JakubDylag/CGM_Prediction_LSTM

This project is a classic implementation of a standard LSTM architecture applied to a well-studied medical time-series problem. With only 11 stars and no activity in over three years, it functions primarily as a student project or personal reference implementation rather than a viable tool or library. Defensibility is near zero as it lacks proprietary datasets (likely uses OhioT1DM or similar public sets), clinical validation, or a unique architectural approach. In the professional medical AI space, LSTMs have largely been superseded by more sophisticated architectures like Temporal Fusion Transformers (TFT) or Informers. Furthermore, the market for CGM prediction is dominated by hardware giants (Dexcom, Abbott, Medtronic) and platform players (Apple, Google/Fitbit) who have massive data moats and regulatory clearance. A standalone GitHub repository of this nature faces immediate displacement by any dedicated health-tech startup or the internal R&D of hardware manufacturers.