## 7 Conclusions and Future Work  
Given a target patient \(p\), DataToCare uses an abnormality-based data-driven criterion to identify patients relevant to \(p\) in some state, builds a machine learning model on those relevant patients for each treatment \(r\), and applies that model to suggest whether to give treatment \(r\) to \(p\) in that state. Given that diagnoses are quite fluid early in a patient's stay in the Intensive Care Unit, these suggestions show high precision and recall, particularly for short time horizons, such as two hours. Such a time horizon constitutes a useful time scale for the fast-paced environment of an ICU.  
The abnormality-driven criterion to identify patients from which to build a machine learning model is of particular help when predicting rare treatments compared to a strategy using a uniform Random Selection of patients. For common treatments, a strategy using a uniform Random Selection of patients is nearly as good.  
Some areas of future work include the following.  
* The current method does not take drug-drug interactions into account, but that is an important post-processing step: given the DataToCare's recommendations and the current drugs being taken and any drug-drug interactions, the medical staff would need to consider whether a recommended drug that conflicts with a drug the patient is already taking should not be recommended or whether the already-taken drug should be removed.
* Incorporating dosage in recommendations: Suggesting a dosage requires additional information, including pharmacokinetics, in the context of each patient's conditions. While our goal has been to remind physicians of possibly unaddressed issues and the potentially best medicine, including the dosage is a worthwhile research question.
* DataToCare currently predicts the next treatments to be given to a target patient. A useful generalization would be to use this same framework to predict the tests to give to that patient.
* One of our reviewers suggested the following point for future work: In an Intensive Care Unit, or in any medical setting for that matter, future work might lead to suggestions of a specific treatment to increase or decrease a particular measured lab value or symptom based on established protocols. It would also be useful to suggest the sequence of treatments.  
Conceptualization, A.A., A.S., S.R. and D.S.; methodology, A.A., A.S., S.R. and D.S.; software, S.R., E.S. and Y.L.; validation, S.R., A.S. and D.S.; formal analysis, S.R., A.S., A.A. and D.S.; data curation, S.R., E.S. and Y.L.; writing--original draft preparation, All; writing--review and editing, S.R., A.S., A.A. and D.S.; visualization, S.R.; supervision, A.S., A.A. and D.S.; funding acquisition, A.A. and D.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award CG001. Additional support came from NYU WIRELESS, grants 1R01 GM121753 (U.S. NIH), 1934388 (U.S. NSF), and 1840761 (U.S. NSF).  
Data Availability Statement: Publicly available data were used in this study. The MIMIC-III v1.4 Clinical Database is available from PhysioNet at [[https://physionet.org/content/mimiciii/1.4/](https://physionet.org/content/mimiciii/1.4/)]([https://physionet.org/content/mimiciii/1.4/](https://physionet.org/content/mimiciii/1.4/)) (accessed on 19 May 2025).  
The authors would like to acknowledge the helpful feedback and guidance of [PERSON] and [PERSON]. [PERSON], Critical Care & Sleep Medicine, Yale University School of Medicine, was also kind enough to point out the utility of DataToCare. [PERSON], provided valuable insights. We would also like to thank the anonymous reviewers for their very helpful comments.  
The authors declare no conflicts of interest.