{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T03:16:21Z","timestamp":1771298181087,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010830","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000}}],"reference-count":9,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Talent Cultivation Project of Guangdong Medical Equipment Society Medical Informatization Yangcheng Forum","award":["GDYZ2021A05"],"award-info":[{"award-number":["GDYZ2021A05"]}]},{"name":"Sun Yat-sen University Youth Development Project","award":["19ykpy200"],"award-info":[{"award-number":["19ykpy200"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"Summary<\/jats:title>\nThe survival path mapping approach has been proposed for dynamic prognostication of cancer patients using time-series survival data. The SurvivalPath R package was developed to facilitate building personalized survival path models. The package contains functions to convert time-series data into time-slices data by fixed interval based on time information of input medical records. After the pre-processing of data, under a user-defined parameters on covariates, significance level, minimum bifurcation sample size and number of time slices for analysis, survival paths can be computed using the main function, which can be visualized as a tree diagram, with important parameters annotated. The package also includes function for analyzing the connections between exposure\/treatment and node transitions, and function for screening patient subgroup with specific features, which can be used for further exploration analysis. In this study, we demonstrate the application of this package in a large dataset of patients with hepatocellular carcinoma, which is embedded in the package. The SurvivalPath R package is freely available from CRAN, with source code and documentation hosted at https:\/\/github.com\/zhangt369\/SurvivalPath<\/jats:ext-link>.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010830","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T18:53:32Z","timestamp":1673031212000},"page":"e1010830","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["SurvivalPath:A R package for conducting personalized survival path mapping based on time-series survival data"],"prefix":"10.1371","volume":"19","author":[{"given":"Lujun","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinqing","family":"Mo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changsheng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiquan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangru","family":"Ke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingdong","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8757-647X","authenticated-orcid":true,"given":"Weijun","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"issue":"1","key":"pcbi.1010830.ref001","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1038\/s41467-018-04633-7","article-title":"Dynamically prognosticating patients with hepatocellular carcinoma through survival paths mapping based on time-series data","volume":"9","author":"L Shen","year":"2018","journal-title":"Nat Commun"},{"key":"pcbi.1010830.ref002","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.1016\/j.csbj.2020.08.019","article-title":"Artificial intelligence (AI) and big data in cancer and precision oncology","volume":"18","author":"Z Dlamini","year":"2020","journal-title":"Computational and structural biotechnology journal"},{"key":"pcbi.1010830.ref003","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.jbi.2017.04.001","article-title":"Predicting healthcare trajectories from medical records: A deep learning approach","volume":"69","author":"T Pham","year":"2017","journal-title":"J Biomed Inform"},{"issue":"10","key":"pcbi.1010830.ref004","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review","volume":"25","author":"C Xiao","year":"2018","journal-title":"J Am Med Inform Assoc"},{"issue":"3","key":"pcbi.1010830.ref005","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.cell.2019.06.011","article-title":"Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction","volume":"178","author":"DM Kurtz","year":"2019","journal-title":"Cell"},{"issue":"3","key":"pcbi.1010830.ref006","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10985-007-9045-1","article-title":"Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure","volume":"13","author":"X Xue","year":"2007","journal-title":"Lifetime Data Anal"},{"issue":"1","key":"pcbi.1010830.ref007","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/s13643-019-1069-6","article-title":"Usability and acceptability of four systematic review automation software packages: a mixed method design","volume":"8","author":"G Cleo","year":"2019","journal-title":"Syst Rev"},{"issue":"1","key":"pcbi.1010830.ref008","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1111\/j.0006-341X.2005.030814.x","article-title":"Survival model predictive accuracy and ROC curves","volume":"61","author":"PJ Heagerty","year":"2005","journal-title":"Biometrics"},{"issue":"Suppl 4","key":"pcbi.1010830.ref009","doi-asserted-by":"crossref","first-page":"iv238","DOI":"10.1093\/annonc\/mdy308","article-title":"Hepatocellular carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up","volume":"29","author":"A Vogel","year":"2018","journal-title":"Annals of oncology: official journal of the European Society for Medical Oncology \/ ESMO"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1010830","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010830","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T18:46:20Z","timestamp":1674153980000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010830"}},"subtitle":[],"editor":[{"given":"Feng","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,1,6]]},"references-count":9,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1,6]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1010830","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1010830","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,6]]}}}