# Artificial Intelligence Algorithms for Treatment of Diabetes
## 2 Methods
### ML with Electronic Health Records for Personalized Medicine and Decision Making in T2D
The constraint may also be formulated in other analogous forms [73], such as false negative rate, false positive rate, false omission rate, and false discovery rate [74]. Based on which stage of the ML training processes are considered, existing approaches fall into three categories: pre-processing approaches, in-processing approaches, and post-processing approaches [78; 79]. The pre-processing approaches are applied before training machine learning models by modifying the labels and/or attributes in the training data; For example, one can reweight the datasets to be less biased to sensitive attributes before feeding them into an algorithm. In-processing approaches aim to incorporate constraints during model training to ensure fairness. For example, adversarial debiasing aims to train a fair algorithm with adversarial learning to minimize the discrepancy between different sensitive groups, and post-processing methods develop mechanisms to change predicted labels. For example, one can calibrate the predictions based on fairness criteria such as Equalized odds. It is important to consider privacy when building a fair ML model on EHR data as it is often highly confidential due to legal compliance. Apart from ensuring fairness in various medical applications, it is also essential to preserve privacy in handling all kinds of medical data. Privacy protection is one of the core components in jurisdictions on healthcare data across the globe. Ensuring privacy in ML models for healthcare applications has been an increasingly critical concern. ML models can have serious privacy leakage issues for healthcare information. For example, a study shows that ML models can be utilized to identify the individuals in healthcare data repositories despite the removal of protected sensitive information [80]. Therefore, multiple privacy protection methods including differential privacy, local differential privacy, and federated learning can be incorporated to develop privacy-preserving ML models with healthcare data. For instance, differential private logistic regression and naive Bayes are proposed for breast cancer classification and diabetes prediction problems [81]. Federated learning is applied for heart failure disease prediction in a private manner [82]. Thus, it is important to study how to ensure fairness under privacy. Due to the restrictions such as Electronic Communications Privacy Act (ECPA), General Data Protection Regulation (GDPR) and medical data requirements, it is usually infeasible to directly get access to the original sensitive data and effective privacy protection methods are needed [83]. Various privacy protection approaches are proposed to protect the privacy of sensitive data including multiparty computation (MC), differential privacy (DP), local differential privacy (LDP) and federated learning (FL). Such privacy mechanisms are widely deployed in medical analysis applications. There is currently limited research on the intersection of privacy and fairness in ML. For example, with regards to the problem of fairness under LDP, we can observe the experiment results on two mainstream fairness datasets (ADULT [84], COMPAS [85; 86]) and conclude that stronger privacy guarantee requirements lead to worse fairness performance for debiasing models such as adversarial debiasing (Figure 2). Therefore, there is a trade-off between the debiasing model and the LDP privacy mechanism. More research is needed for the direction of private and fair ML. In addition to privacy, we also propose to deal with the scenario when we have limited/unknown sensitive attributes (e.g., gender, race) to ensure fairness in ML models. In real-world medical systems, user-sensitive attributes are often limited or even unavailable due to legal restrictions or security protection. In [31], we study the problem of learning fair classifiers by exploring related attributes.