Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning

Yasodhara, Angeline; Dong, Victor; Azhie, Amirhossein; Goldenberg, Anna; Bhat, Mamatha

doi:10.1002/lt.25930

Cited by 18 publications

(28 citation statements)

References 30 publications

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“…This study by Yasodhara et al proves to us that long-standing diabetes mellitus increases the risk for poor outcomes more so than new-onset diabetes mellitus. (2) This makes sense. The longer one has diabetes mellitus, the more likely it is to have consequences.…”

Section: See Article On Page 536mentioning

confidence: 98%

“…There is great promise in the application of these methods to the practice of hepatology and liver transplantation, and Yasodhara et al, in this issue, aptly illustrate this. (2) Although modern practices have improved longevity following liver transplantation, morbidity and mortality owing to metabolic complications still abound. (3,4) Of liver transplantation (LT) recipients, 11% to 15% have preexisting diabetes mellitus, whereas up to 30% have diabetes mellitus after transplantation.…”

Section: See Article On Page 536mentioning

confidence: 99%

“…There is great promise in the application of these methods to the practice of hepatology and liver transplantation, and Yasodhara et al, in this issue, aptly illustrate this. ( 2 )…”

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confidence: 99%

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Scientific Relief: When Science and Technology Agree and Lead

Khan

Watt

2021

Liver Transpl.

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Section: See Article On Page 536mentioning

confidence: 98%

Section: See Article On Page 536mentioning

confidence: 99%

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Scientific Relief: When Science and Technology Agree and Lead

Khan

Watt

2021

Liver Transpl.

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“…identified survival factors for diabetic liver transplantation recipients using ML analysis. They found that among patients with diabetes, hypertension, renal dysfunction, and use of sirolimus were top ranked features that affected survival post‐transplantation 63 …”

Section: Artificial Intelligence In Electronic Health Recordsmentioning

confidence: 99%

Artificial intelligence in precision medicine in hepatology

Kao

2021

J of Gastro and Hepatol

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The advancement of investigation tools and electronic health records (EHR) enables a paradigm shift from guideline‐specific therapy toward patient‐specific precision medicine. The multiparametric and large detailed information necessitates novel analyses to explore the insight of diseases and to aid the diagnosis, monitoring, and outcome prediction. Artificial intelligence (AI), machine learning, and deep learning (DL) provide various models of supervised, or unsupervised algorithms, and sophisticated neural networks to generate predictive models more precisely than conventional ones. The data, application tasks, and algorithms are three key components in AI. Various data formats are available in daily clinical practice of hepatology, including radiological imaging, EHR, liver pathology, data from wearable devices, and multi‐omics measurements. The images of abdominal ultrasonography, computed tomography, and magnetic resonance imaging can be used to predict liver fibrosis, cirrhosis, non‐alcoholic fatty liver disease (NAFLD), and differentiation of benign tumors from hepatocellular carcinoma (HCC). Using EHR, the AI algorithms help predict the diagnosis and outcomes of liver cirrhosis, HCC, NAFLD, portal hypertension, varices, liver transplantation, and acute liver failure. AI helps to predict severity and patterns of fibrosis, steatosis, activity of NAFLD, and survival of HCC by using pathological data. Despite of these high potentials of AI application, data preparation, collection, quality, labeling, and sampling biases of data are major concerns. The selection, evaluation, and validation of algorithms, as well as real‐world application of these AI models, are also challenging. Nevertheless, AI opens the new era of precision medicine in hepatology, which will change our future practice.

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“…Multiple models have been proposed in recent years. Risk features for acute, postoperative outcomes, including mortality and acute kidney injury, were identified using a gradient boosting machine (13,14). Molinari et al (17) bridged traditional methods by developing a point-system for predicting 90-day mortality in liver transplant using variables identified through artificial neural networks, classification tree analysis, and logistic regression.…”

Section: Predicting Clinical Outcomes After Solid Organ Transplantation With Machine Learningmentioning

confidence: 99%

Machine Learning Applications in Solid Organ Transplantation and Related Complications

et al. 2021

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The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.

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Identifying Modifiable Predictors of Long‐Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning

Cited by 18 publications

References 30 publications

Scientific Relief: When Science and Technology Agree and Lead

Scientific Relief: When Science and Technology Agree and Lead

Artificial intelligence in precision medicine in hepatology

Machine Learning Applications in Solid Organ Transplantation and Related Complications

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