Randhawa, ShanShojaee, AbbasSorrentino, ElisaLi, YifanAbouzied, AzzaShasha, Dennis2025-06-252025-06-252025-05-26Randhawa, S.; Shojaee, A.; Sorrentino, E.; Li, Y.; Abouzied, A.; Shasha, D. DataToCare: Predicting Treatments for Intensive Care Unit Patients Based on Similarity of Abnormalities. Algorithms 2025, 18, 311.https://hdl.handle.net/10919/135602Clinical 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 <span style="font-variant: small-caps;">DataToCare</span> system receives demographic information, initial diagnoses and measurements from the first hours of a patient&rsquo;s arrival in an Intensive Care Unit. From that information, <span style="font-variant: small-caps;">DataToCare</span> 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. <span style="font-variant: small-caps;">DataToCare</span> 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.application/pdfenCreative Commons Attribution 4.0 InternationalArticle - Refereed2025-06-25https://doi.org/10.3390/a18060311