In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals. We use a contrastive learning based approach to learn similar embeddings of patients with similar physiological signal data. We also introduce a number of neighbor selection algorithms to determine the patients with the highest similarity on the generated embeddings. To validate the effectiveness of our framework for measuring patient similarity, we select the detection of Atrial Fibrillation (AF) through photoplethysmography (PPG) signals obtained from smartwatch devices as our case study. We present extensive experimentation of our framework on a dataset of over 170 individuals and compare the performance of our framework with other baseline methods on this dataset.

Clinical decision support has gained significant attention in recent years, especially with the advancement of data analytics techniques. One active research area in this domain is survival prediction or deterioration prediction of critical care patients, such as intensive care unit (ICU) patients. Usually, ICUs are equipped with continuous monitoring devices, which monitor vital signs such as heart rate, blood pressure, Oxygen saturation and so on. In addition to this, ICU patients also undergo different pathological (i.e., lab) tests. Recent studies claim that vital signs can be used to predict the near future status of a patient, with the help of predictive analytics. However, in this work, we investigate the usefulness of lab test results in patient survival prediction, which have been rarely used for this purpose. We propose a framework for utilizing the lab test data for this clinical prediction task. We encounter several challenges associated with this task, including variable-length feature vector, longitudinal features, missing data, class imbalance and high dimensionality. The proposed work addresses most of these challenges under this single framework. In this framework we propose a novel orthogonal clustering technique to reduce data dimensions as well as missing data. We also propose a systematic approach to inject informative background knowledge into the data and increase the prediction performance. The proposed technique has been evaluated on a real ICU patients database, achieving notable success in reducing 66% of the data dimensions without discarding any feature, while improving the weighted average F1-score 5% on average and achieving about 3 times speedup. We believe that the proposed technique will provide a powerful framework in the field of clinical and healthcare data analytics and healthcare decision support.