TL;DR:
- Machine learning models, specifically those built with artificial neural networks, can improve the prediction of 28-day mortality among patients with hepatic encephalopathy.
- A study was conducted using data from 601 patients with hepatic encephalopathy and four different machine-learning algorithms to develop models.
- The artificial neural networks model demonstrated the highest level of performance with an AUC of 0.837 and was well-calibrated.
- Factors such as APSIII, SOFA, mechanical ventilation, INR, TBIL, albumin, and AKI were identified as crucial in predicting 28-day mortality.
- The use of machine learning models in the prediction of mortality holds great potential and could lead to significant advancements in the prognosis and quality of life for hepatic encephalopathy patients.
Main AI News:
In a recent study published in BMC Gastroenterology, researchers have found that the use of machine learning models, specifically those built with artificial neural networks, can greatly improve the prediction of 28-day mortality among patients with hepatic encephalopathy. This is a significant discovery as, despite improved outcomes for hepatic encephalopathy patients over the past decade, the prognosis and quality of life for these patients remain poor.
The study was conducted using data from 601 patients with hepatic encephalopathy from the Medical Information Mart for Intensive Care database. The researchers used four different machine learning algorithms to develop the models, including artificial neural networks, gradient boosting machines, ‘random forest,’ and ‘bagged trees’ algorithms.
The independent risk factors for mortality were identified as acute physiology score III (APSIII), sepsis-related organ failure assessment (SOFA), international normalized ratio (INR), total bilirubin (TBIL), albumin, blood urea nitrogen (BUN), acute kidney injury (AKI), and mechanical ventilation.
The results showed that the artificial neural networks model demonstrated the highest level of performance, with an area under the curve (AUC) of 0.837 (95% CI, 0.774-0.901). This model was also found to be well-calibrated, offering an improved prediction for 28-day mortality compared to previously established MELD and MELD-Na score AUCs of 0.728 (95% CI, 0.677-0.779) and 0.711 (95% CI, 0.658-0.765), respectively. The ‘bagged trees’ algorithm demonstrated the lowest discriminative ability, with an AUC of 0.741 (95% CI, 0.654-0.829).
The study highlights the crucial role of factors such as APSIII, SOFA, mechanical ventilation, INR, TBIL, albumin, and AKI in predicting 28-day mortality. The use of machine learning models, specifically artificial neural networks, was shown to have superior performance compared to the MELD score or MELD-Na score in predicting 28-day mortality among hepatic encephalopathy patients. This study opens up the possibility of real-time prediction of mortality risk, which could lead to the optimization of treatment and improvement of the clinical prognosis for these patients.
Conlcusion:
The results of this study show that the use of machine learning models, specifically artificial neural networks, can greatly improve the prediction of 28-day mortality among patients with hepatic encephalopathy. This holds significant potential for the healthcare market as it could lead to more accurate predictions and improved outcomes for patients with this condition.
The identification of key independent risk factors such as APSIII, SOFA, mechanical ventilation, INR, TBIL, albumin, and AKI further highlights the potential of this technology in the healthcare industry. As such, this study is likely to drive further research and development in the field of machine learning and its application in healthcare, which could ultimately lead to advancements in the prognosis and quality of life for patients with hepatic encephalopathy.
