Deep Learning Applications for Biomedical Data and Natural Language Processing

The human brain can be seen as an ensemble of interconnected neurons, more or less specialized to solve different cognitive and motor tasks. In computer science, the term deep learning is often applied to signify sets of interconnected nodes, where deep means that they have several computational layers. Development of deep learning is essentially a quest to mimic how the human brain, at least partially, operates.In this thesis, I will use machine learning techniques to tackle two different domain of problems. The first is a problem in natural language processing. We improved classification of relations within images, using text associated with the pictures. The second domain is regarding heart transplant. We created models for pre- and post-transplant survival and simulated a whole transplantation queue, to be able to asses the impact of different allocation policies. We used deep learning models to solve these problems.As introduction to these problems, I will present the basic concepts of machine learning, how to represent data, how to evaluate prediction results, and how to create different models to predict values from data. Following that, I will also introduce the field of heart transplant and some information about simulation

Liver transplantation

Purpose of review: Long-term survival of liver transplant recipients is threatened by increased rates of de-novo malignancy and recurrence of hepatocellular carcinoma (HCC), both events tightly related to immunosuppression. Recent findings: There is accumulating evidence linking increased exposure to immunosuppressants and carcinogenesis, particularly concerning calcineurin inhibitors (CNIs), azathioprine and antilymphocyte agents. A recent study including 219 HCC transplanted patients showed that HCC recurrence rates were halved if a minimization of CNIs was applied within the first month after liver transplant. With mammalian target of rapamycin (mTOR) inhibitors as approved immunosuppressants for liver transplant patients, pooled data from several retrospective studies have suggested their possible benefit for reducing HCC recurrence. Summary: Randomized controlled trials with sufficiently long follow-up are needed to evaluate the influence of different immunosuppression protocols in preventing malignancy after LT. Currently, early minimization of CNIs with or without mTOR inhibitors or mycophenolate seems a rational strategy for patients with risk factors for de-novo malignancy or recurrence of HCC after liver transplant. A deeper understanding of the immunological pathways of rejection and cancer would allow for designing more specific and safer drugs, and thus to prevent cancer after liver transplant

Liver transplantation for alcoholic cirrhosis: Long term follow-up and impact of disease recurrence

Background. Alcoholic liver disease has emerged as a leading indication for hepatic transplantation, although it is a controversial use of resources. We aimed to examine all aspects of liver transplantation associated with alcohol abuse. Methods. Retrospective cohort analysis of 123 alcoholic patients with a median of 7 years follow-up at one center. Results. In addition to alcohol, 43 (35%) patients had another possible factor contributing to cirrhosis. Actuarial patient and graft survival rates were, respectively, 84% and 81% (1 year); 72% and 66% (5 years); and 63% and 59% (7 years). After transplantation, 18 patients (15%) manifested 21 noncutaneous de novo malignancies, which is significantly more than controls (P=0.0001); upper aerodigestive squamous carcinomas were over-represented (P=0.03). Thirteen patients had definitely relapsed and three others were suspected to have relapsed. Relapse was predicted by daily ethanol consumption (P=0.0314), but not by duration of pretransplant sobriety or explant histology. No patient had alcoholic hepatitis after transplantation and neither late onset acute nor chronic rejection was significantly increased. Multiple regression analyses for predictors of graft failure identified major biliary/vascular complications (P=0.01), chronic bile duct injury on biopsy (P=0.002), and pericellular fibrosis on biopsy (P=0.05); graft viral hepatitis was marginally significant (P=0.07) on univariate analysis. Conclusions. Alcoholic liver disease is an excellent indication for liver transplantation in those without coexistent conditions. Recurrent alcoholic liver disease alone is not an important cause of graft pathology or failure. Potential recipients should be heavily screened before transplantation for coexistent conditions (e.g., hepatitis C, metabolic diseases) and other target-organ damage, especially aerodigestive malignancy, which are greater causes of morbidity and mortality than is recurrent alcohol liver disease

Objective Estimates Improve Risk Stratification for Primary Graft Dysfunction after Lung Transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112244/1/ajt13262.pd

Validated Prognostic Scores to Predict Outcomes in ECLS-Bridged Patients to Lung Transplantation

Selection of patients who may benefit from extracorporeal life support (ECLS) as a bridge to lung transplant (LTx) is crucial. The aim was to assess if validated prognostic scores could help in selecting patients who may benefit from ECLS-bridging predicting their outcomes. Clinical data of patients successfully ECLS-bridged to LTx from 2009 to 2021 were collected from two European centers. For each patient, we calculated Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score III (SAPS III), Acute Physiology and Chronic Health Evaluation II (APACHE II), before placing ECLS support, and then correlated with outcome. Median values of SOFA, SAPS III, and APACHE II were 5 (IQR 3-9), 57 (IQR 47.5-65), and 21 (IQR 15-26). In-hospital, 30 and 90 days mortality were 21%, 14%, and 22%. SOFA, SAPS III, and APACHE II were analyzed as predictors of in-hospital, 30 and 90 days mortality (SOFA C-Index: 0.67, 0.78, 0.72; SAPS III C-index: 0.48, 0.45, 0.51; APACHE II C-Index: 0.49, 0.45, 0.52). For SOFA, the score with the best performance, a value ≥9 was identified to be the optimal cut-off for the prediction of the outcomes of interest. SOFA may be considered an adequate predictor in these patients, helping clinical decision-making. More specific and simplified scores for this population are necessary