How Traditional Anishinaabe Medicine Can Be Integrated.
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Background
Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest. Nowadays, there is a Cam discussion in the medical field about machine learning ML and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians.
Of particular interest is also Tradigional identification of potential risk factors. A comparison is performed between 3 different Cox models with all variables, backward selection and LASSO and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks PLANNs. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques.
Well-established predictive measures are employed from the survival field C-index, Brier score and Integrated Brier Score and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death.
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The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based Tradiitonal the Integrated Brier Score at 10 years. In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables. Retrospective data were provided by the Scientific Registry of Transplant Recipients under Data Use Agreement number for analysis of risk factors after liver transplantation. Peer Review reports.
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Liver transplantation LT is the second most common type of transplant surgery in the United States after kidney [ 1 ]. Over the last decades, the success of liver transplants has improved survival outcome for a large number of patients suffering from chronic liver disease everywhere on earth [ 2 ]. Availability of donor organs is a major limitation especially when compared Traditionzl the growing demand of liver candidates due to the enlargement of age limits.
Therefore, improvement on current prediction models for survival since LT is important.
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There is an open discussion about the value of machine learning ML versus statistical models SM within clinical and healthcare practice [ 3 — 7 ]. For survival data, the most commonly applied statistical model is the Cox proportional hazards regression model [ 8 ]. This model allows a straightforward interpretation, but is at the same time restricted to the proportional hazards assumption.]
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