scholarly journals Artificial Intelligence Based Study on Analyzing of Habits and with History of Diseases of Patients for Prediction of Recurrence of Disease Due to COVID-19

2020 ◽  
Author(s):  
Samir Kumar Bandyopadhyay ◽  
Shawni Dutta
Keyword(s):  
Chronic Renal Disease ◽  
Research Work ◽  
Young Male ◽  
Support Vector ◽  
General Tendency ◽  
Health Records ◽  
General Drug ◽  
Patient Will ◽  
History Of ◽  
Serious Disease

A patient will visit physicians when he/she feels ill. This illness is not for COVID-19 but it is a general tendency of human being to visit doctor probably it can not be controlled by general drug. When a patient comes to a doctor, the doctor examines him/her after knowing his/her problem. The physician always asks him/her about some questions related to him/her daily life. For example, if a young male patient comes to a doctor with a symptom of fever and cough, the first question doctor asked him that he has a habit of smoking. Then doctor asks him whether this type of symptom appeared often to him previously or not. If the answers of both questions are yes, then the first one is habit and the second one is that he may suffering from some serious disease or a disease due to the weather. The aim of this paper is to consider habit of the patient as well as he/she has been affected by a critical disease. This information is used to build a model that will predict whether there is any possibility of his/her being affected by COVID-19. This research work contributes to tackle the pandemic situation occurred due to Corona Virus Infectious Disease, 2019 (Covid-19). Outbreak of this disease happens based on numerous factors such as past health records and habits of patients. Health records include diabetes tendency, cardiovascular disease existence, pregnancy, asthma, hypertension, pneumonia; chronic renal disease may contribute to this disease occurrence. Past lifestyles such as tobacco, alcohol consumption may be analyzed. A deep learning based framework is investigated to verify the relationship between past health records, habits of patients and covid-19 occurrence. A stacked Gated Recurrent Unit (GRU) based model is proposed in this paper that identifies whether a patient can be infected by this disease or not. The proposed predictive system is compared against existing benchmark Machine Learning classifiers such as Support Vector Machine (SVM) and Decision Tree (DT).

Diagnostic approach to causes of spinal pain

2018 ◽  
pp. 66-70
Author(s):  
F. D. Nasirova
Keyword(s):  
Spinal Pain ◽  
Rapid Loss ◽  
Loss Of Weight ◽  
History Of ◽  
Radiologic Investigation ◽  
Males And Females ◽  
Serious Disease ◽  
Pain At Rest ◽  
Oncologic Diseases ◽  
Selection Of

Causes of spinal pain are extremely varying. Sex composition of patients referring with spinal pain at the age of 16 to 35 was 35% and 65% for males and females, respectively. Peak number of complaints was observed in 30-40 years age group of highest work ability. The followings should be considered as precautions in spinal pain: onset of pain at the age of 20 and after 50, family history of oncologic diseases, walking disorders or dysfunctions of sphincters, numbness in extremities, general malaise and rapid loss of weight, pain at rest and primarily at night, as these conditions may be a warning of underlying serious disease. Selection of algorithm for radiologic investigation is decided by the treating physician.

scholarly journals Prediction of Incident Cancers in the Lifelines Population-Based Cohort

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2133
Author(s):  
Francisco O. Cortés-Ibañez ◽  
Sunil Belur Nagaraj ◽  
Ludo Cornelissen ◽  
Gerjan J. Navis ◽  
Bert van der Vegt ◽  
...  
Keyword(s):  
Cancer Incidence ◽  
Binary Classification ◽  
Predictive Performance ◽  
Population Based ◽  
Support Vector ◽  
Clinical Variables ◽  
Incident Cancer ◽  
History Of ◽  
Diagnosis Of Cancer ◽  
Auc Value

Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) <0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively); age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.

Young Male with Bilateral ICA Dissection: Beyond CADISS Trial

2021 ◽  
pp. 251660852098428
Author(s):  
Vikas Bhatia ◽  
Chirag Jain ◽  
Sucharita Ray ◽  
jay Kumar
Keyword(s):  
Management Strategies ◽  
Young Male ◽  
Acute Onset ◽  
The Body ◽  
Artery Dissection ◽  
Cervical Artery Dissection ◽  
Stroke Study ◽  
History Of

Objective: To report a case of young male with stroke and bilateral internal carotid artery (ICA) dissection. Background: Cervical Artery Dissection in Stroke Study trial has provided some insight on management of patients with ICA dissection. However, there is a need to modify the management strategies as per specific clinical scenario. Design/Methods: Case report and literature review. Results: A 45-year-old male presented with 1 month old history of acute onset numbness of right half of the body with slurring of speech. Computed tomography angiography showed complete occlusion of left cervical ICA just beyond origin with presence of fusiform dilatation and spiral flap in right extracranial cervical ICA. The patient was started on antiplatelets and taken for endovascular procedure using 2-mesh-based carotid stents. Patient was discharged after 3 days on antiplatelet therapy. At 1-year follow-up, there were no fresh symptoms. Conclusion: This case emphasizes the role of successful endovascular management of carotid dissection in a young male. These clinical situations may not be fully represented in trials, and a case-based approach is required.

Ensemble classification technique for heart disease prediction with meta-heuristic-enabled training system

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Parvathaneni Rajendra Kumar ◽  
Suban Ravichandran ◽  
Satyala Narayana
Keyword(s):  
Heart Disease ◽  
Dimensionality Reduction ◽  
Research Work ◽  
Feature Reduction ◽  
Ensemble Classification ◽  
Support Vector ◽  
Disease Prediction ◽  
Central Tendency ◽  
Sea Lion ◽  
Qualitative Variation

AbstractObjectivesThis research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification.MethodsAs the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with Canberra Distance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)-based feature reduction approach is deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier.ResultsAn elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.ConclusionsFrom the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively.ResultsFinally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.

scholarly journals Serum neurofilament levels reflect outer retinal layer changes in multiple sclerosis

2021 ◽  
Vol 14 ◽  
pp. 175628642110034
Author(s):  
Caspar B. Seitz ◽  
Falk Steffen ◽  
Muthuraman Muthuraman ◽  
Timo Uphaus ◽  
Julia Krämer ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Optic Neuritis ◽  
Previous History ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Retinal Layer ◽  
Neurofilament Light Chain ◽  
Neurofilament Light ◽  
Retinal Layers ◽  
History Of

Background: Serum neurofilament light chain (sNfL) and distinct intra-retinal layers are both promising biomarkers of neuro-axonal injury in multiple sclerosis (MS). We aimed to unravel the association of both markers in early MS, having identified that neurofilament has a distinct immunohistochemical expression pattern among intra-retinal layers. Methods: Three-dimensional (3D) spectral domain macular optical coherence tomography scans and sNfL levels were investigated in 156 early MS patients (female/male: 109/47, mean age: 33.3 ± 9.5 years, mean disease duration: 2.0 ± 3.3 years). Out of the whole cohort, 110 patients had no history of optic neuritis (NHON) and 46 patients had a previous history of optic neuritis (HON). In addition, a subgroup of patients ( n = 38) was studied longitudinally over 2 years. Support vector machine analysis was applied to test a regression model for significant changes. Results: In our cohort, HON patients had a thinner outer plexiform layer (OPL) volume compared to NHON patients ( B = −0.016, SE = 0.006, p = 0.013). Higher sNfL levels were significantly associated with thinner OPL volumes in HON patients ( B = −6.734, SE = 2.514, p = 0.011). This finding was corroborated in the longitudinal subanalysis by the association of higher sNfL levels with OPL atrophy ( B = 5.974, SE = 2.420, p = 0.019). sNfL levels were 75.7% accurate at predicting OPL volume in the supervised machine learning. Conclusions: In summary, sNfL levels were a good predictor of future outer retinal thinning in MS. Changes within the neurofilament-rich OPL could be considered as an additional retinal marker linked to MS neurodegeneration.

scholarly journals Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

2020 ◽  
pp. 002029402096482
Author(s):  
Sulaiman Khan ◽  
Abdul Hafeez ◽  
Hazrat Ali ◽  
Shah Nazir ◽  
Anwar Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition Task ◽  
Convolution Neural Network ◽  
Medium Size ◽  
Support Vector ◽  
Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

scholarly journals Venipuncture Induced Complex Regional Pain Syndrome Presenting as Inflammatory Arthritis

2016 ◽  
Vol 2016 ◽  
pp. 1-4
Author(s):  
Punit Pruthi ◽  
Pramod Arora ◽  
Manoj Mittal ◽  
Anugrah Nair ◽  
Waqia Sultana
Keyword(s):  
Complex Regional Pain Syndrome ◽  
Inflammatory Arthritis ◽  
Young Male ◽  
Pain Syndrome ◽  
Poor Response ◽  
History Of ◽  
Diagnostic Modalities ◽  
Musculoskeletal Ultrasonography ◽  
The Right

Venipuncture is one of the most commonly done medical procedures. We report a unique case of a 23-year-old young male who presented with features suggestive of inflammatory arthritis. The symptoms, which initially started on the right side, also involved the other side after a few weeks. Although the patient’s symptoms and signs were simulating inflammatory arthritis, he had atypical features like poor response to anti-inflammatory medicines and normal laboratory parameters. His musculoskeletal ultrasonography was also not suggestive of arthritis. His history was reviewed and on direct questioning he revealed a history of venipuncture for blood sample withdrawal, done from right antecubital region for routine health check on the day prior to the onset of symptoms. Complex regional pain syndrome was suspected and triple-phase radioisotope bone scan was done which was highly suggestive of this diagnosis. The patient was managed with multidimensional approach and responded very well to the treatment. Complex regional pain syndrome is usually not thought of in the initial differential diagnosis of inflammatory arthritis. In this report we highlight the need to elicit the often overlooked history of trivial trauma like venipuncture, especially in atypical cases of arthritis. Also the role of newer diagnostic modalities in such cases is emphasized.

scholarly journals Prevalence of NAFLD in Healthy and Young Male Individuals

2011 ◽  
Vol 2011 ◽  
pp. 1-4 ◽  
Author(s):  
Asif Niaz ◽  
Zafar Ali ◽  
Shaista Nayyar ◽  
Naureen Fatima
Keyword(s):  
Liver Disease ◽  
Fatty Liver ◽  
Alcohol Intake ◽  
Young Male ◽  
Difficult Problem ◽  
Liver Fat ◽  
Renal Diseases ◽  
Nonalcoholic Fatty Liver ◽  
History Of ◽  
Ultrasound Evidence

Introduction. Nonalcoholic fatty liver disease (NAFLD) is an important cause of liver disease in adults and the most common cause of liver disease in children (Lavine and Schwimmer 2004). The abnormalities include increased liver fat without inflammation (steatosis) and nonalcoholic steatohepatitis (NASH). NASH may lead to fibrosis, cirrhosis, and ultimately liver failure if it is not treated (Matteoni et al. 1999). The objective of the study is to estimate the magnitude of the problem which will help us to formulate strategies in managing the potentially difficult problem. Materials and Methods. We included 1000 individuals between the ages of 30 and 50 years who came for annual checkup. The patients with other comorbidities like diabetes, ischemic heart disease, chronic liver disease, or renal diseases were excluded from the study. History of alcohol ingestion was also taken; any individual with history of alcohol intake was also excluded. All of them underwent investigations including CBC, LFTs, height and weight. The individuals who were found to have increased ALT (50 to 150 u/L) further underwent investigations including ultrasound of abdomen hepatitis b and c serology RA and ANA antibodies. All the individuals who were found to have viral or autoimmune illness were excluded from the study. The individuals having raised ALT levels and ultrasound evidence of fatty liver were taken. Results. 13.5% of the individuals were found to have NAFLD among those selected for the study. Conclusion. Mass campaign regarding physical and dietary measures needs to be undertaken in general masses regarding the gravity and potential prevention of the disease.

Sign in / Sign up

Export Citation Format

Share Document