Clinical management of sepsis can be improved by artificial intelligence: no

Implementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care

This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers' minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding.

Addressing antibiotic resistance: computational answers to a biological problem?

The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.

A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis

Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting clinicians for real-time treatment of sepsis. While a value function quantifies the performance of policies in such decision-making processes, most value-based DRL algorithms cannot evaluate the target value function precisely and are not as safe as clinical experts. In this study, we propose a Weighted Dueling Double Deep Q-Network with embedded human Expertise (WD3QNE). A target Q value function with adaptive dynamic weight is designed to improve the estimate accuracy and human expertise in decision-making is leveraged. In addition, the random forest algorithm is employed for feature selection to improve model interpretability. We test our algorithm against state-of-the-art value function methods in terms of expected return, survival rate, action distribution and external validation. The results demonstrate that WD3QNE obtains the highest survival rate of 97.81% in MIMIC-III dataset. Our proposed method is capable of providing reliable treatment decisions with embedded clinician expertise.

Anticardiolipin Autoantibodies as Useful Biomarkers for the Prediction of Mortality in Septic Patients

Background. The detection of antiphospholipid antibodies (aPL) is of interest because of their importance in the pathogenesis of arterial or venous thrombosis. They could be a “second hit” of an inflammatory event such as infection. The aim of our study was to assess the performance of antiphospholipid antibody biomarker to predict in-hospital mortality in intensive care unit (ICU) septic patients. Methods. We conducted a prospective single-center observational study including consecutive critically ill septic adults admitted to the intensive care unit. Clinical and laboratory data including enzyme-linked immunosorbent assay for antiphospholipid antibodies (anticardiolipin (aCL), antiphosphatidylserine (aPS)) were obtained. Blood samples were collected on days 1, 3, 5, 8, and 10 of hospitalization. The primary study endpoint was ICU mortality defined as death before ICU discharge. Secondary end points included correlation between SOFA score and biological parameters. Results. A total of 53 patients were enrolled. 18.8% of patients were aPL positive. In-hospital mortality rate was 60%. Multivariate analysis showed that age and aCL at days 3 and 5 along with SOFA at day 3 were independent outcome predictors. A significant positive correlation existed between SOFA at days 3, 5, and 8 and antiphospholipid antibody concentrations. Conclusions. Our data showed that antiphospholipid was useful biomarkers for the prediction of mortality in critically ill septic patients. We found a positive correlation between SOFA score and antiphospholipid antibodies.

Artificial Intelligence for Clinical Decision Support in Sepsis

Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.