Applying Deep Learning Model to Predict Diagnosis Code of Medical Records.

The International Classification of Diseases (ICD) code is a diagnostic classification standard that is frequently used as a referencing system in healthcare and insurance. However, it takes time and effort to find and use the right diagnosis code based on a patient's medical records. In response, deep learning (DL) methods have been developed to assist physicians in the ICD coding process. Our findings propose a deep learning model that utilized clinical notes from medical records to predict ICD-10 codes. Our research used text-based medical data from the outpatient department (OPD) of a university hospital from January to December 2016. The dataset used clinical notes from five departments, and a total of 21,953 medical records were collected. Clinical notes consisted of a subjective component, objective component, assessment, plan (SOAP) notes, diagnosis code, and a drug list. The dataset was divided into two groups: 90% for training and 10% for test cases. We applied natural language processing (NLP) technique (word embedding, Word2Vector) to process the data. A deep learning-based convolutional neural network (CNN) model was created based on the information presented above. Three metrics (precision, recall, and F-score) were used to calculate the achievement of the deep learning CNN model. Clinically acceptable results were achieved through the deep learning model for five departments (precision: 0.53-0.96; recall: 0.85-0.99; and F-score: 0.65-0.98). With a precision of 0.95, a recall of 0.99, and an F-score of 0.98, the deep learning model performed the best in the department of cardiology. Our proposed CNN model significantly improved the prediction performance for an automated ICD-10 code prediction system based on prior clinical information. This CNN model could reduce the laborious task of manual coding and could assist physicians in making a better diagnosis.

Deep-ADCA: Development and Validation of Deep Learning Model for Automated Diagnosis Code Assignment Using Clinical Notes in Electronic Medical Records.

Currently, the International Classification of Diseases (ICD) codes are being used to improve clinical, financial, and administrative performance. Inaccurate ICD coding can lower the quality of care, and delay or prevent reimbursement. However, selecting the appropriate ICD code from a patient's clinical history is time-consuming and requires expert knowledge. The rapid spread of electronic medical records (EMRs) has generated a large amount of clinical data and provides an opportunity to predict ICD codes using deep learning models. The main objective of this study was to use a deep learning-based natural language processing (NLP) model to accurately predict ICD-10 codes, which could help providers to make better clinical decisions and improve their level of service. We retrospectively collected clinical notes from five outpatient departments (OPD) from one university teaching hospital between January 2016 and December 2016. We applied NLP techniques, including global vectors, word to vectors, and embedding techniques to process the data. The dataset was split into two independent training and testing datasets consisting of 90% and 10% of the entire dataset, respectively. A convolutional neural network (CNN) model was developed, and the performance was measured using the precision, recall, and F-score. A total of 21,953 medical records were collected from 5016 patients. The performance of the CNN model for the five different departments was clinically satisfactory (Precision: 0.50~0.69 and recall: 0.78~0.91). However, the CNN model achieved the best performance for the cardiology department, with a precision of 69%, a recall of 89% and an F-score of 78%. The CNN model for predicting ICD-10 codes provides an opportunity to improve the quality of care. Implementing this model in real-world clinical settings could reduce the manual coding workload, enhance the efficiency of clinical coding, and support physicians in making better clinical decisions.

The psychometric properties of the Bangla Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): preliminary reports from a large-scale validation study.

BACKGROUND: The Posttraumatic Stress Disorder Checklist (PCL-5) is the most widely used screening tool in assessing posttraumatic stress disorder symptoms, based on the Diagnostic and Statistical Manual of Mental disorders (DSM-5) criteria. This study aimed to evaluate the psychometric properties of the newly translated Bangla PCL-5. METHODS: A cross-sectional survey was carried out among 10,605 individuals (61.0% male; mean age: 23.6 ± 5.5 [13-71 years]) during May and June 2020, several months after the onset of the COVID-19 outbreak in Bangladesh. The survey included the Bangla PCL-5 and the PHQ-9 depression scale. We used confirmatory factor analysis to test the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor hybrid model. RESULTS: The Bangla PCL-5 displayed adequate internal consistency (Cronbach's alpha = 0.90). The Bangla PCL-5 score was significantly correlated with scores of the PHQ-9 depression scale, confirming strong convergent validity. Confirmatory factor analyses indicated the models had a good fit to the data, including the four-factor DSM-5 model, the six-factor Anhedonia model, and the seven-factor hybrid model. Overall, the seven-factor hybrid model exhibited the best fit to the data. CONCLUSIONS: The Bangla PCL-5 appears to be a valid and reliable psychometric screening tool that may be employed in the prospective evaluation of posttraumatic stress disorder in Bangladesh.

Depressive symptoms associated with COVID-19 preventive practice measures, daily activities in home quarantine and suicidal behaviors: Findings from a large-scale online survey in Bangladesh.

BACKGROUND: The world is facing a public health emergency situation caused by the COVID-19 pandemic. Psychological wellbeing among individuals worldwide has been negatively affected by the pandemic especially in low- and middle-income countries such as Bangladesh. The present study aimed to assess the estimate of depressive symptoms and investigated its associations with COVID-19 preventive practice measures, daily activities in home quarantine, and suicidal behaviors in a large-scale Bangladeshi online survey. METHODS: An online-based cross-sectional survey was widely distributed to Bangladeshi citizens. A total of 13,654 participants (61.0% male; mean age = 24.0 years [SD = 6.0]; age range 18-65 years) completed the survey between May and June (2020). The survey included socio-demographics and COVID-19-related questions, along with lifestyle, suicidal, and psychometric measures. Hierarchical regression was performed to determine significant associations between depression and examined variables. RESULTS: The estimate of depressive symptoms during the COVID-19 pandemic was 43.5%. Based on hierarchical regression analysis, depression was significantly associated with not engaging in COVID-19 preventive measures, daily activities in home quarantine (e.g., playing videogames), and suicidal behaviors. CONCLUSIONS: Depressive symptoms appeared to be high during the COVID-19 pandemic in Bangladesh. To fight against the pandemic, mental health issues as well as physical health issues need to be taken into consideration.

Problematic internet use among young and adult population in Bangladesh: Correlates with lifestyle and online activities during the COVID-19 pandemic.

BACKGROUND: Although internet use can boost individuals' quality of life in various aspects, activities on the internet (e.g., gambling, video gaming, social media use, pornography use, etc.) can be used as coping strategy to deal with psychological stressors and mood states (e.g., fear, anxiety, depression) particularly during the global COVID-19 pandemic. OBJECTIVES: The present study assessed problematic internet use (PIU) among Bangladeshi youth and adults in Bangladesh and examined its correlation with lifestyle and online activities during the COVID-19 pandemic. METHODS: An online cross-sectional survey was utilized between May and June 2020 comprising 13,525 Bangladeshi individuals (61.3% male; age range 18-50 years; mean age 23.7 years) recruited from various online platforms. The self-report survey included questions concerning socio-demographics, lifestyle, and online activities during the COVID-19 pandemic, as well as psychometric scales such as the nine-item Internet Disorder Scale-Short Form (IDS9-SF). RESULTS: Utilizing hierarchical regression analysis, problematic internet use was significantly and positively associated with those who were younger, having a higher level of education, living with a nuclear family, engaging in less physical exercise, avoiding household chores, playing online videogames, social media use, and engaging in recreational online activities. CONCLUSIONS: Excessive internet use appears to have been commonplace during the COVID-19 pandemic period and young adults were most vulnerable to problematic internet use.

Predicting Diagnosis Code from Medication List of an Electronic Medical Record Using Convolutional Neural Network.

Automated coding and classification systems play a role in healthcare for quality of care. Our objective was to predict diagnosis code from medication list of electronic medical record (EMR) using convolutional neural network (CNN). We collected the clinical note from outpatient department (OPD) of Wanfang hospital, Taiwan of 2016 and used three physicians from three departments. The dataset was split into two parts, 90% for training and 10% for test cases. We used medication list as input and International Statistical Classification of Diseases 10 (ICD 10) code as output. After data preprocess, we used word2vector CNN to predict ICD 10 code. This study shows all the three physicians from three departments achieved better performance. The best performance of model was a physician from cardiology department achieved precision 69%, recall 89% and F measure 78%. We need to include more component such as text data, lab report for evaluation.

Roles of Sedentary Behaviors and Unhealthy Foods in Increasing the Obesity Risk in Adult Men and Women: A Cross-Sectional National Study.

Sedentary behaviors and dietary intake are independently associated with obesity risk. In the literature, only a few studies have investigated gender differences for such associations. The present study aims to assess the association of sedentary behaviors and unhealthy foods intake with obesity in men and women in a comparative manner. The analysis presented in this study was based on the data from a population-based, cross-sectional, nationally representative survey (Indonesian Basic Health Research 2013/RISKESDAS 2013). In total, 222,650 men and 248,590 women aged 19­55 years were enrolled. A validated questionnaire, physical activity card, and food card were used for the assessments. The results showed that the prevalence of obesity (body mass index of ≥27.5 kg/m²) was higher in women (18.71%) than in men (8.67%). The mean body mass index in women tended to be higher than in men. After adjusting for age and education, the gender effect on obesity persisted in women and was more significant than in men. There was also a positive and significant effect on obesity of sedentary behaviors and unhealthy foods intake. Moreover, fatty and fried foods displayed a positive multiplicative interaction, increasing obesity risk in women more than in men and indicating a possible dietary risk in in women in relation to obesity. The study suggests that the implementation of educational programs on nutrition and physical activity is particularly important for promoting a healthy body weight among Indonesian women.

Gender-based personalized pharmacotherapy: a systematic review.

PURPOSE: In general, male and female are prescribed the same amount of dosage even if most of the cases female required less dosage than male. Physicians are often facing problem on appropriate drug dosing, efficient treatment, and drug safety for a female in general. To identify and synthesize evidence about the effectiveness of gender-based therapy; provide the information to patients, providers, and health system intervention to ensure safety treatment; and minimize adverse effects. METHODS: We performed a systematic review to evaluate the effect of gender difference on pharmacotherapy. Published articles from January 1990 to December 2015 were identified using specific term in MEDLINE (PubMed), EMBASE, and the Cochrane library according to search strategies that strengthen the reporting of observational and clinical studies. RESULTS: Twenty-six studies fulfilled the inclusion criteria for this systematic review, yielding a total of 6309 subjects. We observed that female generally has a lower the gastric emptying time, gastric PH, lean body mass, and higher plasma volume, BMI, body fat, as well as reduce hepatic clearance, difference in activity of Cytochrome P450 enzyme, and metabolize drugs at different rate compared with male. Other significant factors such as conjugation, protein binding, absorption, and the renal elimination could not be ignored. However, these differences can lead to adverse effects in female especially for the pregnant, post-menopausal, and elderly women. CONCLUSION: This systematic review provides an evidence for the effectiveness of dosage difference to ensure safety and efficient treatment. Future studies on the current topic are, therefore, recommended to reduce the adverse effect of therapy.

Benzodiazepine Use and Risk of Dementia in the Elderly Population: A Systematic Review and Meta-Analysis.

BACKGROUND: Benzodiazepines are a widely used medication in developed countries, particularly among elderly patients. However, benzodiazepines are known to affect memory and cognition and might thus enhance the risk of dementia. The objective of this review is to synthesize evidence from observational studies that evaluated the association between benzodiazepines use and dementia risk. SUMMARY: We performed a systematic review and meta-analysis of controlled observational studies to evaluate the risk of benzodiazepines use on dementia outcome. All control observational studies that compared dementia outcome in patients with benzodiazepine use with a control group were included. We calculated pooled ORs using a random-effects model. Ten studies (of 3,696 studies identified) were included in the systematic review, of which 8 studies were included in random-effects meta-analysis and sensitivity analyses. Odds of dementia were 78% higher in those who used benzodiazepines compared with those who did not use benzodiazepines (OR 1.78; 95% CI 1.33-2.38). In subgroup analysis, the higher association was still found in the studies from Asia (OR 2.40; 95% CI 1.66-3.47) whereas a moderate association was observed in the studies from North America and Europe (OR 1.49; 95% CI 1.34-1.65 and OR 1.43; 95% CI 1.16-1.75). Also, diabetics, hypertension, cardiac disease, and statin drugs were associated with increased risk of dementia but negative association was observed in the case of body mass index. There was significant statistical and clinical heterogeneity among studies for the main analysis and most of the sensitivity analyses. There was significant statistical and clinical heterogeneity among the studies for the main analysis and most of the sensitivity analyses. Key Messages: Our results suggest that benzodiazepine use is significantly associated with dementia risk. However, observational studies cannot clarify whether the observed epidemiologic association is a causal effect or the result of some unmeasured confounding variable. Therefore, more research is needed.