{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:19:08Z","timestamp":1760059148285,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T00:00:00Z","timestamp":1748217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NYUAD Center for Interacting Urban Networks","award":["CG001","1R01GM121753","1934388","1840761"],"award-info":[{"award-number":["CG001","1R01GM121753","1934388","1840761"]}]},{"name":"Tamkeen under the NYUAD Research Institute Award","award":["CG001","1R01GM121753","1934388","1840761"],"award-info":[{"award-number":["CG001","1R01GM121753","1934388","1840761"]}]},{"name":"NYU WIRELESS","award":["CG001","1R01GM121753","1934388","1840761"],"award-info":[{"award-number":["CG001","1R01GM121753","1934388","1840761"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Clinical decision-making lies at the heart of health care. Medical data collection has made it possible for clinical decision-making to be data-driven. However, data-driven systems for decision-making have so far worked only for a limited set of clinical conditions. It is still unclear whether a pure data-driven clinical decision-making system can work for a wide set of clinical conditions in real-time environments such as Intensive Care Units. Our DataToCare system receives demographic information, initial diagnoses and measurements from the first hours of a patient\u2019s arrival in an Intensive Care Unit. From that information, DataToCare suggests treatments to offer the patient, based on the treatments given to similar patients. Patients are considered similar if they have abnormal measurements in common. This paper describes the analytics pipeline and the results of its evaluation. DataToCare has the potential to increase patient safety and transfer expertise across medical teams. Though we apply these ideas in the context of Intensive Care Units, the approach could potentially be applied more broadly within medicine.<\/jats:p>","DOI":"10.3390\/a18060311","type":"journal-article","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T02:50:05Z","timestamp":1748227805000},"page":"311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DataToCare: Predicting Treatments for Intensive Care Unit Patients Based on Similarity of Abnormalities"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1974-4036","authenticated-orcid":false,"given":"Shan","family":"Randhawa","sequence":"first","affiliation":[{"name":"School of Information, University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abbas","family":"Shojaee","sequence":"additional","affiliation":[{"name":"Biology Department, New York University, New York, NY 10012, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1926-126X","authenticated-orcid":false,"given":"Elisa","family":"Sorrentino","sequence":"additional","affiliation":[{"name":"Fujifilm Healthcare Italia, 20090 Milano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Li","sequence":"additional","affiliation":[{"name":"Virginia Tech Carilion School of Medicine, Roanoke, VA 24016, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Azza","family":"Abouzied","sequence":"additional","affiliation":[{"name":"Department of Computer Science, New York University, Abu Dhabi P.O. Box 129188, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7036-3312","authenticated-orcid":false,"given":"Dennis","family":"Shasha","sequence":"additional","affiliation":[{"name":"Department of Computer Science, New York University, New York, NY 10012, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0933-3657(96)00376-4","article-title":"CADIAG-2 and MYCIN-like systems","volume":"9","author":"Daniel","year":"1997","journal-title":"Artif. Intell. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/0010-4825(85)90014-9","article-title":"CADIAG: Approaches to computer-assisted medical diagnosis","volume":"15","author":"Adlassnig","year":"1985","journal-title":"Comput. Biol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0933-3657(89)90018-3","article-title":"Cadiag-2\/gall: An experimental expert system for the diagnosis of gallbladder and biliary tract diseases","volume":"1","author":"Adlassng","year":"1989","journal-title":"Artif. Intell. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1109\/51.334629","article-title":"Automated delivery of muscle relaxants using fuzzy logic control","volume":"13","author":"Mason","year":"1994","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/0165-0114(95)00124-7","article-title":"Fuzzy modelling of the expert\u2019s knowledge in ECG-based ischaemia detection","volume":"77","author":"Presedo","year":"1996","journal-title":"Fuzzy Sets Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41746-020-0221-y","article-title":"An overview of clinical decision support systems: Benefits, risks, and strategies for success","volume":"3","author":"Sutton","year":"2020","journal-title":"npj Digit. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/s13012-017-0644-2","article-title":"What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation","volume":"12","author":"Liberati","year":"2017","journal-title":"Implement. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pincay, J., Ter\u00e1n, L., and Portmann, E. (2019, January 24\u201326). Health recommender systems: A state-of-the-art review. Proceedings of the 2019 Sixth International Conference on eDemocracy & eGovernment (ICEDEG), Quito, Ecuador.","DOI":"10.1109\/ICEDEG.2019.8734362"},{"key":"ref_9","unstructured":"Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., and Sun, J. (2016, January 19\u201320). Doctor ai: Predicting clinical events via recurrent neural networks. Proceedings of the Machine Learning for Healthcare Conference, Los Angeles, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, S., McDermott, M.B., Chauhan, G., Ghassemi, M., Hughes, M.C., and Naumann, T. (2020, January 2\u20134). Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii. Proceedings of the ACM Conference on Health, Inference, and Learning, Toronto, ON, Canada.","DOI":"10.1145\/3368555.3384469"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, X., Qian, B., Cao, S., Li, Y., Chen, H., Zheng, Y., and Davidson, I. (2020, January 6\u201310). INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, USA.","DOI":"10.1145\/3394486.3403087"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","year":"2018","journal-title":"npj Digit. Med."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., and Sun, J. (2017, January 13\u201317). GRAM: Graph-based attention model for healthcare representation learning. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098126"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., and Gao, J. (2017, January 13\u201317). Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098088"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/JBHI.2016.2633963","article-title":"Deepr: A Convolutional Net for Medical Records","volume":"21","author":"Nguyen","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_16","unstructured":"Choi, E., Bahadori, M.T., Sun, J., Kulas, J.A., Schuetz, A., and Stewart, W. (2016, January 5\u201310). RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. Proceedings of the NIPS, Barcelona, Spain."},{"key":"ref_17","unstructured":"Suresh, H., Hunt, N., Johnson, A., Celi, L.A., Szolovits, P., and Ghassemi, M. (2017, January 18\u201319). Clinical intervention prediction and understanding with deep neural networks. Proceedings of the Machine Learning for Healthcare Conference, Boston, MA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Esteban, C., Staeck, O., Baier, S., Yang, Y., and Tresp, V. (2016, January 4\u20137). Predicting clinical events by combining static and dynamic information using recurrent neural networks. Proceedings of the 2016 IEEE International Conference on Healthcare Informatics (ICHI), Chicago, IL, USA.","DOI":"10.1109\/ICHI.2016.16"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.jarmac.2015.07.001","article-title":"A naturalistic decision making perspective on studying intuitive decision making","volume":"4","author":"Klein","year":"2015","journal-title":"J. Appl. Res. Mem. Cogn."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.jbi.2018.04.007","article-title":"Benchmarking deep learning models on large healthcare datasets","volume":"83","author":"Purushotham","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cui, S., Wang, J., Zhong, Y., Liu, H., Wang, T., and Ma, F. (2024, January 8\u201320). Automated fusion of multimodal electronic health records for better medical predictions. Proceedings of the 2024 SIAM International Conference on Data Mining (SDM), Houston, TX, USA.","DOI":"10.1137\/1.9781611978032.41"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103112","DOI":"10.1016\/j.eclinm.2025.103112","article-title":"Multicenter validation of a machine learning model to predict Intensive Care Unit readmission within 48 hours after discharge","volume":"81","author":"Lim","year":"2025","journal-title":"eClinicalMedicine"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, G.H., Kim, J.W., Kim, K.H., Maeng, J.Y., Shin, Y.G., and Park, S. (2025). Transformer-based model for predicting length of stay in intensive care unit in sepsis patients. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1473533"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1038\/s41746-025-01643-w","article-title":"Artificial intelligence based multispecialty mortality prediction models for septic shock in a multicenter retrospective study","volume":"8","author":"Wang","year":"2025","journal-title":"Npj Digit. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e079243","DOI":"10.1136\/bmjopen-2023-079243","article-title":"Forecasting disease trajectories in critical illness: Comparison of probabilistic dynamic systems to static models to predict patient status in the Intensive Care Unit","volume":"14","author":"Duggal","year":"2024","journal-title":"BMJ Open"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fabbri, A., Tascioglu, A.B., Bertini, F., and Montesi, D. (2025). Overnight Stay in the Emergency Department and In-Hospital Mortality Among Elderly Patients: A 6-Year Follow-Up Italian Study. J. Clin. Med., 14.","DOI":"10.3390\/jcm14092879"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1093\/jamia\/ocz205","article-title":"Temporal convolutional networks allow early prediction of events in critical care","volume":"27","author":"Catling","year":"2020","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jin, B., Yang, H., Sun, L., Liu, C., Qu, Y., and Tong, J. (2018, January 19\u201323). A treatment engine by predicting next-period prescriptions. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3220095"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, W., He, X., and Zha, H. (2018, January 19\u201323). Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3219961"},{"key":"ref_30","unstructured":"Bajor, J., and Lasko, T. (2017, January 24\u201326). Predicting Medications from Diagnostic Codes with Recurrent Neural Networks. Proceedings of the ICLR, Toulon, France."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"104788","DOI":"10.1016\/j.knosys.2019.05.031","article-title":"Learning and recommending treatments using electronic medical records","volume":"181","author":"Hoang","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jbi.2018.06.001","article-title":"Patient similarity for precision medicine: A systematic review","volume":"83","author":"Parimbelli","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101782","DOI":"10.1016\/j.artmed.2019.101782","article-title":"A fusion framework to extract typical treatment patterns from electronic medical records","volume":"103","author":"Chen","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_34","first-page":"1126","article-title":"Gamenet: Graph augmented memory networks for recommending medication combination","volume":"33","author":"Shang","year":"2019","journal-title":"AAAI Conf. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shang, J., Ma, T., Xiao, C., and Sun, J. (2019, January 10\u201316). Pre-training of Graph Augmented Transformers for Medication Recommendation. Proceedings of the IJCAI, Macao, China.","DOI":"10.24963\/ijcai.2019\/825"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, R., Tang, J., Stewart, W.F., and Sun, J. (2017, January 13\u201317). LEAP: Learning to prescribe effective and safe treatment combinations for multimorbidity. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data MINING, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098109"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci. Data"},{"key":"ref_38","unstructured":"(2022, May 30). MIMIC-IV Documentation. Mimic-IV Note Information. Available online: https:\/\/mimic.mit.edu\/docs\/iv\/modules\/note\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1186\/s13613-018-0458-7","article-title":"Caring for the critically ill patients over 80: A narrative review","volume":"8","author":"Guidet","year":"2018","journal-title":"Ann. Intensive Care"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1111\/aas.13021","article-title":"Attitudes of physicians towards the care of critically ill elderly patients - a European survey","volume":"62","author":"Guidet","year":"2017","journal-title":"Acta Anaesthesiol. Scand."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1111\/j.1399-6576.2005.00759.x","article-title":"Impact of gender on treatment and outcome of ICU patients","volume":"49","author":"Reinikainen","year":"2005","journal-title":"Acta Anaesthesiol. Scand."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1377\/hlthaff.2012.1400","article-title":"The quality of care delivered to patients within the same hospital varies by insurance type","volume":"32","author":"Spencer","year":"2013","journal-title":"Health Aff."},{"key":"ref_43","unstructured":"McInnes, L., Healy, J., and Melville, J. (2020). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/6\/311\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:40:17Z","timestamp":1760031617000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/6\/311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,26]]},"references-count":43,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["a18060311"],"URL":"https:\/\/doi.org\/10.3390\/a18060311","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,5,26]]}}}