JUNE , 2023 | THE HEALTH

SEPSIS ALERT

BY ASSOC PROF DR TAN TOH LEONG

AND DR DIAN NASRIANA NASURUDDIN

Sepsis is a life-threatening condition . Therefore , it must be promptly diagnosed and treated appropriately .

Healthcare practitioners can improve their capacity to diagnose sepsis effectively , stratify patient risk , and optimise treatment approaches through the analysis of enormous volumes of real-world patient data and the application of AI algorithms . In addition to examining how big data and AI are changing sepsis care , we will try and elucidate the field ’ s possibilities and future developments .

DIAGNOSIS OF SEPSIS

Big data plays a critical role in diagnosing sepsis by leveraging and combining numerous patient data points , such as laboratory results , clinical measures and vital signs allowing AI systems to examine this complicated dataset , looking for patterns and locating biomarkers that suggest sepsis . These algorithms continuously learn and enhance their diagnostic precision by utilising machine learning approaches , assisting physicians in making quick and educated decisions .

AI-powered solutions can also help with the early diagnosis of sepsis . AI algorithms can spot minor changes in vital signs , biomarkers or clinical symptoms that may point to the beginning of sepsis by analysing real-time data from numerous internet-of-things sources , including wearable technology and electronic health records . By enabling proactive action , this early warning system could potentially save lives and lessen the severity of consequences caused by sepsis .

STRATIFICATION OF RISK

Accurately stratifying patients based on their risk is one of the major difficulties in sepsis therapy .

By including a variety of patient characteristics , including age , comorbidities , vital signs , test results , and therapy response , big data analytics and AI algorithms shine in this field .

These methods can evaluate a patient ’ s sepsis severity , forecast the likelihood of complications , and calculate the likelihood of mortality . These risk classification algorithms help healthcare providers allocate the right resources and customise treatment plans to meet the unique needs of each patient i . e ., personalised medicine .

Big data and AI have enormous potential for improving sepsis treatment plans . AI systems can find trends and suggest treatments based on previous successes by analysing enormous volumes of patient data , including treatment results and responsiveness to therapy . With this method , clinicians can choose the most successful therapies , lowering the possibility of therapy failure and enhancing patient outcomes .

Furthermore , by combining patient data with the most recent evidencebased recommendations , AI systems can offer real-time decision support . These resources support clinicians in making evidence-based choices regarding fluid resuscitation , antibiotic medication , and other crucial therapies .

Assoc Prof Dr Tan Toh Leong is Senior Lecturer and Emergency Medical Consultant , UKM Medical Center , Universiti Kebangsaan Malaysia ( UKM ) and President & Founder of Malaysian Sepsis Alliance ( MySepsis ) while Dr Dian Nasriana Nasuruddin is Senior Lecturer and Chemical Pathologist Consultant , UKM Medical Center , UKM and Treasurer of MySepsis

AI algorithms improve the accuracy and effectiveness of sepsis care by incorporating patient-specific characteristics and continuously learning from new data .

FUTURE DIRECTIONS AND PROSPECTS

Big data and AI have great promise for managing and diagnosing sepsis . The precision and effectiveness of diagnostic models will considerably increase as technology develops and larger , more complete datasets become accessible .

Genetic profiling , continuous patient monitoring , and the integration of wearable device data may further improve risk stratification and early detection , enabling more proactive and individualised therapies .

Additionally , by combining natural language processing ( NLP ) methods with AI algorithms , unstructured clinical notes might yield insightful data that enriches the analysis of the data . This skill will help us better understand sepsis , allowing for more precise diagnosis and individualised treatment regimens . – The Health