A novel computer based risk prediction model for vocal cord palsy before thyroidectomy.

BACKGROUND: Thyroidectomy may be performed for clinical indications that include malignancy, benign nodules or cysts suspicious findings on fine needle aspiration (FNA) biopsy, dyspnea from airway compression or dysphagia from cervical esophageal compression, etc. The incidences of vocal cord palsy (VCP) caused by thyroid surgery were reported to range from 3.4% to 7.2% and 0.2% to 0.9% for temporary and permanent vocal fold palsy respectively which is a serious complication of thyroidectomy that is worrisome for patients. OBJECTIVE: Therefore, it is aimed to determine the patients who have the risk of developing vocal cord palsy before thyroidectomy by using machine learning methods in the study. In this way, the possibility of developing palsy can be reduced by applying appropriate surgical techniques to individuals in the high-risk group. METHOD: For this aim, 1039 patients with thyroidectomy, between the years 2015 and 2018, have been used from Karadeniz Technical University Medical Faculty Farabi Hospital at the department of general surgery. The clinical risk prediction model was developed using the proposed sampling and random forest classification method on the dataset. CONCLUSION: As a result, a novel quite a satisfactory prediction model with 100% accuracy was developed for VCP before thyroidectomy. Using this clinical risk prediction model, physicians can be helped to identify patients at high risk of developing post-operative palsy before the operation.

Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques.

The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using deep learning algorithms. With this aim, a prospective study was designed and the data was taken from 489 patients between the years 2019 and 2021, and informed consent was obtained. The clinical decision support system for the diagnosis of GD was developed using the generated dataset with deep learning algorithms and Bayesian optimization. As a result, a novel successful decision support model was developed using RNN-LSTM with Bayesian optimization that gave 95% sensitivity and 99% specificity on the dataset for the diagnosis of patients in the GD risk group by obtaining 98% AUC (95% CI (0.95-1.00) and p < 0.001). Thus, with the clinical diagnosis system developed to assist physicians, it is planned to save both cost and time, and reduce possible adverse effects by preventing unnecessary OGTT for patients who are not in the GD risk group.

A prospective study for gestational diabetes mellitus: Analysis of risk factors in Turkish women for early prediction.

OBJECTIVE: To define risk factors for the early prediction of gestational diabetes mellitus (GDM) because the risk of pre-eclampsia and preterm birth increases in mothers who are diagnosed with GDM. MATERIALS AND METHODS: A prospective study was designed and the data were collected by physicians prospectively from the patients who came to the clinic between the years 2019 and 2021; informed consent was obtained from the women. The prospective data comprised 489 patient records with 72 variables and the risk factors for early prediction of GDM were determined using logistic regression and random forest (RF), which is an advanced analysis method. RESULTS: The obtained sensitivity and specificity values are 90% and 75% for logistic regression and 71% and 90% for the RF, respectively. CONCLUSION: In this prospective study of GDM in Turkish women; age, body mass index, level of hemoglobin A1c, level of fasting blood sugar, physical activity time in first trimester, gravidity, triglycerides, and high-density lipoprotein cholesterol were confirmed to be risk factors in analysis results.

Associations of maternal characteristics and dietary factors with anemia and iron-deficiency in pregnancy.

OBJECTIVE: Anemia and iron deficiency during pregnancy influence maternal and fetal health, birth outcomes, and the risk of chronic disease in offspring. This study aimed to examine the association with sociodemographic, maternal factors, supplement use and dietary intakes, and anemia and iron deficiency in pregnancy. METHODS: A cross-sectional study was conducted on 165 pregnant women aged between 19 and 45 years who were interviewed, and dietary intake was assessed by 24-hours dietary recall, supplement records and food frequency questionnaire. Learning Vector Quantization feature selection method which is one of the machine learning techniques was used to extract important variables from sociodemographic, maternal, and dietary factors. RESULTS: The prevalence of anemia was 15.2% and prevalence of iron deficiency was 65.5%. Total intake of iron, phosphorus, vitamin B1 and B2 were importance factors for iron deficiency while age, number of births, use of folic acid supplement, dietary folate equivalent and total iron intake were importance factors for anemia. CONCLUSIONS: Maternal and dietary characteristics were the most crucial risk factors for anemia while dietary factors were the most important risk factor for iron deficiency in pregnancy. The development of anemia and iron deficiency is associated with the coexistence of many nutrient deficiencies.