A method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data

BACKGROUND: We consider the user task of designing clinical trial protocols and propose a method that discovers and outputs the most appropriate eligibility criteria from a potentially huge set of candidates. Each document d in our collection D is a clinical trial protocol which itself contains a set of eligibility criteria. Given a small set of sample documents [Formula: see text] , a user has initially identified as relevant e.g., via a user query interface, our scoring method automatically suggests eligibility criteria from D, D ⊃ D', by ranking them according to how appropriate they are to the clinical trial protocol currently being designed. The appropriateness is measured by the degree to which they are consistent with the user-supplied sample documents D'. METHOD: We propose a novel three-step method called LDALR which views documents as a mixture of latent topics. First, we infer the latent topics in the sample documents using Latent Dirichlet Allocation (LDA). Next, we use logistic regression models to compute the probability that a given candidate criterion belongs to a particular topic. Lastly, we score each criterion by computing its expected value, the probability-weighted sum of the topic proportions inferred from the set of sample documents. Intuitively, the greater the probability that a candidate criterion belongs to the topics that are dominant in the samples, the higher its expected value or score. RESULTS: Our experiments have shown that LDALR is 8 and 9 times better (resp., for inclusion and exclusion criteria) than randomly choosing from a set of candidates obtained from relevant documents. In user simulation experiments using LDALR, we were able to automatically construct eligibility criteria that are on the average 75% and 70% (resp., for inclusion and exclusion criteria) similar to the correct eligibility criteria. CONCLUSIONS: We have proposed LDALR, a practical method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data. Results from our experiments suggest that LDALR models can be used to effectively find appropriate eligibility criteria from a large repository of clinical trial protocols

Seeking Help for Intimate Betrayal Trauma: The Lived Experience of Unsuspecting Wives Receiving Counseling Messages from Mental Health Professionals after Discovering Their Sexually Addicted Spouse\u27s Out-of-Control Behavior

After unsuspecting wives (UWs) discover their sexually addicted spouse’s (SAS’s) out-of-control behavior (OCB) outside of their committed relationship, they may seek support from mental health professionals. Depending on a mental health professional’s theoretical framework for treating sexual addiction (SA) and partner betrayal, women may receive messages based on a family systems approach for addiction counseling or from a trauma model that prioritizes the client’s need for safety, stabilization, and grief work, with the goal of reconnecting the UW to a redefined sense of reality. Components of trauma work along with validation of an UW’s experience offers UWs the safety to cope with the nature of an intimate betrayal trauma (IBT) and experience growth as part of the process. This transcendental phenomenological study explored the experience of UWs who sought counseling after discovering their SAS’s OCB, and how messages they received from their mental health professionals impacted their recovery. Semi-structured interviews were used for data collection, and data were analyzed and coded based on conceptualizations from the Multidimensional Partner Trauma Model (MPTM), transcendental phenomenological approach required the researcher to suspend bias and work intentionally to honor participants\u27 narratives with objectivity and curiosity. Phenomenological data analysis revealed three themes and eight subthemes. A subsequent discussion included an expansion of the literature, its application in practice, implications, recommendations for actions, and recommendations for further study

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs\u27 recapitulation of human tumors

Pursuing High-Value, Cost-Conscious Care:The Role of Medical Education

It is estimated that 30% of healthcare costs could be avoided, and eliminating these costs would not negatively affect quality of care. High-value, cost-conscious care (HV3C) refers to care aimed at balancing the benefit, harm, and cost of interventions, and focused on delivering care that adds value. HV3C is essential to keep our healthcare system affordable and accessible to all. This research was conducted with the goal of understanding of how future physicians are trained to deliver HV3C and how medical education can support this training. This research revealed the important role of the environment in which future physicians are trained. Besides important elements of training such as knowledge transfer (healthcare costs, patients preferences) and reflective practice (feedback, reflective questions), we should focus on the norms and beliefs of the workplace. Individual approaches, of both residents and supervisors, strongly influence if and how HV3C is taught. The final chapter contains 12 tips for training physicians in the delivery of high-value, cost-conscious care

CANCER PAIN PROCESSES IN THE HOSPICE CARING TRIAD: A GROUNDED THEORY STUDY

The author conducted this constructivist grounded theory study to describe perceptions, behaviors, and communication that hospice caring triads engage in while managing cancer pain, specifically how these social processes can be assessed and used to improve poorly-controlled pain. Three hospice caring triads comprised of patients, family caregivers, and nurses along with one nurse-patient dyad, were recruited into this longitudinal qualitative study. Each group was observed during nursing visits. Triad and dyad members were individually interviewed. Nurses participated in a focus group and survey. The author used constant comparative methods of data analysis, including line-by-line gerund coding, theoretical codes from cancer pain assessment standards, theoretical sampling of early codes, memo writing, and concept-mapping to raise codes into concepts and domains for a theoretical framework. Study trustworthiness included appropriate methodology to answer research questions, prolonged and repeated participant engagement, triangulation of content across participants, reflexive memos, and use of multiple data sources. Results were presented in a triadic case study comparing and contrasting the two cancer pain social processes domains related to pain control: Controlling Cancer Pain and Proximity. The first domain included perceptions of pain control, goals, and efficacy. The second domain included physical and emotional distance between triad members, presence of communication, and level of agreement. When triads were compared, one triad with close emotional and physical proximity had a shared perception of pain meaning and goals for control, and effective communication for pain management behaviors. The other triads had more physical and emotional distance, communication that was vague, and differing perceptions of pain control, pain meaning, or control goals. An important difference for these other triads was a lack of agreement about pain perception and pain severity, as well as vague communication about pain perception, with subsequent impact on pain goals. Controlling Cancer Pain and Proximity social processes are inextricable with cancer pain management for hospice caring triads. Assessment tools for proximity-related social processes which measure closeness, communication, and agreement among hospice caring triad members should be developed and tested for improving cases of poorly controlled pain. Development and testing of simple open-ended functional goal assessments is needed

An ontology for formal representation of medication adherence-related knowledge : case study in breast cancer

Indiana University-Purdue University Indianapolis (IUPUI)Medication non-adherence is a major healthcare problem that negatively impacts the health and productivity of individuals and society as a whole. Reasons for medication non-adherence are multi-faced, with no clear-cut solution. Adherence to medication remains a difficult area to study, due to inconsistencies in representing medicationadherence behavior data that poses a challenge to humans and today’s computer technology related to interpreting and synthesizing such complex information. Developing a consistent conceptual framework to medication adherence is needed to facilitate domain understanding, sharing, and communicating, as well as enabling researchers to formally compare the findings of studies in systematic reviews. The goal of this research is to create a common language that bridges human and computer technology by developing a controlled structured vocabulary of medication adherence behavior—“Medication Adherence Behavior Ontology” (MAB-Ontology) using breast cancer as a case study to inform and evaluate the proposed ontology and demonstrating its application to real-world situation. The intention is for MAB-Ontology to be developed against the background of a philosophical analysis of terms, such as belief, and desire to be human, computer-understandable, and interoperable with other systems that support scientific research. The design process for MAB-Ontology carried out using the METHONTOLOGY method incorporated with the Basic Formal Ontology (BFO) principles of best practice. This approach introduces a novel knowledge acquisition step that guides capturing medication-adherence-related data from different knowledge sources, including adherence assessment, adherence determinants, adherence theories, adherence taxonomies, and tacit knowledge source types. These sources were analyzed using a systematic approach that involved some questions applied to all source types to guide data extraction and inform domain conceptualization. A set of intermediate representations involving tables and graphs was used to allow for domain evaluation before implementation. The resulting ontology included 629 classes, 529 individuals, 51 object property, and 2 data property. The intermediate representation was formalized into OWL using Protégé. The MAB-Ontology was evaluated through competency questions, use-case scenario, face validity and was found to satisfy the requirement specification. This study provides a unified method for developing a computerized-based adherence model that can be applied among various disease groups and different drug categories

Computational Tools for the Untargeted Assignment of FT-MS Metabolomics Datasets

Metabolomics is the study of metabolomes, the sets of metabolites observed in living systems. Metabolism interconverts these metabolites to provide the molecules and energy necessary for life processes. Many disease processes, including cancer, have a significant metabolic component that manifests as differences in what metabolites are present and in what quantities they are produced and utilized. Thus, using metabolomics, differences between metabolomes in disease and non-disease states can be detected and these differences improve our understanding of disease processes at the molecular level. Despite the potential benefits of metabolomics, the comprehensive investigation of metabolomes remains difficult. A popular analytical technique for metabolomics is mass spectrometry. Advances in Fourier transform mass spectrometry (FT-MS) instrumentation have yielded simultaneous improvements in mass resolution, mass accuracy, and detection sensitivity. In the metabolomics field, these advantages permit more complicated, but more informative experimental designs such as the use of multiple isotope-labeled precursors in stable isotope-resolved metabolomics (SIRM) experiments. However, despite these potential applications, several outstanding problems hamper the use of FT-MS for metabolomics studies. First, artifacts and data quality problems in FT-MS spectra can confound downstream data analyses, confuse machine learning models, and complicate the robust detection and assignment of metabolite features. Second, the assignment of observed spectral features to metabolites remains difficult. Existing targeted approaches for assignment often employ databases of known metabolites; however, metabolite databases are incomplete, thus limiting or biasing assignment results. Additionally, FT-MS provides limited structural information for observed metabolites, which complicates the determination of metabolite class (e.g. lipid, sugar, etc. ) for observed metabolite spectral features, a necessary step for many metabolomics experiments. To address these problems, a set of tools were developed. The first tool identifies artifacts with high peak density observed in many FT-MS spectra and removes them safely. Using this tool, two previously unreported types of high peak density artifact were identified in FT-MS spectra: fuzzy sites and partial ringing. Fuzzy sites were particularly problematic as they confused and reduced the accuracy of machine learning models trained on datasets containing these artifacts. Second, a tool called SMIRFE was developed to assign isotope-resolved molecular formulas to observed spectral features in an untargeted manner without a database of expected metabolites. This new untargeted method was validated on a gold-standard dataset containing both unlabeled and 15N-labeled compounds and was able to identify 18 of 18 expected spectral features. Third, a collection of machine learning models was constructed to predict if a molecular formula corresponds to one or more lipid categories. These models accurately predict the correct one of eight lipid categories on our training dataset of known lipid and non-lipid molecular formulas with precisions and accuracies over 90% for most categories. These models were used to predict lipid categories for untargeted SMIRFE-derived assignments in a non-small cell lung cancer dataset. Subsequent differential abundance analysis revealed a sub-population of non-small cell lung cancer samples with a significantly increased abundance in sterol lipids. This finding implies a possible therapeutic role of statins in the treatment and/or prevention of non-small cell lung cancer. Collectively these tools represent a pipeline for FT-MS metabolomics datasets that is compatible with isotope labeling experiments. With these tools, more robust and untargeted metabolic analyses of disease will be possible

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems