Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review

Background: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: 1) medical note classification, 2) clinical entity recognition, 3) text summarisation, 4) deep learning (DL) and transfer learning architecture, 5) information extraction, 6) Medical language translation and 7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and Discussion: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification

Translational Bioinformatics for Human Reproductive Biology Research: Examples, Opportunities and Challenges for a Future Reproductive Medicine

Since 1978, with the first IVF (in vitro fertilization) baby birth in Manchester (England), more than eight million IVF babies have been born throughout the world, and many new techniques and discoveries have emerged in reproductive medicine. To summarize the modern technology and progress in reproductive medicine, all scientific papers related to reproductive medicine, especially papers related to reproductive translational medicine, were fully searched, manually curated and reviewed. Results indicated whether male reproductive medicine or female reproductive medicine all have made significant progress, and their markers have experienced the progress from karyotype analysis to single-cell omics. However, due to the lack of comprehensive databases, especially databases collecting risk exposures, disease markers and models, prevention drugs and effective treatment methods, the application of the latest precision medicine technologies and methods in reproductive medicine is limited.This research was funded by Project of Natural Science Foundation of Gansu Province (20JR5RA363); Project of Gansu Provincial Education Department (2020B-003)

Artificial intelligence applications in disease diagnosis and treatment: recent progress and outlook

The use of computers and other technologies to replicate human-like intelligent behaviour and critical thinking is known as artificial intelligence (AI).The development of AI-assisted applications and big data research has accelerated as a result of the rapid advancements in computing power, sensor technology, and platform accessibility that have accompanied advances in artificial intelligence. AI models and algorithms for planning and diagnosing endodontic procedures. The search engine evaluated information on artificial intelligence (AI) and its function in the field of endodontics, and it also incorporated databases like Google Scholar, PubMed, and Science Direct with the search criterion of original research articles published in English. Online appointment scheduling, online check-in at medical facilities, digitization of medical records, reminder calls for follow-up appointments and immunisation dates for children and pregnant women, as well as drug dosage algorithms and adverse effect warnings when prescribing multidrug combinations, are just a few of the tasks that already use artificial intelligence. Data from the review supported the conclusion that AI can play a significant role in endodontics, including the identification of apical lesions, classification and numbering of teeth, detection of dental caries, periodontitis, and periapical disease, diagnosis of various dental problems, aiding dentists in making referrals, and helping them develop more precise treatment plans for dental disorders. Although artificial intelligence (AI) has the potential to drastically alter how medicine is practised in ways that were previously unthinkable, many of its practical applications are still in their infancy and need additional research and development. Over the past ten years, artificial intelligence in ophthalmology has grown significantly and will continue to do so as imaging techniques and data processing algorithms improve

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

Repeatable and reusable research - Exploring the needs of users for a Data Portal for Disease Phenotyping

Background: Big data research in the field of health sciences is hindered by a lack of agreement on how to identify and define different conditions and their medications. This means that researchers and health professionals often have different phenotype definitions for the same condition. This lack of agreement makes it hard to compare different study findings and hinders the ability to conduct repeatable and reusable research. Objective: This thesis aims to examine the requirements of various users, such as researchers, clinicians, machine learning experts, and managers, for both new and existing data portals for phenotypes (concept libraries). Methods: Exploratory sequential mixed methods were used in this thesis to look at which concept libraries are available, how they are used, what their characteristics are, where there are gaps, and what needs to be done in the future from the point of view of the people who use them. This thesis consists of three phases: 1) two qualitative studies, including one-to-one interviews with researchers, clinicians, machine learning experts, and senior research managers in health data science, as well as focus group discussions with researchers working with the Secured Anonymized Information Linkage databank, 2) the creation of an email survey (i.e., the Concept Library Usability Scale), and 3) a quantitative study with researchers, health professionals, and clinicians. Results: Most of the participants thought that the prototype concept library would be a very helpful resource for conducting repeatable research, but they specified that many requirements are needed before its development. Although all the participants stated that they were aware of some existing concept libraries, most of them expressed negative perceptions about them. The participants mentioned several facilitators that would encourage them to: 1) share their work, such as receiving citations from other researchers; and 2) reuse the work of others, such as saving a lot of time and effort, which they frequently spend on creating new code lists from scratch. They also pointed out several barriers that could inhibit them from: 1) sharing their work, such as concerns about intellectual property (e.g., if they shared their methods before publication, other researchers would use them as their own); and 2) reusing others' work, such as a lack of confidence in the quality and validity of their code lists. Participants suggested some developments that they would like to see happen in order to make research that is done with routine data more reproducible, such as the availability of a drive for more transparency in research methods documentation, such as publishing complete phenotype definitions and clear code lists. Conclusions: The findings of this thesis indicated that most participants valued a concept library for phenotypes. However, only half of the participants felt that they would contribute by providing definitions for the concept library, and they reported many barriers regarding sharing their work on a publicly accessible platform such as the CALIBER research platform. Analysis of interviews, focus group discussions, and qualitative studies revealed that different users have different requirements, facilitators, barriers, and concerns about concept libraries. This work was to investigate if we should develop concept libraries in Kuwait to facilitate the development of improved data sharing. However, at the end of this thesis the recommendation is this would be unlikely to be cost effective or highly valued by users and investment in open access research publications may be of more value to the Kuwait research/academic community

Artificial intelligence in sickle disease

Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.Open Access funding provided by the Qatar National Library.Scopu