# Forecast of Medical Costs in Health Companies Using Models Based on Advanced Analytics
## 2 Related Work  
The cost forecast is one of the main objectives of different time series methods when these methods are applied in diverse fields. A time series is a sequence of measurements over time rarely mapped in equal intervals. Time series forecasting can be applied to diverse sectors, and in this case, specifically to the prediction of medication costs as performed in papers by, e.g., [PERSON] and [PERSON] [12; 22], using different techniques such as ARIMA and LSTM. Another work by [PERSON] [23] using RL, RNN, and LSTM showed a sustainable approach to forecast the future demands of hospital beds, considering the hospital capacity and the population of the region in order to plan the future increase in required hospital beds. [PERSON] [24] used electronic medical records for Finnish citizens over sixty-five years of age to develop a sequential deep learning model to predict the use of health services in the following year using RNN and LSTM networks. Another work which uses clustering techniques is that by [PERSON] [25]. This author studied hip fracture care in Ireland and, using k-means clustering, showed that elderly patients are grouped according to three variables: age, length of stay, and time to surgery. According to [PERSON], the cost of treating a hip fracture was estimated to be approximately EUR 12,600. He identified hip fractures as one of the most serious injuries with long hospital admissions.  
In addition, [PERSON] [26] used k-means to find the most appropriate clinical variables between 23 and 26 variables capable of efficiently separating patients diagnosed with type 2 diabetes mellitus (T2 DM) with underlying diseases such as arterial hypertonia (AH), ischemic heart disease (CHD), diabetic polynneuropathy (DPNP), and diabetic microangiopathy (DMA).  
The following Table 1 provides a summary of the related papers and their input variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Method** & **Cost Variables** & **No-Cost Variables** \\ \hline \hline [PERSON] (2017) [12] & Arima, LSTM & Medication cost & \begin{tabular}{c} Demographic variables of patients (age, gender, region, year of birth) and clinical variables of patients (type of admission, diagnoses, and procedure codes) \\ \end{tabular} \\  & & & Predict the average weekly expenditure of patients \\ [PERSON] (2020) [22] & Arima, LMP, LSTM & Medication cost & \begin{tabular}{c} Predic the average weekly expenditure of patients \\ on certain pain medications, selecting two \\ medications that are among the ten most \\ prescribed pain medications in the US \\ \end{tabular} \\ Kabir (2021) [23] & RL, RNN, LSTM & Bed cost & \begin{tabular}{c} Number of beds, occupation, and patients \\ \end{tabular} \\ [PERSON] (2020) [24] & Lasso, LightGBM, LSTM & \
\begin{tabular}{c} Cost of visits by \\ family doctor \\ \end{tabular} &
\begin{tabular}{c} Number of patients, number of visits, average \\ visits per patient, procedure codes, and diagnoses \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Models and input variables from related papers.