Development, validation and deployment of a novel prediction model utilizing artificial intelligence on clinical and proteomic features to predict mortality among patients with acute-on-chronic liver failure

Liver cirrhosis is the most common cause ofdeath among gastrointestinal diseases, with a global burden of 1.5 billionpersons being affected annually. It is the 4th and 10thmost common cause of disease in males and females, respectively.Acute-on-chronic liver failure (ACLF) development is the most common cause ofdeath in these patients. The mortality of this dynamic syndrome ranges from15-100% within 30-days and 90-100% within one year of presentation. Multiplelife-threatening events like sepsis, organ failures, and gastrointestinalbleeding may occur during its course. This proposal is aimed to develop,validate, and deploy a novel model to predict mortality among patients withacute-on-chronic liver failure (ACLF). The model will be derived from clinical,biochemical, radiological and plasma proteomics data obtained from patientswith ACLF. Principles of AI, machine learning, and bioinformatics will beutilized to build such a model. The model aims to incorporate the systemsapproach to medicine with clinical sciences and information technology sector.The proposal includes multiple innovations. It will develop a multimediaprocessing engine (converting the existing data in a ready format for ML and AImodeling), development, and deployment of AI-derived model for real-timepredictive analytics. The models’ deployment will contribute to the developmentof user-friendly data analytic apps. The models may be used to assess thefutility of care and allocate significant resources like ICU and mechanicalventilators to deserving patients. Importantly, patients’ lives can be savedwith a timely decision for definitive treatment (liver transplant) using thesemodels.

Thisproposal aligns with the mandate of TiH in terms of knowledge generation(development of clinical and proteomic database), technology productdevelopment (portable AI model), skill development (integration of skills suchas clinical, biochemical, bioinformatics and information technology),collaboration (between clinicians, basic scientist, computational biologist,and information technologist).