An intelligent recommender system based on short-term disease risk prediction for patients with chronic diseases in a telehealth environment

Clinical decisions are usually made based on the practitioners' experiences with limited support from data-centric analytic processes from medical databases. This often leads to undesirable biases, human errors and high medical costs affecting the quality of services provided to patients. Recently, the use of intelligent technologies in clinical decision making in the telehealth environment has begun to play a vital role in improving the quality of patients' lives and reducing the costs and workload involved in their daily healthcare. In the telehealth environment, patients suffering from chronic diseases such as heart disease or diabetes have to take various medical tests such as measuring blood pressure, blood sugar and blood oxygen, etc. This practice adversely affects the overall convenience and quality of their everyday living. In this PhD thesis, an effective recommender system is proposed utilizing a set of innovative disease risk prediction algorithms and models for short-term disease risk prediction to provide chronic disease patients with appropriate recommendations regarding the need to take a medical test on the coming day. The input sequence of sliding windows based on the patient's time series data, is analyzed in both the time domain and the frequency domain. The time series medical data obtained for each chronicle disease patient is partitioned into consecutive sliding windows for analysis in both the time and the frequency domains. The available time series data are readily available in time domains which can be used for analysis without any further conversion. For data analysis in the frequency domain, Fast Fourier Transformation (FFT) and Dual-Tree Complex Wavelet Transformation (DTCWT) are applied to convert the data into the frequency domain and extract the frequency information. In the time domain, four innovative predictive algorithms, Basic Heuristic Algorithm (BHA), Regression-Based Algorithm (RBA) and Hybrid Algorithm (HA) as well as a structural graph-based method (SG), are proposed to study the time series data for producing recommendations. While, in the frequency domain, three predictive classifiers, Artificial Neural Network, Least Squares-Support Vector Machine, and Naïve Bayes, are used to produce the recommendations. An ensemble machine learning model is utilized to combine all the used predictive models and algorithms in both the time and frequency domains to produce the final recommendation. Two real-life telehealth datasets collected from chronic disease patients (i.e., heart disease and diabetes patients) are utilized for a comprehensive experimental evaluation in this study. The results show that the proposed system is effective in analysing time series medical data and providing accurate and reliable (very low risk) recommendations to patients suffering from chronic diseases such as heart disease and diabetes. This research work will help provide high-quality evidence-based intelligent decision support to clinical disease patients that significantly reduces workload associated with medical checkups would otherwise have to be conducted every day in a telehealth environment

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition

Pulmonary Image Segmentation and Registration Algorithms: Towards Regional Evaluation of Obstructive Lung Disease

Pulmonary imaging, including pulmonary magnetic resonance imaging (MRI) and computed tomography (CT), provides a way to sensitively and regionally measure spatially heterogeneous lung structural-functional abnormalities. These unique imaging biomarkers offer the potential for better understanding pulmonary disease mechanisms, monitoring disease progression and response to therapy, and developing novel treatments for improved patient care. To generate these regional lung structure-function measurements and enable broad clinical applications of quantitative pulmonary MRI and CT biomarkers, as a first step, accurate, reproducible and rapid lung segmentation and registration methods are required. In this regard, we first developed a 1H MRI lung segmentation algorithm that employs complementary hyperpolarized 3He MRI functional information for improved lung segmentation. The 1H-3He MRI joint segmentation algorithm was formulated as a coupled continuous min-cut model and solved through convex relaxation, for which a dual coupled continuous max-flow model was proposed and a max-flow-based efficient numerical solver was developed. Experimental results on a clinical dataset of 25 chronic obstructive pulmonary disease (COPD) patients ranging in disease severity demonstrated that the algorithm provided rapid lung segmentation with high accuracy, reproducibility and diminished user interaction. We then developed a general 1H MRI left-right lung segmentation approach by exploring the left-to-right lung volume proportion prior. The challenging volume proportion-constrained multi-region segmentation problem was approximated through convex relaxation and equivalently represented by a max-flow model with bounded flow conservation conditions. This gave rise to a multiplier-based high performance numerical implementation based on convex optimization theories. In 20 patients with mild- to-moderate and severe asthma, the approach demonstrated high agreement with manual segmentation, excellent reproducibility and computational efficiency. Finally, we developed a CT-3He MRI deformable registration approach that coupled the complementary CT-1H MRI registration. The joint registration problem was solved by exploring optical-flow techniques, primal-dual analyses and convex optimization theories. In a diverse group of patients with asthma and COPD, the registration approach demonstrated lower target registration error than single registration and provided fast regional lung structure-function measurements that were strongly correlated with a reference method. Collectively, these lung segmentation and registration algorithms demonstrated accuracy, reproducibility and workflow efficiency that all may be clinically-acceptable. All of this is consistent with the need for broad and large-scale clinical applications of pulmonary MRI and CT

Imaging Biomarkers of Pulmonary Structure and Function

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airflow limitations resulting from airway obstruction and/or tissue destruction. The diagnosis and monitoring of these pulmonary diseases is primarily performed using spirometry, specifically the forced expiratory volume in one second (FEV1), which measures global airflow obstruction and provides no regional information of the different underlying disease pathologies. The limitations of spirometry and current therapies for lung disease patients have motivated the development of pulmonary imaging approaches, such as computed tomography (CT) and magnetic resonance imaging (MRI). Inhaled hyperpolarized noble gas MRI, specifically using helium-3 (3He) and xenon-129 (129Xe) gases, provides a way to quantify pulmonary ventilation by visualizing lung regions accessed by gas during a breath-hold, and alternatively, regions that are not accessed - coined “ventilation defects.” Despite the strong foundation and many advantages hyperpolarized 3He MRI has to offer research and patient care, clinical translation has been inhibited in part due to the cost and need for specialized equipment, including multinuclear-MR hardware and polarizers, and personnel. Accordingly, our objective was to develop and evaluate imaging biomarkers of pulmonary structure and function using MRI and CT without the use of exogenous contrast agents or specialized equipment. First, we developed and compared CT parametric response maps (PRM) with 3He MR ventilation images in measuring gas-trapping and emphysema in ex-smokers with and without COPD. We observed that in mild-moderate COPD, 3He MR ventilation abnormalities were related to PRM gas-trapping whereas in severe COPD, ventilation abnormalities correlated with both PRM gas-trapping and PRM emphysema. We then developed and compared pulmonary ventilation abnormalities derived from Fourier decomposition of free-breathing proton (1H) MRI (FDMRI) with 3He MRI in subjects with COPD and bronchiectasis. This work demonstrated that FDMRI and 3He MRI ventilation defects were strongly related in COPD, but not in bronchiectasis subjects. In COPD only, FDMRI ventilation defects were spatially related with 3He MRI ventilation defects and emphysema. Based on the FDMRI biomarkers developed in patients with COPD and bronchiectasis, we then evaluated ventilation heterogeneity in patients with severe asthma, both pre- and post-salbutamol as well as post-methacholine challenge, using FDMRI and 3He MRI. FDMRI free-breathing ventilation abnormalities were correlated with but under-estimated 3He MRI static ventilation defects. Finally, based on the previously developed free-breathing MRI approach, we developed a whole-lung free-breathing pulmonary 1H MRI technique to measure regional specific-ventilation and evaluated both asthmatics and healthy volunteers. These measurements not only provided similar information as specific-ventilation measured using plethysmography, but also information about regional ventilation defects that were correlated with 3He MRI ventilation abnormalities. These results demonstrated that whole-lung free-breathing 1H MRI biomarker of specific-ventilation may reflect ventilation heterogeneity and/or gas-trapping in asthma. These important findings indicate that imaging biomarkers of pulmonary structure and function using MRI and CT have the potential to regionally reveal the different pathologies in COPD and asthma without the use of exogenous contrast agents. The development and validation of these clinically meaningful imaging biomarkers are critically required to accelerate pulmonary imaging translation from the research workbench to being a part of the clinical workflow, with the overall goal to improve patient outcomes

Towards longitudinal data analytics in Parkinson's Disease

The CloudUPDRS app has been developed as a Class I med- ical device to assess the severity of motor symptoms for Parkinson’s Disease using a fully automated data capture and signal analysis pro- cess based on the standard Unified Parkinson’s Disease Rating Scale. In this paper we report on the design and development of the signal pro- cessing and longitudinal data analytics microservices developed to carry out these assessments and to forecast the long-term development of the disease. We also report on early findings from the application of these techniques in the wild with a cohort of early adopters