Robust training of recurrent neural networks to handle missing data for disease progression modeling

Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect temporal dependencies among measurements and make parametric assumptions about biomarker trajectories. In addition, they do not model multiple biomarkers jointly and need to align subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values. We, therefore, propose a generalized training rule for the most widely used RNN architecture, long short-term memory (LSTM) networks, that can handle missing values in both target and predictor variables. This algorithm is applied for modeling the progression of Alzheimer's disease (AD) using magnetic resonance imaging (MRI) biomarkers. The results show that the proposed LSTM algorithm achieves a lower mean absolute error for prediction of measurements across all considered MRI biomarkers compared to using standard LSTM networks with data imputation or using a regression-based DPM method. Moreover, applying linear discriminant analysis to the biomarkers' values predicted by the proposed algorithm results in a larger area under the receiver operating characteristic curve (AUC) for clinical diagnosis of AD compared to the same alternatives, and the AUC is comparable to state-of-the-art AUCs from a recent cross-sectional medical image classification challenge. This paper shows that built-in handling of missing values in LSTM network training paves the way for application of RNNs in disease progression modeling.Comment: 9 pages, 1 figure, MIDL conferenc

Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values. Instead, we propose a generalized training rule for the most widely used RNN architecture, long short-term memory (LSTM) networks, that can handle both missing predictor and target values. The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i.e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method. The results show that the proposed algorithm achieves a significantly lower mean absolute error (MAE) than the alternatives with p < 0.05 using Wilcoxon signed rank test in predicting values of almost all of the MRI biomarkers. Moreover, a linear discriminant analysis (LDA) classifier applied to the predicted biomarker values produces a significantly larger AUC of 0.90 vs. at most 0.84 with p < 0.001 using McNemar's test for clinical diagnosis of AD. Inspection of MAE curves as a function of the amount of missing data reveals that the proposed LSTM algorithm achieves the best performance up until more than 74% missing values. Finally, it is illustrated how the method can successfully be applied to data with varying time intervals.Comment: arXiv admin note: substantial text overlap with arXiv:1808.0550

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples

Panel studies typically suffer from attrition, which reduces sample size and can result in biased inferences. It is impossible to know whether or not the attrition causes bias from the observed panel data alone. Refreshment samples - new, randomly sampled respondents given the questionnaire at the same time as a subsequent wave of the panel - offer information that can be used to diagnose and adjust for bias due to attrition. We review and bolster the case for the use of refreshment samples in panel studies. We include examples of both a fully Bayesian approach for analyzing the concatenated panel and refreshment data, and a multiple imputation approach for analyzing only the original panel. For the latter, we document a positive bias in the usual multiple imputation variance estimator. We present models appropriate for three waves and two refreshment samples, including nonterminal attrition. We illustrate the three-wave analysis using the 2007-2008 Associated Press-Yahoo! News Election Poll.Comment: Published in at http://dx.doi.org/10.1214/13-STS414 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Effectiveness and cost-effectiveness of a novel, group self-management course for adults with chronic musculoskeletal pain: study protocol for a multicentre, randomised controlled trial (COPERS)

Introduction: Chronic musculoskeletal pain is a common condition that often responds poorly to treatment. Self-management courses have been advocated as a non-drug pain management technique, although evidence for their effectiveness is equivocal. We designed and piloted a self-management course based on evidence for effectiveness for specific course components and characteristics. Methods/analysis: COPERS (coping with persistent pain, effectiveness research into self-management) is a pragmatic randomised controlled trial testing the effectiveness and cost-effectiveness of an intensive, group, cognitive behavioural-based, theoretically informed and manualised self-management course for chronic pain patients against a control of best usual care: a pain education booklet and a relaxation CD. The course lasts for 15 h, spread over 3 days, with a –2 h follow-up session 2 weeks later. We aim to recruit 685 participants with chronic musculoskeletal pain from primary, intermediate and secondary care services in two UK regions. The study is powered to show a standardised mean difference of 0.3 in the primary outcome, pain-related disability. Secondary outcomes include generic health-related quality of life, healthcare utilisation, pain self-efficacy, coping, depression, anxiety and social engagement. Outcomes are measured at 6 and 12 months postrandomisation. Pain self-efficacy is measured at 3 months to assess whether change mediates clinical effect. Ethics/dissemination: Ethics approval was given by Cambridgeshire Ethics 11/EE/046. This trial will provide robust data on the effectiveness and cost-effectiveness of an evidence-based, group self-management programme for chronic musculoskeletal pain. The published outcomes will help to inform future policy and practice around such self-management courses, both nationally and internationally. Trial registration: ISRCTN24426731

Association of prior depressive symptoms and suicide attempts with subsequent victimisation - analysis of population-based data from the Adult Psychiatric Morbidity Survey

Background: Symptoms of mental disorder, particularly schizophrenia, predispose to victimisation. Much less is known about the relationship between depressive symptoms and later victimisation in the general population, the influence of these symptoms on types of subsequent victimisation, or the role of symptom severity. We investigated this in nationally representative data from the UK. Methods: Data were from the Adult Psychiatric Morbidity Survey 2007. Multivariable logistic regressions estimated association between: a. prior depressive symptoms, and b. prior depressive symptoms with suicide attempt, and types of more recent victimisation. Gender-specific associations were estimated using multiplicative interactions. Results: Prior depressive symptoms were associated with greater odds of any recent intimate partner violence (IPV), emotional IPV, sexual victimisation, workplace victimisation, any victimisation, and cumulative victimisation (adjusted odds ratio (aOR) for increasing types of recent victimisation: 1.47, 95% confidence interval (CI): 1.14, 1.89). Prior depressive symptoms with suicide attempt were associated with any recent IPV, emotional IPV, any victimisation, and cumulative victimisation (aOR for increasing types of recent victimisation: 2.33, 95%: 1.22, 4.44). Limitations: Self-reported recalled data on previous depressive symptoms, may have limited accuracy. Small numbers of outcomes for some comparisons resulted in imprecision of these estimates. Conclusion: Aside from severe mental illness such as schizophrenia, previous depressive symptoms in the general population are associated with greater subsequent victimisation. Men and women with prior depressive symptoms may be vulnerable to a range of types of victimisation, and may benefit from interventions to reduce this vulnerability