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Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals 1 – 3 , but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids and vasopressors are suboptimal and likely induce harm in a proportion of patients 1 , 4 – 6 . To tackle this sequential decision-making problem, we developed a reinforcement learning agent, the Artificial Intelligence (AI) Clinician, which extracted implicit knowledge from an amount of patient data that exceeds by many-fold the life-time experience of human clinicians and learned optimal treatment by analyzing a myriad of (mostly suboptimal) treatment decisions. We demonstrate that the value of the AI Clinician’s selected treatment is on average reliably higher than human clinicians. In a large validation cohort independent of the training data, mortality was lowest in patients for whom clinicians’ actual doses matched the AI decisions. Our model provides individualized and clinically interpretable treatment decisions for sepsis that could improve patient outcomes. A reinforcement learning agent, the AI Clinician, can assist physicians by providing individualized and clinically interpretable treatment decisions to improve patient outcomes.

In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.

Harnessing new digital technologies can improve access to health care but can also widen the health divide for those with poor digital literacy. This scoping review aims to assess the current situation of low digital health literacy in terms of its definition, reach, impact on health and interventions for its mitigation. A comprehensive literature search strategy was composed by a qualified medical librarian. Literature databases [Medline (Ovid), Embase (Ovid), Scopus, and Google Scholar] were queried using appropriate natural language and controlled vocabulary terms along with hand-searching and citation chaining. We focused on recent and highly cited references published in English. Reviews were excluded. This scoping review was conducted following the methodological framework of Arksey and O'Malley. A total of 268 articles were identified (263 from the initial search and 5 more added from the references of the original papers), 53 of which were finally selected for full text analysis. Digital health literacy is the most frequently used descriptor to refer to the ability to find and use health information with the goal of addressing or solving a health problem using technology. The most utilized tool to assess digital health literacy is the eHealth literacy scale (eHEALS), a self-reported measurement tool that evaluates six core dimensions and is available in various languages. Individuals with higher digital health literacy scores have better self-management and participation in their own medical decisions, mental and psychological state and quality of life. Effective interventions addressing poor digital health literacy included education/training and social support. Although there is interest in the study and impact of poor digital health literacy, there is still a long way to go to improve measurement tools and find effective interventions to reduce the digital health divide.

Digital data collection during routine clinical practice is now ubiquitous within hospitals. The data contains valuable information on the care of patients and their response to treatments, offering exciting opportunities for research. Typically, data are stored within archival systems that are not intended to support research. These systems are often inaccessible to researchers and structured for optimal storage, rather than interpretability and analysis. Here we present MIMIC-IV, a publicly available database sourced from the electronic health record of the Beth Israel Deaconess Medical Center. Information available includes patient measurements, orders, diagnoses, procedures, treatments, and deidentified free-text clinical notes. MIMIC-IV is intended to support a wide array of research studies and educational material, helping to reduce barriers to conducting clinical research. Measurement(s) Homo sapiens Technology Type(s) Electronic Health Record Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment hospital Sample Characteristic - Location Commonwealth of Massachusetts

Recent years have seen a surge of studies in machine learning in health and biomedicine, driven by digitalization of healthcare environments and increasingly accessible computer systems for conducting analyses. Many of us believe that these developments will lead to significant improvements in patient care. Like many academic disciplines, however, progress is hampered by lack of code and data sharing. In bringing together this PLOS ONE collection on machine learning in health and biomedicine, we sought to focus on the importance of reproducibility, making it a requirement, as far as possible, for authors to share data and code alongside their papers.

Critical care patients are monitored closely through the course of their illness. As a result of this monitoring, large amounts of data are routinely collected for these patients. Philips Healthcare has developed a telehealth system, the eICU Program, which leverages these data to support management of critically ill patients. Here we describe the eICU Collaborative Research Database, a multi-center intensive care unit (ICU)database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States. The database is deidentified, and includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, treatment information, and more. Data are publicly available after registration, including completion of a training course in research with human subjects and signing of a data use agreement mandating responsible handling of the data and adhering to the principle of collaborative research. The freely available nature of the data will support a number of applications including the development of machine learning algorithms, decision support tools, and clinical research.

Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness. To bring AI into maturity in clinical care, we advocate for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which we refer to as “AI-QI” units. We discuss how tools that have long been used in hospital quality assurance and quality improvement can be adapted to monitor static ML algorithms. On the other hand, procedures for continual model updating are still nascent. We highlight key considerations when choosing between existing methods and opportunities for methodological innovation.

Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes transparency, human oversight and task-specific performance reporting. We also introduce an interactive website ( https://tripod-llm.vercel.app/ ) facilitating easy guideline completion and PDF generation for submission. As a living document, TRIPOD-LLM will evolve with the field, aiming to enhance the quality, reproducibility and clinical applicability of LLM research in healthcare through comprehensive reporting. TRIPOD-LLM (transparent reporting of a multivariable model for individual prognosis or diagnosis–large language model) is a checklist of items considered essential for good reporting of studies that are developing or evaluating an LLM for use in healthcare settings. It is a ‘living guideline’ that emphasizes transparency, human oversight and task-specific performance reporting.

MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. Design Type(s) data integration objective Measurement Type(s) Demographics • clinical measurement • intervention • Billing • Medical History Dictionary • Pharmacotherapy • clinical laboratory test • medical data Technology Type(s) Electronic Medical Record • Medical Record • Electronic Billing System • Medical Coding Process Document • Free Text Format Factor Type(s) Sample Characteristic(s) Homo sapiens Machine-accessible metadata file describing the reported data (ISA-Tab format)

PurposeMechanical power (MP) may unify variables known to be related to development of ventilator-induced lung injury. The aim of this study is to examine the association between MP and mortality in critically ill patients receiving invasive ventilation for at least 48 h.MethodsThis is an analysis of data stored in the databases of the MIMIC–III and eICU. Critically ill patients receiving invasive ventilation for at least 48 h were included. The exposure of interest was MP. The primary outcome was in-hospital mortality.ResultsData from 8207 patients were analyzed. Median MP during the second 24 h was 21.4 (16.2–28.1) J/min in MIMIC-III and 16.0 (11.7–22.1) J/min in eICU. MP was independently associated with in-hospital mortality [odds ratio per 5 J/min increase (OR) 1.06 (95% confidence interval (CI) 1.01–1.11); p = 0.021 in MIMIC-III, and 1.10 (1.02–1.18); p = 0.010 in eICU]. MP was also associated with ICU mortality, 30-day mortality, and with ventilator-free days, ICU and hospital length of stay. Even at low tidal volume, high MP was associated with in-hospital mortality [OR 1.70 (1.32–2.18); p < 0.001] and other secondary outcomes. Finally, there is a consistent increase in the risk of death with MP higher than 17.0 J/min.ConclusionHigh MP of ventilation is independently associated with higher in-hospital mortality and several other outcomes in ICU patients receiving invasive ventilation for at least 48 h.