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It has been suggested previously that simple features, such as word unigrams, part-of-speech tags, chunk tags, among others, are sufficient for this task. We show that more careful preprocessing and feature selection can significantly improve the results. We used conditional random field classifier with more linguistically oriented features and outperformed the current state-of-the-art approaches. We also show that the popular and much simpler Viterbi algorithm (hidden Markov model-based classification algorithm) can produce competitive results, when its parameters are tuned using specific optimization techniques. We evaluate these algorithms for the task of extraction of medical events from the corpus developed for SemEval shared Task\u00a012: Clinical TempEval (Temporal Evaluation) 2016, namely, for its two subtasks: (i)\u00a0event detection and (ii) event classification based on contextual modality.<\/jats:p>","DOI":"10.3233\/jifs-169479","type":"journal-article","created":{"date-parts":[[2018,5,18]],"date-time":"2018-05-18T10:38:12Z","timestamp":1526639892000},"page":"2935-2947","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Extracting medical events from clinical records using conditional random fields and parameter tuning for hidden Markov models"],"prefix":"10.1177","volume":"34","author":[{"given":"Carolina","family":"F\u00f3cil-Arias","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Mexico City, Mexico"}]},{"given":"Grigori","family":"Sidorov","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Mexico City, Mexico"}]},{"given":"Alexander","family":"Gelbukh","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Mexico City, Mexico"}]},{"given":"Fernando","family":"Arce","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional, Mexico City, Mexico"}]}],"member":"179","published-online":{"date-parts":[[2018,5,17]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"1256","volume-title":"UtahBMI at SemEval-Task 12: Extracting Temporal Information from Clinical Text","author":"Abdulrahman K.","year":"2016","unstructured":"AbdulrahmanK., VelupillaiS. and MeystreS., UtahBMI at SemEval-Task 12: Extracting Temporal Information from Clinical Text, In Proc of the 10th International Workshop Semantic Evaluation, 2016, pp. 1256\u20131262."},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2010.08.003"},{"key":"e_1_3_3_4_2","first-page":"1274","volume-title":"Brundlefly at SemEval-Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction","author":"Alan Fries J.","year":"2016","unstructured":"Alan FriesJ., Brundlefly at SemEval-Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction, In: Proc of the 10th International Workshop Semantic Evaluation, 2016, pp. 1274\u20131279."},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.17562\/PB-54-1"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2016.7850259"},{"key":"e_1_3_3_7_2","first-page":"1263","volume-title":"ULISBOA at SemEval-Task 12: Extraction of Temporal Expressions, Clinical Events and Relations using IBEnt","author":"Barros M.","year":"2016","unstructured":"BarrosM., LamuriasA., AntunesM., TeixeiraJ., PinheiroA. and CoutoF.M., ULISBOA at SemEval-Task 12: Extraction of Temporal Expressions, Clinical Events and Relations using IBEnt, In: Proc of the 10th International Workshop Semantic Evaluation, 2016, pp. 1263\u20131267."},{"key":"e_1_3_3_8_2","first-page":"56","volume-title":"Medical Entity Recognition: A Comparison of Semantic and Statical Methods","author":"Ben Abacha A.","year":"2011","unstructured":"Ben AbachaA., and ZweigenbaumP., Medical Entity Recognition: A Comparison of Semantic and Statical Methods, In 2011 Workshop on Biomedical Natural Language Processing, 2011, pp. 56\u201364."},{"key":"e_1_3_3_9_2","first-page":"820","volume-title":"SemEval-task 12: Clinical TempEval","author":"Bethard S.","year":"2016","unstructured":"BethardS., GuerganaS., CheW.-T., DerczynskiL., PustejovskyJ. and VerhagenM., SemEval-task 12: Clinical TempEval, In Proceedings of NAACL-HLT 2016 (2016), 820\u2013830."},{"key":"e_1_3_3_10_2","unstructured":"BirdS. 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