Publications by authors named "Oscar Perez Concha"

Encapsulating a patient's clinical narrative into a condensed, informative summary is indispensable to clinical coding. The intricate nature of the clinical text makes the summarisation process challenging for clinical coders. Recent developments in large language models (LLMs) have shown promising performance in clinical text summarisation, particularly in radiology and echocardiographic reports, after adaptation to the clinical domain.

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Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection.

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Encouraged by the success of pretrained Transformer models in many natural language processing tasks, their use for International Classification of Diseases (ICD) coding tasks is now actively being explored. In this study, we investigated two existing Transformer-based models (PLM-ICD and XR-Transformer) and proposed a novel Transformer-based model (XR-LAT), aiming to address the extreme label set and long text classification challenges that are posed by automated ICD coding tasks. The Transformer-based model PLM-ICD, which currently holds the state-of-the-art (SOTA) performance on the ICD coding benchmark datasets MIMIC-III and MIMIC-II, was selected as our baseline model for further optimisation on both datasets.

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Both blood glucose and lactate are well-known predictors of organ dysfunction and mortality in critically ill patients. Previous research has shown that concurrent adjustment for glucose and lactate modifies the relationship between these variables and patient outcomes, including blunting of the association between blood glucose and patient outcome. We aim to investigate the relationship between ICU admission blood glucose and hospital mortality while accounting for lactate and diabetic status.

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Objective: Blood glucose measurements in the intensive care unit (ICU) are typically made at irregular intervals. This presents a challenge in choice of forecasting model. This article gives an overview of continuous time autoregressive recurrent neural networks (CTRNNs) and evaluates how they compare to autoregressive gradient boosted trees (GBT) in forecasting blood glucose in the ICU.

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Background: The narrative free-text data in electronic medical records (EMRs) contain valuable clinical information for analysis and research to inform better patient care. However, the release of free text for secondary use is hindered by concerns surrounding personally identifiable information (PII), as protecting individuals' privacy is paramount. Therefore, it is necessary to deidentify free text to remove PII.

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Early identification of vulnerable children to protect them from harm and support them in achieving their long-term potential is a community priority. This is particularly important in the Northern Territory (NT) of Australia, where Aboriginal children are about 40% of all children, and for whom the trauma and disadvantage experienced by Aboriginal Australians has ongoing intergenerational impacts. Given that shared social determinants influence child outcomes across the domains of health, education and welfare, there is growing interest in collaborative interventions that simultaneously respond to outcomes in all domains.

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Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers. However, this information is mixed with Personally Identifiable Information (PII) that presents risks to patient and clinician confidentiality. This paper presents an end-to-end de-identification framework to automatically remove PII from Australian hospital discharge summaries.

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Background: Medicare-subsidised Team Care Arrangements (TCAs) support Australian general practitioners to implement shared care between collaborating health professionals for patients with chronic medical conditions and complex needs. We assessed the prevalence of TCAs, factors associated with TCA uptake and visits to TCA-subsidised allied health practitioners, for adults newly diagnosed with cancer in New South Wales, Australia.

Methods: We carried out a retrospective individual patient data linkage study with 13 951 45 and Up Study participants diagnosed with incident cancer during 2006-16.

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International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents. HiLAT firstly fine-tunes a pretrained Transformer model to represent the tokens of clinical documents.

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Background: Dietary guidelines recommend limiting the intake of added sugars. However, despite the public health importance, most countries have not mandated the labeling of added-sugar content on packaged foods and beverages, making it difficult for consumers to avoid products with added sugar, and limiting the ability of policymakers to identify priority products for intervention.

Objective: The aim was to develop a machine learning approach for the prediction of added-sugar content in packaged products using available nutrient, ingredient, and food category information.

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Objective: Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care.

Materials And Methods: Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals.

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Purpose: To identify medications used disproportionately more or less among pregnant women relative to women of childbearing age.

Methods: Medication use among pregnant women in New South Wales, Australia was identified using linked perinatal and pharmaceutical dispensing data from 2006 to 2012. Medication use in women of childbearing age (including pregnant women) was identified using pharmaceutical dispensing data for a 10% random sample of the Australian population.

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Background: Cancer of unknown primary (CUP) is a late-stage malignancy with poor prognosis, but we know little about what diagnostic tests and procedures people with CUP receive prior to diagnosis. The purpose of this study was to determine how health service utilisation prior to diagnosis for people with cancer-registry notified CUP differs from those notified with metastatic cancer of known primary.

Methods: We identified people with a cancer registry notification of CUP (n = 327) from the 45 and Up Study, a prospective cohort of 266,724 people ≥45 years in New South Wales, Australia, matched with up to three controls with a diagnosis of metastatic cancer of known primary (n = 977).

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To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45,298 ICU stays for 33,150 patients.

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Background: The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission.

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Rational, Aims And Objectives: The aim of this review was to identify general theoretical frameworks used in online social network interventions for behavioral change. To address this research question, a PRISMA-compliant systematic review was conducted.

Methods: A systematic review (PROSPERO registration number CRD42014007555) was conducted using 3 electronic databases (PsycINFO, Pubmed, and Embase).

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Objective: To introduce and evaluate a method that uses electronic medical record (EMR) data to measure the effects of computer system downtime on clinical processes associated with pathology testing and results reporting.

Materials And Methods: A matched case-control design was used to examine the effects of five downtime events over 11-months, ranging from 5 to 300min. Four indicator tests representing different laboratory workflows were selected to measure delays and errors: potassium, haemoglobon, troponin and activated partial thromboplastin time.

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Objective: To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs).

Materials And Methods: A Bayesian Network model was built to estimate the probability of a hospitalized patient being "at home," in the hospital, or dead for each of the next 7 days. The network utilizes patient-specific administrative and laboratory data and is updated each time a new pathology test result becomes available.

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Background: The last two decades have seen an unprecedented growth in initiatives aimed to improve patient safety. For the most part, however, evidence of their impact remains controversial. At the same time, the healthcare industry has experienced an also unprecedented growth in the amount and variety of available electronic data.

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Background: Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model performance was evaluated alone, and in combination with simple disease-specific models.

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Background: Proposed causes for increased mortality following weekend admission (the 'weekend effect') include poorer quality of care and sicker patients. The aim of this study was to analyse the 7 days post-admission time patterns of excess mortality following weekend admission to identify whether distinct patterns exist for patients depending upon the relative contribution of poorer quality of care (care effect) or a case selection bias for patients presenting on weekends (patient effect).

Methods: Emergency department admissions to all 501 hospitals in New South Wales, Australia, between 2000 and 2007 were linked to the Death Registry and analysed.

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Background: Severe hypertension (SHT) (Blood Pressure, BP ≥ 180/110 mmHg) is associated with considerable morbidity and mortality, yet little is known about how it is managed. The purpose of this study is to examine the management of SHT by Australian general practitioners (GPs) and to explore its variance across patient characteristics and clinical practices.

Methods: Review of electronic medical records for a year before and after a recorded measure of SHT in 7,499 patients by 436 GPs in 167 clinics throughout Australia during 2008-2009.

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Accurate prediction of discharge time and identification of patients at risk of extended length of stay (LOS) can facilitate discharge planning and positively impact both the patient and the hospital in a variety of ways. To date, however, most studies only focus on the prediction of the overall LOS, which is generally estimated at admission time to hospital, emergency department or intensive care unit. This paper explores whether individual laboratory results can improve predictions of time of discharge as the tests become available.

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