JMIR Medical Informatics

IntroductionMedical records and physician notes often contain valuable information not organized in tabular form and usually require extensive manual processes to extract and structure. Large Language Models (LLMs) have shown remarkable abilities to understand, reason, and retrieve information from unstructured data sources (such as plain text), presenting the opportunity to transform clinical data into accessible information for clinical or research purposes. ObjectiveWe present PANDORA, an AI...

BackgroundThe digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This study presents an approach using natural language processing to extract clinical concepts from free-text which are used to automatically form diagnostic criteria for lung cancer from unstructured secondary-care data. MethodsPatients aged 40 and above wh...

There remain many unknowns regarding the onset and clinical course of the ongoing COVID-19 pandemic. We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling), to analyse the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the SESCAM Healthcare Network (Castilla La-Mancha, Spain) from the entire population with available EHRs (1,364,924 patients...

ObjectivesSeveral risk factors have been identified for severe clinical outcomes of COVID-19 caused by SARS-CoV-2. Some can be found in structured data of patients Electronic Health Records. Others are included as unstructured free-text, and thus cannot be easily detected automatically. We propose an automated real-time detection of risk factors using a combination of data mining and Natural Language Processing (NLP). Material and methodsPatients were categorized as negative or positive for SAR...

BackgroundArtificial intelligence (AI)-assisted diagnosis is considered to be the future direction of improving the efficiency and accuracy of pediatric diseases diagnosis, while the existing research based on AI are far from sufficient because of limited data amount, inadequate coverage of disease types, or high construction costs, and have not been applied on a large scale. We aimed to develop an accurate deep learning model trained on millions of real-world data to verify the feasibility of t...

BackgroundThe current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the "German Corona Consensus Dataset" (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data. MethodsBas...

BackgroundThe novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. ObjectiveThe objective of this...

IntroductionImproving postoperative patient recovery after cardiac surgery is a priority, but our current understanding of individual variations in recovery and factors associated with poor recovery is limited. We are using a health-information exchange platform to collect patient-reported outcome measures (PROMs) and wearable device data to phenotype recovery patterns in the 30-day period after cardiac surgery hospital discharge, to identify factors associated with these phenotypes and to inves...

ObjectivesThis study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based on clinical and imaging data. Materials and MethodsClinical data--demographics, signs, symptoms, comorbidities and blood test results--and chest CT scans of 346 patients from two hospitals in the Hubei province, China, were used to develop machine learning models for automated severity assessment of diagnosed COVID-19 cases. We compared the predictive power of clinical a...

BackgroundWith the advent of large language models (LLM), such as ChatGPT, natural language processing (NLP) is revolutionizing healthcare. We systematically reviewed NLPs role in rheumatology and assessed its impact on diagnostics, disease monitoring, and treatment strategies. MethodsFollowing PRISMA guidelines, we conducted a systematic search to identify original research articles exploring NLP applications in rheumatology. This search was performed in PubMed, Embase, Web of Science, and Sco...

IntroductionThe 2019 coronavirus (COVID-19) has led to unprecedented strain on healthcare facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here we present the results of an analytical model, PICTURE (Predicting Intensive Care Transfers and Other UnfoReseen Events), to identify patients at a high risk for imminent intensive care unit (ICU) transfer, resp...

BackgroundSARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision making during the pandemic. However...

BackgroundIn 2021, the Israeli Ministry of Health established the Kineret initiative for standardizing clinical data across its medical centers and making it accessible for secondary use, research, and development. The main objectives were to reduce burdens and bottlenecks in data extraction, data cleaning, and data sharing in order to enable the reuse of patient data for translational research. The Directorate of Government Medical Centers is the governing body for the network of government hea...

BackgroundWhile recent research efforts to reduce pressure ulcers in the clinical context have focused on key retrospective characteristics, little work has focused on creating real-time predictive models to prevent this avoidable hospital-acquired injury. Furthermore, existing machine learning heuristics often fail to surpass traditional statistical models or provide individual-level risk assessments with explanations for each patient. Thus, we sought to compare the predictive performance of fi...

Active screening for Tuberculosis (TB) is needed to optimize detection and treatment. However, current algorithms for verbal screening perform poorly, causing misclassification that leads to missed cases and unnecessary and costly laboratory tests for false positives. We investigated the role of machine learning to improve the predefined one-size-fits-all algorithm used for scoring the verbal screening questionnaire. We present a cost-sensitive machine learning classification for mass tuberculos...

I.A. ObjectiveClinical Decision Support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. We aimed to develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial Electronic Healt...

Medicine is characterized by its inherent ambiguity, i.e., the difficulty to identify and obtain exact outcomes from available data. Regarding this problem, electronic Health Records (EHRs) aim to avoid imprecisions in the data recording, for instance by its recording in an automatic way or by the integration of data that is both readable by humans and machines. However, the inherent biology and physiological processes introduce a constant epistemic uncertainty, which has a deep implication in t...

Acute myocardial infarction poses significant health risks and financial burden on healthcare and families. Prediction of mortality risk among AMI patients using rich electronic health record (EHR) data can potentially save lives and healthcare costs. Nevertheless, EHR-based prediction models usually use a missing data imputation method without considering its impact on the performance and interpretability of the model, hampering its real-world applicability in the healthcare setting. This study...

BackgroundClinical practice guidelines are important tools for clinical decision support, but monitoring guideline adherence manually is highly resource-intensive. Therefore, we developed an automated system for evaluating guideline adherence based on computer-interpretable representations of guidelines. We implemented the system across multiple university hospitals and assessed its validity and performance by comparing its guideline adherence evaluations to those conducted by medical profession...

BackgroundAsthma exacerbation is an acute or sub-acute episode of progressive worsening of asthma symptoms and can have significant impacts on patients daily life. In 2016, 12.4 million current asthmatics (46.9%) in the U.S. had at least one asthma exacerbation in the previous year. ObjectiveThe objectives of this study were to predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. MethodsWe proposed a time-sensitive attentive neural netw...