Accurate privacy-preserving prediction using electronic health record (EHR) data distributed in multiple hospitals is essential to enable stakeholders related to healthcare services to obtain useful information without privacy leakage. In this paper, we propose harmonized embedding-based self-attentive encoder (HarmoSATE), which is a new method for privacy-preserving federated predictive analysis. We extract contextual embeddings of local institutions using Word2Vec, and then harmonize locally-trained embeddings using a neural network-based harmonization technique. The proposed method uses a deep representative encoder based on self-attention to learn complex and dynamic patterns inherent to harmonized embeddings of medical concepts. To evaluate our method, we implemented experiments using sequential medical codes collected from the Medical Information Mart for Intensive Care-III dataset in a distributed setting. It achieved a significant increase in average AUC, ranging from 3% to 8% depending on the experiments compared to baseline models, demonstrating superior prediction accuracy of a patient's diagnosis in the next admission. HarmoSATE can be a useful alternative to obtain accurate and practical results for various predictive tasks that use sensitive and distributed EHR data while preserving patients' privacy.

Recommendation systems have required centralized storage of user data, but due to privacy concerns, recent studies adopted federated learning (FL) that discloses intermediate statistics instead of raw data to build privacy-preserving federated recommendation systems. However, they suffer from inefficiencies in privacy-preserving mechanisms and inaccuracies in simple algorithms that ignore sequential information. This study proposes an extension of Word2Vec for a privacy-preserving federated sequential recommendation system (PPFSRS). This method exploits sequential information to generate contextual item representations for accurate recommendations while concealing privacy-sensitive features efficiently. Specifically, we mixed updates from negative samples to inhibit the direct leakage of purchased items from model updates. In addition, our method computes approximate model updates that can occur when sensitive features only belong to negative samples to prevent inference attacks. In experiments, we used benchmark datasets for recommendation and simulated highly distributed data such that each user stores historical data locally. While preserving privacy with reasonable complexity, the proposed method showed little degradation in recommendation performance compared to FL-based Word2Vec without privacy-preserving mechanisms. Utilizing contextual item representations trained by our method from highly distributed data will be a practical starting point for PPFSRS in a cross-device setting.

The health index measures a person’s overall health status which provides useful information for people to manage their health, so developing a precise and relevant health index is urgent. Currently, many researchers have studied the biological age (BA) estimation, one of the beneficial health indices, by applying machine learning and deep learning techniques to health data. However, most of them have focused on the chronological age prediction or basic latent feature extraction methods. In this paper, we present a new algorithm to estimate BA, called Risk Score-Embedded Autoencoder-based BA (RSAE-BA). RSAE-BA can provide an accurate health index by using deep representation learning with an individual’s health risk. We first proposed a notion of risk score (RS) calculation to monitor a person’s health risk. Then we extracted latent features by using an autoencoder embedding the RS, and used them to generate BA. To evaluate RSAE-BA, we presented a new BA validation method using the RS, which is applicable to both unlabeled and labeled data. We compared the results of RSAE-BA with existing methods, and demonstrated the accuracy of RSAE-BA and its applicability to predict disease incidence. We believe that RSAE-BA will be a useful alternative method to measure a person’s health.