## 5 Edge AI for Early Health Prediction
### Prediction of Future Health Outcomes
#### 5.3.5 Evaluation Metrics of Edge AI Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Application** & **Methods** & **Results** \\ \hline
[77] & 2023 & Clinical language models for a wide range of clinical and operational & Pretraining datasets: NYU & NYUTron has an AUC of 78.7–94.9\%. \\  & & & Notes, NYU & NYUTron improves the AUC by 5.36–14.7\% compared to traditional \\  & & & Notes-Monhattan, NYU & 5.36–14.7\% compared to traditional \\  & & & Notes-Brooklyn. Fine-tuning & models. \\  & & & datasets: NYU Readmission & \\ \hline
[78] & 2023 & Prediction of mental health problems after military deployment. & Neural network models used for prediction. Pre-deployment registry & Approximately 95\% of participants were male. The percentage of individuals deployed in a combat unit was highest for the first deployment (32.7\%), compared to the second (25.6\%) and the third (20.8\%) deployment. \\ \hline
[65] & 2022 & Early-stage & Decision tree, random forest, Support Vector Machine, Gradient Boosting, and voting classifiers. & Evaluation metrics: precision, recall, accuracy. Men are more likely to have dementia than women. Achieved 83\% accuracy on test data \\  & & & Machine learning techniques applied to Alzheimer’s disease datasets. \\ \hline
[57] & 2014 & Risk predictive modeling for diabetes and cardiovascular disease & Collection of data during a prospective study. & Emphasizes the importance of validating existing CVD and diabetes prediction models to improve their adoption in routine practice. \\ \hline
[68] & 2019 & Identifications of patients at risk of uncontrolled & Logistic regression and recurrent neural networks & Best model achieved an AUROC of 0.719. Linear models performed better than recurrent neural networks \\ \hline
[79] & 2021 & Predicting critical state after COVID-19 diagnosis & Prognostic model trained on US electronic health records. Feature reduction process based on SHAP values & ROC AUC: 0.861 [0.838, 0.883], Precision–recall AUC: 0.434 [0.414, 0.485] \\ \hline
[61] & 2022 & Machine learning for healthcare wearable devices. & Homomorphic Encryption (HE). Secure Multiparty Computation (SMPC) & Review of different areas of machine learning research for wearable healthcare devices. \\ \hline
[58] & 2018 & Clinical prediction models (CPMs) with statistical updating & Regression coefficient updating. Meta-model updating & Original ES overestimated mortality (calibration intercept—1.06, slope—0.97). All updating strategies improved calibration performance. \\ \hline
[80] & 2019 & Continuous risk predictions for acute kidney injury. & Binary variable prediction for AKI occurrence. Eight future time horizons for predictions. & Model achieves a higher ROC AUC in shorter time windows. The model achieves a lower PR AUC in shorter time windows. \\ \hline
[71] & 2019 & Deep learning models for early prediction of acute adverse events. & Developing deep learning continuous risk models. Integrating domain knowledge into the technical specification & Continuous risk models identified 55.8\% of AKI cases up to 48 h early with a false positive rate of 2:1. The model correctly predicted 90.2\% of AKI cases requiring dialysis within 90 days. \\ \hline
[48] & 2020 & AI in digital pathology for breast cancer diagnosis. & Image analysis with deep learning (DL). Non-CNN algorithms for segmentation and detection & Review of the basics of digital pathology and AI and the challenges in the field. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._