Large language models for biomedicine: foundations, opportunities, challenges, and best practices.

OBJECTIVES: Generative large language models (LLMs) are a subset of transformers-based neural network architecture models. LLMs have successfully leveraged a combination of an increased number of parameters, improvements in computational efficiency, and large pre-training datasets to perform a wide spectrum of natural language processing (NLP) tasks. Using a few examples (few-shot) or no examples (zero-shot) for prompt-tuning has enabled LLMs to achieve state-of-the-art performance in a broad range of NLP applications. This article by the American Medical Informatics Association (AMIA) NLP Working Group characterizes the opportunities, challenges, and best practices for our community to leverage and advance the integration of LLMs in downstream NLP applications effectively. This can be accomplished through a variety of approaches, including augmented prompting, instruction prompt tuning, and reinforcement learning from human feedback (RLHF). TARGET AUDIENCE: Our focus is on making LLMs accessible to the broader biomedical informatics community, including clinicians and researchers who may be unfamiliar with NLP. Additionally, NLP practitioners may gain insight from the described best practices. SCOPE: We focus on 3 broad categories of NLP tasks, namely natural language understanding, natural language inferencing, and natural language generation. We review the emerging trends in prompt tuning, instruction fine-tuning, and evaluation metrics used for LLMs while drawing attention to several issues that impact biomedical NLP applications, including falsehoods in generated text (confabulation/hallucinations), toxicity, and dataset contamination leading to overfitting. We also review potential approaches to address some of these current challenges in LLMs, such as chain of thought prompting, and the phenomena of emergent capabilities observed in LLMs that can be leveraged to address complex NLP challenge in biomedical applications.

Machine Learning Interpretability Methods to Characterize the Importance of Hematologic Biomarkers in Prognosticating Patients with Suspected Infection.

Early detection of sepsis in patients admitted to the emergency department (ED) is an important clinical objective as early identification and treatment can help reduce morbidity and mortality rate of 20% or higher. Hematologic changes during sepsis-associated organ dysfunction are well established and a new biomarker called Monocyte Distribution Width (MDW) has been recently approved by the US Food and Drug Administration for sepsis. However, MDW, which quantifies monocyte activation in sepsis patients, is not a routinely reported parameter and it requires specialized proprietary laboratory equipment. Further, the relative importance of MDW as compared to other routinely available hematologic parameters and vital signs has not been studied, which makes it difficult for resource constrained hospital systems to make informed decisions in this regard. To address this issue, we analyzed data from a cohort of ED patients (n=10,229) admitted to a large regional safety-net hospital in Cleveland, Ohio with suspected infection who later developed poor outcomes associated with sepsis. We developed a new analytical framework consisting of seven data models and an ensemble of high accuracy machine learning (ML) algorithms (accuracy values ranging from 0.83 to 0.90) for the prediction of outcomes more common in sepsis than uncomplicated infection (3-day intensive care unit stay or death). To characterize the contributions of individual hematologic parameters, we applied the Local Interpretable Model-Agnostic Explanation (LIME) and Shapley Additive Value (SHAP) interpretability methods to the high accuracy ML algorithms. The ML interpretability results were consistent in their findings that the value of MDW is grossly attenuated in the presence of other routinely reported hematologic parameters and vital signs data. Further, this study for the first time shows that complete blood count with differential (CBC-DIFF) together with vital signs data can be used as a substitute for MDW in high accuracy ML algorithms to screen for poor outcomes associated with sepsis.

MaTiLDA: An Integrated Machine Learning and Topological Data Analysis Platform for Brain Network Dynamics.

Topological data analysis (TDA) combined with machine learning (ML) algorithms is a powerful approach for investigating complex brain interaction patterns in neurological disorders such as epilepsy. However, the use of ML algorithms and TDA for analysis of aberrant brain interactions requires substantial domain knowledge in computing as well as pure mathematics. To lower the threshold for clinical and computational neuroscience researchers to effectively use ML algorithms together with TDA to study neurological disorders, we introduce an integrated web platform called MaTiLDA. MaTiLDA is the first tool that enables users to intuitively use TDA methods together with ML models to characterize interaction patterns derived from neurophysiological signal data such as electroencephalogram (EEG) recorded during routine clinical practice. MaTiLDA features support for TDA methods, such as persistent homology, that enable classification of signal data using ML models to provide insights into complex brain interaction patterns in neurological disorders. We demonstrate the practical use of MaTiLDA by analyzing high-resolution intracranial EEG from refractory epilepsy patients to characterize the distinct phases of seizure propagation to different brain regions. The MaTiLDA platform is available at: https://bmhinformatics.case.edu/nicworkflow/MaTiLDA.

NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data.

Introduction: Open science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from schizophrenia and addiction studies. Methods: The NeuroBridge architecture contained the following components: (1) Extensible ontology for modeling study metadata: subject population, imaging techniques, and relevant behavioral, cognitive, or clinical data. Details are described in the companion paper in this special issue; (2) A natural-language based document processor that leveraged pre-trained deep-learning models on a small-sample document corpus to establish efficient representations for each article as a collection of machine-recognized ontological terms; (3) Integrated search using ontology-driven similarity to query PubMed Central and NeuroQuery, which provides fMRI activation maps along with PubMed source articles. Results: The NeuroBridge prototype contains a corpus of 356 papers from 2018 to 2021 describing schizophrenia and addiction neuroimaging studies, of which 186 were annotated with the NeuroBridge ontology. The search portal on the NeuroBridge website https://neurobridges.org/ provides an interactive Query Builder, where the user builds queries by selecting NeuroBridge ontology terms to preserve the ontology tree structure. For each return entry, links to the PubMed abstract as well as to the PMC full-text article, if available, are presented. For each of the returned articles, we provide a list of clinical assessments described in the Section "Methods" of the article. Articles returned from NeuroQuery based on the same search are also presented. Conclusion: The NeuroBridge prototype combines ontology-based search with natural-language text-mining approaches to demonstrate that papers relevant to a user's research question can be identified. The NeuroBridge prototype takes a first step toward identifying potential neuroimaging data described in full-text papers. Toward the overall goal of discovering "enough data of the right kind," ongoing work includes validating the document processor with a larger corpus, extending the ontology to include detailed imaging data, and extracting information regarding data availability from the returned publications and incorporating XNAT-based neuroimaging databases to enhance data accessibility.

NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use.

Background: Despite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not findable or accessible. Users face significant challenges in reusing neuroimaging data due to the lack of provenance metadata, such as experimental protocols, study instruments, and details about the study participants, which is also required for interoperability. To implement the FAIR guidelines for neuroimaging data, we have developed an iterative ontology engineering process and used it to create the NeuroBridge ontology. The NeuroBridge ontology is a computable model of provenance terms to implement FAIR principles and together with an international effort to annotate full text articles with ontology terms, the ontology enables users to locate relevant neuroimaging datasets. Methods: Building on our previous work in metadata modeling, and in concert with an initial annotation of a representative corpus, we modeled diagnosis terms (e.g., schizophrenia, alcohol usage disorder), magnetic resonance imaging (MRI) scan types (T1-weighted, task-based, etc.), clinical symptom assessments (PANSS, AUDIT), and a variety of other assessments. We used the feedback of the annotation team to identify missing metadata terms, which were added to the NeuroBridge ontology, and we restructured the ontology to support both the final annotation of the corpus of neuroimaging articles by a second, independent set of annotators, as well as the functionalities of the NeuroBridge search portal for neuroimaging datasets. Results: The NeuroBridge ontology consists of 660 classes with 49 properties with 3,200 axioms. The ontology includes mappings to existing ontologies, enabling the NeuroBridge ontology to be interoperable with other domain specific terminological systems. Using the ontology, we annotated 186 neuroimaging full-text articles describing the participant types, scanning, clinical and cognitive assessments. Conclusion: The NeuroBridge ontology is the first computable metadata model that represents the types of data available in recent neuroimaging studies in schizophrenia and substance use disorders research; it can be extended to include more granular terms as needed. This metadata ontology is expected to form the computational foundation to help both investigators to make their data FAIR compliant and support users to conduct reproducible neuroimaging research.

Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy.

The rapid adoption of machine learning (ML) algorithms in a wide range of biomedical applications has highlighted issues of trust and the lack of understanding regarding the results generated by ML algorithms. Recent studies have focused on developing interpretable ML models and establish guidelines for transparency and ethical use, ensuring the responsible integration of machine learning in healthcare. In this study, we demonstrate the effectiveness of ML interpretability methods to provide important insights into the dynamics of brain network interactions in epilepsy, a serious neurological disorder affecting more than 60 million persons worldwide. Using high-resolution intracranial electroencephalogram (EEG) recordings from a cohort of 16 patients, we developed high accuracy ML models to categorize these brain activity recordings into either seizure or non-seizure classes followed by a more complex task of delineating the different stages of seizure progression to different parts of the brain as a multi-class classification task. We applied three distinct types of interpretability methods to the high-accuracy ML models to gain an understanding of the relative contributions of different categories of brain interaction patterns, including multi-focii interactions, which play an important role in distinguishing between different states of the brain. The results of this study demonstrate for the first time that post-hoc interpretability methods enable us to understand why ML algorithms generate a given set of results and how variations in value of input values affect the accuracy of the ML algorithms. In particular, we show in this study that interpretability methods can be used to identify brain regions and interaction patterns that have a significant impact on seizure events. The results of this study highlight the importance of the integrated implementation of ML algorithms together with interpretability methods in aberrant brain network studies and the wider domain of biomedical research.

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathology, hippocampal sclerosis, malformation of cortical dysplasia, tumor, encephalitis, and gliosis. We modeled the diagnosis terms together with their microscopy, immunohistochemistry, anatomy, etiologies, and imaging findings using the description logic-based Web Ontology Language (OWL) in the Epilepsy and Seizure Ontology (EpSO). Three tree-based machine learning models were used to classify the neuropathology reports into one or more diagnosis classes with and without ontology-based feature engineering. We used five-fold cross validation to avoid overfitting with a fixed number of repetitions while leaving out one subset of data for testing, and we used recall, balanced accuracy, and hamming loss as performance metrics for the multi-label classification task. The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7%, 54.5%, and 33.3% in logistics regression, random forest, and gradient tree boosting models respectively. The run time performance of all three models improved significantly with ontology-based feature engineering with gradient tree boosting model showing a 93.8% reduction in the time required for training and testing of the model. Although, all three models showed an overall improved performance across the three-performance metrics using ontology-based feature engineering, the rate of improvement was not consistent across all input features. To analyze this variation in performance, we computed feature importance scores and found that microscopy had the highest importance score across the three models, followed by imaging, immunohistochemistry, and anatomy in a decreasing order of importance scores. This study showed that ontologies have an important role in feature engineering to make heterogeneous clinical data accessible to machine learning models and also improve the performance of machine learning models in multilabel multiclass classification tasks.

Enabling Scientific Reproducibility through FAIR Data Management: An ontology-driven deep learning approach in the NeuroBridge Project.

Scientific reproducibility that effectively leverages existing study data is critical to the advancement of research in many disciplines including neuroscience, which uses imaging and electrophysiology modalities as primary endpoints or key dependency in studies. We are developing an integrated search platform called NeuroBridge to enable researchers to search for relevant study datasets that can be used to test a hypothesis or replicate a published finding without having to perform a difficult search from scratch, including contacting individual study authors and locating the site to download the data. In this paper, we describe the development of a metadata ontology based on the World Wide Web Consortium (W3C) PROV specifications to create a corpus of semantically annotated published papers. This annotated corpus was used in a deep learning model to support automated identification of candidate datasets related to neurocognitive assessment of subjects with drug abuse or schizophrenia using neuroimaging. We built on our previous work in the Provenance for Clinical and Health Research (ProvCaRe) project to model metadata information in the NeuroBridge ontology and used this ontology to annotate 51 articles using a Web-based tool called Inception. The Bidirectional Encoder Representations from Transformers (BERT) neural network model, which was trained using the annotated corpus, is used to classify and rank papers relevant to five research hypotheses and the results were evaluated independently by three users for accuracy and recall. Our combined use of the NeuroBridge ontology together with the deep learning model outperforms the existing PubMed Central (PMC) search engine and manifests considerable trainability and transparency compared with typical free-text search. An initial version of the NeuroBridge portal is available at: https://neurobridges.org/.

Social Service Workers' Use of Social Media to Obtain Client Information: Current Practices and Perspectives on a Potential Informatics Platform.

To gain insight into current use of social-media platforms in human services delivery, we systematically surveyed 172 social-service workers from six agencies in a Midwest US city to gather data about social-media usage among social-service providers, potential challenges and benefits of using social media, and whether a social-media-based informatics platform could be valuable. Quantitative analyses showed that approximately half of participants have used social media to collect client-related information; nearly one-quarter indicated "often" or "nearly daily" use. Adjusting for the effects of worker characteristics, social-media use was associated with the type of agency involved and with increased tenure in social services. Adjusted results also showed that participants' comfort with using the potential application was greater in those agencies substantially involved with investigative/legal work. However, trust in the information collected by the potential application was a stronger, independent predictor of comfort using the tool. Qualitative analyses identified numerous challenges and ethical concerns, and positive and negative aspects of a social-media-based informatics platform. If the platform is to be created, work must be done carefully, fully considering ethical issues rightly raised by social service workers, existing agency policies, and professional standards. Future research should investigate ways to negotiate these complex challenges.

Characterizing Brain Network Dynamics using Persistent Homology in Patients with Refractory Epilepsy.

Epilepsy is a common serious neurological disorder that affects more than 65 million persons worldwide and it is characterized by repeated seizures that lead to higher mortality and disabilities with corresponding negative impact on the quality of life of patients. Network science methods that represent brain regions as nodes and the interactions between brain regions as edges have been extensively used in characterizing network changes in neurological disorders. However, the limited ability of graph network models to represent high dimensional brain interactions are being increasingly realized in the computational neuroscience community. In particular, recent advances in algebraic topology research have led to the development of a large number of applications in brain network studies using topological structures. In this paper, we build on a fundamental construct of cliques, which are all-to-all connected nodes with a k-clique in a graph G (V, E), where V is set of nodes and E is set of edges, consisting of k-nodes to characterize the brain network dynamics in epilepsy patients using topological structures. Cliques represent brain regions that are coupled for similar functions or engage in information exchange; therefore, cliques are suitable structures to characterize the dynamics of brain dynamics in neurological disorders. We propose to detect and use clique structures during well-defined clinical events, such as epileptic seizures, to combine non-linear correlation measures in a matrix with identification of geometric structures underlying brain connectivity networks to identify discriminating features that can be used for clinical decision making in epilepsy neurological disorder.

Epilepsy-Connect: An Integrated Knowledgebase for Characterizing Alterations in Consciousness State of Pharmacoresistant Epilepsy Patients.

Alterations in consciousness state are a defining characteristic of focal epileptic seizures. Consequently, understanding the complex changes in neurocognitive networks which underpin seizure-induced alterations in consciousness state is important for advancement in seizure classification. Comprehension of these changes are complicated by a lack of data standardization; however, the use of a common terminological system or ontology in a patient registry minimizes this issue. In this paper, we introduce an integrated knowledgebase called Epilepsy-Connect to improve the understanding of changes in consciousness states during focal seizures of pharmacoresistant epilepsy patients. This registry catalogues over 809 seizures from 70 patients at University Hospital's Epilepsy Center who were undergoing stereotactic electroencephalography (SEEG) monitoring as part of an evaluation for surgical intervention. Although Epilepsy-Connect focuses on consciousness states, it aims to enable users to leverage data from an informatics platform to analyze epilepsy data in a streamlined manner. Epilepsy-Connect is available at https://bmhinformatics.case.edu/Epilepsyconnect/login/.

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy.

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

NeuroIntegrative Connectivity (NIC) Informatics Tool for Brain Functional Connectivity Network Analysis in Cohort Studies.

Objective: Brain functional connectivity measures are often used to study interactions between brain regions in various neurological disorders such as epilepsy. In particular, functional connectivity measures derived from high resolution electrophysiological signal data have been used to characterize epileptic networks in epilepsy patients. However, existing signal data formats as well as computational methods are not suitable for complex multi-step methods used for processing and analyzing signal data across multiple seizure events. To address the significant data management challenges associated with signal data, we have developed a new workflow-based tool called NeuroIntegrative Connectivity (NIC) using the Cloudwave Signal Format (CSF) as a common data abstraction model. Method: The NIC compositional workflow-based tool consists of: (1) Signal data processing component for automated pre- processing and generation of CSF files with semantic annotation using epilepsy domain ontology; and (2) Functional network computation component for deriving functional connectivity metrics from signal data analysis across multiple recording channels. The NIC tool streamlines signal data management using a modular software implementation architecture that supports easy extension with new libraries of signal coupling measures and fast data retrieval using a binary search tree indexing structure called NIC-Index. Result and Conclusion: We evaluated the NIC tool by processing and analyzing signal data for 28 seizure events in two patients with refractory epilepsy. The result shows that certain brain regions have high local measure of connectivity, such as total degree, as compared to other regions during ictal events in both patients. In addition, global connectivity measures, which characterize transitivity and efficiency, increase in value during the initial period of the seizure followed by decrease towards the end of seizure. The NIC tool allows users to efficiently apply several network analysis metrics to study global and local changes in epileptic networks in patient cohort studies.

An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data.

A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.

Semantic Provenance Graph for Reproducibility of Biomedical Research Studies: Generating and Analyzing Graph Structures from Published Literature.

OBJECTIVE: To characterize the scientific reproducibility of biomedical research studies by query and analysis of semantic provenance graphs generated from provenance metadata terms extracted from PubMed articles. METHODS: We develop a new semantic provenance graph generation algorithm that uses a provenance ontology developed as part of the Provenance for Clinical and Health Research (ProvCaRe) project. The ProvCaRe project has processed and extracted provenance metadata from more than 1.6 million full text articles from the PubMed database. RESULTS: The semantic provenance graph generation algorithm is evaluated using provenance terms extracted from 75 selected articles describing sleep medicine research studies. In addition, we use eight provenance queries to evaluate the quality of semantic provenance graphs generated by the new algorithm. CONCLUSION: The ProvCaRe project has created a unique resource to characterize the reproducibility of biomedical research studies and the semantic provenance graph generation algorithm enables users to effectively query and analyze the provenance metadata in the ProvCaRe knowledge repository.

Computation of Brain Functional Connectivity Network Measures in Epilepsy: A Web-Based Platform for EEG Signal Data Processing and Analysis.

Epilepsy is a serious neurological disorder that affects nearly 60 million individuals worldwide and it is characterized by repeated seizures. Graph theoretic approaches have been developed to analyze functional brain networks that underpin epileptogenic network. We have developed a Web-based application that enables neuroscientists to process high resolution Stereotactic Electroencephalogram (SEEG) signal data and compute various kinds of signal coupling measures using an intuitive user interface for study of epilepsy seizure networks. Results of a systematic evaluation of this new application show that it scales with increasing volume of signal data.

Computing Functional Brain Connectivity in Neurological Disorders: Efficient Processing and Retrieval of Electrophysiological Signal Data.

Brain functional network connectivity is an important measure for characterizing changes in a variety of neurological disorders, for example Alzheimer's Disease, Parkinson Disease, and Epilepsy. Epilepsy is a serious neurological disorder affecting more than 50 million persons worldwide with severe impact on the quality of life of patients and their family members due to recurrent seizures. More than 30% of epilepsy patients are refractive to pharmacotherapy and are considered for resection to disrupt epilepsy seizure networks. However, 20-50% of these patients continue to have seizures after surgery. Therefore, there is a critical need to gain new insights into the characteristics of epilepsy seizure networks involving one of more brain regions and accurately delineate epileptogenic zone as a target for surgery. Although there is growing availability of large volume of high resolution stereotactic electroencephalogram (SEEG) data recorded from intracranial electrodes during presurgical evaluation of patients, there are significant informatics challenges associated with processing and analyzing this large signal dataset for characterizing epilepsy seizure networks. In this paper, we describe the development and application of a high-performance indexing structure for efficient retrieval of large-scale SEEG signal data to compute seizure network patterns corresponding to brain functional connectivity networks. This novel Neuro-Integrative Connectivity (NIC) search and retrieval method has been developed by extending the red-black tree index model together with an efficient lookup algorithm. We systematically perform a comparative evaluation of the proposed NIC index using de-identified SEEG data from a patient with temporal lobe epilepsy to retrieve segments of signal data corresponding to multiple seizure events and demonstrate the significant advantages of the NIC index as compared to existing methods. This new NIC Index enables faster computation of brain functional connectivity measures in epilepsy patients for large-scale network analysis and potentially provide new insights into the organization as well as evolution of seizure networks in epilepsy patients.

A Simplified Crossing Fiber Model in Diffusion Weighted Imaging.

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models.

ProvCaRe: Characterizing scientific reproducibility of biomedical research studies using semantic provenance metadata.

OBJECTIVE: Reproducibility of research studies is key to advancing biomedical science by building on sound results and reducing inconsistencies between published results and study data. We propose that the available data from research studies combined with provenance metadata provide a framework for evaluating scientific reproducibility. We developed the ProvCaRe platform to model, extract, and query semantic provenance information from 435, 248 published articles. METHODS: The ProvCaRe platform consists of: (1) the S3 model and a formal ontology; (2) a provenance-focused text processing workflow to generate provenance triples consisting of subject, predicate, and object using metadata extracted from articles; and (3) the ProvCaRe knowledge repository that supports "provenance-aware" hypothesis-driven search queries. A new provenance-based ranking algorithm is used to rank the articles in the search query results. RESULTS: The ProvCaRe knowledge repository contains 48.9 million provenance triples. Seven research hypotheses were used as search queries for evaluation and the resulting provenance triples were analyzed using five categories of provenance terms. The highest number of terms (34%) described provenance related to population cohort followed by 29% of terms describing statistical data analysis methods, and only 5% of the terms described the measurement instruments used in a study. In addition, the analysis showed that some articles included a higher number of provenance terms across multiple provenance categories suggesting a higher potential for reproducibility of these research studies. CONCLUSION: The ProvCaRe knowledge repository (https://provcare. CASE: edu/) is one of the largest provenance resources for biomedical research studies that combines intuitive search functionality with a new provenance-based ranking feature to list articles related to a search query.

Enhancing Multi-Center Patient Cohort Studies in the Managing Epilepsy Well (MEW) Network: Integrated Data Integration and Statistical Analysis.

Self-management techniques that assist patients with chronic conditions, such as epilepsy, diabetes, and arthritis, play an important role in managing and caring for their conditions. The US Center for Disease Control and Prevention (CDC)-funded Managing Epilepsy Well (MEW) Network consists of 11 study sites across the US that aims to develop and disseminate self-management techniques for epilepsy patients. Epilepsy affects more than 65 million patients worldwide with serious negative impact on their own as well as their family member's quality of life. Taking advantage of advances in biomedical informatics, the MEW Network has created an integrated database (MEW DB) using a common data model and two tiers of study variables. The MEW DB consists of 1680 patient data records covering a wide range of patient population nationwide. Therefore, there is growing interest in the use of the MEW DB for different cohort query analysis. To address the challenges in: (1) selecting appropriate MEW research studies based on inclusion/exclusion criteria; (2) creating a patient cohort for given research hypothesis; and (3) performing appropriate statistical tests; we have developed an integrated data query and statistical analysis informatics tool called Insight. The Insight platform features an intuitive user interface to support the three phases of study selection, patient cohort creation, and statistical testing with the use of an epilepsy domain ontology to support ontology-driven query expansion. We evaluate the Insight platform using two user evaluation methods of "first click testing" and "user satisfaction survey". In addition, we performed a time performance test of the Insight platform using four patient datasets and three statistical test. The results of the user evaluation show that Insight platform is strongly approved by the users and the results of the time performance show that there is marginal difference in performance as the volume of patient data increases in the MEW DB.