Introduction
Sepsis is a pathogenic disease that remains the primary cause of death in critically ill patients. Despite the advancements in medical technology, the morbidity and mortality rates of sepsis remain high due to the delay in diagnosis and treatment. Artificial intelligence system, an evolving field, has helped in the development of multiple clinical decision support systems. Artificial intelligence systems help in the prediction, diagnosis, prognosis, and clinical management of sepsis.
What Is Artificial Intelligence System?
Artificial intelligence (AI) is a technology that simulates human intelligence by machines. It combines datasets and computer science to aid in problem-solving. Specific applications of artificial intelligence systems include language processing, speech recognition, machine vision, and expert systems. Artificial intelligence systems work by feeding on huge amounts of labeled training data, analyzing them, and using them to make predictions about future states. AI systems are developing in the field of medicine and have shown great progress in predicting the condition of the patients and also assisting in clinical-decision making. Artificial intelligence systems use machine learning systems to search medical data and provide insight to improve patient experiences and health outcomes.
What Is Sepsis?
Sepsis is also referred to as septicemia. It occurs as a life-threatening complication of an infection. Septicemia occurs when the chemicals released in response to an infection trigger an inflammatory response throughout the body. This results in a cascade of changes that damage multiple organs, leading to multiple organ failure and death. It is essential to diagnose sepsis sooner because sepsis can result in sudden organ failure and death. Many patients with mild sepsis survive with early diagnosis and proper treatment.
What Is the Role of Artificial Intelligence in the Detection of Sepsis?
Artificial intelligence systems have shown great potential in predicting sepsis. Sepsis has high mortality and morbidity rates; early detection and intervention are of great importance. The management of sepsis is highly challenging, and still, a reliable diagnostic test or treatment method has not been established. But as the application of artificial intelligence systems continues to emerge in the field of medicine, management of sepsis can be individually performed or optimized through AI-derived algorithms. Artificial intelligence system-derived algorithms help in early prediction, mortality prediction, prognosis assessment, and optimal management of sepsis. The application of artificial intelligence in sepsis helps in the prediction, diagnosis, prognosis assessment, subphenotyping, and management of sepsis.
What Are the Applications of Artificial Intelligence Systems in Sepsis?
Early Prediction of Sepsis:
Early detection and treatment of sepsis is important, as mortality increases with a delay in the treatment. Early intervention is possible if the occurrence of sepsis is predicted early. The traditional system of sepsis prediction relies on clinical decision rules using vital signs. This data includes heart rate, blood pressure, and respiratory rate. An AI model developed for the early detection of sepsis predicts the occurrence of sepsis with an accuracy of 83 percent. A predictive model has also been introduced to identify the patients at risk of sepsis. The screening tool screens the hospitalized patients and passes the results to caregivers without the need for manual intervention. A new AI-based algorithm was derived based on electronic medical records. This algorithm can detect sepsis six hours before the occurrence of sepsis.
In the traditional system of medicine, proper treatment was not started due to the delay in the recognition of sepsis. Therefore, AI-based systems facilitate early intervention due to early recognition of sepsis. In addition to these models, deep learning has led to the introduction of models to achieve real-time prediction of sepsis with a superior performance that estimates the risk of sepsis in real-time.
Early Prediction of Septic Shock:
Early prediction of septic shock is of great significance. The development of decision support tools related to the diagnosis of septic shock has led to an improvement in clinical results. Various gradient enhancement algorithms were used to develop septic shock prediction systems. One of the models helps predict septic shock seven hours before its incidence, which helps in intervention several hours before the occurrence of septic shock. Transfer learning is a new field in AI, which was introduced to predict delayed septic shock.
Improved Accuracy in Sepsis Diagnosis:
Sepsis causes multiple organ failure, which affects the accuracy of sepsis diagnosis. Automated diagnostic tools were developed to solve the diagnosis of sepsis. The accuracy of these diagnostic tools was higher than AI-based algorithms. However, AI algorithms based on electronic medical records were 80 percent accurate in the diagnosis of sepsis. However, the diagnosis of neonatal sepsis has not been established by AI systems because of the non-specific signs and symptoms of neonatal sepsis.
Prognosis and Risk Assessment of Sepsis:
The mortality rate of sepsis is high due to the lack of assessment tools. Appropriate assessment tools can help in evaluating the prognosis of sepsis, which improves the accuracy of decision-making, thereby reducing the mortality rate. A new model was developed to evaluate the status of the patient after treatment in the ICU (intensive care unit). This tool helps in predicting the mortality rate within 96 hours after admission. It also assists doctors in identifying patients with poor prognoses, thereby improving treatment plans.
Management of Sepsis:
A hemodynamic assessment method called the passive leg lift technique helps predict fluid responsiveness in patients with sepsis, but limited mobility of the patient precludes the use of this technique. A model was developed using the data from transthoracic echocardiography to predict the fluid responsiveness in sepsis or septic shock.
Other Applications of AI in Sepsis:
A sepsis watch was introduced into the routine clinical care process for sepsis detection and management. It improves the early prediction and management of sepsis. The use of nurses’ experience in the early diagnosis of sepsis in AI algorithms can help in the early diagnosis of sepsis and rapid decision-making. Effective ways to save time and cost help mitigate the burden of sepsis.
Conclusion
Sepsis is a disease with complex pathogenic factors. Early prediction and intervention help reduce mortality rates and improve the overall health and survival of patients with sepsis. Artificial intelligence systems help in the early detection and management of sepsis. However, AI-derived algorithms cannot replace the role of experts in the clinical management of sepsis. AI-based systems should always be considered developmental tools until they can cultivate actions that are compatible with the physiological environment.
