The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource ...(NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.
We used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor.
The authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data.
The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.
Sudden death in epilepsy (SUDEP) is a rare disease in US, however, they account for 8-17% of deaths in people with epilepsy. This disease involves complicated physiological patterns and it is still ...not clear what are the physio-/bio-makers that can be used as an indicator to predict SUDEP so that care providers can intervene and treat patients in a timely manner. For this sake, UTHealth School of Biomedical Informatics (SBMI) organized a machine learning Hackathon to call for advanced solutions https://sbmi.uth.edu/hackathon/archive/sept19.htm .
In recent years, deep learning has become state of the art for many domains with large amounts data. Although healthcare has accumulated a lot of data, they are often not abundant enough for subpopulation studies where deep learning could be beneficial. Taking these limitations into account, we present a framework to apply deep learning to the detection of the onset of slow activity after a generalized tonic-clonic seizure, as well as other EEG signal detection problems exhibiting data paucity.
We conducted ten training runs for our full method and seven model variants, statistically demonstrating the impact of each technique used in our framework with a high degree of confidence.
Our findings point toward deep learning being a viable method for detection of the onset of slow activity provided approperiate regularization is performed.
The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of ...diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt.
IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process.
We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection.
IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts.
Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several ...multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches.
Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, ...medicine and data science are not synchronized. Oftentimes, researchers with a strong data science background do not understand the clinical challenges, while on the other hand, physicians do not know the capacity and limitation of state-of-the-art machine learning methods. The difficulty boils down to the lack of a common interface between two highly intelligent communities due to the privacy concerns and the disciplinary gap. The School of Biomedical Informatics (SBMI) at UTHealth is a pilot in connecting both worlds to promote interdisciplinary research. Recently, the Center for Secure Artificial Intelligence For hEalthcare (SAFE) at SBMI is organizing a series of machine learning healthcare hackathons for real-world clinical challenges. We hosted our first Hackathon themed centered around Sudden Unexpected Death in Epilepsy and finding ways to recognize the warning signs. This community effort demonstrated that interdisciplinary discussion and productive competition has significantly increased the accuracy of warning sign detection compared to the previous work, and ultimately showing a potential of this hackathon as a platform to connect the two communities of data science and medicine.
Epilepsy affects ~2-3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of ...poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.
About 20% of individuals with attention deficit hyperactivity disorder are first diagnosed during adolescence. While preclinical experiments suggest that adolescent-onset exposure to attention ...deficit hyperactivity disorder medication is an important factor in the development of substance use disorder phenotypes in adulthood, the long-term impact of attention deficit hyperactivity disorder medication initiated during adolescence has been largely unexplored in humans. Our analysis of 11,624 adolescent enrollees with attention deficit hyperactivity disorder in the Truven database indicates that temporal medication features, rather than stationary features, are the most important factors on the health consequences related to substance use disorder and attention deficit hyperactivity disorder medication initiation during adolescence.
Seizure clusters may be related to Sudden Unexpected Death in Epilepsy (SUDEP). Two or more generalized convulsive seizures (GCS) were captured during video electroencephalography in 7/11 (64%) ...patients with monitored SUDEP in the MORTEMUS study. It follows that seizure clusters may be associated with epilepsy severity and possibly with SUDEP risk. We aimed to determine if electroclinical seizure features worsen from seizure to seizure within a cluster and possible associations between GCS clusters, markers of seizure severity, and SUDEP risk.
Patients were consecutive, prospectively consented participants with drug-resistant epilepsy from a multi-center study. Seizure clusters were defined as two or more GCS in a 24-h period during the recording of prolonged video-electroencephalography in the Epilepsy monitoring unit (EMU). We measured heart rate variability (HRV), pulse oximetry, plethysmography, postictal generalized electroencephalographic suppression (PGES), and electroencephalography (EEG) recovery duration. A linear mixed effects model was used to study the difference between the first and subsequent seizures, with a level of significance set at
< 0.05.
We identified 112 GCS clusters in 105 patients with 285 seizures. GCS lasted on average 48.7 ± 19 s (mean 49, range 2-137). PGES emerged in 184 (64.6%) seizures and postconvulsive central apnea (PCCA) was present in 38 (13.3%) seizures. Changes in seizure features from seizure to seizure such as seizure and convulsive phase durations appeared random. In grouped analysis, some seizure features underwent significant deterioration, whereas others improved. Clonic phase and postconvulsive central apnea (PCCA) were significantly shorter in the fourth seizure compared to the first. By contrast, duration of decerebrate posturing and ictal central apnea were longer. Four SUDEP cases in the cluster cohort were reported on follow-up.
Seizure clusters show variable changes from seizure to seizure. Although clusters may reflect epilepsy severity, they alone may be unrelated to SUDEP risk. We suggest a stochastic nature to SUDEP occurrence, where seizure clusters may be more likely to contribute to SUDEP if an underlying progressive tendency toward SUDEP has matured toward a critical SUDEP threshold.
Cancer is responsible for approximately 7.6 million deaths per year worldwide. A 2012 survey in the United Kingdom found dramatic improvement in survival rates for childhood cancer because of ...increased participation in clinical trials. Unfortunately, overall patient participation in cancer clinical studies is low. A key logistical barrier to patient and physician participation is the time required for identification of appropriate clinical trials for individual patients. We introduce the Trial Prospector tool that supports end-to-end management of cancer clinical trial recruitment workflow with (a) structured entry of trial eligibility criteria, (b) automated extraction of patient data from multiple sources, (c) a scalable matching algorithm, and (d) interactive user interface (UI) for physicians with both matching results and a detailed explanation of causes for ineligibility of available trials. We report the results from deployment of Trial Prospector at the National Cancer Institute (NCI)-designated Case Comprehensive Cancer Center (Case CCC) with 1,367 clinical trial eligibility evaluations performed with 100% accuracy.
The continued deterioration of riparian ecosystems is a worldwide concern, which can lead to soil erosion, plant degradation, biodiversity loss, and water quality decline. Here, taking into account ...waste resource utilization and eco-environmental friendliness, the sediment-modified planting eco-concrete with both H. verticillata and T. orientalis (SEC-H&T) was prepared and explored for the first time to achieve sustainable riparian restoration. Concrete mechanical characterizations showed that the compressive strength and porosity of SEC with 30% sediment content could reach up to 15.8 MPa and 21.25%, respectively. The mechanical properties and the sediment utilization levels of SEC were appropriately balanced, and potentially toxic element leaching results verified the environmental safety of eco-concrete modified with dredged sediments. Plant physiological parameters of both aquatic plants (biomass, chlorophyll, protein and starch) were observed to reach the normal levels in SEC during the 30-day culture period, and T. orientalis seemed better adapted to SEC environment than H. verticillate. Importantly, compared to SEC-H and SEC-T, SEC-H&T could effectively reduce the concentrations of COD, TN and TP by 58.59%, 74.00% and 79.98% in water, respectively. Notably, water purification mechanisms by SEC-H&T were further elucidated from the perspective of microbial community responses. Shannon index of bacterial diversity and proliferation of specific populations dominating nutrient transformation (such as Bacillus and Nitrospira) was increased under the synergy of SEC and aquatic plants. Correspondingly, functional genes involved in nitrogen and phosphorus transformation (such as nosZ and phoU) were also enriched. Our study can not only showcase an effective and flexible approach to recycle dredged sediments into eco-concrete with low environment impacts, but also provide a promising alternative for sustainable riparian restoration.
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•Recycling dredged sediments to prepare sediment-modified planting eco-concrete (SEC).•The optimal SEC showed good mechanical, planting, and water purification properties.•Microbial response played an important role in nutrient reduction using SEC.•Environmentally friendly SEC was developed for sustainable riparian restoration.
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