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Modelling disease progression of iron deficiency anaemia (IDA) following oral iron supplement prescriptions is a prerequisite for evaluating the cost-effectiveness of oral iron supplements. Electronic health records (EHRs) from the Clinical…

Objective Hospitals register information in the electronic health records (EHR) continuously until discharge or death. As such, there is no censoring for in-hospital outcomes. We aimed to compare different dynamic regression modeling…

The new era of large-scale data collection and analysis presents an opportunity for diagnosing and understanding the causes of health inequities. In this study, we describe a framework for systematically analyzing health disparities using…

Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with…

OBJECTIVE: To propose time-to-event estimators that help evaluate incident diagnostic coding and possible upcoding in Medicare as well as introduce an open-source software package that enables more reproducible methods development relevant…

Heart failure affects millions of people worldwide, significantly reducing quality of life and leading to high mortality rates. Despite extensive research, the relationship between heart failure and mortality rates among ICU patients is not…

Chronic diseases such as diabetes are quite prevalent in the world and are responsible for a significant number of deaths per year. In addition, treatments for such chronic diseases account for a high healthcare cost. However, research has…

The Intensive Care Unit (ICU) is one of the most important parts of a hospital, which admits critically ill patients and provides continuous monitoring and treatment. Various patient outcome prediction methods have been attempted to assist…

Acute brain dysfunction (ABD) is a common, severe ICU complication, presenting as delirium or coma and leading to prolonged stays, increased mortality, and cognitive decline. Traditional screening tools like the Glasgow Coma Scale (GCS),…

Hypoglycemia is a severe condition of decreased blood glucose, specifically below 70 mg/dL (3.9 mmol/L). This condition can often be asymptomatic and challenging to predict in individuals with type 1 diabetes (T1D). Research on hypoglycemic…

This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for…

Worldwide, many millions of people die suddenly and unexpectedly each year, either with or without a prior history of cardiovascular disease. Such events are sparse (once in a lifetime), many victims will not have had prior investigations…

Objectives-Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients (age 65 years and above) functional ability, physical health, and cognitive wellbeing. The majority of these patients suffer from…

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on…

Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests. The patterns of these values may be significant indicators of patients' clinical states and there…

The recent outbreak of the COVID-19 led to the death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US employed different strategies, including the mask mandate order issued by the states'…

Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict…

Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often co-occur with them, are major drivers of high healthcare…

Coding diagnosis and procedures in medical records is a crucial process in the healthcare industry, which includes the creation of accurate billings, receiving reimbursements from payers, and creating standardized patient care records. In…

Purpose: The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help address the COVID-19 pandemic. The purpose of this study was to…