A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis

Andrès, Axel; Montano‐Loza, Aldo J.; Greiner, Russell; Uhlich, Max; Park, Jin; Hoehn, Bret; Bigam, David L.; Shapiro, James; Kneteman, Norman M.

doi:10.1371/journal.pone.0193523

Cited by 29 publications

(33 citation statements)

References 28 publications

(26 reference statements)

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“…In addition to these evaluations using the discriminative concordance measure, we also applied a calibration measure: “D-Calibration” (“D” for “Distribution”) [15, 16], which measures how well a individual survival distribution model is calibrated, using the Hosmer-Lemeshow (HL) [30] goodness-of-fit test; see Appendix B.2. We found that all of our models, for both datasets (METABRIC and KIPAN), passed this calibration test; see Appendix C.2, especially Table 3.…”

Section: Resultsmentioning

confidence: 99%

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Gene expression based survival prediction for cancer patients—A topic modeling approach

Kumar

Greiner

2019

PLoS ONE

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Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional gene expression data. There, a document is represented as a mixture over a relatively small number of topics, where each topic corresponds to a distribution over the words; here, to accommodate the heterogeneity of a patient’s cancer, we represent each patient (≈ document) as a mixture over cancer-topics, where each cancer-topic is a mixture over gene expression values (≈ words). This required some extensions to the standard LDA model—e.g., to accommodate the real-valued expression values—leading to our novel discretized Latent Dirichlet Allocation (dLDA) procedure. After using this dLDA to learn these cancer-topics, we can then express each patient as a distribution over a small number of cancer-topics, then use this low-dimensional “distribution vector” as input to a learning algorithm—here, we ran the recent survival prediction algorithm, MTLR, on this representation of the cancer dataset. We initially focus on the METABRIC dataset, which describes each of n = 1,981 breast cancer patients using the r = 49,576 gene expression values, from microarrays. Our results show that our approach (dLDA followed by MTLR) provides survival estimates that are more accurate than standard models, in terms of the standard Concordance measure. We then validate this “dLDA+MTLR” approach by running it on the n = 883 Pan-kidney (KIPAN) dataset, over r = 15,529 gene expression values—here using the mRNAseq modality—and find that it again achieves excellent results. In both cases, we also show that the resulting model is calibrated, using the recent “D-calibrated” measure. These successes, in two different cancer types and expression modalities, demonstrates the generality, and the effectiveness, of this approach. The dLDA+MTLR source code is available at https://github.com/nitsanluke/GE-LDA-Survival.

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Section: Resultsmentioning

confidence: 99%

“…We show that this predictor performs better than other standard survival analysis tools in terms of concordance. We also found that it was “D-calibrated” [15, 16]; see Appendix B.2.…”

Section: Introductionmentioning

confidence: 95%

Gene expression based survival prediction for cancer patients—A topic modeling approach

Kumar

Greiner

2019

PLoS ONE

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“…In sensitivity analyses, the algorithm remained accurate regardless of baseline phenotype of PSC, degree of bilirubin elevation, or hepatic involvement. In an attempt to identify those patients with PSC most likely to survive post‐LT, Andres et al produced a novel multitime point calibrated model for the prediction of individual survival after LT derived from Scientific Registry of Transplant Recipients (SRTR) database information for 2,769 PSC LT recipients . This model was able to outperform traditional Cox regression analysis in evaluation of long‐term survival for PSC LT recipients.…”

Section: Pscmentioning

confidence: 99%

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

et al. 2020

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Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.

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“…Regarding primary sclerosing cholangitis, a team of researchers derived and validated a risk estimate tool based on GB algorithms to predict the outcomes of the disease[ 155 ]. In a different study, an ML model was developed to predict survival curves for patients with primary sclerosing cholangitis following liver transplantation[ 156 ]. The P value of the χ 2 test of the distributional calibration was 1, indicating excellent calibration of the model[ 156 ].…”

Section: Applications Of Ai In Hepatologymentioning

confidence: 99%

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Christou

Tsoulfas

2021

WJG

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Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

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A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis

Cited by 29 publications

References 28 publications

Gene expression based survival prediction for cancer patients—A topic modeling approach

Gene expression based survival prediction for cancer patients—A topic modeling approach

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

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