{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T14:40:36Z","timestamp":1792075236974,"version":"4.4.0"},"reference-count":113,"publisher":"SAGE Publications","issue":"5-6","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neurotrauma"],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:p>Traumatic intracranial hypertension (tIH) is a common and potentially lethal complication of moderate to severe traumatic brain injury (m-sTBI). It often develops with little warning and is managed reactively with the tiered application of intracranial pressure (ICP)-lowering interventions administered in response to an ICP rising above a set threshold. For over 45 years, a variety of research groups have worked toward the development of technology to allow for the preemptive management of tIH in the hope of improving patient outcomes. In 2022, the first operationalizable tIH prediction system became a reality. With such a system, ICP lowering interventions could be administered prior to the rise in ICP, thus protecting the patient from potentially damaging tIH episodes and limiting the overall ICP burden experienced. In this review, we discuss related approaches to ICP forecasting and IH prediction algorithms, which collectively provide the foundation for the successful development of an operational tIH prediction system. We also discuss operationalization and the statistical assessment of tIH algorithms. This review will be of relevance to clinicians and researchers interested in development of this technology as well as those with a general interest in the bedside application of machine learning (ML) technology.<\/jats:p>","DOI":"10.1089\/neu.2022.0201","type":"journal-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T10:01:38Z","timestamp":1665136898000},"page":"416-434","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":24,"title":["Development of Traumatic Brain Injury Associated Intracranial Hypertension Prediction Algorithms: A Narrative Review"],"prefix":"10.1177","volume":"40","author":[{"given":"Robert","family":"McNamara","sequence":"first","affiliation":[{"name":"Department of Intensive Care Medicine, Royal Perth Hospital, Perth, Western Australia, Australia."},{"name":"School of Medicine, Curtin University, Bentley, Western Australia, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiv","family":"Meka","sequence":"additional","affiliation":[{"name":"Data Innovation Laboratory, Western Australian Department of Health, Perth, Western Australia, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Anstey","sequence":"additional","affiliation":[{"name":"Department of Intensive Care, The Royal Melbourne Hospital, Melbourne, Victoria, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Fatovich","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Royal Perth Hospital, Perth, Western Australia, Australia."},{"name":"Centre for Clinical Research in Emergency Medicine, Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke","family":"Haseler","sequence":"additional","affiliation":[{"name":"Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toby","family":"Jeffcote","sequence":"additional","affiliation":[{"name":"Department of Intensive Care, Alfred Health, Melbourne, Victoria, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Udy","sequence":"additional","affiliation":[{"name":"Department of Intensive Care, Alfred Health, Melbourne, Victoria, Australia."},{"name":"Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rinaldo","family":"Bellomo","sequence":"additional","affiliation":[{"name":"Department of Intensive Care, The Royal Melbourne Hospital, Melbourne, Victoria, Australia."},{"name":"Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia."},{"name":"Department of Critical Care, The University of Melbourne, Melbourne, Victoria, Australia."},{"name":"Department of Intensive Care, Austin Hospital, Melbourne, Australia"},{"name":"Data Analytics Research and Evaluation, Austin Hospital, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melinda","family":"Fitzgerald","sequence":"additional","affiliation":[{"name":"Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia, Australia."},{"name":"Perron Institute for Neurological and Translational Sciences, Nedlands, Western Australia, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"crossref","unstructured":"Poca M Sahuquillo J Baguena M et al. Incidence of Intracranial Hypertension after Severe Head Injury: a Prospective Study Using the Traumatic Coma Data Bank Classification. In: Intracranial Pressure and Neuromonitoring in Brain Injury. Springer: Vienna; 1998; pp. 27\u201330.","DOI":"10.1007\/978-3-7091-6475-4_8"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1208708"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12987-020-00201-8"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcna.2019.11.001"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1097\/TA.0b013e31822820da"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1385\/NCC:4:1:008"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-012-9780-3"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-019-05805-9"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1227\/NEU.0000000000001432"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.nec.2017.12.001"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(21)00138-1"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1102077"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.17075"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1605215"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1507581"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1207363"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2021.5561"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.nec.2016.05.010"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1097\/MCC.0000000000000920"},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2003.10.006"},{"key":"e_1_3_5_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mpaic.2019.10.012"},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mpdhp.2016.08.005"},{"key":"e_1_3_5_24_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2016.0091"},{"key":"e_1_3_5_25_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awh536"},{"key":"e_1_3_5_26_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18113-4"},{"key":"e_1_3_5_27_2","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000001838"},{"key":"e_1_3_5_28_2","doi-asserted-by":"publisher","DOI":"10.3109\/02699052.2013.775497"},{"key":"e_1_3_5_29_2","unstructured":"Smit J. Predicting intracranial hypertension in acute traumatic brain injury patients. University of Twente: Radboud UMC; 2020."},{"key":"e_1_3_5_30_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awab453"},{"key":"e_1_3_5_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropharm.2018.06.005"},{"key":"e_1_3_5_32_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0243427"},{"key":"e_1_3_5_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00701-019-03915-3"},{"key":"e_1_3_5_34_2","doi-asserted-by":"publisher","DOI":"10.1089\/neu.2017.5472"},{"key":"e_1_3_5_35_2","doi-asserted-by":"publisher","DOI":"10.1177\/0300060520941291"},{"key":"e_1_3_5_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-016-0319-x"},{"key":"e_1_3_5_37_2","doi-asserted-by":"publisher","DOI":"10.3171\/JNS\/2008\/109\/10\/0678"},{"key":"e_1_3_5_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02518874"},{"key":"e_1_3_5_39_2","doi-asserted-by":"crossref","unstructured":"Szewczykowski J Dytko P Kunicki A et al. Determination of Critical ICP Levels in Neurosurgical Patients: A Statistical Approach. In: Intracranial Pressure II. Springer: Berlin Heidelberg; 1975; pp. 392\u2013393.","DOI":"10.1007\/978-3-642-66086-3_90"},{"key":"e_1_3_5_40_2","doi-asserted-by":"crossref","unstructured":"Turner JM McDowall DG Gibson RM et al. Computer Analysis of Intracranial Pressure Measurements: Clinical Value and Nursing Response. In: Intracranial Pressure III. Springer: Berlin Heidelberg; 1976; pp. 283\u2013287.","DOI":"10.1007\/978-3-642-66508-0_46"},{"key":"e_1_3_5_41_2","doi-asserted-by":"crossref","unstructured":"Price DJ Dugdale RE Mason J. The Control of ICP Using Three Asynchronous Closed Loops. In: Intracranial Pressure IV. Springer: Berlin Heidelberg; 1980; pp. 395\u2013399.","DOI":"10.1007\/978-3-642-67543-0_86"},{"key":"e_1_3_5_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/0141-5425(83)90073-0"},{"key":"e_1_3_5_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/0141-5425(83)90027-4"},{"key":"e_1_3_5_44_2","doi-asserted-by":"crossref","unstructured":"Janny P Jouan JP Janny L et al. A Statistical Approach to Long-Term Monitoring of Intracranial Pressure. In: Intracranial Pressure II. Springer: Berlin Heidelberg; 1972; pp. 59\u201364.","DOI":"10.1007\/978-3-642-65486-2_11"},{"issue":"1","key":"e_1_3_5_45_2","first-page":"93","article-title":"Application of neural networks to the prediction of intracranial pressure (ICP). A preliminary study","volume":"15","author":"Swiercz M","year":"1995","unstructured":"Swiercz M, , Lewko J, , Mariak Z. Application of neural networks to the prediction of intracranial pressure (ICP). A preliminary study. Biocybern Biomed Eng, 1995; 15(1):93\u2013104.","journal-title":"Biocybern Biomed Eng"},{"key":"e_1_3_5_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.1995.479705"},{"key":"e_1_3_5_47_2","first-page":"601","article-title":"An adaptive neural network in wavelet space for time-series prediction. 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World","volume":"3","author":"Tsui F-C","year":"1996","unstructured":"Tsui F-C, , Li C-C, , Sun M, et al. An adaptive neural network in wavelet space for time-series prediction. 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96 1996; 3:601\u2013604.","journal-title":"ISCAS"},{"key":"e_1_3_5_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSMC.1997.638292"},{"key":"e_1_3_5_49_2","unstructured":"Tsui F-C. Time series prediction using a multiresolution dynamic predictor. University of Pittsburgh: Pittsburgh; 1996."},{"issue":"1","key":"e_1_3_5_50_2","first-page":"7111","article-title":"Artificial neural network based intracranial pressure mean forecast algorithm for medical decision support","volume":"1","author":"Zhang F","year":"2011","unstructured":"Zhang F, , Feng M, , Pan SJ, et al. Artificial neural network based intracranial pressure mean forecast algorithm for medical decision support. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011; 1(1):7111\u20137114.","journal-title":"2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"issue":"1","key":"e_1_3_5_51_2","first-page":"3930","article-title":"Utilization of temporal information for intracranial pressure development trend forecasting in traumatic brain injury","volume":"1","author":"Feng M","year":"2012","unstructured":"Feng M, , Zhang Z, , Guan C, et al. Utilization of temporal information for intracranial pressure development trend forecasting in traumatic brain injury. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012; 1(1):3930\u20133934.","journal-title":"2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"key":"e_1_3_5_52_2","doi-asserted-by":"crossref","unstructured":"Han B Muma M Zoubir A. An online approach for ICP forecasting based on signal decomposition and robust statistics. 2013 IEEE International Conference on Acoustics Speech and Signal Processing. 2013:6239\u20136243.","DOI":"10.1109\/ICASSP.2013.6638865"},{"key":"e_1_3_5_53_2","doi-asserted-by":"publisher","DOI":"10.1097\/TA.0000000000000698"},{"key":"e_1_3_5_54_2","doi-asserted-by":"publisher","DOI":"10.3390\/forecast1010004"},{"key":"e_1_3_5_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/JTEHM.2022.3179874"},{"key":"e_1_3_5_56_2","doi-asserted-by":"publisher","DOI":"10.1089\/neu.2007.9997"},{"key":"e_1_3_5_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2009.2037607"},{"key":"e_1_3_5_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2011.11.010"},{"key":"e_1_3_5_59_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/ab6360"},{"key":"e_1_3_5_60_2","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0b013e3182742d0a"},{"key":"e_1_3_5_61_2","doi-asserted-by":"publisher","DOI":"10.1177\/1099800409349164"},{"key":"e_1_3_5_62_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12028-010-9463-x"},{"key":"e_1_3_5_63_2","doi-asserted-by":"publisher","DOI":"10.4037\/ajcc2008.17.6.545"},{"key":"e_1_3_5_64_2","doi-asserted-by":"crossref","unstructured":"Bray R Sherwood A Halter J et al. Development of a Clinical Monitoring System by Means of ICP Waveform Analysis. In: Intracranial Pressure VI. Springer: Berlin Heidelberg; 1986; pp. 260\u2013264.","DOI":"10.1007\/978-3-642-70971-5_49"},{"issue":"1","key":"e_1_3_5_65_2","first-page":"3977","article-title":"Precursors to rapid elevations in intracranial pressure","volume":"4","author":"McNames J","year":"2001","unstructured":"McNames J, , Crespo C, , Aboy M, et al. Precursors to rapid elevations in intracranial pressure. 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001; 4(1):3977\u20133980.","journal-title":"2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society"},{"key":"e_1_3_5_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2008.2008636"},{"key":"e_1_3_5_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2009.5332749"},{"key":"e_1_3_5_68_2","doi-asserted-by":"publisher","DOI":"10.1007\/s007010050450"},{"key":"e_1_3_5_69_2","doi-asserted-by":"crossref","unstructured":"Rasmussen CE. Gaussian processes in Machine Learning. In: Lecture Notes in Computer Science. Springer: Berlin Heidelberg; 2003; pp. 63\u201371.","DOI":"10.1007\/978-3-540-28650-9_4"},{"key":"e_1_3_5_70_2","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000002080"},{"key":"e_1_3_5_71_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-020-06247-4"},{"issue":"1","key":"e_1_3_5_72_2","first-page":"559","article-title":"Interpretable machine learning in healthcare","volume":"1","author":"Ahmad MA","year":"2018","unstructured":"Ahmad MA, , Eckert C, , Teredesai A. Interpretable machine learning in healthcare. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2018; 1(1):559\u2013560.","journal-title":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics"},{"key":"e_1_3_5_73_2","first-page":"3319","article-title":"Axiomatic Attribution for Deep Networks. In: International Conference on Machine Learning","author":"Sundararajan M","year":"2017","unstructured":"Sundararajan M, , Taly A, , Yan Q. Axiomatic Attribution for Deep Networks. In: International Conference on Machine Learning. PMLR: 2017; pp. 3319\u20133328.","journal-title":"PMLR"},{"key":"e_1_3_5_74_2","doi-asserted-by":"crossref","unstructured":"Paramanandam K Kanagavalli R. A Review on Deep Learning Techniques for Saliency Detection. In: Lecture Notes in Networks and Systems. (Joshi A Mahmud M Ragel RG eds.). Springer Nature Singapore: Singapore; 2023; pp. 279\u2013289.","DOI":"10.1007\/978-981-19-0095-2_29"},{"key":"e_1_3_5_75_2","doi-asserted-by":"crossref","unstructured":"Ren H Xu B Wang Y et al. Time-series anomaly detection service at microsoft. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery: New York; 2019; pp. 3009\u20133017.","DOI":"10.1145\/3292500.3330680"},{"key":"e_1_3_5_76_2","unstructured":"Mundhenk TN Chen BY Friedland G. Efficient saliency maps for explainable AI. arXiv preprint arXiv:1911.11293. 2019."},{"key":"e_1_3_5_77_2","doi-asserted-by":"crossref","unstructured":"Parvatharaju PS Doddaiah R Hartvigsen T et al. Learning Saliency Maps to Explain Deep Time Series Classifiers. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery: Virtual Event Queensland Australia; 2021; pp. 1406\u20131415.","DOI":"10.1145\/3459637.3482446"},{"key":"e_1_3_5_78_2","doi-asserted-by":"crossref","unstructured":"Saadallah A Jakobs M Morik K. Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting. Springer International Publishing: Cham; 2021.","DOI":"10.1007\/978-3-030-86486-6_25"},{"key":"e_1_3_5_79_2","doi-asserted-by":"crossref","unstructured":"Sani HM Lei C Neagu D. Computational Complexity Analysis of Decision Tree Algorithms. In: Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science. (Bramer M Petridis M. eds.). Springer International Publishing: Cham; 2018; pp. 191\u2013197.","DOI":"10.1007\/978-3-030-04191-5_17"},{"key":"e_1_3_5_80_2","doi-asserted-by":"publisher","DOI":"10.1007\/s007010050449"},{"key":"e_1_3_5_81_2","unstructured":"Lipton ZC Berkowitz J Elkan C. A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:150600019. 2015."},{"key":"e_1_3_5_82_2","doi-asserted-by":"crossref","unstructured":"Dietterich TG. Machine Learning for Sequential Data: A Review. Springer: Berlin Heidelberg; 2002.","DOI":"10.1007\/3-540-70659-3_2"},{"key":"e_1_3_5_83_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tree.2019.03.007"},{"key":"e_1_3_5_84_2","doi-asserted-by":"publisher","DOI":"10.51893\/2022.1.OA5"},{"key":"e_1_3_5_85_2","unstructured":"Sutskever I Vinyals O Le QV. Sequence to Sequence Learning with Neural Networks. In: Advances in Neural Information Processing Systems. (Ghahramani Z Welling M Cortes C et al. eds.). Neural Information Processing Systems Foundation Inc. (NeurIPS): Montreal Canada; 2014; pp. 3104\u20133112."},{"key":"e_1_3_5_86_2","doi-asserted-by":"crossref","unstructured":"Weik MH. Sequential Computer. In: Computer Science and Communications Dictionary. Springer US: Boston MA; 2001; pp. 1551.","DOI":"10.1007\/1-4020-0613-6_17011"},{"key":"e_1_3_5_87_2","doi-asserted-by":"publisher","unstructured":"Salehinejad H Sankar S Barfett J et al. Recent advances in recurrent neural networks. arXiv preprint arXiv:150600019. 2017; doi: 10.48550\/arXiv.1801.01078","DOI":"10.48550\/arXiv.1801.01078"},{"key":"e_1_3_5_88_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00161572"},{"key":"e_1_3_5_89_2","doi-asserted-by":"publisher","DOI":"10.1197\/jamia.M2950"},{"key":"e_1_3_5_90_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020192224"},{"key":"e_1_3_5_91_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0224365"},{"key":"e_1_3_5_92_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-021-06446-7"},{"key":"e_1_3_5_93_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2021.712569"},{"key":"e_1_3_5_94_2","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_5_95_2","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","author":"McMahan B","year":"2017","unstructured":"McMahan B, , Moore E, , Ramage D, et al. Communication-Efficient Learning of Deep Networks from Decentralized Data. In: Artificial Intelligence and Statistics. PMLR: 2017; pp. 1273\u20131282.","journal-title":"PMLR"},{"key":"e_1_3_5_96_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03583-3"},{"key":"e_1_3_5_97_2","first-page":"598","article-title":"Sleep-Stage Scoring in Mice: The Influence of Data Pre-Processing on a System's Performance","author":"Katsageorgiou V-M","year":"2015","unstructured":"Katsageorgiou V-M, , Lassi G, , Tucci V, et al. Sleep-Stage Scoring in Mice: The Influence of Data Pre-Processing on a System's Performance. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE: 2015; pp. 598\u2013601.","journal-title":"IEEE"},{"key":"e_1_3_5_98_2","doi-asserted-by":"crossref","unstructured":"Malley B Ramazzotti D Wu JT-y. Data Pre-Processing. In: Secondary Analysis of Electronic Health Records. Springer International Publishing: Cham; 2016; pp. 115\u2013141.","DOI":"10.1007\/978-3-319-43742-2_12"},{"key":"e_1_3_5_99_2","unstructured":"Alpaydin E. Introduction to Machine Learning. MIT Press; 2020."},{"key":"e_1_3_5_100_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2013.12.159"},{"key":"e_1_3_5_101_2","doi-asserted-by":"crossref","unstructured":"Berthet A Dugelay J-L. A Review of Data Preprocessing Modules in Digital Image Forensics Methods Using Deep Learning. In: 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE: 2020; pp. 281\u2013284.","DOI":"10.1109\/VCIP49819.2020.9301880"},{"key":"e_1_3_5_102_2","doi-asserted-by":"crossref","unstructured":"Wilken M H\u00fcske-Kraus D Klausen A et al. Alarm Fatigue: Causes and Effects. In: German Medical Data Sciences: Visions and Bridges: Proceedings of the 62nd Annual Meeting of the German Association of Medical Informatics Biometry and Epidemiology. (Rohriq R Timmer A Binder H et al. eds.). IOS Press: Oldenburg; 2017; pp. 107\u2013111.","DOI":"10.3233\/978-1-61499-808-2-107"},{"key":"e_1_3_5_103_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph17228409"},{"key":"e_1_3_5_104_2","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2016.22.1.46"},{"key":"e_1_3_5_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30219-3"},{"key":"e_1_3_5_106_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m3164"},{"key":"e_1_3_5_107_2","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.4642"},{"key":"e_1_3_5_108_2","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181c30fb2"},{"key":"e_1_3_5_109_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropharm.2018.06.005"},{"key":"e_1_3_5_110_2","doi-asserted-by":"publisher","DOI":"10.4103\/2229-5151.170840"},{"key":"e_1_3_5_111_2","doi-asserted-by":"publisher","DOI":"10.1080\/10903120701732052"},{"key":"e_1_3_5_112_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13049-019-0590-x"},{"key":"e_1_3_5_113_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-020-03370-y"},{"key":"e_1_3_5_114_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-022-06822-x"}],"container-title":["Journal of Neurotrauma"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1089\/neu.2022.0201","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/neu.2022.0201","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/neu.2022.0201","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:41:01Z","timestamp":1777675261000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1089\/neu.2022.0201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":113,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["10.1089\/neu.2022.0201"],"URL":"https:\/\/doi.org\/10.1089\/neu.2022.0201","relation":{},"ISSN":["0897-7151","1557-9042"],"issn-type":[{"value":"0897-7151","type":"print"},{"value":"1557-9042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3]]}}}