In health facilities, sepsis is the leading cause of mortality. Early detection and diagnosis of sepsis, which is crucial in lowering mortality, is problematic since many of its signs and symptoms are similar to those of other less serious illnesses.
What is sepsis
Sepsis is the body’s extreme response to an infection. It is a life-threatening medical emergency. Sepsis happens when an infection you already have triggers a chain reaction throughout your body. Infections that lead to sepsis most often start in the lung, urinary tract, skin, or gastrointestinal tract. Without timely treatment, sepsis can rapidly lead to tissue damage, organ failure, and death
How AI works in healthcare
Artificial intelligence (AI) is revolutionizing healthcare, and its application is becoming a reality across a wide range of medical sectors and specialties. AI, machine learning (ML), natural language processing (NLP), and deep learning (DL) help healthcare stakeholders and professionals to discover healthcare problems and solutions more quickly and accurately, utilizing data patterns to make educated medical or business decisions.
AI can evaluate enormous volumes of data kept by healthcare companies in the form of photographs, clinical research trials, and medical claims, and may uncover patterns and insights that manual human skill sets frequently overlook.
AI algorithms are “trained” to recognize and classify data patterns, whereas NLP enables these algorithms to separate pertinent data. Data is processed and interpreted by computers using expanded knowledge in DL.
SERA (sepsis early risk assessment) algorithm.
Given that the SERA algorithm uses clinical notes and structured data to produce a sepsis risk assessment, each patient consultation instance is the unit of analysis for the algorithm. We use this unit of analysis to ensure that the algorithm can function in a realistic clinical setting in which professionals consult, assess, and diagnose patients. SERA is made up of two interconnected algorithms: a diagnostic algorithm and an early prediction algorithm. The diagnosis algorithm identifies whether or not the patient has sepsis at the time of consultation, and if not, the early prediction algorithm assesses the patient’s risk of developing sepsis in the following 4, 6, 12, 24, and 48 hours.
Examples and Benefits of using sepsis integrated algorithm all around the world.
Since implementing a sepsis warning system in 2017, Fishersville, Va.-based Augusta Health has seen a drop in sepsis death rates, saving an estimated 282 lives. This decrease in fatality rates is promising and may lead to widespread use of this technology. Augusta’s warning system may analyses patients’ vital signs via their electronic health records, detect common warning indicators of sepsis development, and inform physicians and staff if any irregularities occur.
It’s one of the reasons the Duke Institute for Health Innovation created Sepsis Watch, an AI system that detects early symptoms of sepsis. The system, which launched at Duke University Hospital in November 2018, is trained using deep learning and analyses over 32 million data points in real time to assess a patient’s status — and, if necessary, inform the hospital’s quick response team.
Conclusively
Although various sepsis waring systems and algorithm are proven to be highly beneficial for the healthcare system but there are some drawbacks to it aswell. To summarize, there is still a significant gap between the development of AI algorithms and their use in clinical practice. Experts in sepsis cannot be replaced by AI in the clinical management role. AI-based algorithms should always be utilized as development tools until they can integrate actions that are consistent with known physiology and demonstrate that the results can be adjusted in advance in diverse situations.
