Machine Learning Techniques Differentiate Alcohol-Associated Hepatitis From Acute Cholangitis in Patients With Systemic Inflammation and Elevated Liver Enzymes

Ahn, Joseph; Noh, Yung‐Kyun; Rattan, Puru; Buryska, Seth; Wu, Tiffany; Kezer, Camille A.; Choi, Chansong; Arunachalam, Shivaram P.; Simonetto, Douglas A.; Shah, Vijay H.; Kamath, Patrick S.

doi:10.1016/j.mayocp.2022.01.028

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…It can perform a non-linear prediction of outcomes, without relying on the data distribution hypothesis, such as the independence and multiple collinearities of observation. Owing to outstanding model identification capacity, ML can help clinicians make decisions, and it has also been demonstrated to have an outstanding performance in identifying endocrine diseases [ 75 ] and predicting mortality of inflammatory diseases, etc. [ 76 ] .…”

Section: Discussionmentioning

confidence: 99%

Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis

Wang

Yin

Wen

2023

BMC Med Inform Decis Mak

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Purpose With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI. Methodology We systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity. Result A total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance. Conclusion According to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.

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Section: Discussionmentioning

confidence: 99%

Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis

Wang

Yin

Wen

2023

BMC Med Inform Decis Mak

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“…The application of machine learning to predict scores proved to be extremely promising for the use of breath biomarkers for liver function diagnostics. Ahn et al (2022) 19 also investigated machine learning models which efficiently differentiated patients with acute cholangitis and alcoholassociated hepatitis with laboratory tests. Liver disease is a major public health issue.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Radiation-Induced Abnormality in Liver Enzymes from Machine Learning (ML) Algorithms

Shahid¹,

Ghayyur²,

Afraz³

et al. 2022

PJMHS

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Background: Exposure to ionizing radiation from medical radiation equipment during cancer diagnosis and treatment can alter the biochemistry of hospital personnel by triggering the oxidative stress process. Aim: To develop a Simple-Linear-Regression algorithm with supervised learning applied to find the correlation between liver enzymes with the AAED (mSv) in low-dose medical radiation workers. Methodology: Radiology & Nuclear Medicine Radiation workers from INMOLHospital were included. The AAED (annual average effective radiation doses) received from TLDs were measured by Radiation Dosimetry Laboratory. The models were trained and applied to the sample data set. Results: The mean value of AAED was 0.28 mSv. Half of the workers were found with high ALT levels and around 20% were found with altered AST levels. The models were also successfully cross-validated. ALT (R2= 0.025) & AST (R2= 0.00072) were having very weak relationships with AAED. From regression equations, it is inferred that for every unit increase in AAED (mSv), there will be a 12.98 unit decrease in ALT (U/L) and a 0.63 unit increase in AST (U/L) values. Conclusion: Our ML model was successfully implemented to predict the alteration or abnormality in the liver enzymes from radiation exposure. It can assist physicians to detect changes in an individual's biochemistry before exposure to certain toxins. Keywords: Radio-induced liver injury; Annual average effective radiation doses; Liver enzymes; Machine Learning (ML) Model

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“…An example is the MELD score, which was generated by logistic regressionin recent studies, the predictive capabilities of the MELD score have been refined using additional non-biased variables. [67][68][69] Similar predictive analytics have recently been used to help clinicians distinguish differential diagnoses such as cholangitis from AH. 70 Although these advances are exciting and will continue to improve over the coming years as we generate even larger data sets and better analytic capabilities, most of these have not been definitively able to improve upon traditional clinician diagnosis.…”

Section: Digital Biomarkersmentioning

confidence: 99%

Therapeutic advances in alcohol-associated hepatitis

Szabó

Thursz

Shah

2022

Journal of Hepatology

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Machine Learning Techniques Differentiate Alcohol-Associated Hepatitis From Acute Cholangitis in Patients With Systemic Inflammation and Elevated Liver Enzymes

Cited by 7 publications

References 22 publications

Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis

Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis

Prediction of Radiation-Induced Abnormality in Liver Enzymes from Machine Learning (ML) Algorithms

Therapeutic advances in alcohol-associated hepatitis

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