The Integration of Big Data and Artificial Neural Networks for Enhancing Credit Risk Scoring in Emerging Markets: Evidence from Egypt

This study investigates the effectiveness of technology models in credit risk scoring modeling in emerging markets. the study proposes evaluation methods for credit risk scoring modeling for current and potential borrowers through an investigation into the Egyptian banking industry by offering and examining a framework for the integration of big data and artificial neural networks based on systematic and unsystematic risk for both the macroeconomic environment and characteristics of current and potential borrowers. The data for the borrowers under examination covers the period from 2015 to 2019 for 75 firms, excluding 2020 and 2021 data to isolate the impact of COVID-19 on the results of the inferred statistics. Artificial Neural Networks was training within 25 firms under NeuroXL program but examination for 50 firms. The study found the ability of artificial neural networks to rank the commitment of borrowers in Egyptian banks under big data about the firm and Egyptian economy. Additions to discrepancy between the proposed model against some traditional models. Finally; The Integration of Big Data and ANN can help banks to bring out the value of data within create a level of financial stability for banks. Especially in emerging markets characterized by information inefficiency.

Effect of Replacing Vendor QTc Alerts with a Custom QTc Risk Alert in Inpatients

Objective The aim of the study is to implement a customized QTc interval clinical decision support (CDS) alert strategy in our electronic health record for hospitalized patients and aimed at providers with the following objectives: minimize QTc prolongation, minimize exposure to QTc prolonging medications, and decrease overall QTc-related alerts. A strategy that was based on the validated QTc risk scoring tool and replacing medication knowledge vendor alerts with custom QTc prolongation alerts was implemented. Methods This is a retrospective quasi-experimental study with a pre-intervention period (August 2019 to October 2019) and post-intervention period (December 2019 to February 2020). The custom alert was implemented in November 2019. Results In the pre-implementation group, 361 (19.3%) patients developed QTc prolongation, and in the post-implementation group, 357 (19.6%) patients developed QTc prolongation (OR: 1.02, 95% CI: 0.87–1.20, p = 0.81). The odds ratio of an action taken post-implementation compared with pre-implementation was 18.90 (95% CI: 14.03–25.47, p <0. 001). There was also a decrease in total orders for QTc prolonging medications from 7,921 (5.5%) to 7,566 (5.3%) with an odds ratio of 0.96 (95% CI: 0.93–0.99, p = 0.01). Conclusion We were able to decrease patient exposure to QTc prolonging medications while not increasing the rate of QTc prolongation as well as improving alert action rate. Additionally, there was a decrease in QTc prolonging medication orders which illustrates the benefit of using a validated risk score with a customized CDS approach compared with a traditional vendor-based strategy. Further research is needed to confirm if an approach implemented at our organization can reduce QTc prolongation rates.

Practical Risk Scoring System for Predicting Severity of COVID-19 Disease

Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19 disease, has become an international pandemic with numerous casualties. It had been noted that the severity of the COVID-19 disease course depends on several clinical, laboratory, and radiological factors. This has led to risk scoring systems in various populations such as in China, but similar risk scoring systems based on the American veteran population are sparse, particularly with the vulnerable Veteran population. As a simple risk scoring system would be very useful, we propose a simple Jhala Risk Scoring System (JRSS) to assess the severity of disease risk. Methods: A retrospective review of all SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR) tests collected and performed at the regional Veterans Administration Medical Center (VAMC) serving the Philadelphia and surrounding areas from March 17th, 2020 to May 20th, 2020. Data was collected and analyzed in the same year. These tests were reviewed within the computerized medical record system for demographic, medical history, laboratory test history, and clinical course. Information from the medical records were then scored based on the criteria of the Jhala Risk Scoring System (JRSS). Results: The JRSS, based on age, ethnicity, presence of any lung disease, presence of cardiovascular disease, smoking history, and diabetes history with laboratory parameters correlated and predicted (with statistical significance) which patients would be hospitalized. Conclusion: The JRSS may play a role in informing which COVID-19 positive patients in the emergency room/urgent care for risk stratification.

Development And Validation of a Risk Scoring System to Predict Pneumothorax in CT-Guided Percutaneous Transthoracic Needle Biopsy

Objective: To develop and validate a risk scoring system using variables easily obtained for the prediction of pneumothorax in CT-guided percutaneous transthoracic needle biopsy (PTNB).Methods: The derivation cohort was comprised of 1001 patients who underwent CT-guided PTNB. Multivariate logistic regression was used to identify risk factors for pneumothorax, which were treated as the foundation to develop the risk scoring system. To validate the system, a validation cohort group of 230 patients was enrolled.Results: Age, puncture times, puncture depth, smoking index, number of specimens, bleeding from the needle path, and lobular lesion were identified as risk factors in the derivation cohort. A risk scoring system (Hosmer-Lemeshow goodness-of-fit test p =0.33) was developed. The area under the receiver operating characteristic curve (AUROC) was 0.601 by using the risk score system. This risk score system demonstrated a better diagnostic effect with increasing age. In the group of patients older than 80 years, the AUROC was 0.76, showing good predictive power. This risk scoring system was confirmed in the validation cohort with an AUROC of 0.736.Conclusion: This scoring system has a good predictive effect in both derivation and validation cohort.

Immune Score-based Molecular Subtypes and Signature Associated with Clinical Outcome in Hepatoblastoma

Background: This study aimed to identify genes related to the immune score of hepatoblastoma, examine the characteristics of the immune microenvironment of hepatoblastoma, and construct a risk scoring system for predicting the prognosis of hepatoblastoma. Methods: Through using the gene chip data of patients with hepatoblastoma with survival data in the ArrayExpress and GEO databases, the immune score of hepatoblastoma was calculated by the ESITIMATE algorithm, and the prognostic value of immune score in patients with hepatoblastoma was studied by the survival analysis. Genes related to the immune score were identified by the WGCNA algorithm. According to these genes, patients with hepatoblastoma were clustered unsupervised. Finally, the risk scoring system was constructed according to the immune score-related genes. Results: The immune score calculated by the ESTIMATE algorithm had a good prognostic value in patients with hepatoblastoma. Patients with high immune scores had better OS than those with low immune scores (P < 0.001). A total of 146 immune score-related genes were identified by WGCNA analysis, and univariate COX regression analysis indicated that 59 of the genes had prognostic value. According to the unsupervised clustering results of the 146 immune score-related genes, patients with hepatoblastoma could be divided into two subtypes with different prognoses, namely molecular subtype 1 and subtype 2, with molecular subtype 1 having a better prognosis. The immunocyte infiltration analysis results showed that the difference between the two subtypes was mainly in activated CD4 T cells, activated dendritic cells, CD56 bright natural killer cells, the macrophage, and regulatory T cells. According to the immune score-related genes, a risk scoring system was constructed based on a five-gene signature. After the cut-off value was determined, patients with hepatoblastoma were divided into a high-risk group and a low-risk group. The prognosis of the two groups was different. Conclusions: The immune score has a good prognostic value in patients with hepatoblastoma. Based on the different expression patterns of immune score-related genes, hepatoblastoma can be divided into two different prognostic molecular subtypes, showing different immunocyte infiltration patterns. The established risk scoring system based on a five-gene signature has a good predictive value in patients with hepatoblastoma.

Comparative Study of ACR TI-RADS and ATA 2015 for Ultrasound Risk Stratification of Thyroid Nodules

Objective To study the adoption rate of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) scoring system over a 3-year period in a community setting and compare its performance with that of the American Thyroid Association 2015 (ATA 2015) ultrasound risk scoring system. Study Design Case series with prospective data collection and retrospective chart review. Setting Large community-based practice with multiple satellite offices and a dedicated thyroid ultrasound clinic. Methods All patients referred to the thyroid clinic between January 2018 and December 2020 for ultrasound-guided fine-needle biopsy were assigned an ATA 2015 risk score in a prospective manner immediately prior to biopsy. ACR TI-RADS scores were recorded through retrospective chart review of the radiologist report. Performance of the 2 systems was compared with cytology as the gold standard. Results A total of 949 nodules underwent biopsy, of which 236 had available data for both scoring systems. There was a 33.8% increase in adoption of the ACR TI-RADS over the 3-year study period. The ATA 2015 guidelines yielded sensitivity and specificity of 81.6% and 54.5%, respectively, as opposed to 73.7% and 27.0% for the ACR TI-RADS. Conclusion In our community, there has been a gradual increase in adoption of the ACR TI-RADS, although the ATA 2015 risk scoring system has performed better.