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BMC Psychiatry Aug 2023Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of...
BACKGROUND
Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations.
METHODS
Our data originated from the National Health and Nutrition Examination Survey (2005-2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score.
RESULTS
Deep learning had the highest AUC (0.891, 95%CI 0.869-0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904-0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors.
CONCLUSIONS
Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.
Topics: Middle Aged; Humans; Aged; Deep Learning; Depression; Nutrition Surveys; Veterans; Algorithms
PubMed: 37612646
DOI: 10.1186/s12888-023-05109-9 -
Radiation Oncology (London, England) Aug 2023Hypothyroidism (HT) and subclinical HT after radiotherapy is frequent in nasopharyngeal carcinoma (NPC) patients, results in negative impact on patients' quality of... (Randomized Controlled Trial)
Randomized Controlled Trial
Thyroid V40 is a good predictor for subclinical hypothyroidism in patients with nasopharyngeal carcinoma after intensity modulated radiation therapy: a randomized clinical trial.
BACKGROUND
Hypothyroidism (HT) and subclinical HT after radiotherapy is frequent in nasopharyngeal carcinoma (NPC) patients, results in negative impact on patients' quality of life. The percentage of thyroid volume receiving more than 40 Gy (V40) ≤ 85% was reported to be a useful dose constraint to adopt during intensity-modulated radiation therapy (IMRT) planning. This study aims to verify whether V40 ≤ 85% can be used as an effective dose constraint in IMRT planning in a randomized clinical trial.
METHODS
This single-center 1:1 randomized clinical trial was conducted in Fujian province hospital between March 2018 and September 2022. All patients were treated with IMRT and randomized to induction chemo followed by concurrent chemo-IMRT or concurrent chemo-IMRT alone. Ninety-two clinically NPC patients were included in this study. The thyroid function tests were performed for all patients before and after radiation at regular intervals. Thyroid dose-constraint was defined as V40 ≤ 85%. The primary outcome in this study was subclinical HT.
RESULTS
Median follow up was 34 months. Significant difference in the incidence of subclinical HT between the thyroid dose-constraint group and unrestricted group was observed (P = 0.023). The risk of subclinical HT in the thyroid dose-constraint group was lower than that in the unrestricted group (P = 0.022). Univariate and multivariate cox regression analysis indicated that thyroid dose-constraint was a protective effect of subclinical HT (HR = 0.408, 95% CI 0.184-0.904; HR = 0.361, 95% CI 0.155-0.841).
CONCLUSION
V40 ≤ 85% can be used as an effective dose constraint in IMRT planning to prevent radiation-induced subclinical HT.
Topics: Humans; Nasopharyngeal Carcinoma; Quality of Life; Radiotherapy, Intensity-Modulated; Hypothyroidism; Nasopharyngeal Neoplasms
PubMed: 37626342
DOI: 10.1186/s13014-023-02329-x