Objective: We aimed to mine the data in the Electronic Medical Record to automatically discover p... more Objective: We aimed to mine the data in the Electronic Medical Record to automatically discover patients' Rheumatoid Arthritis disease activity at discrete rheumatology clinic visits. We cast the problem as a document classification task where the feature space includes concepts from the clinical narrative and lab values as stored in the Electronic Medical Record. Materials and Methods: The Training Set consisted of 2792 clinical notes and associated lab values. Test Set 1 included 1749 clinical notes and associated lab values. Test Set 2 included 344 clinical notes for which there were no associated lab values. The Apache clinical Text Analysis and Knowledge Extraction System was used to analyze the text and transform it into informative features to be combined with relevant lab values. Results: Experiments over a range of machine learning algorithms and features were conducted. The best performing combination was linear kernel Support Vector Machines with Unified Medical Language System Concept Unique Identifier features with feature selection and lab values. The Area Under the Receiver Operating Characteristic Curve (AUC) is 0.831 (s = 0.0317), statistically significant as compared to two baselines (AUC = 0.758, s = 0.0291). Algorithms demonstrated superior performance on cases clinically defined as extreme categories of disease activity (Remission and High) compared to those defined as intermediate categories (Moderate and Low) and included laboratory data on inflammatory markers. Conclusion: Automatic Rheumatoid Arthritis disease activity discovery from Electronic Medical Record data is a learnable task approximating human performance. As a result, this approach might have several research applications, such as the identification of patients for genome-wide pharmacogenetic studies that require large sample sizes with precise definitions of disease activity and response to therapies.
In a world that is constantly changing, our education systems have more or less stayed the same. ... more In a world that is constantly changing, our education systems have more or less stayed the same. The adoption of smartboards and smart-classroom setups have been the only innovation in teaching methodology. The chalkandtalk style of teaching, is still the most widely used method in Indian Education. Although teaching methodologies have been a part of the growing innovation, there is no way to measure or identify the emotions of a student during a lecture. The entire purpose of a lecture is to impart knowledge to the students and ensure maximum retention of the concept being taught. But, if a student is bored during or halfway through a lecture, it is very rare that he/she has been able to retain more than 50% of the contents taught during that lecture. In this paper, we attempt to discuss potential solutions, based on Machine Learning, that can help track and identify student emotions during a classroom lecture. They have been derived from various methodologies and ideas that have been presented in the literature and portray a view of the current state-of-the-art research in this exciting field.
This project is developed to make the life of blind people easy. This is a camera based system to... more This project is developed to make the life of blind people easy. This is a camera based system to scan the product behind the image and read the description of the product with the help of Id stored in the product. This is very beneficial in case of finding out the description of packaged goods to the blind people and thus helping them in deciding to purchase a product or not especially which are packaged. This is because it becomes very difficult for the blind people to distinguish between the packaged goods. In order to use this system, all the user needs to do is capture the image on the product in the device which then resolves the product which means it scans the image to find out the Id stored. Thus this application really benefits blind and visually impaired people and thus making their work of identifying products easy. This is very easy to use and affordable as it requires a scanner to scan the product and a camera phone to take the picture of the image containing the product. This is now easy to implement as most of the devices today have the required resolution in order to scan the product to identify the Id stored in it and read out the product description. This project can be implemented in any shopping mall, supermarket, Book stores, Medical stores etc 1 Introduction: The ability to identify products such as groceries and other products is very useful for blind and visually impaired persons, for whom such identification of information may be inaccessible. This is now easy to get information & price of product but we need scanner for the same. Mobile phones can also help to get information but it need proper internet access. 2 Lıterature Survey A. Toolkit for Bar Code Recognition and Resolving on Camera Phones-Jump Starting the Internet of Things by Robert Adelmann, Marc Langheinrich, Christian Flörkemeier which describes System developed a freely available EAN-13 bar code recognition and information system that is both lightweight and fast enough for the use on camera-equipped mobile phones. Limitations: If barcode were not in scanable condition, the system won't work properly. B. Real time object detection & tracking system (locally & remotely)with rotating by S. Kresic, D. madej, Fadil Santosa Which describes System present a novel approach to edge detection in bar code signals
Rheumatoid arthritis can be defined as a chronic inflammatory disorder affecting the joints by da... more Rheumatoid arthritis can be defined as a chronic inflammatory disorder affecting the joints by damaging the tissue of the body. Therefore, an effective system analysis is necessary for the identification and detection of rheumatoid arthritis by hand, especially during its development or pre-diagnostic stages.This project is designed to develop an intelligent system to detect rheumatoid arthritis of the hand using image processing techniques and a neural network of convolution.The system comprises of two main phases. The image processing phase is the first stage in which images are processed using image processing. These techniques include preprocessing, image segmentation and feature extraction using gabor filter. The second phase the extracted features being used as inputs for the neural convolution network, which classifies the hand images as normal or abnormal (arthritic). Classification is carried out based on the CNN algorithm, which involves the training of the network with normal and abnormal hand images. The system was tested on the same number of images as the testing set and the experimental results showed that a recognition rate of 83.5% respectively.
Hybrid Detection Model for Crop Disease using CNN and SVM algorithm
International Journal of Advanced Research in Science, Communication and Technology
Plant and crop disease management practices have evolved significantly to limit harm. Utilizing b... more Plant and crop disease management practices have evolved significantly to limit harm. Utilizing big data analytic techniques, it is now possible to forecast the beginning of a change in the severity of diseases using new information and communication technology. The study's findings show that this approach is still in its early stages, with significant obstacles to overcome. The planned study's purpose is to look at a variety of machine algorithms for predicting plant diseases. A plant’s response to the pathogen exhibits some obvious illness symptoms. Shape, size, etc are all visual characteristics that help identify the plant's status. The study paper covers all of these elements and using a variety of machine learning approaches to get a result. The proposed system model is tested on the Plant Disease dataset. Experiments reveal that the proposed model surpasses previous existing models with a classification accuracy of roughly 99 percent.
Journal of emerging technologies and innovative research, 2018
In a world that is constantly changing, our education systems have more or less stayed the same. ... more In a world that is constantly changing, our education systems have more or less stayed the same. The adoption of smartboards and smart-classroom setups have been the only innovation in teaching methodology. The chalk – and – talk style of teaching, is still the most widely used method in Indian Education. Although teaching methodologies have been a part of the growing innovation, there is no way to measure or identify the emotions of a student during a lecture. The entire purpose of a lecture is to impart knowledge to the students and ensure maximum retention of the concept being taught. But, if a student is bored during or half-way through a lecture, it is very rare that he/she has been able to retain more than 50% of the contents taught during that lecture. In this paper, we attempt to discuss potential solutions, based on Machine Learning, that can help track and identify student emotions during a classroom lecture. They have been derived from various methodologies and ideas that...
In the article titled "An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis" [1... more In the article titled "An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis" [1], reference 8 is listed incorrectly and should be corrected as [2].
Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inf... more Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-r...
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Papers by Gitanjali Mate