Health care predictive analytics using artificial intelligence techniques

University of Technology Sydney. Faculty of Engineering and Information Technology.In recent years, advances in Artificial Intelligence (AI) are opening the door for intelligent health care data prediction and decision making. Machine learning, as an increasingly popular approach to AI, has been widely used to learn directly from data, adapt independently, and produce predictive outcomes, which support doctors when encountering complex health care predictive analytics. However, traditional machine learning methods are not always perfectly working in the health field, intrinsically due to little consideration for characteristic problems within health care data. For example, the small sample size problem is common due to complex data collection procedures and privacy concerns. Missing data is also widely encountered since most data are collected as a second-product of patient-care activities instead of following systematic research protocols. The class imbalance is another inevitable problem in the medical data as the normal class always predominates over the disease class. To solve aforementioned issues in health care predictive analytics, this study stands on the principles of machine learning and transfer learning to develop five advanced prediction models. The first model is an output-based transfer least squares support vector machines (LS-SVMs) model which can leverage knowledge from the existing prediction model or on-line tool to facilitate the learning process on the current domain of interest with insufficient data. This model overcomes the small sample size problem and improves the health care data prediction by learning knowledge from the other domain. The second model is a novel additive LS-SVMs model which can make predictions simultaneously considering the influences on the classification error caused by missing features in a dataset. This model can generate valuable explanations regarding the influence levels of missing features for health professionals to improve the future data collection process. The third model is a transfer-based additive LS-SVMs model which can deal with missing data from a transfer learning perspective. It can leverage the model knowledge learned from the complete portion of the dataset to help the learning process on the whole dataset with missing data. The proposed model can provide supplementary information for health professionals to improve the data quality via data cleaning. The forth model is a deep transfer additive LS-SVMs model called DTA-LS-SVMs and its imbalanced version called iDTA-LS-SVMs to enhance the prediction performance on the balanced and imblanced datasets. Inspired by the stacked architecture and transfer learning mechanism, the model stacks multiple additive LS-SVMs based modules layer-by-layer and embeds model transfer between adjacent modules to guarantee their consistency. The fifth model is a deep cross-output transfer LS-SVMs model called DCOT-LS-SVMs and its imbalanced version called IDCOT-LS-SVMs to improve the prediction performance on the balanced and imbalanced datasets. The cross-output transfer is used to transfer the predictive outcome from the previous module to the adjacent higher layer to achieve a better learning. Moreover, modules’ parameters can be randomly assigned in the proposed model which significantly reduces the time for model selection. The proposed models are verified using experiments on the public UCI datasets. Moreover, case studies are conducted to validate and integrate the proposed models with real world applications, including bladder cancer prognosis, prostate cancer diagnosis, and predictions of elderly quality of life (QOL). The results have demonstrated that the models in this study can enhance the prediction performance while taking the characteristic problems within health care data into account, thus exhibiting promising potential for use in different health applications in future

PHYSICS-BASED AND DATA-DRIVEN MODELING OF HYBRID ROBOT MOVEMENT ON SOFT TERRAIN

Navigating an unmapped environment is one of the ten biggest challenges facing the robotics community. Wheeled robots can move fast on flat surfaces but suffer from loss of traction and immobility on soft ground. However, legged machines have superior mobility over wheeled locomotion when they are in motion over flowable ground or a terrain with obstacles but can only move at relatively low speeds on flat surfaces. A question to answer is as follows: If legged and wheeled locomotion are combined, can the resulting hybrid leg-wheel locomotion enable fast movement in any terrain condition? To investigate the rich physics during dynamic interactions between a robot and a granular terrain, a physics-based computational framework based on the smoothed particle hydrodynamics (SPH) method has been developed and validated by using experimental results for single robot appendage interaction with the granular system. This framework has been extended and coupled with a multi-body simulator to model different robot configurations. Encouraging agreement is found amongst the numerical, theoretical, and experimental results, for a wide range of robot leg configurations, such as curvature and shape. Real-time navigation in a challenging terrain requires fast prediction of the dynamic response of the robot, which is useful for terrain identification and robot gait adaption. Therefore, a data-driven modeling framework has also been developed for the fast estimation of the slippage and sinkage of robots. The data-driven model leverages the high-quality data generated from the offline physics-based simulation for the training of a deep neural network constructed from long short-term memory (LSTM) cells. The results are expected to form a good basis for online robot navigation and exploration in unknown and complex terrains

Research on land cover classification of multi-source remote sensing data based on improved U-net network

Abstract In recent years, remote sensing images of various types have found widespread applications in resource exploration, environmental protection, and land cover classification. However, relying solely on a single optical or synthetic aperture radar (SAR) image as the data source for land cover classification studies may not suffice to achieve the desired accuracy in ground information monitoring. One widely employed neural network for remote sensing image land cover classification is the U-Net network, which is a classical semantic segmentation network. Nonetheless, the U-Net network has limitations such as poor classification accuracy, misclassification and omission of small-area terrains, and a large number of network parameters. To address these challenges, this research paper proposes an improved approach that combines both optical and SAR images in bands for land cover classification and enhances the U-Net network. The approach incorporates several modifications to the network architecture. Firstly, the encoder-decoder framework serves as the backbone terrain-extraction network. Additionally, a convolutional block attention mechanism is introduced in the terrain extraction stage. Instead of pooling layers, convolutions with a step size of 2 are utilized, and the Leaky ReLU function is employed as the network's activation function. This design offers several advantages: it enhances the network's ability to capture terrain characteristics from both spatial and channel dimensions, resolves the loss of terrain map information while reducing network parameters, and ensures non-zero gradients during the training process. The effectiveness of the proposed method is evaluated through land cover classification experiments conducted on optical, SAR, and combined optical and SAR datasets. The results demonstrate that our method achieves classification accuracies of 0.8905, 0.8609, and 0.908 on the three datasets, respectively, with corresponding mIoU values of 0.8104, 0.7804, and 0.8667. Compared to the traditional U-Net network, our method exhibits improvements in both classification accuracy and mIoU to a certain extent