{ "ok": true, "doi": "10.46243/jst.2018.v7.i05.pp32-41", "events": [ { "seq": 1, "kind": "register", "at": "2026-09-29 22:00:27", "by": "Administrator (admin)", "by_kind": "user", "note": "registered at Crossref; record read from api.crossref.org", "changes": [ { "path": "abstract.lang", "from": null, "to": "en" }, { "path": "abstract.value", "from": null, "to": "Patient treatment trajectory mining is current research field to access the response of the patient to new treatment data in clinical trials which is available in form high dimensional data. However processing of high dimensional trajectory data using machine learning model named markov chain model, learns the events but it is cumbersome and ambiguous on identifying the latent events and it fails to tackle long term dependencies between the clinical events. In order to tackle those challenges, deep learning model has been employed to effectively classify the patient treatment data to highlight the future outcome of the patients. In this paper, an extensive study has been carried out on deep learning architecture to predict the future outcomes on the patient treatment trajectory data. It is vital and essential task for providing detailed insight on disease progression and interventions. We demonstrate the efficacy of the model on long term dependency for disease progression modelling, intervention recommendation, and future risk prediction on treatment trajectory data. Further prediction on the irregularly distributed data has been analysed on employment of sampling, latent representatives and effective use of loss function. Moreover importance of activation functi on for learning representatives has been exploited for efficient future event prediction on extracted trajectory representation. Finally outline of the proposed methodology as framework to predict the prognosis of the patient has provid ed. 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However processing of high dimensional trajectory data using machine learning model named markov chain model, learns the events but it is cumbersome and ambiguous on identifying the latent events and it fails to tackle long term dependencies between the clinical events. In order to tackle those challenges, deep learning model has been employed to effectively classify the patient treatment data to highlight the future outcome of the patients. In this paper, an extensive study has been carried out on deep learning architecture to predict the future outcomes on the patient treatment trajectory data. It is vital and essential task for providing detailed insight on disease progression and interventions. We demonstrate the efficacy of the model on long term dependency for disease progression modelling, intervention recommendation, and future risk prediction on treatment trajectory data. Further prediction on the irregularly distributed data has been analysed on employment of sampling, latent representatives and effective use of loss function. Moreover importance of activation functi on for learning representatives has been exploited for efficient future event prediction on extracted trajectory representation. Finally outline of the proposed methodology as framework to predict the prognosis of the patient has provid ed. Evaluation of models has been carried out on the ovarian cancer patient treatment data", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-07-18", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/bibm.2015.7359771", "unstructured": "Xiang Ji, Soon Ae Chun, James Geller and Vincent Oria \"Collaborative and trajectory prediction models of medical conditions by mining patients' Social Data\"in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015" }, { "key": "ref2", "doi": "10.1109/cbms.2019.00112", "unstructured": "Martí Zamora, Ricard Gavaldà \"Interpretable Patient Trajectories from Temporally Annotated Health Records\" in IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS),2019" }, { "key": "ref3", "doi": "10.1109/jbhi.2019.2962079", "unstructured": "Huilong Duan; Zhoujian Sun; Wei Dong; Kunlun He; Zhengxing Huang\" On Clinical E vent Prediction in Patient Treatment Trajectory Using Longitudinal Electronic Health Records\" in IEEE Journal of Biomedical and Health Informatics,Volume: 24, Issue: 7, July 2020" }, { "key": "ref4", "doi": "10.1109/cbms.2008.140", "unstructured": "Shusaku Tsumoto; Shoji Hirano\"Mining Trajectories of Laboratory Data using Multiscale Matching and Clustering\" in 21st IEEE International Symposium on Computer-Based Medical Systems, 2008" }, { "key": "ref5", "doi": "10.1109/cbms49503.2020.00118", "unstructured": "Alexander Ylnner Choquenaira Florez; Lucas Scabora; Sihem Amer-Yahia; José Fernando Rodrigues\"Augmentation Techniques for Sequential Clinical Data to Improve Deep Learning Prediction Techniques in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)" }, { "key": "ref6", "doi": "10.1109/icdm.2015.144", "unstructured": "E. Choi, N. Du, R. Chen, L. Song and J. Sun, \"Constructing Disease Network and Temporal Progression Model via Context-Sensitive Hawkes Process,\" in Proc. IEEE Int. Conf. Data Mining, Atlantic City, NJ, 2015, pp. 721-726" }, { "key": "ref7", "doi": "10.1109/tbme.2017.2731158", "unstructured": "Z. Huang, W. Dong, H. Duan and J. Liu, \"A regularized deep learning approach for clinical risk prediction of acute coronary syndrome using electronic healt h records,\" IEEE Trans. Biomed. Eng., vol. 65, no. 5, pp. 956-968, 2018" }, { "key": "ref8", "doi": "10.1145/3219819.3219904", "unstructured": "T. Bai, S. Zhang, B. L. Egleston and S. Vucetic, \"Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time,\" in Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, London, United Kingdom, 2018, pp 43 -" }, { "key": "ref9", "doi": "10.1137/1.9781611975321.30", "unstructured": "T. Ma, C. Xiao and F. Wang, \"Health-ATM: A deep architecture for multifaceted patient health record representation and risk prediction,\" in Proc. 2018 SIAM Int. Conf. Data Mining, San Diego, CA, pp. 261-269" }, { "key": "ref10", "doi": "10.1186/s12911-018-0730-7", "unstructured": "H. Duan, Z. Sun, W. Dong and Z. Huang, \"Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome,\" BMC Med. Inform. Decis. Mak., vol. 19, no. 5, 2019" }, { "key": "ref11", "doi": "10.1177/070674370505000407", "unstructured": "S. Bellino, L. Patria, E, Paradiso, R. D i Lorenzo, C. Zanon, M, Zizza, et al., \"Major depression in patients with borderline personality disorder: a clinical investigation,\" Canadian Journal of Psychiatry, vol. 50,2005, pp. 234-238" }, { "key": "ref12", "unstructured": "K. Hainke, J, Rahnenflihrer, and R. Fried, \"Disease progression models: A review and comparison,\" Dortmund University, Technical Report, 2011" }, { "key": "ref13", "doi": "10.1016/j.jbi.2017.04.001", "unstructured": "T. Pham, T. Tran, D. Phung and S. Venkatesh, \"Predicting healthcare trajectories from medical records: A deep learning approach,\" J. Biomed. Inform., vol. 69, pp. 218-229, 2017" }, { "key": "ref14", "doi": "10.3748/wjg.v16.i9.1232", "unstructured": "S. S. Yarandi, S. Nasseri-Moghaddam, P. Mostajabi, and R. Malekzadeh, \"Overlapping gastroesophageal reflux disease and irritable bowel syndrome: increased dysfunctional symptoms,\" World Journal of Gastroenterology, vol. 16,2010, pp. 1232-1238" }, { "key": "ref15", "doi": "10.1186/s12911-018-0730-7", "unstructured": "H. Duan, Z. Sun, W. Dong and Z. Huang, \"Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome,\" BMC Med. Inform. Decis. Mak., vol. 19, no. 5, 2019" }, { "key": "ref16", "doi": "10.1137/1.9781611975321.30", "unstructured": "T. Ma, C. Xiao and F. Wang, \"Health-ATM: A deep architecture for multifaceted patient health record representation and risk prediction,\" in Proc. 2018 SIAM Int. Conf. Data Mining, San Diego, CA, pp. 261-269" }, { "key": "ref17", "doi": "10.1016/j.ahj.2006.10.004", "unstructured": "E. W. Tang, C. -K. Wong and P. Herbison, \"Global Registry of Acute Coronary Events (GRACE) hospital discharge risk score accurately pre dicts long-term mortality post acute coronary syndrome,\" Am. Heart J., vol. 153, no. 1, pp. 29-35, 2007" }, { "key": "ref18", "doi": "10.1109/jbhi.2018.2882518", "unstructured": "Z. Huang and W. Dong, “Adversarial MACE Prediction after Acute Coronary Syndrome using Electronic Health Records”, IEEE Journal of Biomedical and Healt h Informatics, 23(5):2117-2126, 2019" }, { "key": "ref19", "doi": "10.1007/s10618-014-0379-5", "unstructured": "B. Qian, X, Wang, N, Cao, H, Li, and Y, -G, Jiang, \"A relative similarity based method for interactive patient risk prediction,\" Data Mining and Knowledge Discovery, vol. 29,2015, pp. 1070-1093" }, { "key": "ref20", "unstructured": "L. Jane, \"Latent Continu ous Time Markov Chains for Partially-Observed Multistate Disease Processes,\" Ph. D. Thesis, Univ. Washington, Seattle, WA, 2014" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/473", "sha256_before": null, "sha256_after": "68b14c393f03a670358c11458b18ee79e1ca14bd25d7f23de35f3e205a77e4eb" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:46", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2018.v7.i05.pp32-41", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "A Extensive Study on Deep learning Architecture on Patient Treatment Trajectory Mining", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2018.v7.i05.pp32-41" } ], "agents": [ { "role": "author", "name": { "given": "Jangam Raghunath", "family": "Jangam Raghunath" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-07-18", "date_type": "PublicationDate", "online": "2023-07-18" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "7", "issue": "5", "pages": { "first": "32", "last": "41" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/473", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/473/429", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/473/2363", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/JST070504.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "Patient treatment trajectory mining is current research field to access the response of the patient to new treatment data in clinical trials which is available in form high dimensional data. However processing of high dimensional trajectory data using machine learning model named markov chain model, learns the events but it is cumbersome and ambiguous on identifying the latent events and it fails to tackle long term dependencies between the clinical events. In order to tackle those challenges, deep learning model has been employed to effectively classify the patient treatment data to highlight the future outcome of the patients. In this paper, an extensive study has been carried out on deep learning architecture to predict the future outcomes on the patient treatment trajectory data. It is vital and essential task for providing detailed insight on disease progression and interventions. We demonstrate the efficacy of the model on long term dependency for disease progression modelling, intervention recommendation, and future risk prediction on treatment trajectory data. Further prediction on the irregularly distributed data has been analysed on employment of sampling, latent representatives and effective use of loss function. Moreover importance of activation functi on for learning representatives has been exploited for efficient future event prediction on extracted trajectory representation. Finally outline of the proposed methodology as framework to predict the prognosis of the patient has provid ed. Evaluation of models has been carried out on the ovarian cancer patient treatment data", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-07-18", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/bibm.2015.7359771", "unstructured": "Xiang Ji, Soon Ae Chun, James Geller and Vincent Oria \"Collaborative and trajectory prediction models of medical conditions by mining patients' Social Data\"in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015" }, { "key": "ref2", "doi": "10.1109/cbms.2019.00112", "unstructured": "Martí Zamora, Ricard Gavaldà \"Interpretable Patient Trajectories from Temporally Annotated Health Records\" in IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS),2019" }, { "key": "ref3", "doi": "10.1109/jbhi.2019.2962079", "unstructured": "Huilong Duan; Zhoujian Sun; Wei Dong; Kunlun He; Zhengxing Huang\" On Clinical E vent Prediction in Patient Treatment Trajectory Using Longitudinal Electronic Health Records\" in IEEE Journal of Biomedical and Health Informatics,Volume: 24, Issue: 7, July 2020" }, { "key": "ref4", "doi": "10.1109/cbms.2008.140", "unstructured": "Shusaku Tsumoto; Shoji Hirano\"Mining Trajectories of Laboratory Data using Multiscale Matching and Clustering\" in 21st IEEE International Symposium on Computer-Based Medical Systems, 2008" }, { "key": "ref5", "doi": "10.1109/cbms49503.2020.00118", "unstructured": "Alexander Ylnner Choquenaira Florez; Lucas Scabora; Sihem Amer-Yahia; José Fernando Rodrigues\"Augmentation Techniques for Sequential Clinical Data to Improve Deep Learning Prediction Techniques in 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)" }, { "key": "ref6", "doi": "10.1109/icdm.2015.144", "unstructured": "E. 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