Lung cancer survival period prediction and understanding: Deep learning approaches.

INTRODUCTION: Survival period prediction through early diagnosis of cancer has many benefits. It allows both patients and caregivers to plan resources, time and intensity of care to provide the best possible treatment path for the patients. In this paper, by focusing on lung cancer patients, we build several survival prediction models using deep learning techniques to tackle both cancer survival classification and regression problems. We also conduct feature importance analysis to understand how lung cancer patients' relevant factors impact their survival periods. We contribute to identifying an approach to estimate survivability that are commonly and practically appropriate for medical use. METHODOLOGIES: We have compared the performance across three of the most popular deep learning architectures - Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) while comparing the performing of deep learning models against traditional machine learning models. The data was obtained from the lung cancer section of Surveillance, Epidemiology, and End Results (SEER) cancer registry. RESULTS: The deep learning models outperformed traditional machine learning models across both classification and regression approaches. We obtained a best of 71.18 % accuracy for the classification approach when patients' survival periods are segmented into classes of '<=6 months',' 0.5 - 2 years' and '>2 years' and Root Mean Squared Error (RMSE) of 13.5 % andR2 value of 0.5 for the regression approach for the deep learning models while the traditional machine learning models saturated at 61.12 % classification accuracy and 14.87 % RMSE in regression. CONCLUSIONS: This approach can be a baseline for early prediction with predictions that can be further improved with more temporal treatment information collected from treated patients. In addition, we evaluated the feature importance to investigate the model interpretability, gaining further insight into the survival analysis models and the factors that are important in cancer survival period prediction.

Time-to-event prediction using survival analysis methods for Alzheimer's disease progression.

INTRODUCTION: Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous-time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time. METHODOLOGIES: We simulate the disease progression based on patient profiles using non-linear survival methods-non-linear Cox proportional hazard model (Cox-PH) and neural multi-task logistic regression (N-MTLR). In addition, we evaluate the concordance index (C-index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple-visit details between 2005 and 2017. RESULTS: The experiment results show that our N-MTLR based survival models outperform the CoxPH models, the best of which gives Concordance-Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval. CONCLUSIONS: The proposed deep learning-based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.

Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning.

Purpose: To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis that reveals its essential pathological features via fundus photographs. Methods: We developed a deep learning construct to identify uveitis using reference mouse fundus images and further categorized the severity levels of disease into mild and severe EAU. We evaluated the performance of the model using the area under the receiver operating characteristic curve (AUC) and confusion matrices. We further assessed the clinical relevance of the model by visualizing the principal components of features at different layers and through the use of gradient-weighted class activation maps, which presented retinal regions having the most significant influence on the model. Results: Our model was trained, validated, and tested on 1500 fundus images (training, 1200; validation, 150; testing, 150) and achieved an average AUC of 0.98 for identifying the normal, trace (small and local lesions), and disease classes (large and spreading lesions). The AUCs of the model using an independent subset with 180 images were 1.00 (95% confidence interval [CI], 0.99-1.00), 0.97 (95% CI, 0.94-0.99), and 0.96 (95% CI, 0.90-1.00) for the normal, trace and disease classes, respectively. Conclusions: The proposed deep learning model is able to identify three severity levels of EAU with high accuracy. The model also achieved high accuracy on independent validation subsets, reflecting a substantial degree of generalizability. Translational Relevance: The proposed model represents an important new tool for use in animal medical research and provides a step toward clinical uveitis identification in clinical practice.

Identity Management Systems for the Internet of Things: A Survey Towards Blockchain Solutions.

The Internet of Things aims at connecting everything, ranging from individuals, organizations, and companies to things in the physical and virtual world. The digital identity has always been considered as the keystone for all online services and the foundation for building security mechanisms such as authentication and authorization. However, the current literature still lacks a comprehensive study on the digital identity management for the Internet of Things (IoT). In this paper, we firstly identify the requirements of building identity management systems for IoT, which comprises scalability, interoperability, mobility, security and privacy. Then, we trace the identity problem back to the origin in philosophy, analyze the Internet digital identity management solutions in the context of IoT and investigate recent surging blockchain sovereign identity solutions. Finally, we point out the promising future research trends in building IoT identity management systems and elaborate challenges of building a complete identity management system for the IoT, including access control, privacy preserving, trust and performance respectively.

Evaluation of a real-time low-power cardiorespiratory sensor for the IoT.

A wide variety of sensors have been developed in the biomedical engineering community for telemedicine and personalized healthcare applications. However, they usually focus on sensor connectivity and embedded signal processing, at the expense of the sensing part. This observation lead to the development and exhaustive evaluation of a new ECGbased cardiorespiratory IoT sensor. In order to improve the robustness of our IoT-based sensor, we discuss in detail the influence of electrodes placement and nature. Performance assessment of our sensor resulted in a best-case sensitivity of 99.95% and a precision of 99.89% for an abdominal positioning of wet electrodes, while a sensitivity of 99.47% and a precision of 99.31% were observed using a commercialgrade dry electrodes belt. Consequently, we prove that our sensor is fit for the comfortable medical-grade monitoring of the cardiorespiratory activity in order to provide insights of patients health in a telemedicine context.

Analysis of the pen pressure and grip force signal during basic drawing tasks: The timing and speed changes impact drawing characteristics.

Writing is a complex fine and trained motor skill, involving complex biomechanical and cognitive processes. In this paper, we propose the study of writing kinetics using three angles: the pen-tip normal force, the total grip force signal and eventually writing quality assessment. In order to collect writing kinetics data, we designed a sensor collecting these characteristics simultaneously. Ten healthy right-handed adults were recruited and were asked to perform four tasks: first, they were instructed to draw circles at a speed they considered comfortable; they then were instructed to draw circles at a speed they regarded as fast; afterwards, they repeated the comfortable task compelled to follow the rhythm of a metronome; and eventually they performed the fast task under the same timing constraints. Statistical differences between the tasks were computed, and while pen-tip normal force and total grip force signal were not impacted by the changes introduced in each task, writing quality features were affected by both the speed changes and timing constraint changes. This verifies the already-studied speed-accuracy trade-off and suggest the existence of a timing constraints-accuracy trade-off.