Phenotyping hypotensive patients in critical care using hospital discharge summaries

Among critically-ill patients, hypotension represents a failure in compensatory mechanisms and may lead to organ hypoperfusion and failure. In this work, we adopt a datadriven approach for phenotype discovery and visualization of patient similarity and cohort structure in the intensive care unit (ICU). We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically learn a d-dimensional feature representation of patients that captures the latent 'topic' structure of diseases, symptoms, medications, and findings documented in hospital discharge summaries. We then used the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to convert the d-dimensional latent structure learned from HDP into a matrix of pairwise similarities for visualizing patient similarity and cohort structure. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluated the clinical utility of the discovered topic structure in phenotyping critically-ill patients who experienced hypotensive episodes. Our results indicate that the approach is able to reveal clinically interpretable clustering structure within our cohort and may potentially provide valuable insights to better understand the association between disease phenotypes and outcomes.National Institutes of Health (U.S.) (Grant R01-EB017205)National Institutes of Health (U.S.) (Grant R01-EB001659)National Institutes of Health (U.S.) (Grant R01GM104987

Language modelling for clinical natural language understanding and generation

One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored. Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information. This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces

Discovering prescription patterns in pediatric acute-onset neuropsychiatric syndrome patients

OBJECTIVE: Pediatric acute-onset neuropsychiatric syndrome (PANS) is a complex neuropsychiatric syndrome characterized by an abrupt onset of obsessive-compulsive symptoms and/or severe eating restrictions, along with at least two concomitant debilitating cognitive, behavioral, or neurological symptoms. A wide range of pharmacological interventions along with behavioral and environmental modifications, and psychotherapies have been adopted to treat symptoms and underlying etiologies. Our goal was to develop a data-driven approach to identify treatment patterns in this cohort. MATERIALS AND METHODS: In this cohort study, we extracted medical prescription histories from electronic health records. We developed a modified dynamic programming approach to perform global alignment of those medication histories. Our approach is unique since it considers time gaps in prescription patterns as part of the similarity strategy. RESULTS: This study included 43 consecutive new-onset pre-pubertal patients who had at least 3 clinic visits. Our algorithm identified six clusters with distinct medication usage history which may represent clinician\u27s practice of treating PANS of different severities and etiologies i.e., two most severe groups requiring high dose intravenous steroids; two arthritic or inflammatory groups requiring prolonged nonsteroidal anti-inflammatory drug (NSAID); and two mild relapsing/remitting group treated with a short course of NSAID. The psychometric scores as outcomes in each cluster generally improved within the first two years. DISCUSSION AND CONCLUSION: Our algorithm shows potential to improve our knowledge of treatment patterns in the PANS cohort, while helping clinicians understand how patients respond to a combination of drugs

Studies On Side Effect Profile Of Treated Hypertensives On Selected Pharmacotherapy

Hypertension is a major public health problem because of its consequences. Its treatment is crucial and goals include to decrease ~orbidity and mortality associated with hypertension by decreaSing blood pressure using drugs that have good tolerance, dosing convenience and low cost. As many antihypertensives are now available, it is important to choose the most appropriate drug in terms of efficacy and with least side effect in order to improve compliance and the patient's quality of life. In HUSM, metoprolol is a widely used. Its metabolism is mediated by the polymorphic debrisoquinehydroxylase that exhibits large inter ethnic difference. As most of its adverse reactions could be due to excessive plasma concentrations, its use among our local population may therefore be associated with adverse effects due to reduced capacity of the local population to metabolise the drug. The objedives of this study were therefore to investigate the use of metoprolol in the treatment of hypertension in relation to the incidence of adverse drug reactions it caused. We would also determine whether patients who experienced adverse reactions suffered reduced quality of life. As controls, we used patients who received enalapril or enalapril combined with metoprolol in the treatment of their hypertension

Toward Precision Medicine in Intensive Care: Leveraging Electronic Health Records and Patient Similarity

The growing adoption of Electronic Health Record (EHR) systems has resulted in an unprecedented amount of data. This availability of data has also opened up the opportunity to utilize EHRs for providing more customized care for each patient by considering individual variability, which is the goal of precision medicine. In this context, patient similarity (PS) analytics have been introduced to facilitate data analysis through investigating the similarities in patients’ data, and, ultimately, to help improve the healthcare system. This dissertation is presented in six chapters and focuses on employing PS analytics in data-rich intensive care units. Chapter 1 provides a review of the literature and summarizes studies describing approaches for predicting patients’ future health status based on EHR and PS. Chapter 2 demonstrates the informativeness of missing data in patient profiles and introduces missing data indicators to use this information in mortality prediction. The results demonstrate that including indicators with observed measurements in a set of well-known prediction models (logistic regression, decision tree, and random forest) can improve the predictive accuracy. Chapter 3 builds upon the previous results and utilizes these missing indicators to reveal patient subpopulations based on their similarity in laboratory test ordering being used for them. In this chapter, the Density-based Spatial Clustering of Applications with Noise method, was employed to group the patients into clusters using the indicators generated in the previous study. Results confirmed that missing indicators capture the laboratory-test-ordering patterns that are informative and can be used to identify similar patient subpopulations. Chapter 4 investigates the performance of a multifaceted PS metric constructed by utilizing appropriate similarity metrics for specific clinical variables (e.g. vital signs, ICD-9, etc.). The proposed PS metric was evaluated in a 30-day post-discharge mortality prediction problem. Results demonstrate that PS-based prediction models with the new PS metric outperformed population-based prediction models. Moreover, the multifaceted PS metric significantly outperformed cosine and Euclidean PS metric in k-nearest neighbors setting. Chapter 5 takes the previous results into consideration and looks for potential subpopulations among septic patients. Sepsis is one of the most common causes of death in Canada. The focus of this chapter is on longitudinal EHR data which are a collection of observations of measurements made chronologically for each patient. This chapter employs Functional Principal Component Analysis to derive the dominant modes of variation in septic patients’ EHR's. Results confirm that including temporal data in the analysis can help in identifying subgroups of septic patients. Finally, Chapter 6 provides a discussion of results from previous chapters. The results indicate the informativeness of missing data and how PS can help in improving the performance of predictive modeling. Moreover, results show that utilizing the temporal information in PS calculation improves patient stratification. Finally, the discussion identifies limitations and directions for future research

Exploration and adaptation of large language models for specialized domains

Large language models have transformed the field of natural language processing (NLP). Their improved performance on various NLP benchmarks makes them a promising tool—also for the application in specialized domains. Such domains are characterized by highly trained professionals with particular domain expertise. Since these experts are rare, improving the efficiency of their work with automated systems is especially desirable. However, domain-specific text resources hold various challenges for NLP systems. These challenges include distinct language, noisy and scarce data, and a high level of variation. Further, specialized domains present an increased need for transparent systems since they are often applied in high stakes settings. In this dissertation, we examine whether large language models (LLMs) can overcome some of these challenges and propose methods to effectively adapt them to domain-specific requirements. We first investigate the inner workings and abilities of LLMs and show how they can fill the gaps that are present in previous NLP algorithms for specialized domains. To this end, we explore the sources of errors produced by earlier systems to identify which of them can be addressed by using LLMs. Following this, we take a closer look at how information is processed within Transformer-based LLMs to better understand their capabilities. We find that their layers encode different dimensions of the input text. Here, the contextual vector representation, and the general language knowledge learned during pre-training are especially beneficial for solving complex and multi-step tasks common in specialized domains. Following this exploration, we propose solutions for further adapting LLMs to the requirements of domain-specific tasks. We focus on the clinical domain, which incorporates many typical challenges found in specialized domains. We show how to improve generalization by integrating different domain-specific resources into our models. We further analyze the behavior of the produced models and propose a behavioral testing framework that can serve as a tool for communication with domain experts. Finally, we present an approach for incorporating the benefits of LLMs while fulfilling requirements such as interpretability and modularity. The presented solutions show improvements in performance on benchmark datasets and in manually conducted analyses with medical professionals. Our work provides both new insights into the inner workings of pre-trained language models as well as multiple adaptation methods showing that LLMs can be an effective tool for NLP in specialized domains

Annual SHOT Report 2013

SHOT is affiliated to the Royal College of PathologistsThe current risks from blood and blood component transfusion in the UK remains small with a risk of death at 8.0 and risk of major morbidity 51.8 per 1,000,000 components issued. New strategies are required to reduce the level of error in the transfusion process. Checklists are very useful to ensure all the steps of a process have been completed and should be introduced for transfusion as recommended in 2011 (http://www.shotuk.org/resources/current-resources/ ). Any unexpected transfusion reactions must be promptly recognised and treated and continue to be reported. Appropriate local review of incidents including root cause analysis where indicated will help to identify systems problems which can be remedied. All staff involved in transfusion are reminded that they have a duty of care to report adverse events which potentially or actually affect patient safety

Annual SHOT Report 2020

SHOT is affiliated to the Royal College of Pathologists. This report is produced by SHOT working with MHRAKey SHOT messages • Ensuring transfusion teams are well resourced: Clinical and laboratory teams can function optimally only if adequately staffed and well resourced. Healthcare leaders and management must ensure that staff have access to the correct information technology (IT) equipment and financial resources for safe and effective functioning • Addressing knowledge gaps, cognitive biases, and holistic training: Transfusion training with a thorough and relevant knowledge base in transfusion to all clinical and laboratory staff along with training in patient safety principles, understanding human factors and quality improvement approaches are essential. It is important that staff understand how cognitive biases contribute to poor decision making so that they can be mitigated appropriately • Patient safety culture: Fostering a strong and effective safety culture that is ‘just and learning’ is vital to ensure reduction in transfusion incidents and errors, thus directly improving patient safety • Standard operating procedures (SOP): SOP need to be simple, clear, easy to follow and explain the rationale for each step. This will then ensure staff are engaged and more likely to be compliant and follow the SOP • Learning from near misses: Reporting and investigating near misses helps identify and control risks before actual harm results, thus providing valuable opportunities to improve transfusion safety • Learning from the pandemic: The learning from the pandemic experiences should be captured in every organisation, by everyone in healthcare and used to improve patient safet

Annual SHOT Report 2021

SHOT is affiliated to the Royal College of Pathologists. This report is produced by SHOT working with MHRAPartnering with patients to enhance safety: Staff must ensure that they involve, engage and listen to patients as ‘partners’ in their own care, including transfusion support. Engaging patients, their families, and carers as ‘safety partners’ helps co-create safer systems, identify, and rectify preventable adverse events. Investing in safety - well-resourced systems with safe staffing levels: Healthcare leaders must ensure that systems are designed to support safe transfusion practice and allocate adequate resources in clinical and laboratory areas to ensure safe staffing levels, staff training in technical and non-technical skills and appropriate equipment, including IT systems. Just and learning safety culture: All healthcare leaders must promote a just, learning safety culture with a collective, inclusive, and compassionate leadership. Effective leaders must ensure staff have: access to adequate training, mentorship, and support. All staff in clinical and laboratory areas have a responsibility to speak up in case of any concerns and help embed the safety culture in teams