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Medicina
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Statistical Techniques and Machine Learning Algorithms Applied to the Prediction...
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Statistical Techniques and Machine Learning Algorithms Applied to the Prediction of Survival in Organ Transplants

A special issue of Medicina (ISSN 1648-9144).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 5459

Special Issue Editors

Prof. Dr. Antonio Sarasa Cabezuelo

E-Mail Website
Guest Editor
Facultad de Informática, Universidad Complutense de Madrid, 28040 Madrid, Spain
Interests: NonSQL databases; machine learning; artificial intelligence; e-learning; programming languages
Special Issues, Collections and Topics in MDPI journals
Dr. Amado Andrés Belmonte

E-Mail Website
Guest Editor
Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
Interests: internal medicine; kidney transplant; renal insufficiency; dialysis
Dr. Fernando Gonzlez Fuenzalida

E-Mail Website
Guest Editor
Facultad de Medicina, Hospital del Salvador, Universidad de Chile, Santiago, Chile
Interests: internal medicine; kidney transplant; renal insufficiency; dialysis

Special Issue Information

Dear Colleagues, 

When an organ transplant is performed, it is of great interest to study the survival of transplant patients. With the results of these studies, it is possible to detect the factors on which the success of the transplant depends. Traditionally, this question has been answered using classical statistical techniques. However, in recent years, studies have begun using techniques from the field of artificial intelligence, and more specifically machine learning algorithms. These studies show that this type of algorithms can, in certain situations, obtain more reliable and results than statistical techniques in a more efficient way. However, the trend is to use both techniques in a complementary way so that the results of one serve to confirm the results obtained with the other.

The objective of this Special Issue is to bring together researchers interested in the field of organ transplants, regardless of the type of organ transplanted, and who are also carrying out experimental research on the factors on which the survival of transplanted patients depends using both statistical and machine learning techniques. The topics of interest include:

  • Machine learning applied to organ transplant survival.
  • Study of the survival of organ transplant patients.
  • Artificial intelligence techniques applied to organ transplants.
  • Prediction of organ transplant survival.
  • Classic statistics applied to organ transplant survival.
  • Deep learning applied to organ transplant survival
  • Other approaches to the study of organ transplant survival.
  • Particular studies on the survival of organ transplants of any kind.
  • Definition of survival scores.

Both review articles on the state of the art and experimental or theoretical articles are welcome.

Prof. Dr. Antonio Sarasa Cabezuelo
Dr. Amado Andrés Belmonte
Dr. Fernando Gonzlez Fuenzalida
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Medicina is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning algorithms
  • Statistical techniques
  • prediction
  • survival in organ transplants
  • artificial intelligence

Published Papers (2 papers)

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Research

27 pages, 10364 KiB  
Article
A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients
by Diana Barsasella, Karamo Bah, Pratik Mishra, Mohy Uddin, Eshita Dhar, Dewi Lena Suryani, Dedi Setiadi, Imas Masturoh, Ida Sugiarti, Jitendra Jonnagaddala and Shabbir Syed-Abdul
Medicina 2022, 58(11), 1568; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina58111568 - 31 Oct 2022
Cited by 2 | Viewed by 2509
Abstract
Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates [...] Read more.
Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates a burden on hospitals and affects the overall management and administration of the hospitals. Hence, this study aimed to develop a machine learning (ML) model to predict the Length of Stay (LoS) and mortality among T2DM and HTN inpatients. Materials and Methods: Using Taiwan’s National Health Insurance Research Database (NHIRD), this cohort study consisted of 58,618 patients, where 25,868 had T2DM, 32,750 had HTN, and 6419 had both T2DM and HTN. We analyzed the data with different machine learning models for the prediction of LoS and mortality. The evaluation was done by plotting descriptive statistical graphs, feature importance, precision-recall curve, accuracy plots, and AUC. The training and testing data were set at a ratio of 8:2 before applying ML algorithms. Results: XGBoost showed the best performance in predicting LoS (R2 0.633; RMSE 0.386; MAE 0.123), and RF resulted in a slightly lower performance (R2 0.591; RMSE 0.401; MAE 0.027). Logistic Regression (LoR) performed the best in predicting mortality (CV Score 0.9779; Test Score 0.9728; Precision 0.9432; Recall 0.9786; AUC 0.97 and AUPR 0.93), closely followed by Ridge Classifier (CV Score 0.9736; Test Score 0.9692; Precision 0.9312; Recall 0.9463; AUC 0.94 and AUPR 0.89). Conclusions: We developed a robust prediction model for LoS and mortality of T2DM and HTN inpatients. Linear Regression showed the best performance for LoS, and Logistic Regression performed the best in predicting mortality. The results showed that ML algorithms can not only help healthcare professionals in data-driven decision-making but can also facilitate early intervention and resource planning. Full article
(This article belongs to the Special Issue Statistical Techniques and Machine Learning Algorithms Applied to the Prediction of Survival in Organ Transplants)
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10 pages, 1415 KiB  
Article
Identifying Factors Predicting Kidney Graft Survival in Chile Using Elastic-Net-Regularized Cox’s Regression
by Leandro Magga, Simón Maturana, Marcelo Olivares, Martín Valdevenito, Josefa Cabezas, Javier Chapochnick, Fernando González, Alvaro Kompatzki, Hans Müller, Jacqueline Pefaur, Camilo Ulloa and Ricardo Valjalo
Medicina 2022, 58(10), 1348; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina58101348 - 26 Sep 2022
Cited by 1 | Viewed by 2313
Abstract
Background and Objectives: We developed a predictive statistical model to identify donor–recipient characteristics related to kidney graft survival in the Chilean population. Given the large number of potential predictors relative to the sample size, we implemented an automated variable selection mechanism that [...] Read more.
Background and Objectives: We developed a predictive statistical model to identify donor–recipient characteristics related to kidney graft survival in the Chilean population. Given the large number of potential predictors relative to the sample size, we implemented an automated variable selection mechanism that could be revised in future studies as more national data is collected. Materials and Methods: A retrospective multicenter study was conducted to analyze data from 822 adult kidney transplant recipients from adult donors between 1998 and 2018. To the best of our knowledge, this is the largest kidney transplant database to date in Chile. A procedure based on a cross-validated regularized Cox regression using the Elastic Net penalty was applied to objectively identify predictors of death-censored graft failure. Hazard ratios were estimated by adjusting a multivariate Cox regression with the selected predictors. Results: Seven variables were associated with the risk of death-censored graft failure; four from the donor: age (HR = 1.02, 95% CI: 1.00–1.03), male sex (HR = 0.64, 95% CI: 0.46–0.90), history of hypertension (HR = 1.49, 95% CI: 0.98–2.28), and history of diabetes (HR = 2.04, 95% CI: 0.97–4.29); two from the recipient: years on dialysis log-transformation (HR = 1.29, 95% CI: 0.99–1.67) and history of previous solid organ transplantation (HR = 2.02, 95% CI: 1.18–3.47); and one from the transplant: number of HLA mismatches (HR = 1.13, 95% CI: 0.99–1.28). Only the latter is considered for patient prioritization in deceased kidney allocation in Chile. Conclusions: A risk model for kidney graft failure was developed and trained for the Chilean population, providing objective criteria which can be used to improve efficiency in deceased kidney allocation. Full article
(This article belongs to the Special Issue Statistical Techniques and Machine Learning Algorithms Applied to the Prediction of Survival in Organ Transplants)
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