Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity research is Electronic Health Records(EHRs), which are usually heterogeneous, longitudinal, and sparse. Though existing studies on learning patient similarity from EHRs have shown being useful in solving real clinical problems, their applicability is limited due to the lack of medical interpretations. Moreover, most previous methods assume a vector-based representation for patients, which typically requires aggregation of medical events over a certain time period. As a consequence, temporal information will be lost. In this paper, we propose a patient similarity evaluation framework based on the temporal matching of longitudinal patient EHRs. Two efficient methods are presented, unsupervised and supervised, both of which preserve the temporal properties in EHRs. The supervised scheme takes a convolutional neural network architecture and learns an optimal representation of patient clinical records with medical concept embedding. The empirical results on real-world clinical data demonstrate substantial improvement over the baselines. We make our code and sample data available for further study.
Large collections of electronic clinical records today provide us with a vast source of information on medical practice. However, the utilization of those data for exploratory analysis to support clinical decisions is still limited. Extracting useful patterns from such data is particularly challenging because it is longitudinal, sparse and heterogeneous. In this paper, we propose a Nonnegative Matrix Factorization (NMF) based framework using a convolutional approach for open-ended temporal pattern discovery over large collections of clinical records. We call the method One-Sided Convolutional NMF (OSC-NMF). Our framework can mine common as well as individual shift-invariant temporal patterns from heterogeneous events over different patient groups, and handle sparsity as well as scalability problems well. Furthermore, we use an event matrix based representation that can encode quantitatively all key temporal concepts including order, concurrency and synchronicity. We derive efficient multiplicative update rules for OSC-NMF, and also prove theoretically its convergence. Finally, the experimental results on both synthetic and real world electronic patient data are presented to demonstrate the effectiveness of the proposed method. Personalised modelling aims to create a unique computational diagnostic or prognostic model for an individual. The paper reports a new Integrated Method for Personalised Modelling (IMPM) that applies global optimisation of variables (features) and neighbourhood of appropriate data samples to create an accurate personalised model for an individual. The proposed IMPM allows for adaptation, monitoring and improvement of an individual's model. Three medical decision support problems are used as illustrations: cancer diagnosis and profiling; risk of disease evaluation based on whole genome SNPs data; chronic disease decision support. The method leads to improved accuracy and unique personalised profiling that could be used for personalised treatment and personalised drug design. Prognosis refers to the prediction of the future health status of a patient. Providing prognostic insight to clinicians is critical for physician decision support. In this paper we present a collaborative disease prognosis strategy leveraging the information of the clinically similar patient cohort, using a Local Spline Regression (LSR) based similarity measure. To improve the reliability of the approach, the algorithm can also incorporate physician's feedback in the form of whether the patients in a retrieved cohort are indeed similar to the query patient. The proposed methodology was tested on a real clinical data set containing records of over two hundred thousand patients over three years. We report the retrieval as well as prognosis performance to demonstrate the effectiveness of the system. Identifying historical records of patients who are similar to the new patient could help to retrieve similar reference cases for predicting the clinical outcome of the new patient. Amongst different potential applications, this study illustrates use of patient similarity in predicting survival of patients suffering from hepatocellular carcinoma (HCC) treated with locoregional chemotherapy. This study used 14 similarity measures derived from relevant clinical and imaging parameters to classify the HCC patient pairs into two classes, namely the difference between their survival time being longer or no longer than 12 months. Furthermore, this paper proposes and presents a patient similarity algorithm for the classification, named SimSVM. With the 14 similarity measures as input, SimSVM outputs the predicted class and the degree of similarity or dissimilarity. A dataset was collected from 30 patients, forming 300 and 135 patient pairs as training and test datasets respectively. The trained SimSVM with linear kernel gave the best accuracy (66.7 ), sensitivity (64.8 ) and specificity (67.9 ) on the test dataset.  The widespread availability of electronic health records (EHRs) promises to usher in the era of personalized medicine. However, the problem of extracting useful clinical representations from longitudinal EHR data remains challenging. In this paper, we explore deep neural network models with learned medical feature embedding to deal with the problems of high dimensionality and temporality. Specifically, we use a multi-layer convolutional neural network (CNN) to parameterize the model and is thus able to capture complex non-linear longitudinal evolution of EHRs. Our model can effectively capture local short temporal dependency in EHRs, which is beneficial for risk prediction. To account for high dimensionality, we use the embedding medical features in the CNN model which hold the natural medical concepts. Our initial experiments produce promising results and demonstrate the effectiveness of both the medical feature embedding and the proposed convolutional neural network in risk prediction on cohorts of congestive heart failure and diabetes patients compared with several strong baselines. 