Identifying informative risk factors and predicting bone disease progression via deep belief networks

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new ‘Smart’ healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system.

An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered.

The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently.

A Tour of Unsupervised Deep Learning for Medical Image Analysis

BACKGROUND

Interpretation of medical images for the diagnosis and treatment of complex diseases from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical image analysis. Several reviews on supervised deep learning are published, but hardly any rigorous review on unsupervised deep learning for medical image analysis is available.

OBJECTIVES

The objective of this review is to systematically present various unsupervised deep learning models, tools, and benchmark datasets applied to medical image analysis. Some of the discussed models are autoencoders and its other variants, Restricted Boltzmann machines (RBM), Deep belief networks (DBN), Deep Boltzmann machine (DBM), and Generative adversarial network (GAN). Further, future research opportunities and challenges of unsupervised deep learning techniques for medical image analysis are also discussed.

CONCLUSION

Currently, interpretation of medical images for diagnostic purposes is usually performed by human experts that may be replaced by computer-aided diagnosis due to advancement in machine learning techniques, including deep learning, and the availability of cheap computing infrastructure through cloud computing. Both supervised and unsupervised machine learning approaches are widely applied in medical image analysis, each of them having certain pros and cons. Since human supervisions are not always available or inadequate or biased, therefore, unsupervised learning algorithms give a big hope with lots of advantages for biomedical image analysis.

The Internet of Things: Impact and Implications for Health Care Delivery

The Internet of Things (IoT) is a system of wireless, interrelated, and connected digital devices that can collect, send, and store data over a network without requiring human-to-human or human-to-computer interaction. The IoT promises many benefits to streamlining and enhancing health care delivery to proactively predict health issues and diagnose, treat, and monitor patients both in and out of the hospital. Worldwide, government leaders and decision makers are implementing policies to deliver health care services using technology and more so in response to the novel COVID-19 pandemic. It is now becoming increasingly important to understand how established and emerging IoT technologies can support health systems to deliver safe and effective care. The aim of this viewpoint paper is to provide an overview of the current IoT technology in health care, outline how IoT devices are improving health service delivery, and outline how IoT technology can affect and disrupt global health care in the next decade. The potential of IoT-based health care is expanded upon to theorize how IoT can improve the accessibility of preventative public health services and transition our current secondary and tertiary health care to be a more proactive, continuous, and coordinated system. Finally, this paper will deal with the potential issues that IoT-based health care generates, barriers to market adoption from health care professionals and patients alike, confidence and acceptability, privacy and security, interoperability, standardization and remuneration, data storage, and control and ownership. Corresponding enablers of IoT in current health care will rely on policy support, cybersecurity-focused guidelines, careful strategic planning, and transparent policies within health care organizations. IoT-based health care has great potential to improve the efficiency of the health system and improve population health.

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy

Accurately predicting treatment outcome is crucial for creating personalized treatment plans and follow-up schedules. Electronic health records (EHRs) contain valuable patient-specific information that can be leveraged to improve outcome prediction. We propose a reliable multi-objective ensemble deep learning (MoEDL) method that uses features extracted from EHRs to predict high risk of treatment failure after radiotherapy in patients with lung cancer. The dataset used in this study contains EHRs of 814 patients who had not achieved disease-free status and 193 patients who were disease-free with at least one year follow-up time after lung cancer radiation therapy. The proposed MoEDL consists of three phases: (1) training with dynamic ensemble deep learning; (2) model selection with adaptive multi-objective optimization; and (3) testing with evidential reasoning (ER) fusion. Specifically, in the training phase, we employ deep perceptron networks as base learners to handle various issues with EHR data. The architecture and key hyper-parameters of each base learner are dynamically adjusted to increase the diversity of learners while reducing the time spent tuning hyper-parameters. Furthermore, we integrate the snapshot ensembles (SE) restarting strategy, multi-objective optimization, and ER fusion to improve the prediction robustness and accuracy of individual networks. The SE restarting strategy can yield multiple candidate models at no additional training cost in the training stage. The multi-objective model simultaneously considers sensitivity, specificity, and AUC as objective functions, overcoming the limitations of single-objective-based model selection. For the testing stage, we utilized an analytic ER rule to fuse the output scores from each optimal model to obtain reliable and robust predictive results. Our experimental results demonstrate that MoEDL can perform better than other conventional methods.

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.

Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome

BACKGROUND

Main adverse cardiac events (MACE) are essentially composite endpoints for assessing safety and efficacy of treatment processes of acute coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable for improving the effects of ACS treatments. Most existing tools are specific to predict MACE by mainly using static patient features and neglecting dynamic treatment information during learning.

METHODS

We address this challenge by developing a deep learning-based approach to utilize a large volume of heterogeneous electronic health record (EHR) for predicting MACE after ACS. Specifically, we obtain the deep representation of dynamic treatment features from EHR data, using the bidirectional recurrent neural network. And then, the extracted latent representation of treatment features can be utilized to predict whether a patient occurs MACE in his or her hospitalization.

RESULTS

We validate the effectiveness of our approach on a clinical dataset containing 2930 ACS patient samples with 232 static feature types and 2194 dynamic feature types. The performance of our best model for predicting MACE after ACS remains robust and reaches 0.713 and 0.764 in terms of AUC and Accuracy, respectively, and has over 11.9% (1.2%) and 1.9% (7.5%) performance gain of AUC (Accuracy) in comparison with both logistic regression and a boosted resampling model presented in our previous work, respectively. The results are statistically significant.

CONCLUSIONS

We hypothesize that our proposed model adapted to leverage dynamic treatment information in EHR data appears to boost the performance of MACE prediction for ACS, and can readily meet the demand clinical prediction of other diseases, from a large volume of EHR in an open-ended fashion.

Deep Belief Network for the Enhancement of Ultrasound Images with Pelvic Lesions

It is well known that ultrasound images are cost-efficient and exhibit hassle-free usage. However, very few works have focused on exploiting the ultrasound modality for lesion diagnosis. Moreover, there is no reliable contribution reported in the literature for diagnosing pelvic lesions from the pelvic portion of humans, especially females. While few contributions are found for diagnosis of lesions in the pelvic region, no effort has been made on enhancing the images. Inspired from the neural network (NN), our methodology adopts deep belief NN for enhancing the ultrasound image with pelvic lesions. The higher-order statistical characteristics of image textures, such as entropy and autocorrelation, are considered to enhance the image from its noisy environment. The alignment problem is considered using skewness. The proposed method is compared with the existing NN method to demonstrate its enhancement performance.

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHRs). While primarily designed for archiving patient information and performing administrative healthcare tasks like billing, many researchers have found secondary use of these records for various clinical informatics applications. Over the same period, the machine learning community has seen widespread advances in the field of deep learning. In this review, we survey the current research on applying deep learning to clinical tasks based on EHR data, where we find a variety of deep learning techniques and frameworks being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification. We identify several limitations of current research involving topics such as model interpretability, data heterogeneity, and lack of universal benchmarks. We conclude by summarizing the state of the field and identifying avenues of future deep EHR research.

A hybrid classifier based on nonlinear-PCA and deep belief networks with applications in dysphagia diagnosis

Traditional dysphagia prescreening diagnostic methods require doctors specialists to give patients a total score based on a water swallow test scale. This method is limited by the high dimensionality of the diagnostic elements in the water swallow test scale with heavy workload (Towards each patient, the scale requires the doctors give score for 18 diagnostic elements respectively) as well as the difficulties of extracting and using the diagnostic scale data's non-linear features and hidden expertise information (Even with the scale scores, specific diagnostic conclusions are still given by expert doctors under the expertise). In this paper, a hybrid classifier model based on Nonlinear-Principal Component Analysis (NPCA) and Deep Belief Networks (DBN) is proposed in order to effectively extract the diagnostic scale data's nonlinear features and hidden information and to provide the key scale elements' locating methods towards the diagnostic results (The key scale elements that affect different diagnostic conclusions are given to improve the efficiency and pertinence of diagnosis and reduce the workload of diagnosis). We demonstrate the effectiveness of the proposed method using the frame of 'information entropy theory'. Real dysphagia diagnosis examples from the China-Japanese Friendship Hospital are used to demonstrate applications of the proposed methods. The examples show satisfactory results compared to the traditional classifier.