Cross-domain learning in the medical field is a research challenge as many conditions like in oncology imaging use different imaging modalities. Moreover, in most of the medical learning applications, the sample training size is relatively small. Although few-shot learning (FSL) through the use of Siamese neural network was able to be trained on small sample with remarkable accuracy, FSL fails to be effective for the use in multiple domains as their convolution weights are set for task-specific application. In this paper, we are addressing this problem by enabling FSL to posses the ability to shift cross domains through designing two-layer FSL network that can learn individually from each domain and produce shared features map with extra modulation to be used at the second layer that can recognize important targets from mix domains. Our initial experimentations based on mixed medical dataset like the Medical-MNIST reveals promising results. We aim to continue this research to perform full scale-analytics for testing our cross-domain FSL learning.

Clinical research and practice are generating important new findings at exponential rate which need to be readily available to clinicians. However, clinicians are confronted with serious challenges when they try to seek such information for their evidence-based decision making or to generate new clinical case report. One important challenge is the long time needed to browse, filter, summarize and compile information from different resources. The other important challenge is to identify relevant important evidence-based information resources required to answer clinical questions or support a clinical finding. Artificial intelligence can help in solving both challenges based on the automatic question answering (Q&A) and generative technologies. However, Q&A and generative techniques are not trained to answer clinical queries that can be used for evidence-based practice nor it can respond to structured clinical questioning protocol like PICO (Patient/Problem, Intervention, Comparison and Outcome). This article describes the use of deep learning techniques for Q&A that is based on generative models like BERT and GPT to answer PICO clinical questions that can be used for evidence-based practice extracted from sound medical research resources like PubMed. We are reporting acceptable clinical answers that are supported by findings from PubMed. Our generative methods are reaching state of the art performance based on two staged bootstrapping process involving filtering relevant articles followed by identifying articles that support the requested outcome expressed by the PICO question.

Train delay prediction is a key technology for intelligent train scheduling and passenger services. We propose a train delay prediction model that takes into account the asynchrony of train events, the dynamics of train operations, and the diversity of influencing factors. Firstly, we consider train operations as discrete sequences of train events and propose a train arrival neural temporal point process (TANTPP) framework focused on predicting train delays that explicitly models the asynchrony of train events. Secondly, we introduce a multi-source dynamic spatiotemporal embedding method for the feature encoder in the TANTPP framework, which enhances the capability to capture the features of train operation networks. Thirdly, to better capture the distribution of train events in the TANTPP framework, we utilize a log-normal mixture hybrid method to learn the probability density distribution of train arrival events. Finally, the experimental result on real-world datasets demonstrates that the TANTPP model outperforms current state-of-the-art models, reducing the MAE by 10.85%, the RMSE by 9.8%, the RRSE by 3.78% and the MAPE by 10.11% on average. To the best of our knowledge, this is the first study to utilize neural temporal point processes to enhance train delay prediction.

Cataract image severity classification according to contrast sensitivity chart like VTSC represents a challenge to classical machine learning. Typically cataract images hide significant retina features because of the fogy nature of these images. Contrast sensitivity measurement provides important information on the functioning of the visual system that cannot simple detected by the visual acuity tests. In this paper, we are introducing a framework for segmenting fundus images into regions of salient contrast. The framework start by converting the RGB color channels into joint entropy where it can be used to categorized anchor images into contrast based classes. It has been found that classes like normal fundus, fundus with cataract or fundus having cataract with additional complications like availability of cotton-wool spots or fibrosis. We found that contrast sensitivity scoring correlate nicely with the joint entropy scoring. For this purpose, our last level of the framework employs a triplet loss Siamese neural network that has been trained on few contrast classified fundus images to detect the severity of the cataract. We used a similar contrast sensitivity scale like those used by the optometrist with classification rate that reaches 72%.

Practicing evidence based medicine requires establishing relevant and focused clinical questions that allow physicians to seek appropriate answers from rigor of the research and medical practice. These researchable question is not only important for diagnosis, prognosis, treatment and therapy but also for compiling clinical case reports when clinician encounters unanswered issues in current clinical practice. However, clinicians face multiple challenges in formulating the questions manually, searching medical literature as well as summarizing the methods and outcome from the finding of the most relevant articles. This paper extends our QL4POMR to provide a PICO clinical questioning wrapper as well as literature finding summarizers based on two pre-trained models like the BART and Bio-BERT. We also managed to integrate a clinical case report generator that can synthesis information on the clinical case based on its SOAP(s) description as well as the PICO questions and the clinical finding literature summary related to these PICO questions. Compiling the variations of the PICO questions and the integration harmonization with the SOAP cases has been done using the GraphQL API and the flexibility of the Neo4J graph-based representations for both the SOAP cases and the PubMed literature objects.

Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn’s disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours for reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning including deep learning has failed to report robust results because of the lack of large samples to train its neural nets. In this paper we are describing a thick data approach that learns from few anchor images provided by sound data sets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies at any CE video and allow these candidate frames to a feature extraction process to provide representative measures on the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach as reported accuracy of detecting Crohn’s Disease based on availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine tune our approach by adding more thick data methods for enhancing the diagnosis accuracy.

Data integration of health through connected services can save lives in the event of a medical emergency or to provide efficient and effective interventions for the benefit of the patients through the integration of bedside and bench side clinical research. Such integration will support all wind of change in healthcare for being predictive, pre-emptive, personalized, problem-oriented and participatory. Prototyping a healthcare system that enables data integration is a big challenge for healthcare for a long time. However, an innovative solution started to emerge by focusing on problem lists where everything can connect the problem list forming a growing graph. This notion has been introduced by Dr. Lawrence Weed in early 70’s but the enabling technologies weren’t mature enough to provide a successful implementation prototype. In this article we are describing our efforts in prototyping Dr. Lawrence Weed problem-oriented medical record (POMR) and his patient case schema (SOAP) to shape a prototype for connected health. For this we are using the TypeGraphQL API and our enterprise-based QL4POMR to describe a Web-Based gateway for healthcare services connectivity. Our prototype has reported success in connecting to the HL7 FHIR medical record and the OpenTarget biomedical repositories.

Crohn's disease (CD) is a chronic inflammatory disease characterized by transmural inflammation and may affect any part of the gastrointestinal (GI) tract, from the mouth to the perianal area. Crohn's disease most commonly affects the colon and the last part of the small intestine (ileum). Crohn’s disease causes various lesions in the mucosa, which is the inner layer of the gut. CD presents with focal ulcerations, erythema and edema adjacent to areas of normal appearing mucosa resulting in heterogeneous patchy patterns of disease. Knowing the type and the extent of these patterns is important for the clinicians to provide the right treatments. The medical treatment aims at keeping the disease in remission and abating flares, whereas surgical treatment is indicated to address complications that are beyond the efficacy of the medical treatment. The videocolonoscopy is considered the gold standard in examining the colon and the terminal ileum and the video capsule endoscopy (VCE) to examine the entire small bowel. Examination of the video of both viewing procedures can be enhanced by using computer vision and machine learning techniques. In this paper, we have conducted our first investigation to cluster capsule endoscopy video frames from the small bowel into five CD clusters. We call our approach the CD lesion variability sensing as the uses pipeline of variability recognition utilizing thick data image augmentation techniques and deep learning that have the ability to learn such variability from few samples using Siamese neural network (SSN) with triple loss and fuzzy filter that uses structural similarity index (SSIM). The accuracy of our SSN with the triple loss function reached 68% and our added fuzzy filter increased it to reach over 75%. This is only the start of our investigation in this complex field.

Comorbidity is the presence of two or more clinical conditions encountering a patient at the time of visit, hospitalization or being with the patient for a considerable time. Most of these conditions are chronic conditions. Treatment of comorbidity and patients with comorbid diseases poses a major health issue for millions of people worldwide and requires collaboration between clinical providers as well as the use of knowledge cross domains of practice. Having multiple diseases inevitably lead to the use of multiple drugs, a condition known as polypharmacy. This article investigates internists might how to tackle polypharmacy using our previously developed QL4POMR which is kind of medical translation system. QL4POMR provide clinicians with SOAP to describe the patient case at the bedside and link it to the biomedical repositories like the OpenTargets and DrugBank. Based on this link clinician can identify issues associated with polypharmacy like disease to drug interactions or drug to drug interactions. Polypharmacy for comorbid conditions like asthma and hypertension has been investigated as proof of concept.

Research in translational medicine (TM) is growing rapidly due to the growing believe that most valuable data source for the biomedical discoveries come from the basic research on clinical care extracted from patient cases descriptions. Bridging the clinical care findings at the bedside with the biomedical knowledge at the bench provide insights that can improve patient outcomes and lead to the development of new drugs and therapies. Research on translational medicine gained a great deal of prominence through the establishment of governmental and institutional boards like the US Clinical and Translational Science Awards (CTSA) and the EU Seventh Framework Program (FP7). TM research is ultimately requires data integration on a massive scale between variety of clinical data. In order to accelerate medical discovery, the integration of biomedical data needs to happen both at the physician point of care level via the electronic health records (EHRs) and at bench top applications and repositories. However, data integration is a great challenge to healthcare as the legacy technologies used are built around the REST APIs to get data into these biomedical applications and systems. The REST protocol is aging as it has been the technology used for the last 20 years. Major information technology venders like Facebook and Google have replaced REST with GraphQL allowing changes on the client side to fetch new data from the server without the need for extra work on the server. In this paper we are describing our efforts to integrate the problem oriented medical record (called QL4POMR that is based on GraphQL) which has been defined around the notion of SOAP (Subjective, Objective, Assessment and Planning) to describe patient cases with the HL7 FHIR electronic record and the HL7 IPS patient summary as well as with other external biomedical repositories like the OpenTargets and the Drug Bank. The integration has been described via adding a data layer on top of the QL4POMR to enable the schema integration and querying from all these external sources via the GraphQL server. Our initial experimentations show success in discovering drug adverse events and in recommending alternative drugs at the time of prescribing new medication to patients and completing the planning section of SOAP. More research will continue to draw other translational associations based on the integration provided by our QL4POMR framework with OpenTargets and Drug Bank.

The papers in this special section focus on data analytics for public health care. Growing and intensive attention for the use of data analytics in healthcare due to its proven benefits to gain insights on the macro and micro depths to support decisionmaking at the patient, clinical, bench side and business levels. Health data analytics contributed to the improvements to patient care, faster and more accurate diagnoses, preventive measures, more personalized treatment, narrow the gap between bedside and bench side and provide more informed decision-making. However, the recent pandemic of COVID-19 draw the world attention to need to extend the power of data analytics beyond the legacy data channels including Electronic Health Records (EHRs) and Patient Portals. Data analytics can assist population health management in improving patient outcomes, enhancing care management, and address social determinants of health. As the population health analytics continues to grow in the healthcare space, healthcare systems must be able to gather data from multiple sources, apply variety of data analytics (e.g., Descriptive Analytics, Predictive Analytics Prescriptive Analytics), and manage the provide insights for the population.

Since late 1960s when Dr. Larry Weed painted the roadmap to the formal process of medical diagnosis and the way it should structured around problem list based on what he calls SOAP note (Subjective, Objective, Assessment and the Plan), no real implementation or integration of his vision in providing the 'glue' to link the number of required layers, including the SOAP structuring, semantics of assessments and workflows, clinical decision support systems logic, task planning and querying each case based on flexible schema. Diagnosis, as Weed put it, a process that can describe a clear, logical and systematic approach for clinical diagnosis based on Weed's approach when a patient encountered a clinical issue. This process need to be modeled a service that can integrate with the other clinical systems and services including the medical record systems. In this article we are proposing such service based on Weeds approach for the clinical diagnosis purposes. The described service starts with a SOAP schema that utilizes the GraphQL standard and its associated toolkits to offer physicians and pre-clerkships the mean for describing diagnosis of clinical cases according to the Weed's approach. Particular attention has been given to processing and refining of the of SOAP diagnosis note through primitives like create, retrieve, update and delete (CRUD).

Machine learning including deep learning cannot effectively result in reliable and robust accuracy in classifying medical images unless the neural net is trained on extremely large annotated samples. With many medical practices like the inflammatory bowel diseases (IBD), the annotated training data is relatively small requiring more additive techniques to bring focus to the context and then to predict important indicators like identifying acute ulcerative colitis regions of interest. This paper introduced the notion of thick data that can bring the required context by adding the expert heuristics in the form of anchor images and along with the use of Siamese neural network that uses triple ranking loss, it can rate the ulcerative colitis severity level according to the Mayo endoscopic (MES) scoring system. Many other thick data techniques can be added as cascading filters on this core model. We are anticipating many other results to follow in our future research.

Physicians and Clerkships follow the SOAP note in describing and presenting patient cases. The SOAP note was originated from the problem-oriented medical record (POMR) developed nearly 60 years ago by Lawrence Weed, MD. However, the POMR/SOAP is not commonly found in electronic medical records (EMR) used today due to the flexible nature of building patient cases that requires complex harmonization with variety of patient case schemas. In this article we attempted to use the GraphQL API for harmonizing patient case data communicated with different care interface and providing the query on the data. This harmonization interface is called QL4POMR as a GraphQL implementation to the POMR SOAP note. Physicians can used this interface to describe and present any patient case for the purpose of diagnosis and prognosis with a varying backend. The QL4POMR implemented a mapping module to map graphs from POMR to HL7 FHIR and vise versa.

Classification tasks face several issues when applied to complex data sets and sophisticated images such as CT scans. Long training times are needed to properly train traditional networks to classify images, as well as the need for large amounts of data for these networks to draw accurate conclusions. Even when supplied with large datasets, popular neural networks like VGG and ResNet fail to classify images accurately and consistently for sensitive tasks like identifying COVID-19 in a CT lung scan. To overcome these challenges, we apply Siamese neural network architecture, which has been reported to reduce training times and required training data, to a sequential network. To further empower this network, we incorporate thick data heuristics into the CT image dataset, specifically, we annotate areas of interest in the images that a radiologist would be looking for to make a diagnosis, such as ground glass opacities. Our network outperforms five leading image classification neural networks by about 3% when classifying the same CT lung scan images as positive or negative for COVID-19. By applying data thickening heuristics, we have shown that accuracy is improved, and suspect that the accuracy will continue to increase as more heuristics based on more radiologists and imaging experts are to be added on top of what we have considered in this paper.

Abstract Social computing is a disruptive technology that is changing how we do business and building the enterprises. Mining consumer insights and consumer segmentation are the new drivers that are likely to be the cornerstone of developing new service innovations and social interactions. This special issue was designed to stimulate research on the data mining tools and methods for social computing. In this editorial, we are outlining the broad research framework on social computing, including the papers in this special issue and areas for future research.

As blockchain technology is becoming a driving force in the global economy, it is also gaining critical acclaim in the e-commerce industry. Both the blockchain and e-commerce are inseparable as they involve transactions. Blockchain protect transactions and e-commerce activities rely on them. Blockchain technology enables a decentralized marketplace to support important business activities like secure payments, managing the supply chain and reducing the fraud to mention few. In this special issue editorial we are introducing 11 research articles in this hot area of research that were selected by our reviewers from over than 250 submissions. As blockchain technology is becoming a driving force in the global economy, it is also gaining critical acclaim in the e-commerce industry. Both the blockchain and e-commerce are inseparable as they involve transactions. Blockchain protect transactions and e-commerce activities rely on them. Blockchain technology enables a decentralized marketplace to support important business activities like secure payments, managing the supply chain and reducing the fraud to mention few. In this special issue editorial we are introducing 11 research articles in this hot area of research that were selected by our reviewers from over than 250 submissions.

Every healthcare unit is experiencing tremendous pressure to improve its practice quality across several dimensions. Multiple bodies of literature support the importance of establishing community of practice (CoP) to enrich the professional practice and add the expert context on the patient cases. The CoP emphasizes the importance of qualitative social learning and connectivity as preferred sources of knowledge updates to guide the practice rather than using the mere direct quantitative evidence. Social learning and connectivity in CoP is a complex sociotechnical process that takes an abstract idea through a cycle of participation and reification to derive more thickened context and refined knowledge that will help largely the accuracy of decision making. This process is not a straightforward one requiring the use of suitable hyper structure for representing the contextual evolving knowledge as well as a flexible infrastructure to enable CoP learning from experts, agents and connected services as well as other sources of data and knowledge. This article focuses on using the notion of workflow as the hyper structure and Node-RED as the platform that can facilitate CoP learning and connectivity. The focus is on using the CoP Node-RED workflows in healthcare settings to provide basic collaboration and connectivity as well as extensions to facilitate higher participation, learning, and connectivity to arrive at reification of the practice experience. With Node-RED workflows ideas can be represented as flows and sub flows where it can be shared with other CoP members as JSON hyper structure for further improvement, analytics and decision making.

SUMMARY: The Pathogen-Host Analysis Tool (PHAT) is an application for processing and analyzing next-generation sequencing (NGS) data as it relates to relationships between pathogens and their hosts. Unlike custom scripts and tedious pipeline programming, PHAT provides an integrative platform encompassing raw and aligned sequence and reference file input, quality control (QC) reporting, alignment and variant calling, linear and circular alignment viewing, and graphical and tabular output. This novel tool aims to be user-friendly for life scientists studying diverse pathogen-host relationships. AVAILABILITY AND IMPLEMENTATION: The project is available on GitHub (https://github.com/chgibb/PHAT) and includes convenient installers, as well as portable and source versions, for both Windows and Linux (Debian and RedHat). Up-to-date documentation for PHAT, including user guides and development notes, can be found at https://chgibb.github.io/PHATDocs/. We encourage users and developers to provide feedback (error reporting, suggestions and comments).

In everyday software development, you often encounter the same problems again and again. Rather than solve the same problem each time, it makes sense to document the solution and re-use it. Design patterns are essentially documented, generalized solutions to common problems in application development. This is particularly the case in designing collaborative virtual learning systems that work under ubiquitous peer-to-peer grid environments, in which change is inevitable. This article introduces a generic design model for such systems based on the model-view-controller design pattern, which solves the traditional design problems by decoupling data access, business logic, and data presentation and user interaction.