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Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. Various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR) and International Classification of Diseases (ICD-10) patient examination database using the Akin Method and Friendship method. In this Health Indicators and Disease Symptoms Extraction (HIDSE), the evidence checking procedures find and collect all possible medical evidence from the existing patient examination report. Akin Method is making the hypothesis decision from count-based evidence principles. The health indicators extraction scheme extracts all relevant information based on the health indicators query and partial input. Similarly, the friendship method is used for making information associations between medical data attributes. This Akin-Friendship model helps to build hypothesis structures and trait-based feature extraction principles. This is called as Composite Akin Friendship Model (CAFM). This proposed model consists of various test cases for developing the medical hypothesis systems. On the other hand, it provides limited accuracy in disease classification. In this regard, the proposed HIDSE implements Deep Learning (DL) based Akin Friendship Method (DLAFM) for improving the accuracy of this medical hypothesis model. The proposed DLAFM, Convolutional Neural Networks (CNN) associated Legacy Prediction Model for Health Indicator (LPHI) is developed to tune the CAFM principles. The results show the proposed health indicator extraction scheme has 8-10% of better system performance than other existing techniques., Competing Interests: Conflict of interestThe submitted work is unpublished and the authors confirm no conflict of interest associated with this., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Purpose: The need for computerized medical assistance for accurate detection of brain hemorrhage from Computer Tomography (CT) images is more mandatory than conventional clinical tests. Recent technologies and advanced computerized algorithms follow Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to improve medical diagnosis platforms. This technology is making the diagnosis practice of brain issues easier for medical practitioners to analyze and identify diseases with an assured degree of precision and performance., Methods: As the existing CT image analysis models use standard procedures to detect hemorrhages, the need for DL-based data analysis is essential to provide more accurate results. Generally, the existing techniques are limited with image training efficiency, image filtering procedures, and runtime system tuning modules. On the scope, this work develops a DL-based automated analysis of CT scan slices to find various levels of brain hemorrhages. Notably, this proposed system integrates Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) architectures as Integrated Generative Adversarial-Convolutional Imaging Model (IGACM) for extracting the CT image features for detecting brain hemorrhages., Results: This system produces good results and takes lesser training time than existing techniques. This proposed system effectively works over CT images and classifies the abnormalities with more accuracy than current techniques. The experiments and results deliver the optimal detection of hemorrhages with better accuracy. It shows that the proposed system works with 5% to 10% of the better performance compared to other diagnostic techniques., Conclusion: The complex nature of CT images leads to noncorrelated feature complexities in diagnosis models. Considering the issue, the proposed system used GAN-based effective sampling techniques for enriching complex image samples into CNN training phases. This concludes the effective contribution of the proposed IGACM technique for detecting brain hemorrhages than the existing diagnosis models., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 V. Pandimurugan et al.)

Background: The diabetes electronic medical record (DEMR) has emerged as an effective information management tool with the potential to improve diabetes care and research. This study reports on the usefulness of the DEMR system at Dr. Mohan's Diabetes Specialities Centre (DMDSC), Chennai, India, for clinical and research purposes., Methods: The DEMR, set up in 1996 at DMDSC, connects data of nine centers/clinics in different geographical areas in Southern India. The present data analysis is based on a total of 226,228 patients registered in the DEMR system at DMDSC between the years 1991 and 2010., Results: The DEMR included data of 139,906 male and 86,322 female patients, of whom 92.6% had type 2 diabetes mellitus (T2DM), 1.4% had type 1 diabetes mellitus (T1DM), and the rest had other types. Patients with T2DM had higher prevalence rates of neuropathy (33.1% vs 13.0%, p < .001), microalbuminuria (25.5% vs 20.0%, p < .001), coronary artery disease (17.5% vs 9.2%, p < .001) and peripheral vascular disease (3.9% vs 2.8%, p = .017) compared with T1DM patients, while prevalence of diabetic retinopathy was similar (37.9% vs 35.7%, p = .06). Prevalence of microvascular and macrovascular complications of diabetes increased with increasing glycated hemoglobin levels (p for trend < .001) and increasing diabetes duration (p for trend < .001)., Conclusions: The DEMR helps track diabetes care and is a valuable tool for research., (© 2011 Diabetes Technology Society.)