Predicting kidney transplant survival using multiple feature representations for HLAs.

Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose 4 new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.

A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation.

Kidney transplantation is the preferred treatment for people suffering from end-stage renal disease. Successful kidney transplants still fail over time, known as graft failure; however, the time to grant failure, or graft survival time, can vary significantly between different recipients. A significant biological factor affecting graft survival times is the compatibility between the human leukocyte antigens (HLAs) of the donor and recipient. We propose to model HLA compatibility using a network, where the nodes denote different HLAs of the donor and recipient, and edge weights denote compatibilities of the HLAs, which can be positive or negative. The network is indirectly observed, as the edge weights are estimated from transplant outcomes rather than directly observed. We propose a latent space model for such indirectly-observed weighted and signed networks. We demonstrate that our latent space model can not only result in more accurate estimates of HLA compatibilities, but can also be incorporated into survival analysis models to improve accuracy for the downstream task of predicting graft survival times.

Empirical Comparison of Continuous and Discrete-time Representations for Survival Prediction.

Survival prediction aims to predict the time of occurrence of a particular event of interest, such as the time until a patient dies. The main challenge in survival prediction is the presence of incomplete observations due to censoring. The classical formulation for survival prediction treats the survival time as a continuous outcome, which leads to a censored regression problem. Recent work has reformulated the survival prediction problem by discretizing time into a finite number of bins and then applying multi-task binary classification. While the discrete-time formulation is convenient and potentially requires less assumptions than the continuous-time approach, it also loses information by discretizing time. In this paper, we empirically investigate continuous and discrete-time representations for survival prediction to try to quantify the trade-offs between the two formulations. We find that discretizing time does not necessarily decrease prediction accuracy. Furthermore, discrete-time models can result in even more accurate predictors than continuous-time models, but the number of time bins used for discretization has a significant effect on accuracy and should thus be tuned as a hyperparameter rather than specified for convenience.

Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs.

Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.

Multicriteria Similarity-Based Anomaly Detection Using Pareto Depth Analysis.

We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based anomaly detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) anomaly detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets.

Detecting divisions of the autonomic nervous system using wearables.

The ability to assess a user's emotional reaction from biometrics has applications in personalization, recommendation, and enhancing user experiences, among other areas. Unfortunately, understanding the connection between biometric signals and user reactions has previously focused on black box techniques that are opaque to the underlying physiology of the user. In this paper, we explore a novel user study connecting biometric reaction to external stimuli and changes in the user's autonomic nervous system. Specifically, we focus on two competing responses, namely the sympathetic and parasympathetic nervous system, and how differing activations are related to different user responses. Our experiments demonstrate how prior psychophysiological research distinguishing this activation can be replicated using biometric data collected from wearables. The insights from this work have applications in better understanding emotional state from biometric sensors.