No abstract

Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine‐learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep‐learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural‐language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology‐focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.

Early prediction of patient outcomes is key to unlocking the potential for targeted preventive care. This protocol describes a practical workflow for developing deep learning risk models for early prediction of various clinical and operational outcomes using structured electronic health record (EHR) data, discussing the prediction of acute kidney injury (AKI) as an exemplar. The protocol consists of 34 steps grouped into the following stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty estimation, generalisability evaluation. Additionally, we demonstrate the application of this protocol to three other endpoints -mortality, length of stay and 30-day hospital readmission -for both continuous predictions (e.g. triggered every 6h) and static predictions (e.g. triggered at 24h post admission). The performance on these additional endpoints exceeded most comparable literature benchmarks. This protocol is accompanied by an open-source codebase that illustrates key considerations for EHR modeling and may be customised to alternate data formats and prediction tasks.

We developed a digitally enabled care pathway for acute kidney injury (AKI) management incorporating a mobile detection application, specialist clinical response team and care protocol. Clinical outcome data were collected from adults with AKI on emergency admission before (May 2016 to January 2017) and after (May to September 2017) deployment at the intervention site and another not receiving the intervention. Changes in primary outcome (serum creatinine recovery to ≤120% baseline at hospital discharge) and secondary outcomes (30-day survival, renal replacement therapy, renal or intensive care unit (ICU) admission, worsening AKI stage and length of stay) were measured using interrupted time-series regression. Processes of care data (time to AKI recognition, time to treatment) were extracted from casenotes, and compared over two 9-month periods before and after implementation (January to September 2016 and 2017, respectively) using pre–post analysis. There was no step change in renal recovery or any of the secondary outcomes. Trends for creatinine recovery rates (estimated odds ratio (OR) = 1.04, 95% confidence interval (95% CI): 1.00–1.08, p = 0.038) and renal or ICU admission (OR = 0.95, 95% CI: 0.90–1.00, p = 0.044) improved significantly at the intervention site. However, difference-in-difference analyses between sites for creatinine recovery (estimated OR = 0.95, 95% CI: 0.90–1.00, p = 0.053) and renal or ICU admission (OR = 1.06, 95% CI: 0.98–1.16, p = 0.140) were not significant. Among process measures, time to AKI recognition and treatment of nephrotoxicity improved significantly ( p < 0.001 and 0.047 respectively).