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General General

TargetDB: A target information aggregation tool and tractability predictor.

In PloS one ; h5-index 176.0

When trying to identify new potential therapeutic protein targets, access to data and knowledge is increasingly important. In a field where new resources and data sources become available every day, it is crucial to be able to take a step back and look at the wider picture in order to identify potential drug targets. While this task is routinely performed by bespoke literature searches, it is often time-consuming and lacks uniformity when comparing multiple targets at one time. To address this challenge, we developed TargetDB, a tool that aggregates public information available on given target(s) (links to disease, safety, 3D structures, ligandability, novelty, etc.) and assembles it in an easy to read output ready for the researcher to analyze. In addition, we developed a target scoring system based on the desirable attributes of good therapeutic targets and machine learning classification system to categorize novel targets as having promising or challenging tractrability. In this manuscript, we present the methodology used to develop TargetDB as well as test cases.

De Cesco Stephane, Davis John B, Brennan Paul E


General General

Effects of pain, sedation and delirium monitoring on clinical and economic outcome: A retrospective study.

In PloS one ; h5-index 176.0

BACKGROUND : Significant improvements in clinical outcome can be achieved by implementing effective strategies to optimise pain management, reduce sedative exposure, and prevent and treat delirium in ICU patients. One important strategy is the monitoring of pain, agitation and delirium (PAD bundle). We hypothesised that there is no sufficient financial benefit to implement a monitoring strategy in a Diagnosis Related Group (DRG)-based reimbursement system, therefore we expected better clinical and decreased economic outcome for monitored patients.

METHODS : This is a retrospective observational study using routinely collected data. We used univariate and multiple linear analysis, machine-learning analysis and a novel correlation statistic (maximal information coefficient) to explore the association between monitoring adherence and resulting clinical and economic outcome. For univariate analysis we split patients in an adherence achieved and an adherence non-achieved group.

RESULTS : In total 1,323 adult patients from two campuses of a German tertiary medical centre, who spent at least one day in the ICU between admission and discharge between 1. January 2016 and 31. December 2016. Adherence to PAD monitoring was associated with shorter hospital LoS (e.g. pain monitoring 13 vs. 10 days; p<0.001), ICU LoS, duration of mechanical ventilation shown by univariate analysis. Despite the improved clinical outcome, adherence to PAD elements was associated with a decreased case mix per day and profit per day shown by univariate analysis. Multiple linear analysis did not confirm these results. PAD monitoring is important for clinical as well as economic outcome and predicted case mix better than severity of illness shown by machine learning analysis.

CONCLUSION : Adherence to PAD bundles is also important for clinical as well as economic outcome. It is associated with improved clinical and worse economic outcome in comparison to non-adherence in univariate analysis but not confirmed by multiple linear analysis.

TRIAL REGISTRATION : NCT02265263, Registered 15 October 2014.

Deffland Marc, Spies Claudia, Weiss Bjoern, Keller Niklas, Jenny Mirjam, Kruppa Jochen, Balzer Felix


General General

Real-World Implications of Rapidly Responsive COVID-19 Spread Model with Time Dependent Parameters Via Deep Learning: Algorithm Development and Validation.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has been a major shock to the whole world since March 2020. From the experience of the 1918 influenza pandemic, we know that decreases in infection rates of COVID-19 do not guarantee continuity of the trend.

OBJECTIVE : This study was conducted to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning responding promptly to the dynamic situation of the outbreak to proactively minimize damage.

METHODS : In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from Korea Centers for Disease Control & Prevention (KCDC) and Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Since the data consist of confirmed, recovered, and deceased cases, we selected the SIR (Susceptible - Infected - Recovered) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters, and designed suitable loss functions.

RESULTS : We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta of order 4 (RK4) model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from KCDC, the Korean government, and news media. We also cross-validated our model by using data from CSSE for Italy, Sweden, and US.

CONCLUSIONS : The methodology and new model of this study could be employed for short term prediction of COVID-19, by which the government can be prepared for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.


Jung Se Young, Jo Hyeontae, Son Hwijae, Hwang Hyung Ju


General General

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data.

In IEEE journal of biomedical and health informatics

Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS.

Schwab Patrick, Karlen Walter


General General

Demystifying Deep Learning in Predictive Spatiotemporal Analytics: An Information-Theoretic Framework.

In IEEE transactions on neural networks and learning systems

Deep learning has achieved incredible success over the past years, especially in various challenging predictive spatiotemporal analytics (PSTA) tasks, such as disease prediction, climate forecast, and traffic prediction, where intrinsic dependence relationships among data exist and generally manifest at multiple spatiotemporal scales. However, given a specific PSTA task and the corresponding data set, how to appropriately determine the desired configuration of a deep learning model, theoretically analyze the model's learning behavior, and quantitatively characterize the model's learning capacity remains a mystery. In order to demystify the power of deep learning for PSTA in a theoretically sound and explainable way, in this article, we provide a comprehensive framework for deep learning model design and information-theoretic analysis. First, we develop and demonstrate a novel interactively and integratively connected deep recurrent neural network (I²DRNN) model. I²DRNN consists of three modules: an input module that integrates data from heterogeneous sources; a hidden module that captures the information at different scales while allowing the information to flow interactively between layers; and an output module that models the integrative effects of information from various hidden layers to generate the output predictions. Second, to theoretically prove that our designed model can learn multiscale spatiotemporal dependence in PSTA tasks, we provide an information-theoretic analysis to examine the information-based learning capacity (i-CAP) of the proposed model. In so doing, we can tackle an important open question in deep learning, that is, how to determine the necessary and sufficient configurations of a designed deep learning model with respect to the given learning data sets. Third, to validate the I²DRNN model and confirm its i-CAP, we systematically conduct a series of experiments involving both synthetic data sets and real-world PSTA tasks. The experimental results show that the I²DRNN model outperforms both classical and state-of-the-art models on all data sets and PSTA tasks. More importantly, as readily validated, the proposed model captures the multiscale spatiotemporal dependence, which is meaningful in the real-world context. Furthermore, the model configuration that corresponds to the best performance on a given data set always falls into the range between the necessary and sufficient configurations, as derived from the information-theoretic analysis.

Tan Qi, Liu Yang, Liu Jiming


General General

A Novel Multiresolution-Statistical Texture Analysis Architecture: Radiomics-Aided Diagnosis of PDAC Based on Plain CT Images.

In IEEE transactions on medical imaging ; h5-index 74.0

Early screening of PDAC (pancreatic ductal adenocarcinoma) based on plain CT (computed tomogra-phy) images is of great significance. Therefore, this work conducted a radiomics-aided diagnosis analysis of PDAC based on plain CT images. We explored a novel MSTA (multiresolution-statistical texture analysis) architecture to extract texture features and built machine learning models to classify PDACs and HPs (healthy pancreases). We also performed significance tests of differences to analyze the relationships between histopathological characteristics and texture features. The MSTA architecture originates from the analysis of histopathological characteristics and combines multiresolution analysis and statistical analysis to extract texture features. The MSTA architecture achieved better experimental results than the traditional architecture that scales the coefficient matrices of the multiresolution analysis. In the validation of the classifica-tions, the MSTA architecture achieved an accuracy of 81.19% and an AUC (area under the ROC (receiver operating characteristic) curve) of 0.88 (95% confidence interval: 0.84-0.92). In the test of the classifications, it achieved an accuracy of 77.66% and an AUC of 0.79 (95% confidence interval: 0.71-0.87). Moreover, the significance tests of differences showed that the extracted texture features may be relevant to the histopathological characteristics. The MSTA architecture is beneficial for the radiomics-aided diagnosis of PDAC based on plain CT images. Its texture features can potentially enhance radiologists' imaging interpretation abilities.

Qiu Jia-Jun, Yin Jin, Qian Wei, Liu Jin-Heng, Huang Zi-Xing, Yu Hao-Peng, Ji Lin, Zeng Xiao-Xi