Background/Objectives: The number of individuals with lower limb loss (LLL) is rising. Therefore, identifying the walking potential in individuals with LLL and prescribing adequate prosthetic systems are crucial. Various factors can influence participants’ walking ability, to different extents. The aim of the present study was to apply machine learning methods to develop a predictive mode. This model can assist rehabilitation and limb loss care teams in making informed decisions regarding prosthesis prescription and predicting walking ability in individuals with LLL. Methods: The present study was designed as a prospective cross-sectional study encompassing 104 consecutively recruited participants with LLL (average age 62.1 ± 10.9 years, 80 (76.9%) men) at the Medical Rehabilitation Clinic. Demographic, physical, psychological, and social status data of patients were collected at the beginning of the rehabilitation program. At the end of the treatment, K-level estimation of functional ability, a Timed Up and Go Test (TUG), and a Two-Minute Walking Test (TMWT) were performed. Support vector machines (SVM) were used to develop the prediction model. Results: Three decision trees were created, one for each output, as follows: K-level, TUG, and TMWT. For all three outputs, there were eight significant predictors (balance, body mass index, age, Beck depression inventory, amputation level, muscle strength of the residual extremity hip extensors, intact extremity (IE) plantar flexors, and IE hip extensors). For the K-level, the ninth predictor was The Multidimensional Scale of Perceived Social Support (MSPSS). Conclusions: Using the SVM model, we can predict the K-level, TUG, and TMWT with high accuracy. These clinical assessments could be incorporated into routine clinical practice to guide clinicians and inform patients of their potential level of ambulation.

Epilepsy can be effectively controlled with appropriately selected antiepileptic drugs and carefully titrated dosage regimens. Although lamotrigine exhibits favorable pharmacokinetic properties following oral administration, fluctuations in plasma concentration may still occur due to interindividual variability, irregular dosing, and pharmacokinetic interactions. In this study, a subcutaneous implant capable of monitoring plasma lamotrigine levels and adjusting drug delivery accordingly was developed to maintain stable therapeutic concentrations. The proposed system combines intermittent drug release with continuous concentration monitoring using an enzymatic biosensor. A pharmacokinetic model based on first-order absorption and elimination kinetics was implemented in MATLAB/Simulink using clinical lamotrigine concentration data obtained from patients receiving chronic therapy. In the closed-loop configuration, biosensor measurements were used as feedback for a proportional–integral (PI) controller that adjusted the implant release rate in real time. System performance was evaluated using in silico simulations. The open-loop system produced rapid concentration peaks (Cmax ≈ 0.06 mmol/L) followed by a decline below the therapeutic threshold within approximately 80 min. In contrast, the closed-loop system achieved lower peak concentrations (Cmax ≈ 0.045 mmol/L) and maintained plasma concentrations within the therapeutic range of 0.02–0.03 mmol/L with reduced fluctuations. These findings support further investigation of biosensor-guided closed-loop lamotrigine delivery systems.