Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature

Background: Healthcare-associated infections (HAIs) are the most frequent adverse events in healthcare and a global public health concern. Surveillance is the foundation for effective HAIs prevention and control. Manual surveillance is labor intensive, costly and lacks standardization. Artificial Intelligence (AI) and machine learning (ML) might support the development of HAI surveillance algorithms aimed at understanding HAIs risk factors, improve patient risk stratification, identification of transmission pathways, timely or real-time detection. Scant evidence is available on AI and ML implementation in the field of HAIs and no clear patterns emerges on its impact. Methods: We conducted a systematic review following the PRISMA guidelines to systematically retrieve, quantitatively pool and critically appraise the available evidence on the development, implementation, performance and impact of ML-based HAIs detection models. Results: Of 3445 identified citations, 27 studies were included in the review, the majority published in the US (n = 15, 55.6%) and on surgical site infections (SSI, n = 8, 29.6%). Only 1 randomized controlled trial was included. Within included studies, 17 (63%) ML approaches were classified as predictive and 10 (37%) as retrospective. Most of the studies compared ML algorithms' performance with non-ML logistic regression statistical algorithms, 18.5% compared different ML models' performance, 11.1% assessed ML algorithms' performance in comparison with clinical diagnosis scores, 11.1% with standard or automated surveillance models. Overall, there is moderate evidence that ML-based models perform equal or better as compared to non-ML approaches and that they reach relatively high-performance standards. However, heterogeneity amongst the studies is very high and did not dissipate significantly in subgroup analyses, by type of infection or type of outcome. Discussion: Available evidence mainly focuses on the development and testing of HAIs detection and prediction models, while their adoption and impact for research, healthcare quality improvement, or national surveillance purposes is still far from being explored

Advances in mass spectrometry-based cancer research and analysis: from cancer proteomics to clinical diagnostics

Introduction: The last 20 years have seen significant improvements in the analytical capabilities of biological mass spectrometry. Studies using advanced mass spectrometry (MS) have resulted in new insights into cell biology and the aetiology of diseases as well as its use in clinical applications. Areas Covered: This review will discuss recent developments in MS-based technologies and their cancer-related applications with a focus on proteomics. It will also discuss the issues around translating the research findings to the clinic and provide an outline of where the field is moving. Expert Opinion: Proteomics has been problematic to adapt for the clinical setting. However, MS-based techniques continue to demonstrate potential in novel clinical uses beyond classical cancer proteomics

Prognostics of recovery in hip fracture patients

Proximal femoral fractures (often denoted as hip fractures) are amongst the most prevalent fractures in older patients and associated with significant mortality and morbidity.Failure to recover to prefracture levels of function has important social and economic implications, as these patient’s risk losing their independence and self-reliance. The primary aim of this thesis is to provide a better understanding of the factors relevant for the functional prognosis of patients with a proximal femoral fracture.This thesis covers two parts, focusing on the effects of surgical aspects and patient demographics.​​​​​​​Outcomes of previously performed studies on prognostic factors of recovery proved hard to compare. This can be attributed to the high level of heterogeneity and methodology of these studies, for instance in the method to objectify recovery. For the studies in this thesis, we have opted to compare outcomes with the patients’ individual prefracture level of function. Surgical aspects, such as different approaches to place a prosthesis, seemed to have a reserved effect on recovery. Factors which seemed of conclusive relevance were health scores based on the comorbidity and prefracture level of function. This emphasizes the importance of a holistic and geriatric approach for patients with proximal hip fractures. ChipSoft, Castor EDC, Nutricia Nederland BV, Mathys Orthopaedics BV, ABN AMRO, Stichting Anna Fonds|NOREF, Haaglanden Medisch Centrum, Traumacentrum West, the department of Public Health and Primary Care of the LUMC, the Leiden University Libraries.LUMC / Geneeskund

Visualization, Prediction, and Causal Inference: Applications in Healthcare

The recent wave of data collection in the field of healthcare has opened up an ocean of possibilities to learn and develop new exploratory, diagnostic, and prognostic methods. This thesis explores how three fields of statistics (1) data visualization, (2) prediction, (3) and causal inference, can help us leverage this data in order to answer a wide range of questions in healthcare.Part I of this thesis presents a software package called superheat that can be used by researchers to visualize complex datasets and multi-faceted modeling results. The primary users of this software so far have been those in the medical research industry. In this thesis, we apply superheat in three case studies including (1) using a publicly available global organ donation database curated by the World Health Organization to understand and summarize the global organ donation trends, (2) visualizing groups of topics that appear in text data scraped from Google News, (3) examining model performance for a model designed to predict the brain's response to images using fMRI data. The theme of Part 1 of this thesis is visualization in healthcare.Part II of this thesis introduces an analysis for predicting a patient's risk of developing a Surgical Site Infection (SSI) following surgery. A SSI is an infection that occurs at the site of a surgery within 30 days post surgery, and is responsible for up to 30% of hospital acquired infections. This method was developed in collaboration with healthcare professionals including infectious disease experts and surgeons at UC Davis. The theme of Part 2 of this thesis is prediction in healthcare.Part III of this thesis presents a novel application of instrumental variables in causal inference, asking about the possible effectiveness of a "survival-benefit"-based liver transplant allocation scheme. The conclusion is that while there could be substantial benefit yielded from rethinking how organs are allocated, the feasibility of implementing such a scheme that relies drawing causal inferences from complex observational data is extremely difficult. The theme of Part 3 of this thesis is causal inference in healthcare

Utilizing artificial intelligence in perioperative patient flow:systematic literature review

Abstract. The purpose of this thesis was to map the existing landscape of artificial intelligence (AI) applications used in secondary healthcare, with a focus on perioperative care. The goal was to find out what systems have been developed, and how capable they are at controlling perioperative patient flow. The review was guided by the following research question: How is AI currently utilized in patient flow management in the context of perioperative care? This systematic literature review examined the current evidence regarding the use of AI in perioperative patient flow. A comprehensive search was conducted in four databases, resulting in 33 articles meeting the inclusion criteria. Findings demonstrated that AI technologies, such as machine learning (ML) algorithms and predictive analytics tools, have shown somewhat promising outcomes in optimizing perioperative patient flow. Specifically, AI systems have proven effective in predicting surgical case durations, assessing risks, planning treatments, supporting diagnosis, improving bed utilization, reducing cancellations and delays, and enhancing communication and collaboration among healthcare providers. However, several challenges were identified, including the need for accurate and reliable data sources, ethical considerations, and the potential for biased algorithms. Further research is needed to validate and optimize the application of AI in perioperative patient flow. The contribution of this thesis is summarizing the current state of the characteristics of AI application in perioperative patient flow. This systematic literature review provides information about the features of perioperative patient flow and the clinical tasks of AI applications previously identified

Small animal clinic and surgery

As part of the last step in concluding the Master’s degree in veterinary medicine, the current report was carried out. It is divided in three parts. The first part includes statistics regarding pathologies or symptoms of animals brought to Priory Veterinary Surgeons during the traineeship, with brief detailing of a disease or procedure relevant to each clinical and surgical area of small animal medicine. The second part is a review of available literature regarding canine non-Hodgkin lymphoma addressing its aetiology, diagnosis, immunophenotypes, presentations, therapeutics and prognosis. The third and last part is a description of a canine lymphoma case in a 9-year-old border collie with data regarding the diagnosis, management and the treatment protocol; Resumo: Clínica e Cirurgia de Pequenos Animais - O seguinte relatório foi elaborado como parte da última etapa para completar o mestrado em medicina veterinária. Está dividido em três partes. A primeira parte inclui a estatística das patologias ou sintomas dos animais que foram levados à Priory Veterinary Surgeons durante o período de estágio curricular, com um abreve revisão de uma doença ou procedimento relevante a cada área da clínica e cirurgia de medicina de animais de companhia. A segunda parte é uma revisão da literatura disponível sobre linfoma não-Hodgkin canino abordando a sua etiologia, diagnóstico, imunofenótipo, manifestação clínica, tratamento e prognóstico. A terceira e última parte é uma descrição de um caso de linfoma não Hodgkin canino de um border collie de 9 anos com dados sobre o diagnóstico, acompanhamento e tratamento

Parenteral application of drugs, uretheral catheterization

Obtaining parenteral application of drugs and urethral catheterization are essential skills for all physicians. Peripheral intravenous access is one of the simplest invasive procedures, but mastering this potentially lifesaving intervention requires refined skills and experience. It is required in a broad range of clinical applications, including intravenous drug administration, intravenous hydration, and transfusions of blood or blood components, as well as during surgery, during emergency care, and in other situations in which direct access to the bloodstream is needed