A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems.

The objective of this review is to describe the implementation of human factors principles for the design of alerts in clinical information systems. First, we conduct a review of alarm systems to identify human factors principles that are employed in the design and implementation of alerts. Second, we review the medical informatics literature to provide examples of the implementation of human factors principles in current clinical information systems using alerts to provide medication decision support. Last, we suggest actionable recommendations for delivering effective clinical decision support using alerts. A review of studies from the medical informatics literature suggests that many basic human factors principles are not followed, possibly contributing to the lack of acceptance of alerts in clinical information systems. We evaluate the limitations of current alerting philosophies and provide recommendations for improving acceptance of alerts by incorporating human factors principles in their design

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

The case for open science: rare diseases.

The premise of Open Science is that research and medical management will progress faster if data and knowledge are openly shared. The value of Open Science is nowhere more important and appreciated than in the rare disease (RD) community. Research into RDs has been limited by insufficient patient data and resources, a paucity of trained disease experts, and lack of therapeutics, leading to long delays in diagnosis and treatment. These issues can be ameliorated by following the principles and practices of sharing that are intrinsic to Open Science. Here, we describe how the RD community has adopted the core pillars of Open Science, adding new initiatives to promote care and research for RD patients and, ultimately, for all of medicine. We also present recommendations that can advance Open Science more globally

Unlocking the Future of Drug Development:Generative AI, Digital Twins, and Beyond

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery

Doctor of Philosophy

dissertationMedical knowledge learned in medical school can become quickly outdated given the tremendous growth of the biomedical literature. It is the responsibility of medical practitioners to continuously update their knowledge with recent, best available clinical evidence to make informed decisions about patient care. However, clinicians often have little time to spend on reading the primary literature even within their narrow specialty. As a result, they often rely on systematic evidence reviews developed by medical experts to fulfill their information needs. At the present, systematic reviews of clinical research are manually created and updated, which is expensive, slow, and unable to keep up with the rapidly growing pace of medical literature. This dissertation research aims to enhance the traditional systematic review development process using computer-aided solutions. The first study investigates query expansion and scientific quality ranking approaches to enhance literature search on clinical guideline topics. The study showed that unsupervised methods can improve retrieval performance of a popular biomedical search engine (PubMed). The proposed methods improve the comprehensiveness of literature search and increase the ratio of finding relevant studies with reduced screening effort. The second and third studies aim to enhance the traditional manual data extraction process. The second study developed a framework to extract and classify texts from PDF reports. This study demonstrated that a rule-based multipass sieve approach is more effective than a machine-learning approach in categorizing document-level structures and iv that classifying and filtering publication metadata and semistructured texts enhances the performance of an information extraction system. The proposed method could serve as a document processing step in any text mining research on PDF documents. The third study proposed a solution for the computer-aided data extraction by recommending relevant sentences and key phrases extracted from publication reports. This study demonstrated that using a machine-learning classifier to prioritize sentences for specific data elements performs equally or better than an abstract screening approach, and might save time and reduce errors in the full-text screening process. In summary, this dissertation showed that there are promising opportunities for technology enhancement to assist in the development of systematic reviews. In this modern age when computing resources are getting cheaper and more powerful, the failure to apply computer technologies to assist and optimize the manual processes is a lost opportunity to improve the timeliness of systematic reviews. This research provides methodologies and tests hypotheses, which can serve as the basis for further large-scale software engineering projects aimed at fully realizing the prospect of computer-aided systematic reviews

Designing high fidelity simulation to maximize student registered nursing decision-making ability

The current healthcare environment is a complex system of patients, procedures, and equipment that strives to deliver safe and effective medical care. High fidelity simulation provides healthcare educators with a tool to create safety conscious practitioners utilizing an environment that replicates practice without risk to patients. Using HFS learning opportunities to refine a learner\u27s clinical decision-making skills under time pressure and high stakes outcomes could provide new opportunities for training the healthcare workforce of the future. This design based research project explored how to structure HFS training to facilitate the development of decision-making in second semester Registered Nursing learners. Borrowing from the research base of aviation and the military, a framework of Situation Awareness was used to define decision-making skills. Using a naturalistic decision-making approach, the research sought to understand how the design of the HFS learning event impacted the ability of participants to demonstrate behaviors of Situation Awareness. Findings of this study demonstrated that design based research is a powerful tool to create a rich understanding of the high fidelity simulation learning experience. The results also supported the work of Jeffries (2005) reiterating that HFS simulation design must be created using strong pedagogical principles that support specific learning outcomes. Particular attention should be focused on maintenance of fidelity, understanding complexity and scaffolding learning opportunities through a multi-phased approach that minimally includes debriefing. The research related to this small group suggests that the briefing stage of HFS learning should be further explored for its influence on learning in HFS. The influence of the facilitator/faculty on the HFS was emphasized in this research suggesting that faculty development would be important for use of this new tool. Additional implications of the research suggest that high fidelity simulation has a role in team training and development of communication skills

Multi-omics of AML

Acute myeloid leukemia (AML) is one of the most aggressive hematopoietic malignancies and has been recognized as a heterogeneous disease due to a lack of unifying characteristics. It is driven by different genome aberrations, gene expression changes, and epigenomic dysregulations. Therefore a multi-omics approach is needed to unravel the complex biology of this disease. This thesis deals with the challenges of identifying driver events that account for differences in clinical phenotypes and responses to treatment. The work presented here investigates the driver events of AML and epigenetics drug response profiles. The thesis consists of three main projects. The first study identifies recurrent mutations in AML carrying t(8;16)(p11;p13), a rare abnormality. The second project is identifying prospective drivers of mutation- negative nkAML. The third project concentrates on epigenetic changes after AML drugs. t(8;16) AML is a rare and distinguishable clinicopathological entity. Some previous reports that rep- resented the characteristics of patients with this type of AML suggest that the t(8;16) translocation could be sufficient to induce hematopoietic cell transformation to AML without acquiring other genetic alterations. Therefore here I evaluate the frequently mutated genes and compare them with the most frequent mutated genes in AML in general and AML carrying t(8;16) translocation. FLT3 mutation was found in 3 patients of my cohort, a potential target for therapy with tyrosine kinase inhibitors. However, exciting finding was the mutations in EYS, KRTAP9-1, PSIP1, and SPTBN5 that were depicted earlier in AML. Elucidating different layers of aberrations in normal karyotype no-driver acute myeloid leukemia pro- vides better biology insight and may impact risk-group stratification and new potential driver events. Therefore, this study aimed to detect such anomalies in samples without known driver genetic abnor- malities using multi-omic molecular profiling. Samples were analyzed using RNA sequencing (n=43), whole genome sequencing (n=43), and EPIC DNA methylation array (n=42). In 33 of 43 patients, all three layers of data were available. I developed a pipeline looking for a driver in any layer of data by connecting the information of all layers of data and utilizing public genomic, transcriptomic, and clinical data available from TCGA. Genetic alterations of somatic cells can drive malignant clone formation and promote leukemogenesis. Therefore I first built a mutation prioritization workflow that checks each patient’s genomic mutation drivers. Here I use the information on the allele frequency of the specific mu- tation combining information from WGS and RNA sequencing data. Finally, I compared each mutation on a positional level with AML and other TCGA cancer cohorts to assess the causative genomic muta- tions. I found potential driver stopgain mutation in genes implicated in chromosome segregation during mitosis and some tumor suppressor genes. I found new stopgain mutations in cancer genes (NIPBL and NF1). Since fusions are increasingly acknowledged as oncology therapeutic targets, I investigated potential driver fusion events by evaluating high-confidence and in-frame cancer-related fusion findings. As a result, I found specific gene fusion patterns. Kinases activated by gene fusions define a meaningful class of oncogenes associated with hematopoietic malignancies. I identify several novel and recurrent fusions involving kinases that potentially play a role in leukemogenesis. I detected previously unreported fusions involving known cancer-related genes, such as PIM3- RAC2 and PROK2- EIF4E3. In addition, outliers, such as gene expression levels, can pinpoint potential pathogenic events. Therefore, combining my AML cohort with a healthy control group, I determined aberrant gene expression levels as possible pathogenic events using the deep learning method. Finally, I combined the data and looked for a com- parison to the methylation pattern of each patient. Overall, the analysis uncovered a rich landscape of potential drivers. In different data layers, I found an altered genomic and transcriptomic signature of different GTPases, which are known to be involved in many stages of tumorigenesis. My methods and results demonstrate the power of integrating multi-omics data to study complex driver alterations in AML and point to future directions of research that aim to bridge gaps in research and clinical applications. Furthermore, I provide in vitro evidence for antileukemic cooperativity and epigenetic activity between DAC and ATRA. I performed differential methylation analysis on CpG resolution and across genomic and transposable elements regions, enhancing the results’ statistical power and interpretabil- ity. I demonstrated that single-agent ATRA caused no global demethylation, nor did ATRA improve the demethylation mediated by DAC. In summary, combining multi-omics profiling is a powerful tool for studying dysregulated patterns in AML. Furthermore, multi-omics profiling performed on mutation- negative nkAML reveals several promising drivers. My findings not only go beyond augmenting my understanding of the heterogeneity landscape of AML but also may have immediate implications for new targeted therapy studies

The value of semantics in biomedical knowledge graphs

Knowledge graphs use a graph-based data model to represent knowledge of the real world. They consist of nodes, which represent entities of interest such as diseases or proteins, and edges, which represent potentially different relations between these entities. Semantic properties can be attached to these nodes and edges, indicating the classes of entities they represent (e.g. gene, disease), the predicates that indicate the types of relationships between the nodes (e.g. stimulates, treats), and provenance that provides references to the sources of these relationships.Modelling knowledge as a graph emphasizes the interrelationships between the entities, making knowledge graphs a useful tool for performing computational analyses for domains in which complex interactions and sequences of events exist, such as biomedicine. Semantic properties provide additional information and are assumed to benefit such computational analyses but the added value of these properties has not yet been extensively investigated.This thesis therefore develops and compares computational methods that use these properties, and applies them to biomedical tasks. These are: biomarker identification, drug repurposing, drug efficacy screening, identifying disease trajectories, and identifying genes targeted by disease-associated SNPs located on the non-coding part of the genome.In general, we find that methods which use concept classes, predicates, or provenance improves achieve a superior performance over methods that do not use them. We thereby demonstrate the added value of these semantic properties for computational analyses performed on biomedical knowledge graphs.<br/