u2010person evaluations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Data from two cohorts of cancer pain patients were analyzed. Variables included sociodemographic and clinical data, including age, sex, ECOG performance status, metastases, bone metastases, pain type, breakthrough cancer pain (BTCP), and rapid onset opioids (ROOs) therapy. The main outcome was the number of televisits (one versus multiple). For preprocessing, datasets from the two cohorts were harmonized by aligning variable definitions, coding schemes, and data formats. Six models were tested: logistic regression, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), k\u2010nearest neighbors (KNNs), and multilayer perceptron (MLP). Training and tuning used a 7\u2010repeated 5\u2010fold cross\u2010validation approach. Performance was evaluated on a hold\u2010out test set using\n                      <jats:italic>F<\/jats:italic>\n                      1\u2010score, accuracy, and AUC\u2010ROC. A sensitivity analysis with two scenarios was performed to verify the effects of class weighting and excluding the cohort variable.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The final dataset included 270 patients. No statistically significant associations were identified between the available variables and the number of televisits.\n                      <jats:italic>F<\/jats:italic>\n                      1\u2010scores across models ranged from 0.33 (RF) to 0.65 (MLP), accuracy from 0.45 (RF) to 0.55 (SVM), and AUC\u2010ROC from 0.43 (RF) to 0.65 (LR). DeLong tests showed no significant differences between algorithms (\n                      <jats:italic>p<\/jats:italic>\n                      &gt; 0.05). Although the MLP achieved the highest\n                      <jats:italic>F<\/jats:italic>\n                      1\u2010score, it exhibited instability, with 91% of null\n                      <jats:italic>F<\/jats:italic>\n                      1\u2010scores. Incorporating class weights slightly improved SVM (\n                      <jats:italic>F<\/jats:italic>\n                      1\u2009=\u20090.58 and AUC\u2009=\u20090.62) and LR (\n                      <jats:italic>F<\/jats:italic>\n                      1\u2009=\u20090.53 and AUC\u2009=\u20090.63) though not significantly.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Although no model demonstrated strong predictive power, this ML\u2010based framework shows the potential of using structured telemedicine data to model clinical workload and optimize follow\u2010up strategies in cancer pain care.<\/jats:p>\n                    <jats:p>\n                      <jats:bold>Trial Registration:<\/jats:bold>\n                      ClinicalTrials.gov identifier: NCT04726228 and NCT07038434\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/prm\/9211861","type":"journal-article","created":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:28:47Z","timestamp":1778948927000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feasibility Assessment of Telehealth\u2010Based Cancer Pain Management Through Machine Learning: A Prospective Clinical Study"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4044-1217","authenticated-orcid":false,"given":"Sergio","family":"Coluccia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8455-3328","authenticated-orcid":false,"given":"Anna","family":"Crispo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2901-3855","authenticated-orcid":false,"given":"Alessandro","family":"Ottaiano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2689-3014","authenticated-orcid":false,"given":"Mariachiara","family":"Santorsola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8127-2355","authenticated-orcid":false,"given":"Massimo A.","family":"Innamorato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5657-6959","authenticated-orcid":false,"given":"Valentina","family":"Cerrone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9573-5096","authenticated-orcid":false,"given":"Rosario","family":"De Feo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9377-1791","authenticated-orcid":false,"given":"Vincenzo","family":"Cascella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8889-0249","authenticated-orcid":false,"given":"Dalila","family":"Esposito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5541-0684","authenticated-orcid":false,"given":"Maria Pia","family":"Bruno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6431-8278","authenticated-orcid":false,"given":"Francesco","family":"Sabbatino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3321-4331","authenticated-orcid":false,"given":"Gianluigi","family":"Franci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2377-3765","authenticated-orcid":false,"given":"Alessandro","family":"Vittori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5236-3132","authenticated-orcid":false,"given":"Marco","family":"Cascella","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,5,16]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15030591"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpainsymman.2015.12.340"},{"key":"e_1_2_10_3_2","article-title":"Adherence to ESMO Guidelines on Cancer Pain Management and Their Applicability to Specialist Palliative Care Centers: An Observational, Prospective, and Multicenter Study","author":"Carbonara L.","year":"2024","journal-title":"Pain Practice"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00520-018-4437-1"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/20406223231153097"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1093\/pm\/pnac128"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.7573\/dic.2022-11-7"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph182312416"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.21873\/invivo.13090"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/curroncol29080439"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s44158-024-00202-1"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-70073-7"},{"key":"e_1_2_10_13_2","unstructured":"Paindatafor_Telemedicine_Ml https:\/\/zenodo.org\/records\/6944310."},{"key":"e_1_2_10_14_2","unstructured":"Search https:\/\/zenodo.org\/search?q=6944310%26l=list%26p=1%26s=10%26sort=bestmatch."},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-021-01205-4"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.5870"},{"key":"e_1_2_10_17_2","unstructured":"PDF A Practical Guide to Support Vector Classication | Semantic Scholar https:\/\/www.semanticscholar.org\/paper\/A-Practical-Guide-to-Support-Vector-Classication-Hsu-Chang\/7abeda3a20c13bfee416d94efa313ff870656fec."},{"key":"e_1_2_10_18_2","unstructured":"Robust Non-Linear Normalization of Heterogeneous Feature Distributions With Adaptive Tanh-Estimators https:\/\/proceedings.mlr.press\/v238\/guimera-cuevas24a.html."},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"key":"e_1_2_10_20_2","unstructured":"Telemedicine_Ml_Workflow https:\/\/zenodo.org\/records\/17392667."},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.3122\/jabfm.2024.240388r2"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11185484"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2023010140"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-6413-7"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1097\/j.pain.0000000000001363"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.2196\/56969"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14163889"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1426-2"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.11124\/jbies-24-00042"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10759-6"},{"key":"e_1_2_10_31_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16193417"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10652-8"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.3390\/biomedinformatics4010042"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.4c02329"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.23736\/s0375-9393.23.17697-8"},{"key":"e_1_2_10_36_2","doi-asserted-by":"publisher","DOI":"10.20944\/preprints202510.1661.v1"}],"container-title":["Pain Research and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/prm\/9211861","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/prm\/9211861","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/prm\/9211861","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:28:50Z","timestamp":1778948930000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/prm\/9211861"}},"subtitle":[],"editor":[{"given":"Harleen","family":"Khatra","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1155\/prm\/9211861"],"URL":"https:\/\/doi.org\/10.1155\/prm\/9211861","archive":["Portico"],"relation":{},"ISSN":["1203-6765","1918-1523"],"issn-type":[{"value":"1203-6765","type":"print"},{"value":"1918-1523","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"2026-01-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-15","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9211861"}}