Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity

Harabor, Valeriu; Mogos, Raluca; Nechita, Aurel; Adam, A. H.; Adam, Gigi; Melinte-Popescu, Alina-Sinziana; Melinte-Popescu, Marian; Stuparu-Crețu, Mariana; Vasilache, Ingrid-Andrada; Mihălceanu, Elena; Cărăuleanu, Alexandru; Bivoleanu, Anca; Harabor, Anamaria

doi:10.3390/ijerph20032380

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…Our results indicated that patients with urosepsis had an increased likelihood of premature rupture of membranes (aOR: 5.59, 95%CI: 2.02-15.40, p < 0.001) and preterm birth (aOR: 2.47, 95%CI: 1.15-5.33, p = 0.02). The role of urinary infections in the determinism of preterm birth is well established, and immunosuppressive conditions favor the development of severe forms of infection, as we determined in this study [41][42][43][44][45].…”

Section: Discussionmentioning

confidence: 99%

Pregnancy Outcomes in Patients with Urosepsis and Uncomplicated Urinary Tract Infections—A Retrospective Study

Radu,

Vicoveanu,

Cărăuleanu

et al. 2023

Medicina

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Background and Objectives: Urinary tract infections (UTIs) are an important cause of perinatal and maternal morbidity and mortality. The aim of this study was to describe and compare the main pregnancy outcomes among pregnant patients with complicated and uncomplicated UTIs; Materials and Methods: This retrospective study included 183 pregnant patients who were evaluated for uncomplicated UTIs and urosepsis in the Urology Department of ‘C.I. Parhon’ University Hospital, and who were followed up at a tertiary maternity hospital—‘Cuza-voda’ from Romania between January 2014 and October 2023. The control group (183 patients) was randomly selected from the patient’s cohort who gave birth in the same time frame at the maternity hospital without urinary pathology. Clinical and paraclinical data were examined. Descriptive statistics and a conditional logistic regression model were used to analyze our data. Results: Our results indicated that patients with urosepsis had increased risk of premature rupture of membranes (aOR: 5.59, 95%CI: 2.02–15.40, p < 0.001) and preterm birth (aOR: 2.47, 95%CI: 1.15–5.33, p = 0.02). We could not demonstrate a statistically significant association between intrauterine growth restriction and pre-eclampsia with the studied urological pathologies. Conclusions: Careful UTI screening during pregnancy is needed for preventing maternal–fetal complications.

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Section: Discussionmentioning

confidence: 99%

Pregnancy Outcomes in Patients with Urosepsis and Uncomplicated Urinary Tract Infections—A Retrospective Study

Radu,

Vicoveanu,

Cărăuleanu

et al. 2023

Medicina

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“…Artificial intelligence and risk stratification have been gaining more interest in the field of predictive medicine, and they have been frequently used in imaging data modeling in recent years [41][42][43][44][45][46]. Moreover, the correct reporting of positive circumferential margin on MRI has been the subject of debate.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Feasibility of Curative Resection in Low Rectal Cancer: Insights from a Prospective Observational Study on Preoperative Magnetic Resonance Imaging Accuracy

Volovat,

Scripcariu,

Boboc

et al. 2024

Medicina

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Background and Objectives: A positive pathological circumferential resection margin is a key prognostic factor in rectal cancer surgery. The point of this prospective study was to see how well different MRI parameters could predict a positive pathological circumferential resection margin (pCRM) in people who had been diagnosed with rectal adenocarcinoma, either on their own or when used together. Materials and Methods: Between November 2019 and February 2023, a total of 112 patients were enrolled in this prospective study and followed up for a 36-month period. MRI predictors such as circumferential resection margin (mCRM), presence of extramural venous invasion (mrEMVI), tumor location, and the distance between the tumor and anal verge, taken individually or combined, were evaluated with univariate and sensitivity analyses. Survival estimates in relation to a pCRM status were also determined using Kaplan–Meier analysis. Results: When individually evaluated, the best MRI predictor for the detection of a pCRM in the postsurgical histopathological examination is mrEMVI, which achieved a sensitivity (Se) of 77.78%, a specificity (Sp) of 87.38%, a negative predictive value (NPV) of 97.83%, and an accuracy of 86.61%. Also, the best predictive performance was achieved by a model that comprised all MRI predictors (mCRM+ mrEMVI+ anterior location+ < 4 cm from the anal verge), with an Se of 66.67%, an Sp of 88.46%, an NPV of 96.84%, and an accuracy of 86.73%. The survival rates were significantly higher in the pCRM-negative group (p < 0.001). Conclusions: The use of selective individual imaging predictors or combined models could be useful for the prediction of positive pCRM and risk stratification for local recurrence or distant metastasis.

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“…The database was divided into two sets: 70% for testing and 30% for training. Most machine-learning-based studies choose this configuration, especially for small datasets such as ours [ 13 , 14 , 15 , 22 ].…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, artificial intelligence has gained more interest for its applicability in the prediction of disease occurrence, progression, and/or recurrence [ 13 , 14 , 15 ]. In the field of oncology, and specifically for the prediction of CRC local recurrence or distant metastasis, several machine learning (ML)-based algorithms or artificial neural networks (ANN) have been developed.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data

Volovat,

Scripcariu,

Boboc

et al. 2024

Diagnostics

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(1) Background: Numerous variables could influence the risk of rectal cancer recurrence or metastasis, and machine learning (ML)-based algorithms can help us refine the risk stratification process of these patients and choose the best therapeutic approach. The aim of this study was to assess the predictive performance of 4 ML-based models for the prediction of local recurrence or distant metastasis in patients with locally advanced low rectal adenocarcinomas who underwent neoadjuvant chemoradiotherapy and surgical treatment; (2) Methods: Patients who were admitted at the first Oncologic Surgical Clinic from the Regional Institute of Oncology, Iasi, Romania were retrospectively included in this study between November 2019 and July 2023. Decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF) were used to analyze imagistic, surgical, and pathological data retrieved from the medical files, and their predictive performance was assessed; (3) Results: The best predictive performance was achieved by RF when used to predict disease recurrence (accuracy: 90.85%) or distant metastasis (accuracy: 89.63%). RF was closely followed by SVM (accuracy for recurrence 87.8%; accuracy for metastasis: 87.2%) in terms of predictive performance. NB and DT achieved moderate predictive power for the evaluated outcomes; (4) Conclusions: Complex algorithms such as RF and SVM could be useful for improving the prediction of adverse oncological outcomes in patients with low rectal adenocarcinoma.

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Machine Learning Approaches for the Prediction of Hepatitis B and C Seropositivity

Cited by 8 publications

References 42 publications

Pregnancy Outcomes in Patients with Urosepsis and Uncomplicated Urinary Tract Infections—A Retrospective Study

Pregnancy Outcomes in Patients with Urosepsis and Uncomplicated Urinary Tract Infections—A Retrospective Study

Predicting the Feasibility of Curative Resection in Low Rectal Cancer: Insights from a Prospective Observational Study on Preoperative Magnetic Resonance Imaging Accuracy

Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data

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