QUANTITATIVE EVALUATION OF MEDICAL RECORD DOCUMENTATION IN IMAM REZA HOSPITAL, MASHHAD, IRAN

Introduction: Diabetes is the most common endocrine disease. Given the importance of medical record documentation for diabetic patients and its significant impact on accurate treatment process, as well as early diagnosis and treatment of acute and chronic complications, this study aimed to qualitatively evaluate medical record documentation of diabetic patients. Methods: This descriptive and cross-sectional study was conducted on all medical records of diabetic patients (1200 cases) in the comprehensive Diabetes Center of Imam Reza Hospital. A checklist was prepared according to the main sectors and their sub-data elements to conduct a qualitative evaluation on documentation of medical records of diabetic patients.  Descriptive statistics were used to report the results. Results: In this study, 1200 (710 women and 490 men) cases were evaluated. Mean documentation of main sectors of diabetic patients’ records were as follows: 49% demographic characteristics, 14% patient referral, 4% diagnosis, 50% lab tests, 25% diabetes medications,13% nephropathy screening test, 10% diabetic neuropathy, 41% specialty and subspecialty consultations and internal medicine physicians visits did not complete for all the patients. Conclusion: According to the results of this study, qualitative evaluation of medical record documentation of diabetic patients Showed poor documentation in this regard. It is suggested that results of this study be accessible to physicians of healthcare centers to take a positive step toward improved documentation of medical records. In addition, it seems necessary to modify diabetic medical records

DETERMINING THE MINIMUM DATA SET FOR DIABETES REGISTRY

Introduction: The number of people with diabetes's increasing. More than 220 million people have diabetes, more than 70% of whom live in middle and lower-income countries. already exist many innovations around the world on improving the managed care of diabetes  .diabetes registries are one of them. in Iran, development and evaluation of diabetes information systems is one of the most research priorities. since defining health regulations and evaluation of diabetes prevention programs depend on the powerful information system, but in Iran don't exist complete information about incidence and prevalence of diabetes. determine standard data elements (Des) and design diabetes registry is one the most important country requirements. the main purpose of this study is investigating to this subject.   Methods: This is a descriptive- analytic study. Resource related to diabetes DEs collected from selective minimum data sets. Then diabetes DEs set derived from selective minimum data sets were investigated in focus group sessions with endocrine specialists, health informatics, and health information management. Duplicate DEs were removed and similar DEs were combined. Then seven endocrine specialists evaluated diabetes DEs set. They determine the value of each DEs using the Delphi technique (scores range from 0 to 5). The DEs that received more than 75% of grade 4 and 5 remained in the study. Following the expert opinion, the final version of the diabetes DEs set was designed.   Results: According to literature review 455 DEs included studying, after Delphi sessions, 293 data element remained to study. Main categories of DEs are:1-patient demographic characterizes (12 DEs), 2-patient referral (5 DEs), 3-diabetes care follow up (15 DEs), 4-physical exam, chief complaint and assessment (40 DEs), 5-history (such as: individual, grow up, family, drug abuse) (10 DEs), 6-pregnancy management (13 DEs), 7-screening (10 DEs), 8-specialty evolutions ( such as: cardiovascular (18 DEs), neuropathy (16 DEs), nephropathy (7 DEs), teeth and mouse (3 DEs), eyes (14 DEs), psychology situation (2 DEs),  sexual ability (1 DEs)), 9-laboratory exams (33 DEs), 10-drugs (oral antidiabetics drugs (14 DEs), injectable antidiabetics (7 DEs), lipid (11 DEs), hypertension (20 DEs), anti placates (2 DEs)), cardiac (3 DEs), preparing insulin method (5 DEs)), 11-physical activity (4 DEs),12- diet (12 DEs), 13-education and self care (13 DEs). Conclusion: In the study diabetes, DEs set were determined that provide appropriate yield for data gathering and record all required information for diabetes care. Hence diabetes is a chronic disease and Patients suffer from it for years, implementation diabetes DEs can improve documentation and improve diabetes care.&nbsp

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Factors Associated with Length of Hospital Stay: A Systematic Review

Introduction: The Length of Stay (LOS) in the hospital is used as an indirect indicator of resources consumption and efficiency in hospitals. Identifying factors associated with this systematic review can be valuable in planning to optimize the utilization of the existing resources. The goal of the present study was to investigate factors associated with length of stay and it has been conducted as a systematic review. Method: In this systematic review, papers were retrieved by the use of specified key terms in their titles and no restricted time in Persian and English databases. Papers were selected according to how they were in line with the criteria for inclusion and exclusion and finally, information were extracted and entered to Excel 2010 software for analysis. Results: 18 articles out of 347 were selected. These studies introduced four criteria associated with length of stay including clinical, demographic, administrative, and hospital factors. Applied methods for identifying these criteria were statistical techniques and data mining techniques such as decision tree regression and artificial neural networks. The goal of all studies was making a new model for identifying factors associated with LOS or was evaluating other methods introduced in other studies. Conclusion: Findings of this study represent that identifying factors associated with LOS can be variable according to data collection place, studied variables, and applied data mining techniques. So we suggest researchers to help hospital managers and planners with identifying and reducing factors associated with LOS

Enhancing the Security of Web Sites and Patients’ Portals by Detecting Malicious Web Robots Using Machine Learning Techniques

We illustrate a detailed compositional characterization of hydrothermal liquefaction (HTL) oils derived from two biochemically distinct microalgae, Nannochloropsis gaditana and Chlorella sp. (DOE 1412), for a range of reaction temperature as observed by high-resolution electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS). The unique capability to unequivocally derive molecular formulae directly from FT-ICR MS-measured mass-to- charge ratio (for several thousand compounds in each oil) shows that lipids are completely reacted/converted for any reaction temperature above 200 degrees C and reveals the formation of non-lipid reaction products with increasing temperature. Specifically, lipid-rich oil is obtained at low reaction temperature (less than 225 degrees C) for both microalgal strains. For positive ion mode, the major lipid components in Chlorella sp. and N. gaditana HTL oils are betaine lipids and acylglycerols, respectively. Acidic species in the HTL oils (observed by negative ion mode) are dominated by free fatty acids (FFA) regardless of reaction temperature. HTL oils obtained at higher-temperatures (ge 225 degrees C) are comprised of a variety of basic nitrogen- and oxygen-containing compounds that originate from protein and carbohydrate degradation at elevated temperature. Similar structural features are observed for the abundant nitrogen heterocyclics between the two strains with slightly lower carbon number for Chlorella sp., overall

Deep learning prediction models based on EHR trajectories : A systematic review

BACKGROUND: Electronic health records (EHRs) are generated at an ever-increasing rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' future health-related risks. It enables healthcare systems to increase the quality of care through early identification and primary prevention. Deep learning techniques have shown great capacity for analyzing complex data and have been successful for prediction tasks using complex EHR trajectories. This systematic review aims to analyze recent studies to identify challenges, knowledge gaps, and ongoing research directions.METHODS: For this systematic review, we searched Scopus, PubMed, IEEE Xplore, and ACM databases from Jan 2016 to April 2022 using search terms centered around EHR, deep learning, and trajectories. Then the selected papers were analyzed according to publication characteristics, objectives, and their solutions regarding existing challenges, such as the model's capacity to deal with intricate data dependencies, data insufficiency, and explainability.RESULTS: After removing duplicates and out-of-scope papers, 63 papers were selected, which showed rapid growth in the number of research in recent years. Predicting all diseases in the next visit and the onset of cardiovascular diseases were the most common targets. Different contextual and non-contextual representation learning methods are employed to retrieve important information from the sequence of EHR trajectories. Recurrent neural networks and the time-aware attention mechanism for modeling long-term dependencies, self-attentions, convolutional neural networks, graphs for representing inner visit relations, and attention scores for explainability were frequently used among the reviewed publications.CONCLUSIONS: This systematic review demonstrated how recent breakthroughs in deep learning methods have facilitated the modeling of EHR trajectories. Research on improving the ability of graph neural networks, attention mechanisms, and cross-modal learning to analyze intricate dependencies among EHRs has shown good progress. There is a need to increase the number of publicly available EHR trajectory datasets to allow for easier comparison among different models. Also, very few developed models can handle all aspects of EHR trajectory data