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Generative artificial intelligence (GAI) is rapidly transforming the healthcare landscape, offering innovative solutions in areas such as medical imaging, drug discovery, and clinical decision support. This comprehensive review examines the current role of GAI in healthcare, its potential benefits, drawbacks, challenges, and future research directions. By synthesizing recent literature and expert perspectives, this review provides a critical analysis of GAI’s impact on healthcare delivery, patient outcomes, and ethical considerations. While GAI shows promise in enhancing diagnostic accuracy, accelerating drug development, and improving healthcare efficiency, it also faces significant challenges related to data privacy, regulatory compliance, and ethical implementation. This review aims to inform healthcare professionals, researchers, and policymakers about the current state and future potential of GAI in healthcare, emphasizing the need for responsible development and deployment of these technologies.

Abstract Prognosticating Amyotrophic Lateral Sclerosis (ALS) presents a formidable challenge due to patients exhibiting different onset sites, progression rates, and survival times. In this study, we have developed and evaluated Machine Learning (ML) algorithms that integrate Ensemble and Imbalance Learning techniques to classify patients into Short and Non-Short survival groups based on data collected during diagnosis. We aimed to identify individuals at high risk of mortality within 24 months of symptom onset through analysis of patient data commonly encountered in daily clinical practice. Our Ensemble-Imbalance approach underwent evaluation employing six ML algorithms as base classifiers. Remarkably, our results outperformed those of individual algorithms, achieving a Balanced Accuracy of 88% and a Sensitivity of 96%. Additionally, we used the Shapley Additive Explanations framework to elucidate the decision-making process of the top-performing model, pinpointing the most important features and their correlations with the target prediction. Furthermore, we presented helpful tools to visualize and compare patient similarities, offering valuable insights. Confirming the obtained results, our approach could aid physicians in devising personalized treatment plans at the time of diagnosis or serve as an inclusion/exclusion criterion in clinical trials.

Abstract Background As the healthcare sector becomes increasingly reliant on technology, it is crucial for universities to offer bachelor’s degrees in health informatics (HI). HI professionals bridge the gap between IT and healthcare, ensuring that technology complements patient care and clinical workflows; they promote enhanced patient outcomes, support clinical research, and uphold data security and privacy standards. This study aims to evaluate accredited HI academic programs in Saudi Arabia. Methods This study employed a quantitative, descriptive, cross-sectional design utilising a self-reported electronic questionnaire consisting of predetermined items and response alternatives. Probability-stratified random sampling was also performed. Result The responses rates were 39% (n = 241) for students and 62% (n = 53) for faculty members. While the participants expressed different opinions regarding the eight variables being examined, the faculty members and students generally exhibited a strong level of consensus on many variables. A notable association was observed between facilities and various other characteristics, including student engagement, research activities, admission processes, and curriculum. Similarly, a notable correlation exists between student engagement and the curriculum in connection to research, attrition, the function of faculty members, and academic outcomes. Conclusion While faculty members and students hold similar views about the institution and its offerings, certain areas of divergence highlight the distinct perspectives and priorities of each group. The perception disparity between students and faculty in areas such as admission, faculty roles, and internships sheds light on areas of improvement and alignment for universities.

Introduction Venous thromboembolism (VTE) prevention and management are susceptible issues that require specific rules to sustain and oversee their functioning, as preventing VTE is a vital patient safety priority. This paper aims to investigate and provide recommendations for VTE assessment and reassessment through policy enhancement and development. Methods We reviewed different papers and policies to propose recommendations and theme analysis for policy modifications and enhancements to improve VTE prophylaxis and management. Results Recommendations were set to enhance the overall work of VTE prophylaxis, where the current VTE protocols and policies must ensure high levels of patient safety and satisfaction. The recommendations included working through a well-organized multidisciplinary team and staff engagement to support and enhance VTE’s work. Nurses’, pharmacists’, and physical therapists’ involvement in setting up the plan and prevention is the way to share the knowledge and paradigm of experience to standardize the management. Promoting policies regarding VTE prophylaxis assessment and reassessment using electronic modules as a part of the digital health process was proposed. A deep understanding of the underlying issues and the incorporation of generic policy recommendations were set. Conclusion This article presents recommendations for stakeholders, social media platforms, and healthcare practitioners to enhance VTE prophylaxis and management.

Summary: Difference-in-differences (DID) is a key tool for causal impact evaluation but faces challenges when applied to sensitive data restricted by privacy regulations. Obtaining consent can shrink sample sizes and reduce statistical power, limiting the analysis’s effectiveness. Federated learning addresses these issues by sharing aggregated statistics rather than individual data, though advanced federated DID software is limited. We developed a federated version of the Callaway and Sant’Anna difference-in-differences (CSDID), integrated into the DataSHIELD platform, adhering to stringent privacy protocols. Our approach reproduces key estimates and standard errors while preserving confidentiality. Using simulated and real-world data from a malaria intervention in Mozambique, we demonstrate that federated estimates increase sample sizes, reduce estimation uncertainty, and enable analyses when data owners cannot share treated or untreated group data. Our work contributes to facilitating the evaluation of policy interventions or treatments across centers and borders.

Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them.

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults. [ABSTRACT FROM AUTHOR]

AbstractPurposeMaterials and methodsResultsConclusion(s)\nIMPLICATIONS FOR REHABILITATIONTo examine (1) how much participation is represented in the benchmark Unified Medical Language System (UMLS) resource, and (2) to what extent that representation reflects the definition of child and youth participation and/or its related constructs per the family of Participation-Related Constructs framework.We searched and analysed UMLS concepts related to the term “participation.” Identified UMLS concepts were rated according to their representation of participation (i.e., attendance, involvement, both) as well as participation-related constructs using deductive content analysis.363 UMLS concepts were identified. Of those, 68 had at least one English definition, resulting in 81 definitions that were further analysed. Results revealed 2 definitions (2/81; 3%; 2/68 UMLS concepts) representing participation “attendance” and 18 definitions (18/81; 22%; 14/68 UMLS concepts) representing participation “involvement.” No UMLS concept definition represented both attendance and involvement (i.e., participation). Most of the definitions (11/20; 55%; 9/16 UMLS concepts) representing attendance or involvement also represent a participation-related construct.The representation of participation within the UMLS is limited and poorly aligned with the contemporary definition of child and youth participation. Expanding ontological resources to represent child and youth participation is needed to enable better data analytics that reflect contemporary paediatric rehabilitation practice.The representation of participation within the Unified Medical Language System (UMLS) is limited and poorly aligned with the contemporary definition of child and youth participation.From a contemporary paediatric rehabilitation perspective, using the current UMLS concepts for data analytics might result in misrepresentation of child and youth participation.There is need to expand ontological resources within the UMLS to fully and exclusively represent participation dimensions (attendance and involvement) in daily life activities to enable better data analytics that reflect contemporary paediatric rehabilitation practice.The representation of participation within the Unified Medical Language System (UMLS) is limited and poorly aligned with the contemporary definition of child and youth participation.From a contemporary paediatric rehabilitation perspective, using the current UMLS concepts for data analytics might result in misrepresentation of child and youth participation.There is need to expand ontological resources within the UMLS to fully and exclusively represent participation dimensions (attendance and involvement) in daily life activities to enable better data analytics that reflect contemporary paediatric rehabilitation practice. [ABSTRACT FROM AUTHOR]

Background: As healthcare increasingly integrates digital technologies, it is crucial to ensure that medical professionals possess robust digital competencies to maximize the benefits of these technologies. Mastering fundamental digital skills is essential before engaging with more complex digital health technologies. This study aimed to assess the general digital competencies of doctors in Chișinău, Moldova, and to identify specific areas needing improvement, thereby providing a foundation for tailored educational programs. Materials and Methods: In April 2024, data were collected using the My DigiSkills tool, a self-assessment online questionnaire developed under the European Commission's DigCompSAT project by ALL DIGITAL. This cross-sectional study involved resident doctors and specialists and from various medical institutions in Chișinău, who self-assessed their digital competence levels across five areas: Information and Data Literacy, Communication and Collaboration, Digital Content Creation, Safety, and Problem Solving. Upon completing the questionnaire, participants received their results via email and subsequently forwarded the results to the researchers. Results: The study revealed that over 50% of doctors in Chișinău demonstrated advanced skills in information and data literacy and communication. However, significant deficiencies were found in digital safety, with only 19% showing advanced skills in this area. Younger doctors generally exhibited higher digital competence levels. The results highlight the urgent need for customized training initiatives to address these gaps and enhance digital competencies among medical professionals. Conclusions: The study underscores the necessity for tailored educational programs to improve digital competencies, particularly in digital safety, because data privacy and security are paramount in the medical context. [ABSTRACT FROM AUTHOR]

Background: Medication dosing calculation errors can cause significant harm to patients, especially in the pediatric population. Crushing tablets for dose division purposes may increase the risk of calculation errors, which can lead to incorrect dosing and compromised patient safety. This study aimed to develop a calculator to eliminate calculation errors associated with dose division. Methods: Using the Wix platform, a group of pharmacists created a user-friendly webpage "Dose 4 You." To enable accurate dose division calculations, the advanced language model Chat GPT and Visual Studio were used. The tool assists healthcare professionals through a step-by-step process, allowing them to enter the necessary dose and medication requirements. The Dose 4 You web page's reliability and feasibility were assessed using retrospective data and validated questionnaires, including the System Usability Scale (SUS), respectively and a Likert scale-based acceptance questionnaire. Results: The Dose 4 You website calculated the required amount of powdered tablet to achieve the desired dose with 100% accuracy. The obtained SUS score was 88.38, indicating excellent usability. The average score of all questions for acceptance was found to be 4.7 ± 0.15 indicating a strong agreement on the tool's usefulness and effectiveness. Conclusion: Dose 4 You is a reliable tool that improves patient safety by streamlining dose calculations and lowering calculation errors. The tool's ease of use, practicality in daily clinical practice, and potential to reduce medication errors are highlighted by the positive perception among healthcare professionals. Dose 4 You's successful implementation demonstrates the power of technology and collaboration in transforming medication administration and improving patient outcomes. Similar innovative solutions to optimize healthcare practices can be explored in future health informatics endeavors. [ABSTRACT FROM AUTHOR]

Background: Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. Objectives: We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. Methods: In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Patients: Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. Results: The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. Conclusion: This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients. [ABSTRACT FROM AUTHOR]

In response to challenges associated with extensive documentation practices within the NHS, this paper presents the outcomes of a structured brainstorming session as part of the Chief Nurse Fellows project titled 'Digital Documentation in Healthcare: Empowering Nurses and Patients for Optimal Care." Grounded in Dr. Rozzano Locsin's theory of "Technological Competency as Caring in Nursing," this project leverages a Venn diagram framework to integrate Digital Maturity Assessment (DMA) results with the "What Good Looks Like" (WGLL) Framework, the ANCC Pathway to Excellence, and the eHospital EPR program vision of University Hospitals of Leicester NHS Trust. Participants, including Clinical IT facilitators and nursing leaders, engaged in identifying synergies and gaps across digital proficiency, nursing excellence, and patient-centric care, contributing actionable insights towards an optimized digital patient care model. The findings emphasize the need for holistic digital solutions that enhance documentation efficiency, support staff excellence, and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Prognosticating Amyotrophic Lateral Sclerosis (ALS) presents a formidable challenge due to patients exhibiting different onset sites, progression rates, and survival times. In this study, we have developed and evaluated Machine Learning (ML) algorithms that integrate Ensemble and Imbalance Learning techniques to classify patients into Short and Non-Short survival groups based on data collected during diagnosis. We aimed to identify individuals at high risk of mortality within 24 months of symptom onset through analysis of patient data commonly encountered in daily clinical practice. Our Ensemble-Imbalance approach underwent evaluation employing six ML algorithms as base classifiers. Remarkably, our results outperformed those of individual algorithms, achieving a Balanced Accuracy of 88% and a Sensitivity of 96%. Additionally, we used the Shapley Additive Explanations framework to elucidate the decision-making process of the top-performing model, pinpointing the most important features and their correlations with the target prediction. Furthermore, we presented helpful tools to visualize and compare patient similarities, offering valuable insights. Confirming the obtained results, our approach could aid physicians in devising personalized treatment plans at the time of diagnosis or serve as an inclusion/exclusion criterion in clinical trials. [ABSTRACT FROM AUTHOR]

Background: As the healthcare sector becomes increasingly reliant on technology, it is crucial for universities to offer bachelor's degrees in health informatics (HI). HI professionals bridge the gap between IT and healthcare, ensuring that technology complements patient care and clinical workflows; they promote enhanced patient outcomes, support clinical research, and uphold data security and privacy standards. This study aims to evaluate accredited HI academic programs in Saudi Arabia. Methods: This study employed a quantitative, descriptive, cross-sectional design utilising a self-reported electronic questionnaire consisting of predetermined items and response alternatives. Probability-stratified random sampling was also performed. Result: The responses rates were 39% (n = 241) for students and 62% (n = 53) for faculty members. While the participants expressed different opinions regarding the eight variables being examined, the faculty members and students generally exhibited a strong level of consensus on many variables. A notable association was observed between facilities and various other characteristics, including student engagement, research activities, admission processes, and curriculum. Similarly, a notable correlation exists between student engagement and the curriculum in connection to research, attrition, the function of faculty members, and academic outcomes. Conclusion: While faculty members and students hold similar views about the institution and its offerings, certain areas of divergence highlight the distinct perspectives and priorities of each group. The perception disparity between students and faculty in areas such as admission, faculty roles, and internships sheds light on areas of improvement and alignment for universities. [ABSTRACT FROM AUTHOR]

Biomedical informatics can be considered as a multidisciplinary research and educational field situated at the intersection of computational sciences (including computer science, data science, mathematics, and statistics), biology, and medicine. In recent years, there have been advances in the field of biomedical informatics. The current article highlights some interesting state-of-the-art research outcomes in these fields. These include research outcomes in areas like (i) computational biology and medicine, (ii) explainable artificial intelligence (XAI) in biomedical research and clinical practice, (iii) machine learning (including deep learning) methods and application for bioinformatics and healthcare, (iv) imaging informatics, as well as (v) medical statistics and data science. Moreover, the current article also discusses some existing challenges and potential future directions for these research areas to advance the fields of biomedical informatics. [ABSTRACT FROM AUTHOR]

COVID-19 surveillance across the United States is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen's kappa) and agreement across all datasets (Fleiss' kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic. [ABSTRACT FROM AUTHOR]

Key Clinical Message: Phenylketonuria (PKU) is a hereditary disorder caused by phenylalanine hydroxylase enzyme (PAH) defects that might cause severe brain damage. The current main treatment, dietary management, can prevent the symptoms if commenced early. However, it has side effects if used for a long time. Additionally, some patients with mild hyperphenylalaninemia (mHPA), who has serum phenylalanine levels <360 μmol/L, do not require treatment. Since the correlation between genotype and metabolic phenotype has been demonstrated earlier, genotype‐based detection of patients who do not need treatment might help with genetic counseling and choosing the most appropriate treatment option. In this study, we report an asymptomatic adult with mHPA who had never taken any medical intervention to control or lower her serum phenylalanine level (Phe). She had 179 μmol/L serum phenylalanine level and carried p.[V230A];[V230I] genotype. Her child was affected with phenylketonuria and had p.[V230A];[V230A] genotype. Both pathogenic variants detected in the asymptomatic adult with mHPA were computationally analyzed to assess their pathogenicity and the p.V230I pathogenic variant was demonstrated to be responsible for the mHPA phenotype in the asymptomatic adult detected in this study. The findings in this study could contribute to genetic counseling and treatment for families and individuals with p.[V2030I];[V230A] genotype. [ABSTRACT FROM AUTHOR]

Study Design: Modified DELPHI Consensus Process Objective: To agree a single unifying term and definition. Globally, cervical myelopathy caused by degenerative changes to the spine is known by over 11 different names. This inconsistency contributes to many clinical and research challenges, including a lack of awareness. Method: AO Spine RECODE-DCM (Research objectives and Common Data Elements Degenerative Cervical Myelopathy). To determine the index term, a longlist of candidate terms and their rationale, was created using a literature review and interviews. This was shared with the community, to select their preferred terms (248 members (58%) including 149 (60%) surgeons, 45 (18%) other healthcare professionals and 54 (22%) People with DCM or their supporters) and finalized using a consensus meeting. To determine a definition, a medical definition framework was created using inductive thematic analysis of selected International Classification of Disease definitions. Separately, stakeholders submitted their suggested definition which also underwent inductive thematic analysis (317 members (76%), 190 (59%) surgeons, 62 (20%) other healthcare professionals and 72 (23%) persons living with DCM or their supporters). Using this definition framework, a working definition was created based on submitted content, and finalized using consensus meetings. Results: Degenerative Cervical Myelopathy was selected as the unifying term, defined in short, as a progressive spinal cord injury caused by narrowing of the cervical spinal canal Conclusion: A consistent term and definition can support education and research initiatives. This was selected using a structured and iterative methodology, which may serve as an exemplar for others in the future. [ABSTRACT FROM AUTHOR]

The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based machine learning toolkit—to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics. [ABSTRACT FROM AUTHOR]

Background: Patient-reported outcomes (PROs) have potential to support integrated health and social care research and practice; however, evidence of their utilisation has not been synthesised. Objective: To identify PRO measures utilised in integrated care and adult social care research and practice and to chart the evidence of implementation factors influencing their uptake. Design: Scoping review of peer-reviewed literature. Data sources: Six databases (01 January 2010 to 19 May 2023). Study selection: Articles reporting PRO use with adults (18+ years) in integrated care or social care settings. Review methods: We screened articles against pre-specified eligibility criteria; 36 studies (23%) were extracted in duplicate for verification. We summarised the data using thematic analysis and descriptive statistics. Results: We identified 159 articles reporting on 216 PRO measures deployed in a social care or integrated care setting. Most articles used PRO measures as research tools. Eight (5.0%) articles used PRO measures as an intervention. Articles focused on community-dwelling participants (35.8%) or long-term care home residents (23.9%), with three articles (1.9%) focussing on integrated care settings. Stakeholders viewed PROs as feasible and acceptable, with benefits for care planning, health and wellbeing monitoring as well as quality assurance. Patient-reported outcome measure selection, administration and PRO data management were perceived implementation barriers. Conclusion: This scoping review showed increasing utilisation of PROs in adult social care and integrated care. Further research is needed to optimise PROs for care planning, design effective training resources and develop policies and service delivery models that prioritise secure, ethical management of PRO data. [ABSTRACT FROM AUTHOR]

Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods. [ABSTRACT FROM AUTHOR]

Background: Appendicitis is an inflammatory condition that requires timely and effective intervention. Despite being one of the most common surgically treated diseases, the condition is difficult to diagnose because of atypical presentations. Ultrasound and computed tomography (CT) imaging improve the sensitivity and specificity of diagnoses, yet these tools bear the drawbacks of high operator dependency and radiation exposure, respectively. However, new artificial intelligence tools (such as machine learning) may be able to address these shortcomings. Methods: We conducted a state-of-the-art review to delineate the various use cases of emerging machine learning algorithms for diagnosing and managing appendicitis in recent literature. The query ("Appendectomy" OR "Appendicitis") AND ("Machine Learning" OR "Artificial Intelligence") was searched across three databases for publications ranging from 2012 to 2022. Upon filtering for duplicates and based on our predefined inclusion criteria, 39 relevant studies were identified. Results: The algorithms used in these studies performed with an average accuracy of 86% (18/39), a sensitivity of 81% (16/39), a specificity of 75% (16/39), and area under the receiver operating characteristic curves (AUROCs) of 0.82 (15/39) where reported. Based on accuracy alone, the optimal model was logistic regression in 18% of studies, an artificial neural network in 15%, a random forest in 13%, and a support vector machine in 10%. Conclusions: The identified studies suggest that machine learning may provide a novel solution for diagnosing appendicitis and preparing for patient-specific post-operative complications. However, further studies are warranted to assess the feasibility and advisability of implementing machine learning-based tools in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Personalized medicine offers targeted therapy options for cancer treatment. However, the decision whether to include a patient into next-generation sequencing (NGS) testing is not standardized. This may result in some patients receiving unnecessary testing while others who could benefit from it are not tested. Typically, patients who have exhausted conventional treatment options are of interest for consideration in molecularly targeted therapy. To assist clinicians in decision-making, we developed a decision support tool using routine data from a precision oncology program. Methods: We trained a machine learning model on clinical data to determine whether molecular profiling should be performed for a patient. To validate the model, the model’s predictions were compared with decisions made by a molecular tumor board (MTB) using multiple patient case vignettes with their characteristics. Results: The prediction model included 440 patients with molecular profiling and 13,587 patients without testing. High area under the curve (AUC) scores indicated the importance of engineered features in deciding on molecular profiling. Patient age, physical condition, tumor type, metastases, and previous therapies were the most important features. During the validation MTB experts made the same decision of recommending a patient for molecular profiling only in 10 out of 15 of their previous cases but there was agreement between the experts and the model in 9 out of 15 cases. Conclusion: Based on a historical cohort, our predictive model has the potential to assist clinicians in deciding whether to perform molecular profiling. [ABSTRACT FROM AUTHOR]

Objective To identify factors influencing implementation of machine learning algorithms (MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a validated implementation framework. Materials and methods A systematic review of studies of implemented or trialed real-time clinical deterioration prediction MLAs was undertaken, which identified: how MLA implementation was measured; impact of MLAs on clinical processes and patient outcomes; and barriers, enablers and uncertainties within the implementation process. Review findings were then mapped to the SALIENT end-to-end implementation framework to identify the implementation stages at which these factors applied. Results Thirty-seven articles relating to 14 groups of MLAs were identified, each trialing or implementing a bespoke algorithm. One hundred and seven distinct implementation evaluation metrics were identified. Four groups reported decreased hospital mortality, 1 significantly. We identified 24 barriers, 40 enablers, and 14 uncertainties and mapped these to the 5 stages of the SALIENT implementation framework. Discussion Algorithm performance across implementation stages decreased between in silico and trial stages. Silent plus pilot trial inclusion was associated with decreased mortality, as was the use of logistic regression algorithms that used less than 39 variables. Mitigation of alert fatigue via alert suppression and threshold configuration was commonly employed across groups. Conclusions : There is evidence that real-world implementation of clinical deterioration prediction MLAs may improve clinical outcomes. Various factors identified as influencing success or failure of implementation can be mapped to different stages of implementation, thereby providing useful and practical guidance for implementers. [ABSTRACT FROM AUTHOR]

Radiology departments face challenges in delivering timely and accurate imaging reports, especially in high-volume, subspecialized settings. In this retrospective cohort study at a tertiary cancer center, we assessed the efficacy of an Automatic Assignment System (AAS) in improving radiology workflow efficiency by analyzing 232,022 CT examinations over a 12-month period post-implementation and compared it to a historical control period. The AAS was integrated with the hospital-wide scheduling system and set up to automatically prioritize and distribute unreported CT examinations to available radiologists based on upcoming patient appointments, coupled with an email notification system. Following this AAS implementation, despite a 9% rise in CT volume, coupled with a concurrent 8% increase in the number of available radiologists, the mean daily urgent radiology report requests (URR) significantly decreased by 60% (25 ± 12 to 10 ± 5, t = -17.6, p < 0.001), and URR during peak days (95th quantile) was reduced by 52.2% from 46 to 22 requests. Additionally, the mean turnaround time (TAT) for reporting was significantly reduced by 440 min for patients without immediate appointments and by 86 min for those with same-day appointments. Lastly, patient waiting time sampled in one of the outpatient clinics was not negatively affected. These results demonstrate that AAS can substantially decrease workflow interruptions and improve reporting efficiency. [ABSTRACT FROM AUTHOR]

Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R². Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort. [ABSTRACT FROM AUTHOR]