Explore the vast range of titles available.

\"This book covers the global digital revolution in the field of healthcare sector. The book encompasses chapters belong to the blockchain, Artificial Intelligence, and Big health data technologies\"-- Provided by publisher.

Background Medical databases normally contain large amounts of data in a variety of forms. Although they grant significant insights into diagnosis and treatment, implementing data exploration into current medical databases is challenging since these are often based on a relational schema and cannot be used to easily extract information for cohort analysis and visualization. As a consequence, valuable information regarding cohort distribution or patient similarity may be missed. With the rapid advancement of biomedical technologies, new forms of data from methods such as Next Generation Sequencing (NGS) or chromosome microarray (array CGH) are constantly being generated; hence it can be expected that the amount and complexity of medical data will rise and bring relational database systems to a limit. Description We present Graph4Med, a web application that relies on a graph database obtained by transforming a relational database. Graph4Med provides a straightforward visualization and analysis of a selected patient cohort. Our use case is a database of pediatric Acute Lymphoblastic Leukemia (ALL). Along routine patients’ health records it also contains results of latest technologies such as NGS data. We developed a suitable graph data schema to convert the relational data into a graph data structure and store it in Neo4j. We used NeoDash to build a dashboard for querying and displaying patients’ cohort analysis. This way our tool (1) quickly displays the overview of patients’ cohort information such as distributions of gender, age, mutations (fusions), diagnosis; (2) provides mutation (fusion) based similarity search and display in a maneuverable graph; (3) generates an interactive graph of any selected patient and facilitates the identification of interesting patterns among patients. Conclusion We demonstrate the feasibility and advantages of a graph database for storing and querying medical databases. Our dashboard allows a fast and interactive analysis and visualization of complex medical data. It is especially useful for patients similarity search based on mutations (fusions), of which vast amounts of data have been generated by NGS in recent years. It can discover relationships and patterns in patients cohorts that are normally hard to grasp. Expanding Graph4Med to more medical databases will bring novel insights into diagnostic and research.

\"This book aims to highlight the gaps and the transparency issues in the clinical research and trials processes and how there is a lack of information flowing back to researchers and patients involved in those trials. Lack of data transparency is an underlying theme within the clinical research world and causes issues of corruption, fraud, errors and a problem of reproducibility. Blockchain can prove to be a method to ensure a much more joined up and integrated approach to data sharing and improving patient outcomes. Surveys undertaken by creditable organisations in the healthcare industry are analysed in this book that show strong support for using blockchain technology regarding strengthening data security, interoperability and a range of beneficial use cases where mostly all respondents of the surveys believe blockchain will be important for the future of the healthcare industry. Another aspect considered in the book is the coming surge of healthcare wearables using Internet of Things (IoT) and the prediction that the current capacity of centralised networks will not cope with the demands of data storage. The benefits are great for clinical research, but will add more pressure to the transparency of clinical trials and how this is managed unless a secure mechanism like, blockchain is used\"--Publisher's description.

Large databases are now frequently utilized to identify and diagnose medical disorders using extreme learning procedures. Due to its promising implementation and processing speed, the fundamental model of the study utilized for real-time applications is the ensemble classifier. Standard approaches associated with extreme learning practices project the inability for error prediction based on output layer hidden's selection under static weight selection. In this study, a unique weighted extreme learning machine (WELM) for predicting medical conditions is introduced. This research will help to solve the classification problems in the field of healthcare and medicine holds significant promise for improving the accuracy, validation, accountability, and reliability of medical data classification tasks. Moreover, the key objective for the weighted extreme learner's approach is to predict the illness by outlining the high-dimensional data applied for the case study. Typically, the practice proposed an ensemble model which functions the enhances the accurate predictions of high-dimensional cancer showing significant impact of the diagnosis and treatment planning. Furthermore, the WELM model effectiveness is validated through other ensemble learning simulations that include neural networks, random forest, PSO + NN, and ACO + NN techniques. Evaluation for the outcomes is verified through various medical datasets with attributes of the liver, ovarian, lung, diabetes, and DLBCL-Stanford. The results show that the WELM described is very computationally efficient which is related to true positive rate, accuracy, and error rate.

\"Artificial Intelligence and Cybersecurity are two emerging fields that have phenomenal contribution towards technological advancement. As cyber-attacks increase, there is a need to identify threats and thwart attacks. This book will incorporate the recent developments that artificial intelligence brings to the cybersecurity world. This edited book provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered, and solutions adopted in the fields of Artificial Intelligence and Cybersecurity\"-- Provided by publisher.

BACKGROUND: Previous studies have identified large breed, male, outdoor dogs of herding or working groups to be at increased risk for Leptospira infection. Exposure risk factors may change over time, altering the signalment of dogs most commonly diagnosed with leptospirosis. OBJECTIVES: The objectives of this study were to evaluate possible signalment changes by decade in canine leptospirosis cases diagnosed at university veterinary hospitals in the United States and Canada using reports to the Veterinary Medical DataBase (VMDB) over a 40‐year period (1970–2009). ANIMALS: One thousand and ninety‐one dogs with leptospirosis diagnosed among 1,659,146 hospital visits. METHODS: Hospital prevalence of leptospirosis by decade was determined by age, sex, weight, and breed groups. Multivariable logistic regression models were created to evaluate the association between variables and the odds of disease for each decade. RESULTS: Veterinary Medical DataBase hospital prevalence of leptospirosis in dogs, after a marked decrease in the 1970s and low rates in the 1980s, began increasing in the 1990s. Hospital prevalence significantly increased in dogs between 2 and 9.9 years of age (P < .05) and in male dogs (P < .05) in each decade since the 1980s. Among weight groups in the most recent decade (2000–2009), dogs weighing <15 pounds had the greatest odds of being diagnosed with leptospirosis (P = .003). CONCLUSIONS AND CLINICAL IMPORTANCE: Hospital prevalence rates by age, weight, sex, and breed groups differed by decade. These changes may reflect changes in exposure risk, Leptospira vaccination practices for dogs, or both.

Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset.

Umbilical cord clamping strategies at preterm birth have the potential to affect important health outcomes. The aim of this study was to compare the effectiveness of deferred cord clamping, umbilical cord milking, and immediate cord clamping in reducing neonatal mortality and morbidity at preterm birth. We conducted a systematic review and individual participant data meta-analysis. We searched medical databases and trial registries (from database inception until Feb 24, 2022; updated June 6, 2023) for randomised controlled trials comparing deferred (also known as delayed) cord clamping, cord milking, and immediate cord clamping for preterm births (<37 weeks' gestation). Quasi-randomised or cluster-randomised trials were excluded. Authors of eligible studies were invited to join the iCOMP collaboration and share individual participant data. All data were checked, harmonised, re-coded, and assessed for risk of bias following prespecified criteria. The primary outcome was death before hospital discharge. We performed intention-to-treat one-stage individual participant data meta-analyses accounting for heterogeneity to examine treatment effects overall and in prespecified subgroup analyses. Certainty of evidence was assessed with Grading of Recommendations Assessment, Development, and Evaluation. This study is registered with PROSPERO, CRD42019136640. We identified 2369 records, of which 48 randomised trials provided individual participant data and were eligible for our primary analysis. We included individual participant data on 6367 infants (3303 [55%] male, 2667 [45%] female, two intersex, and 395 missing data). Deferred cord clamping, compared with immediate cord clamping, reduced death before discharge (odds ratio [OR] 0·68 [95% CI 0·51–0·91], high-certainty evidence, 20 studies, n=3260, 232 deaths). For umbilical cord milking compared with immediate cord clamping, no clear evidence was found of a difference in death before discharge (OR 0·73 [0·44–1·20], low certainty, 18 studies, n=1561, 74 deaths). Similarly, for umbilical cord milking compared with deferred cord clamping, no clear evidence was found of a difference in death before discharge (0·95 [0·59–1·53], low certainty, 12 studies, n=1303, 93 deaths). We found no evidence of subgroup differences for the primary outcome, including by gestational age, type of delivery, multiple birth, study year, and perinatal mortality. This study provides high-certainty evidence that deferred cord clamping, compared with immediate cord clamping, reduces death before discharge in preterm infants. This effect appears to be consistent across several participant-level and trial-level subgroups. These results will inform international treatment recommendations. Australian National Health and Medical Research Council.

Objective To examine the potential for publication bias, data availability bias, and reviewer selection bias in recently published meta-analyses that use individual participant data and to investigate whether authors of such meta-analyses seemed aware of these issues.Design In a database of 383 meta-analyses of individual participant data that were published between 1991 and March 2009, we surveyed the 31 most recent meta-analyses of randomised trials that examined whether an intervention was effective. Identification of relevant articles and data extraction was undertaken by one author and checked by another.Results Only nine (29%) of the 31 meta-analyses included individual participant data from “grey literature” (such as unpublished studies) in their primary meta-analysis, and the potential for publication bias was discussed or investigated in just 10 (32%). Sixteen (52%) of the 31 meta-analyses did not obtain all the individual participant data requested, yet five of these (31%) did not mention this as a potential limitation, and only six (38%) examined how trials without individual participant data might affect the conclusions. In nine (29%) of the meta-analyses reviewer selection bias was a potential issue, as the identification of relevant trials was either not stated or based on a more selective, non-systematic approach. Investigation of four meta-analyses containing data from ≥10 trials revealed one with an asymmetric funnel plot consistent with publication bias, and the inclusion of studies without individual participant data revealed additional heterogeneity between trials.Conclusions Publication, availability, and selection biases are a potential concern for meta-analyses of individual participant data, but many reviewers neglect to examine or discuss them. These issues warn against uncritically viewing any meta-analysis that uses individual participant data as the most reliable. Reviewers should seek individual participant data from all studies identified by a systematic review; include, where possible, aggregate data from any studies lacking individual participant data to consider their potential impact; and investigate funnel plot asymmetry in line with recent guidelines.

•There are significant disparities in reporting practices among different contributors to the MAUDE database.•Manufacturers reported a 29.2% rate of product problems. The Device Problem Codes (DPCs) were primarily device-related (36.3%), followed by unknown issues (32%) and clinical concerns (19.3%).•Distributors reported the lowest rate of product problems (2.7%); primarily citing clinical issues codes (85.7%).•User facilities had the highest rate of product problem indications (56.7%), primarily attributed to device issues codes (54.3%), with additional reports citing user codes (30.8%) and unknown codes (11.4%). Increasing medical device usage raises concerns regarding unexpected, potentially life-threatening events that pose public health risks. Such events are reported to the Food and Drug Administration (FDA), and cataloged in the Manufacturer and User Facility Device Experience (MAUDE) database, a vital tool for post market surveillance that requires information of high quality and integrity, particularly concerning reporting sources. To analyze reporting behavior among different reporters, including manufacturers, distributors, and user facilities, by examining differences in reported factors, namely: (1) device types, (2) product problem attribution, and (3) selection of Device Problem Codes (DPCs) associated with the root causes of events. Data spanning from 2005 to 2022 was retrieved from the MAUDE database using Python. Reports were grouped by reporter type and divided according to device type, and the reporter's indication of association with a product problem. Furthermore, events were classified by their respective DPCs, which were manually grouped into four categories: device issues, user issues, clinical issues, or unknown. The analysis revealed significant variations among reporters across all examined aspects (P < 0.00001 in all comparisons according to the proportion test). Manufacturers predominantly focused on infusion pumps (10.1%) and Implant, Endosseous, Root-Form (IER) devices (7.6%), with a product problem indication rate of 29.2% in their reports. Device issues codes were the most frequently observed category in their reports, comprising 36.3%, followed by unknown codes (32%) and clinical codes (19.3%). Distributors, on the other hand, primarily reported on IER devices (89%) and exhibited the lowest product problem indication rate at 2.7%. Clinical issues codes predominated in their reports, constituting 85.7%, followed by unknown codes (6.7%). User facilities concentrated on intravascular sets (4.7%), Electrosurgical, Cutting & Coagulation & Accessories (4.2%), and Ventricular (Assist) Bypass (4.1%). Remarkably, their product problem indication rate was the highest at 56.7%. They predominantly reported device issues codes (54.3%), followed by use codes (30.8%), and unknown codes (11.4%) The notable variation among different reporters underscores the importance of incorporating diverse sources when analyzing the database, particularly in cases where majority of reports originate from manufacturers. Decision-makers must approach database information comprehensively, considering data sources and diverse perspectives to inform regulatory decisions effectively. Developing strategies that encourage various reporters to contribute their unique and complementary viewpoints is advisable.