## 2 Related Work  
We organize our related work discussion into three themes: (i) prior work with similar _data cleaning and pre-processing_ techniques, (ii) _specialized treatment prediction models_ that focus on a small set of clinical interventions or treatments and (iii) _generic treatment recommendation systems_. **Data Cleaning and Preprocessing. ** [PERSON] et al. [20] designed a pipeline to clean prescriptions from the MIMIC III database to feed into their deep learning models for common clinical prediction tasks, such as mortality prediction, length of stay prediction and ICD-9 code prediction. They focused particularly on standardizing the inconsistency in prescription units and dosages, which helped them to extract cleaner medication data, consisting of both generic and brand name medications, and increased their models' performance. Similar to their model, our pipeline maps medications from MIMIC III to cleaned generic drug names. Closely related, [PERSON] et al. [10] provide an open-source pipeline, MIMIC-Extract, that includes (among other functions) standardized data processing functions such as unit conversion, outlier detection, deduplication and missing data imputation for raw vital signs and lab results. A study by [PERSON] et al. [21] on the automated fusion of multimodal electronic health records (EHRs) emphasized the importance of integrating diverse data types, such as tabular demographics, discrete medical codes, continuous monitoring data from ICU stays, and unstructured clinical notes, to improve various prediction tasks. Their approach uses a two-stage searchable module that optimizes feature selection and fusion operations, facilitating the effective processing of complex EHR data. **Outcome Prediction in ICU Settings. ** The following papers make predictions about patient outcomes in Intensive Care Unit settings. [PERSON] et al. [22] propose a technique to predict whether a patient will be readmitted within two days of discharge. Their technique used a consistent set of 30 variables comprising demographic features and laboratory models. [PERSON] et al. [23] use transformer models to predict the length of stay in Intensive Care Units for sepsis patients. [PERSON] et al. [24] predict mortality in septic shock cases based on 34 features having to do with blood test signs, age and surgical histories. They used a fusion model whose sub-models were tree/forest-based methods, Bayesian methods, and support-vector-based methods. [PERSON] et al. [25] proposed a method to forecast the illness trajectory of COVID-19 patients using multinomial logistic models. [PERSON] et al. [26]showed a negative result. They showed that overnight stays prior to securing a bed in a hospital do not influence mortality. **Specialized Treatment Prediction Models in the ICU. ** [PERSON] et al. (2017) used data from different ICU sources (vitals, labs, notes, and demographics) to predict five kinds of treatments: invasive ventilation, non-invasive ventilation, vasopressors, colloid boluses, and crystalloid boluses. Their long short-term memory network (LTSM) approach focused on learning the onset and withdrawal of these treatments. The predictions are made with a six-hour time horizon to support clinically actionable planning. They outperform the convolutional neural networks method on the five treatments mentioned. [PERSON] and [PERSON] (2017) employed temporal convolutional networks (TCNs) to enhance the early prediction of critical care events by encoding longitudinal patient data. Their objective was to predict clinical interventions and mortality within a 1-6-hour window for ICU admissions. The TCN-based model demonstrated improved prediction accuracy over recurrent neural networks (RNNs) for certain events, especially when the prediction timeframe varied.