Technical Approaches Research Articles

Investigating Self-Rationalizing Models for Commonsense Reasoning

The rise of explainable natural language processing spurred a bulk of work on datasets augmented with human explanations, as well as technical approaches to leverage them. Notably, generative large language models offer new possibilities, as they can output a prediction as well as an explanation in natural language. This work investigates the capabilities of fine-tuned text-to-text transfer Transformer (T5) models for commonsense reasoning and explanation generation. Our experiments suggest that while self-rationalizing models achieve interesting results, a significant gap remains: classifiers consistently outperformed self-rationalizing models, and a substantial fraction of model-generated explanations are not valid. Furthermore, training with expressive free-text explanations substantially altered the inner representation of the model, suggesting that they supplied additional information and may bridge the knowledge gap. Our code is publicly available, and the experiments were run on open-access datasets, hence allowing full reproducibility.

Hyperspectral Imaging for Fresh-Cut Fruit and Vegetable Quality Assessment: Basic Concepts and Applications

During the last two decades, hyperspectral imaging (HSI) has been one of the most studied and applied techniques in the field of nondestructive monitoring systems for the fruit and vegetable supply chain. This review provides HSI technical aspects (i.e., device features) and data analysis approaches (i.e., data processing and qualitative/quantitative modeling) for fresh-cut products, focusing on the different applications which the literature offers and the possible scale-up for process monitoring. Moreover, new frontiers in the development of possible process analytical technologies of cost-effective and hand-held HSI devices are presented and discussed. Even though the performance of these new proximal sensing tools needs to be carefully evaluated, new applicative research perspectives in the development of a proximal sensing approach based on HSI sensor networks are ready to be studied and developed for finding field applications (i.e., precision agriculture, food processing, and more) and enabling faster and more convenient analysis while maintaining the accuracy and capabilities of traditional HSI systems.

Synergistic Integration of Blockchain and Machine Learning: A Path to a Decentralized Intelligent Future

The integration of blockchain and machine learning has emerged as a promising paradigm that can revolutionize various industries and applications. Blockchain’s decentralized and immutable nature, coupled with the analytical capabilities of machine learning, presents new opportunities for secure and transparent data sharing, collaborative model training, and intelligent decision-making. This research paper explores the concept of synergistic integration of blockchain and machine learning, providing an overview of the underlying technologies, related work, and existing frameworks. It proposes a novel Decentralized Intelli- gent Learning Network (DILN) framework that combines the strengths of both technologies to create a decentralized and efficient ecosystem for collaborative machine learning applications. The paper presents case studies in healthcare, finance, supply chain management, IoT, and academic research to showcase the potential impact of this integration. Furthermore, it discusses technical approaches, challenges, and ethical considerations to address in the deployment of decentralized intelligent systems. The research paper concludes by encourag- ing further research and development in the field to unlock the full potential of this transformative technology.

Photoelectrochemical Water Splitting at Elevated Pressure: A Model-Based Evaluation

Photoelectrochemical (PEC) water splitting is one of the few truly renewable pathways towards “green” H2. Although PEC water splitting demonstration devices have been mainly operated at atmospheric pressure, most applications and processes that use H2 require it to be supplied at elevated pressure. Although PEC-generated H2 can be pressurized afterwards with e.g. mechanical compression, operating the PEC water-splitting device itself at elevated pressure offers an intriguing alternative and several possible advantages. For example, bubble formation is strongly reduced at elevated pressure, which lowers bubble-induced electrode deactivation and bubble-induced product crossover rate.[1,2] In addition, the optical reflection and diffraction losses induced by bubbles can be minimized.[3] Finally, the electrochemical production of hydrogen at higher pressure requires only a small (though not insignificant) increase of the thermodynamic cell voltage (29 mV for 10-fold increase in pressure).[4] The main disadvantage of a higher pressure is the increasing mechanical demands on the cell design, and the challenge is to find a pressure range at which optimum conditions will be achieved.To evaluate these pressure-induced effects, we have developed a two-dimensional multiphysics model that considers semi-empirical pressure-dependent bubble characteristics (e.g., bubble formation efficiency, bubble diameter) and their impacts on the multi-phase flow, electrochemistry, and mass transport in a membrane-free PEC water-splitting device. Based on the simulation results, a quantitative comparison between two technical approaches to generate pressurized H2 from a PEC device, namely operating PEC water-splitting at atmospheric vs. elevated pressure, has been conducted, as shown in the schematic diagram in Fig. 1a. Various performance loss factors in a PEC water-splitting device were considered, including bubble-induced product crossover, optical loss, and pH gradient-induced concentration overpotential. These losses (translated to kWh unit) were then compared with the specific work required to mechanically compress H2 from the collection pressure (p 1 in Fig. 1a) to 700 bar (p 2 in Fig. 1a). Our results show that bubble-induced optical loss is the predominant factor among all the losses considered. The overall loss diminishes as pressure increases, as shown in Fig. 1b. By comparing the benefit of conducting PEC water-splitting at elevated pressure (defined as the amount of losses avoided vs. operation at 1 bar) with the thermodynamic cell voltage penalty (see Fig. 1c), we found that no significant benefit can be gained by operating beyond 6 – 8 bar. Therefore, this pressure range represents a desirable range for the operation of a membrane-free PEC water-splitting device. Keywords : (photo)electrochemistry, water splitting, elevated pressure, multiphysics modeling, quantitative evaluation.

The importance of cybersecurity frameworks to regulate emergent AI technologies for space applications

Over the past decades, industries and governments have progressively been relying upon space data-centric and data-dependant systems. This led to the emergence of malicious activities, also known as cyber-threats, targeting such systems. To counter these threats, new technologies such as Artificial Intelligence (AI) have been implemented and deployed. Today, AI is highly capable of delivering fast, precise, and reliable command-and-control decision-making, as well as providing reliable vulnerability analysis using well-proven cutting-edge techniques, at least when applied to terrestrial applications. In fact, this might not yet be the case when used for space applications. AI can also play a transformative and important role in the future of space cybersecurity, and it poses questions on what to expect in the near-term future.Challenges and opportunities deriving from the adoption of AI-based solutions to achieve cybersecurity and later cyber defence objectives in both civil and military operations require rethinking of a new framework and new ethical requirements. In fact, most of these technologies are not designed to be used or to overcome challenges in space. Because of the highly contested and congested environment, as well as the highly interdisciplinary nature of threats to AI and Machine Learning (ML) technologies, including cybersecurity issues, a solid and open understanding of the technology itself is required, as well as an understanding of its multidimensional uses and approaches. This includes the definition of legal and technical frameworks, ethical dimensions and other concerns such as mission safety, national security, and technology development for future uses.The continuous endeavours to create a framework and regulate interdependent uses of combined technologies such as AI and cybersecurity to counter “new” threats require the investigation and development of “living concepts” to determine in advance the vulnerabilities of networks and AI.This paper defines a cybersecurity risk and vulnerability taxonomy to enable the future application of AI in the space security field. Moreover, it assesses to what extent a network digital twins’ simulation can still protect networks against relentless cyber-attacks in space against users and ground segments. Both concepts are applied to the case study of Earth Observation (EO) operations, which allows for conclusions to be drawn based on the business impact (reputational, environmental, and social) of a cyber malicious activity. Since AI technologies are developing on a daily basis, a regulatory framework is proposed using ethical and technical approaches for this technology and its use in space.

A Diagnostic Analysis of Observational Sketching: Examples from the University of Latvia

The importance of freehand sketching is being updated and revised at a time when sketching by hand is, in many cases, being replaced by sketching with digital technologies. In addition, in Latvia, since the reform of the general education curriculum, sketching has been included in the new primary education curriculum, which requires primary school teachers to have experience and understanding of sketching. Freehand sketching is also part of the curriculum for future designers’ education. Researchers at the University of Latvia developed a task, criteria, and a description of the assessment levels (rubric) for sketching from an image to assess the initial preparedness and observational sketching skills of students on design and primary school education teachers’ programmes. The conclusion was that students’ sketching skills could be developed and extended by encouraging the use of different technical approaches and means of expression, as well as by practising the accuracy of observation.

A Survey on Unmanned Underwater Vehicles: Challenges, Enabling Technologies, and Future Research Directions.

Unmanned underwater vehicles (UUVs) are becoming increasingly important for a variety of applications, including ocean exploration, mine detection, and military surveillance. This paper aims to provide a comprehensive examination of the technologies that enable the operation of UUVs. We begin by introducing various types of unmanned vehicles capable of functioning in diverse environments. Subsequently, we delve into the underlying technologies necessary for unmanned vehicles operating in underwater environments. These technologies encompass communication, propulsion, dive systems, control systems, sensing, localization, energy resources, and supply. We also address general technical approaches and research contributions within this domain. Furthermore, we present a comprehensive overview of related work, survey methodologies employed, research inquiries, statistical trends, relevant keywords, and supporting articles that substantiate both broad and specific assertions. Expanding on this, we provide a detailed and coherent explanation of the operational framework of UUVs and their corresponding supporting technologies, with an emphasis on technical descriptions. We then evaluate the existing gaps in the performance of supporting technologies and explore the recent challenges associated with implementing the Thorp model for the distribution of shared resources, specifically in communication and energy domains. We also address the joint design of operations involving unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and UUVs, which necessitate collaborative research endeavors to accomplish mission objectives. This analysis highlights the need for future research efforts in these areas. Finally, we outline several critical research questions that warrant exploration in future studies.

Modern opportunities for treatment of patients with rectovaginal fistulas: literature review

The review is devoted to the most pressing issues of etiology, classification, diagnosis and treatment of rectovaginal fistulas. The psychosocial impact of a rectovaginal fistula on women’s quality of life cannot be overemphasized. The most common causes of rectovaginal fistula are postoperative and obstetric complications, inflammatory bowel disease, complications of radiation therapy, and neoplasms. A combination of advanced imaging techniques, physical examination, and clinical experience can help determine the location and cause of a fistula. The therapeutic options are varied, but the results are often unsatisfactory. In this regard, the choice of a strategy for the treatment of rectovaginal fistulas should be based on etiology, localization and concomitant pathology. Despite the prevalence and serious consequences of rectovaginal fistulas, there is no generally accepted scientifically based approach to surgical treatment. Collaboration between obstetricians-gynecologists and colorectal surgeons, detailed preoperative assessment, consideration of optimal treatment options and correct surgical technique can achieve a successful outcome in most patients. The aim of the study is to evaluate, on the basis of literature sources, the problem of diagnosis and treatment of patients with rectovaginal fistulas and modern possibilities for improving the results of surgical interventions in this pathology. The methodological basis of the analysis was the study of domestic and foreign literature devoted to this problem. Scientific articles were selected for the period 2016–2021, with the largest randomized controlled trials in this area. The article provides an overview of the main management strategies and technical approaches that are currently effective in eliminating this socially disabling condition.

Carbon Emission Efficiency and Reduction Potential Based on Three-Stage Slacks-Based Measure with Data Envelopment Analysis and Malmquist at the City Scale in Fujian Province, China

Increased carbon emissions led to extreme weather, global warming, and other environmental problems. In order to control energy input and reduce carbon emissions, this study first combines a three-stage Slacks-Based Measure with Data Envelopment Analysis (SBM-DEA) and uses the Malmquist index to quantify energy consumption at the city scale and the related carbon emission efficiency in Fujian Province for the period 2015–2020. Second, we explore the carbon reduction potential on the city scale from the perspective of improving carbon emission efficiency. Our results demonstrate that (i) the carbon emission efficiency of the nine cities increases overall in the first stage, when technical efficiency approaches the efficiency frontier state and efficiency shortage is mainly caused by the lack of pure technical efficiency. (ii) Regression by stochastic frontier analysis in the second stage reveals that the secondary industry correlates positively at 1% significance with fossil energy consumption and power consumption, indicating that the carbon emission efficiency decreases as the secondary industry increases. (iii) Putian and Xiamen reduced their carbon emission efficiency in the third stage due to (a) the input redundancy of fossil energy and social power consumption and (b) excessive undesirable output carbon emissions. (iv) There were improvements in carbon emission efficiency peaks in 2015, with Longyan, Ningde, and Sanming improving by about 50%. This improvement then decreased up to the year 2020, when the improvement in the carbon emission efficiency of Ningde and Zhangzhou was 6.02% and 9.50%, respectively, and that of all other cities was less than 1%. Therefore, we suggest that carbon emission reduction in the future can be further improved by improving technology, optimizing industrial structure, and various other ways to further improve carbon emission efficiency.

A novel CNN gap layer for growth prediction of palm tree plantlings.

Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.

Circulating tumor cells in gastric cancer: developments and clinical applications.

Circulating tumor cells (CTCs), which are shed from primary tumor or metastatic sites into the bloodstream and subsequently seed into distant tissues, are considered as the precursors of metastases. Gastric cancer (GC) is a highly heterogeneous malignant tumor. With regard to the diagnosis of GC, secondary pathological biopsy is difficult, while invasive examination is harmful to patients. In recent years, CTCs have made great progress in tumor diagnosis, prognosis prediction, efficacy detection and treatment guidance, but the research on the role of CTCs in GC remains limited. The following sections review the landmark studies demonstrating the technical approaches of CTCs monitoring in the field of GC. Moreover, we highlight the clinical application of CTCs numbers and phenotypes in monitoring the therapeutic efficacy and judging patient prognosis by sequential blood analyses.

Neuromuscular junction transmission failure in aging and sarcopenia: The nexus of the neurological and muscular systems.

Sarcopenia, or age-related decline in muscle form and function, exerts high personal, societal, and economic burdens when untreated. Integrity and function of the neuromuscular junction (NMJ), as the nexus between the nervous and muscular systems, is critical for input and dependable neural control of muscle force generation. As such, the NMJ has long been a site of keen interest in the context of skeletal muscle function deficits during aging and in the context of sarcopenia. Historically, changes of NMJ morphology during aging have been investigated extensively but primarily in aged rodent models. Aged rodents have consistently shown features of NMJ endplate fragmentation and denervation. Yet, the presence of NMJ changes in older humans remains controversial, and conflicting findings have been reported. This review article describes the physiological processes involved in NMJ transmission, discusses the evidence that supports NMJ transmission failure as a possible contributor to sarcopenia, and speculates on the potential of targeting these defects for therapeutic development. The technical approaches that are available for assessment of NMJ transmission, whether each approach has been applied in the context of aging and sarcopenia, and the associated findings are summarized. Like morphological studies, age-related NMJ transmission deficits have primarily been studied in rodents. In preclinical studies, isolated synaptic electrophysiology recordings of endplate currentsor potentials have been mostly used, and paradoxically, have shown enhancement, rather than failure, with aging. Yet, in vivo assessment of single muscle fiber action potential generation using single fiber electromyography and nerve-stimulated muscle force measurements show evidence of NMJ failure in aged mice and rats. Together these findings suggest that endplate response enhancement may be a compensatory response to post-synaptic mechanisms of NMJ transmission failure in aged rodents. Possible, but underexplored, mechanisms of this failure are discussed including the simplification of post-synaptic folding and altered voltage-gated sodium channel clustering or function. In humans, there is limited clinical data that has selectively investigated single synaptic function in the context of aging. If sarcopenic older adults turn out to exhibit notable impairments in NMJ transmission (this has yet to be examined but based on available evidence appears to be plausible) then these NMJ transmission defects present a well-defined biological mechanism and offer a well-defined pathway for clinical implementation. Investigation of small molecules that are currently available clinically or being testing clinically in other disorders may provide a rapid route for development of interventions for older adults impacted by sarcopenia.

Updates on the Surgical Approach to Fibroids: The Importance of Radiofrequency Ablation.

Uterine fibroids are the most common benign neoplasm of the female pelvis and have a lifetime prevalence exceeding 80% among African American women and approaching 70% among Caucasian women. Approximately 50% of women with fibroids experience symptoms which can range from heavy menstrual bleeding and bulk-related symptoms such as pelvic pressure with bladder and bowel dysfunction to reproductive dysfunction (e.g., infertility or obstetric complications) and pain. The choice of treatment is primarily guided by the type of symptoms in the individual patient and whether they prefer to retain fertility. While hysterectomy provides definitive resolution of fibroid symptoms and remains the most common treatment option, this procedure is invasive with a long recovery window. Radiofrequency ablation (RFA) is now emerging as a uterine preserving and minimally invasive therapy for symptomatic fibroids. Since its introduction, growing evidence for safety and efficacy of RFA has been generated with low rates of complications. This review will discuss RFA for the management of symptomatic uterine fibroids with a special focus on technical approaches, short- and long-term outcomes including fertility outcomes.

Mesoscale calcium imaging in vivo: evolution and contribution to developmental neuroscience.

Calcium imaging is commonly used to visualize neural activity in vivo. In particular, mesoscale calcium imaging provides large fields of view, allowing for the simultaneous interrogation of neuron ensembles across the neuraxis. In the field of Developmental Neuroscience, mesoscopic imaging has recently yielded intriguing results that have shed new light on the ontogenesis of neural circuits from the first stages of life. We summarize here the technical approaches, basic notions for data analysis and the main findings provided by this technique in the last few years, with a focus on brain development in mouse models. As new tools develop to optimize calcium imaging in vivo, basic principles of neural development should be revised from a mesoscale perspective, that is, taking into account widespread activation of neuronal ensembles across the brain. In the future, combining mesoscale imaging of the dorsal surface of the brain with imaging of deep structures would ensure a more complete understanding of the construction of circuits. Moreover, the combination of mesoscale calcium imaging with other tools, like electrophysiology or high-resolution microscopy, will make up for the spatial and temporal limitations of this technique.

Ultralow flow liquid chromatography and related approaches: A focus on recent bioanalytical applications.

Ultralow flow LC employs ultra-narrow bore columns and mid-range pL/min to low nL/min flow rates (i.e., ≤20 nL/min). The separation columns that are used under these conditions are typically 2-30μm in inner diameter. Ultralow flow LC systems allow for exceptionally high sensitivity and frequently high resolution. There has been an increasing interest in the analysis of scarce biological samples, for example, circulating tumor cells, extracellular vesicles, organelles, and single cells, and ultralow flow LC was efficiently applied to such samples. Hence, advances towards dedicated ultralow flow LC instrumentation, technical approaches, and higher throughput (e.g., tens-to-hundreds of single cells analyzed per day) were recently made. Here, we review the types of ultralow flow LC technology, followed by a discussion of selected representative ultralow flow LC applications, focusing on the progress made in bioanalysis of amount-limited samples during the last 10 years. We also discuss several recently reported high-sensitivity applications utilizing flow rates up to 100 nL/min, which are below commonly used nanoLC flow rates. Finally, we discuss the path forward for future developments of ultralow flow LC.

Application of Silicon Nanowire Field Effect Transistor (SiNW-FET) Biosensor with High Sensitivity.

As a new type of one-dimensional semiconductor nanometer material, silicon nanowires (SiNWs) possess good application prospects in the field of biomedical sensing. SiNWs have excellent electronic properties for improving the detection sensitivity of biosensors. The combination of SiNWs and field effect transistors (FETs) formed one special biosensor with high sensitivity and target selectivity in real-time and label-free. Recently, SiNW-FETs have received more attention in fields of biomedical detection. Here, we give a critical review of the progress of SiNW-FETs, in particular, about the reversible surface modification methods. Moreover, we summarized the applications of SiNW-FETs in DNA, protein, and microbial detection. We also discuss the related working principle and technical approaches. Our review provides an extensive discussion for studying the challenges in the future development of SiNW-FETs.

Electrospun Polymeric Fibers Decorated with Silk Microcapsules via Encapsulation and Surface Immobilization for Drug Delivery.

Hollow polymer microcapsules as drug carriers have the advantages of drug protection, storage, and controlled release. Microcapsules combined with tissue engineering scaffolds such as electrospun microfibers can enhance long-term local drug retention. However, the combination methods of microcapsules and fibers still need to be further explored. Here, we developed different technical approaches to functionalize electrospun polycaprolactone (PCL) microfibers with silk fibroin (SF) microcapsules through encapsulation and surface immobilization, including direct blending and emulsion electrospinning for encapsulation, as well as covalent and cleavable disulfide-linkage for surface immobilization. The results of "blending" approach showed that silk microcapsules with different sizes could be uniformly encapsulated inside electrospun fibers without aggregation. To further reduce the use of organic solvents, the microcapsules in the aqueous phase can be uniformly distributed in the PCL organic phase and successfully electrospun into fibers using surfactant span-80. For surface immobilization, silk microcapsules were efficiently covalent binding to the surface of electrospun PCL fibers via click chemistry and exhibited noncytotoxic. Based on this method, with the incorporation of a disulfide bond, the linkages between microcapsule and fiber could be cleaved under reducing conditions. These microcapsule-electrospun fiber combination methods provide sufficient options for different drug delivery requirements. This article is protected by copyright. All rights reserved.

Ten Topics to Get Started in Medical Informatics Research.

The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

Optimization of bioanalysis of dried blood samples

IntroductionPharmacokinetic/pharmacodynamic modelling has emerged as a valuable technique for understanding drug exposure and response relationships in drug development. Pharmacokinetic data are often obtained by taking multiple blood samples, which may disturb physiological parameters and complicate study designs. Wearable automatic blood sampling systems can improve this limitation by collecting dried blood samples at programmable time points without disrupting cardiovascular parameters. It is the objective of this study to evaluate the bioanalysis of DBS in comparison to conventional blood sampling techniques and to optimize the recovery of various compounds spiked into canine blood dried on filter paper tape. MethodsIncubated blood samples from Beagle dogs were spiked with 16 different compounds and half of the whole blood sample was centrifuged to obtain plasma. After the dried blood sample drops were dried, liquid chromatography-mass spectrometry methods were used to analyze the samples. The study explored different anticoagulants, sample preparation methods and technical approaches to best determine the compound concentrations in dried blood samples. ResultsWith the two anticoagulants tested and using the optimized sample preparation methods and technical approaches we employed, the bioanalysis of dried blood samples can provide equivalent results to conventional blood sampling techniques. DiscussionAutomated blood sampling systems have the potential to provide increased numbers of blood samples, providing substantially more Pharmacokinetic data within safety pharmacology studies without disrupting physiological parameters. They can provide a viable alternative to traditional methods of obtaining blood for various other types of studies or analyses.

Semantically enabling clinical decision support recommendations

BackgroundClinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through evidence-based recommendations. These recommendations are typically derived from clinical practice guidelines created by clinical specialties or healthcare organizations. Although there have been many different technical approaches to encoding guideline recommendations into decision support systems, much of the previous work has not focused on enabling system generated recommendations through the formalization of changes in a guideline, the provenance of a recommendation, and applicability of the evidence. Prior work indicates that healthcare providers may not find that guideline-derived recommendations always meet their needs for reasons such as lack of relevance, transparency, time pressure, and applicability to their clinical practice.ResultsWe introduce several semantic techniques that model diseases based on clinical practice guidelines, provenance of the guidelines, and the study cohorts they are based on to enhance the capabilities of clinical decision support systems. We have explored ways to enable clinical decision support systems with semantic technologies that can represent and link to details in related items from the scientific literature and quickly adapt to changing information from the guidelines, identifying gaps, and supporting personalized explanations. Previous semantics-driven clinical decision systems have limited support in all these aspects, and we present the ontologies and semantic web based software tools in three distinct areas that are unified using a standard set of ontologies and a custom-built knowledge graph framework:(i) guideline modeling to characterize diseases,(ii) guideline provenance to attach evidence to treatment decisions from authoritative sources, and(iii) study cohort modeling to identify relevant research publications for complicated patients.ConclusionsWe have enhanced existing, evidence-based knowledge by developing ontologies and software that enables clinicians to conveniently access updates to and provenance of guidelines, as well as gather additional information from research studies applicable to their patients’ unique circumstances. Our software solutions leverage many well-used existing biomedical ontologies and build upon decades of knowledge representation and reasoning work, leading to explainable results.