... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics最新文献

Mechanical thrombectomy (MTB) is one of the two standard treatment options for Acute Ischemic Stroke (AIS) patients. Current clinical guidelines instruct the use of pretreatment imaging to characterize a patient's cerebrovascular flow, as there are many factors that may underlie a patient's successful response to treatment. There is a critical need to leverage pretreatment imaging, taken at admission, to guide potential treatment avenues in an automated fashion. The aim of this study is to develop and validate a fully automated machine learning algorithm to predict the final modified thrombolysis in cerebral infarction (mTICI) score following MTB. A total 321 radiomics features were computed from segmented pretreatment MRI scans for 141 patients. Successful recanalization was defined as mTICI score >= 2c. Different feature selection methods and classification models were examined in this study. Our best performance model achieved 74.42±2.52% AUC, 75.56±4.44% sensitivity, and 76.75±4.55% specificity, showing a good prediction of reperfusion quality using pretreatment MRI. Results suggest that MR images can be informative to predicting patient response to MTB, and further validation with a larger cohort can determine the clinical utility.

Long-term endocrine therapy (e.g. Tamoxifen, aromatase inhibitors) is crucial to prevent breast cancer recurrence, yet rates of adherence to these medications are low. To develop, evaluate, and sustain future interventions, individual-level modeling can be used to understand breast cancer survivors' behavioral mechanisms of medication-taking. This paper presents interdisciplinary research, wherein a model employing randomized neural networks was developed to predict breast cancer survivors' daily medication-taking behavior based on their survey data over three time periods (baseline, 4 months, 8 months). The neural network structure was guided by random utility theory developed in psychology and behavioral economics. Comparative analysis indicates that the proposed model outperforms existing computational models in terms of prediction accuracy under conditions of randomness.

There are multiple study design choices to be selected in order to perform evaluations of predictive patient monitoring algorithms related to the event and true positive alarm definitions (e.g., how far ahead of the event is a true positive alarm). Often, passively collected patient monitoring datasets from clinical environments are available to perform these types of studies, so that the effects of different study design choices can be simulated to evaluate the robustness of an algorithm to those choices. Here, we simulate the effects of varying alarm and event definition criteria on the reported performance of the early warning score to predict hypotensive events. A total of 432 combinations of study design choices were simulated. Area under the receiver-operating characteristic curve varied from greater than 0.8 to less than 0.5 by adjusting alarm and event definition criteria. Traditional metrics for evaluating diagnostic systems were modulated across a wide range by adjusting study design choices for a predictive algorithm using a patient monitoring dataset. This highlights the importance of examining study design choices for new predictive patient monitoring algorithms and presents an approach to simulate different study designs with retrospective patient monitoring data as part of a robustness evaluation.

Missense SNPs are key factors contributing towards many Mendelian disorders and complex diseases. Identifying whether a single amino acid substitution will lead to pathological effects is important for interpreting personal genome and for precision medicine. In this study, we describe a novel method for predicting whether a missense SNP likely brings about pathological effects. Our approach integrates sequence information, biophysical properties, and topological properties of protein structures. In our test dataset consisting of 500 deleterious variants and 500 neutral, our method achieves an accuracy of 0.823. The ROC curve of model has an AUC of 0.910. Our methods outperforms two well known methods, and is comparable with the widely used Polyphen-2 method, while requiring a much smaller amount (approximately 25%) of training data. Our method can be used to aid in distinguishing driver and passenger mutations in cancer and in assessing missense mutations assocaited with rare diseases. It can also be used to identifying mutations in rare disease where only limited patient exome data exsit.

Hearing loss is an increasingly prevalent condition resulting from damage to the inner ear which causes a reduction in speech intelligibility. The societal need for assistive hearing devices has increased exponentially over the past two decades; however, actual human performance with such devices has only seen modest gains relative to advancements in digital signal processing (DSP) technology. A major challenge with clinical hearing technologies is the limited ability to run complex signal processing algorithms requiring high computation power. The CCi-MOBILE platform, developed at UT-Dallas, provides the research community with an open-source, flexible, easy-to-use, software-mediated, powerful computing research interface to conduct a wide variety of listening experiments. The platform supports cochlear implants (CIs) and hearing aids (HAs) independently, as well as bimodal hearing (i.e., a CI in one ear and HA in the contralateral ear). The platform is ideally suited to address hearing research for: both quiet and naturalistic noisy conditions, sound localization, and lateralization. The platform uses commercially available smartphone/tablet devices as portable sound processors and can provide bilateral electric and acoustic stimulation. The hardware components, firmware, and software suite are presented to demonstrate safety to the speech scientist and CI/HA user, highlight user-specificity, and outline various applications of the platform for research.

Current automated heart monitoring tools use supervised learning methods to recognize heart disorders based on ECG signal morphology. We develop a new ECG processing algorithm that enables early prediction of disorders through a novel deviation analysis. The idea is developing a patient-specific ECG baseline and characterizing the deviation of signal morphology towards any of the abnormality classes with specific morphological features. To enable this feature, a novel controlled non-linear transformation is designed to achieve maximal symme- try in the feature space. Our results using benchmark MIT-BIH database show that the proposed method achieves a classification accuracy of 96% and can be used to trigger yellow alarms to warn patients from increased risk of upcoming heart abnormalities (5% to 10% increase with respect to normal conditions). This feature can be used in health monitoring devices to advise patients to take preventive and precaution actions before critical situations.

Approximately 0.5-1% of the global population is afflicted with epilepsy, a neurological disorder characterized by repeated seizures. Sudden Unexpected Death in Epilepsy (SUDEP) is a poorly understood complication that claims the lives of nearly 1-in-1000 epilepsy patients every year. This paper aims to explore diagnosis codes, demographic and payment features on mortality of epilepsy patients. We design a mortality prediction model with diagnosis codes and non-diagnosis features extracted from US commercial insurance claims data. We present classification accuracy of 0.91 and 0.85 by using different feature vectors. After analyzing the aforementioned features in prediction model, we extend the work to causal inference between modified diagnosis codes and selected non-diagnosis features. The uplift test of causal inference using three algorithms indicates that a patient is more likely to survive if upgrading from a low-coverage healthcare plan into a high-coverage plan.