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Prognostic great need of interleukin-17 inside solid tumors: a new meta-analysis.
We also made a comparison study of using lesion region images and full OCT images on this task. Experiments shows that using the full OCT images can obtain better performance. Different deep neural networks such as AlexNet, VGG-16, GooLeNet and ResNet-50 were compared, and the modified ResNet-50 is more suitable for predicting the effectiveness of anti-VEGF.Clinical Relevance - This prediction model can give an estimation of whether anti-VEGF is effective for patients with CNV or CME, which can help ophthalmologists make treatment plan.An Anterior Cruciate Ligament (ACL) injury can cause a serious burden, especially for athletes participating in relatively risky sports. This raises a growing incentive for designing injury-prevention programs. For this purpose, the analysis of the drop jump landing test, for example, can provide a useful asset for recognizing those who are more likely to sustain knee injuries. Knee flexion angle plays a key role within these test scenarios. Multiple research efforts have been conducted on engaging existing technologies such as the Microsoft Kinect sensor and Motion Capture (MoCap) to investigate the connection between the lower limb angle ranges during jump tests and the injury risk associated with them. Even though these technologies provide sufficient capabilities to researchers and clinicians, they need certain levels of knowledge to enable them to utilize these facilities. Moreover, these systems demand special requirements and setup procedures which make them limiting. Due to recent advances in the area of Deep Learning, numerous powerful 3D pose estimation algorithms have been developed over the last few years. Having access to relatively reliable and accurate 3D body keypoint information can lead to successful detection and prevention of injury. The idea of combining temporal convolutions in video sequences with deep Convolutional Neural Networks (CNNs) offer a substantial opportunity to tackle the challenging task of accurate 3D human pose estimation. Using the Microsoft Kinect sensor as our ground truth, we analyze the performance of CNN-based 3D human pose estimation in everyday settings. The qualitative and quantitative results are convincing enough to give an incentive to pursue further improvements, especially in the task of lower extremity kinematics estimation. In addition to the performance comparison between Kinect and CNN, we have also verified the high-margin of consistency between two Kinect sensors.Effective pain management can significantly improve quality of life and outcomes for various types of patients (e.g. elderly, adult, young) and often requires assisted living for a significant number of people worldwide. In order to improve our understanding of patients' response to pain and needs for assisted living we need to develop adequate data processing techniques that would enable us to understand underlying interdependencies. To this purpose in this paper we develop several different algorithms that can predict the need for medically assisted living outcomes using a large database obtained as a part of the national health survey. Selleck GSK2578215A As a part of the survey the respondents provided detailed information about general health care state, acute and chronic problems as well as personal perception of pain associated with performing two simple talks walking on the flat surface and walking upstairs. We model the correspondent responses using multinomial random variables and propose structured deep learning models based on maximum likelihood estimation and machine learning for information fusion. For comparison purposes we also implement fully connected deep learning network and use its results as benchmark measurements. We evaluate the performance of the proposed techniques using the national survey data and split them into two parts used for training and testing. Our preliminary results indicate that the proposed models can potentially be useful in forecasting the need for medically assisted living.Epileptic Seizure (Epilepsy) is a neurological disorder that occurs due to abnormal brain activities. Epilepsy affects patients' health and lead to life-threatening situations. Early prediction of epilepsy is highly effective to avoid seizures. Machine Learning algorithms have been used to classify epilepsy from Electroencephalograms (EEG) data. These algorithms exhibited reduced performance when classes are imbalanced. This work presents an integrated machine learning approach for epilepsy detection, which can effectively learn from imbalanced data. This approach utilizes Principal Component Analysis (PCA) at the first stage to extract both high- and low- variant Principal Components (PCs), which are empirically customized for imbalanced data classification. Conventionally, PCA is used for dimension reduction of a dataset leveraging PCs with high variances. In this paper, we propose a model to show that PCs associated with low variances can capture the implicit pattern of minor class of a dataset. The selected PCs are then fed into different machine learning classifiers to predict seizures. We performed experiments on the Epileptic Seizure Recognition dataset to evaluate our model. The experimental results show the robustness and effectiveness of the proposed model.Freezing of Gait is the most disabling gait disturbance in Parkinson's disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson's disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.More than one million people currently live with Parkinson's Disease (PD) in the U.S. alone. Medications, such as levodopa, can help manage PD symptoms. However, medication treatment planning is generally based on patient history and limited interaction between physicians and patients during office visits. This limits the extent of benefit that may be derived from the treatment as disease/patient characteristics are generally non-stationary. Wearable sensors that provide continuous monitoring of various symptoms, such as bradykinesia and dyskinesia, can enhance symptom management. However, using such data to overhaul the current static medication treatment planning approach and prescribe personalized medication timing and dosage that accounts for patient/care-giver/physician feedback/preferences remains an open question. We develop a model to prescribe timing and dosage of medications, given the motor fluctuation data collected using wearable sensors in real-time. We solve the resulting model using deep reinforcement learning (DRL).
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