Cancer Hallmark Text Classification Using Convolutional Neural Networks

Methods based on deep learning approaches have recently achieved state-of-the-art performance in a range of machine learning tasks and are increasingly applied to natural language processing (NLP). Despite strong results in various established NLP tasks involving general domain texts, here is only limited work applying these models to biomedical NLP. In this paper, we consider a Convolutional Neural Network (CNN) approach to biomedical text classification. Evaluation using a recently introduced cancer domain dataset involving the categorization of documents according to the well-established hallmarks of cancer shows that a basic CNN model can achieve a level of performance competitive with a Support Vector Machine (SVM) trained using complex manually engineered features optimized to the task. We further show that simple modifications to the CNN hyperparameters, initialization, and training process allow the model to notably outperform the SVM, establishing a new state of the art result at this task. We make all of the resources and tools introduced in this study available under open licenses from https://cambridgeltl.github.io/cancer-hallmark-cnn/ .The first author is funded by the Commonwealth Scholarship and the Cambridge Trust. This work is supported by Medical Research Council grant MR/M013049/1 and the Google Faculty Award

Using Clinical Notes with Time Series Data for ICU Management

Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments. In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management. While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together. We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model. Our implementation can be found at \url{https://github.com/kaggarwal/ClinicalNotesICU}.Comment: Accepted at EMNLP 201

Development of an efficient Computational Model for classification of Tissue remodeling

Tissue remodeling is one of the most important and crucial biological process. Process in which tissue reorganization and renovation takes place is called tissue remodeling. Mean of recovery in human beings is tissue remodeling in which damaged tissue are replaced completely with new tissue or through tissue repairmen types physiological and pathological tissue remodeling are two derivatives of Tissue remodeling. Normal Tissue remodeling is referred to as Physiological tissue remodeling, however abnormal process which may lead to a disease is known as pathological tissue remodeling. From past till now different techniques like histopathology and chemicals were being used to identify abnormality in tissues. Which is a time taking and costly processes. There is no such computational method which can be used for the identification of the physiological and pathological tissue remodeling. The current article aims to develop a classification model which has ability to classify weather the given sequence is physiological or pathological process. Three classifiers RF, ANN and SVM will be used for practice and evaluation of proposed classification model

Clinical Screening Prediction in the Portuguese National Health Service: Data Analysis, Machine Learning Models, Explainability and Meta-Evaluation

This paper presents an analysis of the calls made to the Portuguese National Health Contact Center (SNS24) during a three years period. The final goal was to develop a system to help nurse attendants select the appropriate clinical pathway (from 59 options) for each call. It examines several aspects of the calls distribution like age and gender of the user, date and time of the call and final referral, among others and presents comparative results for alternative classification models (SVM and CNN) and different data samples (three months, one and two years data models). For the task of selecting the appropriate pathway, the models, learned on the basis of the available data, achieved F1 values that range between 0.642 (3 months CNN model) and 0.783 (2 years CNN model), with SVM having a more stable performance (between 0.743 and 0.768 for the corresponding data samples). These results are discussed regarding error analysis and possibilities for explaining the system decisions. A final meta evaluation, based on a clinical expert overview, compares the different choices: the nurse attendants (reference ground truth), the expert and the automatic decisions (2 models), revealing a higher agreement between the ML models, followed by their agreement with the clinical expert, and minor agreement with the reference.This research work was funded by FCT—Fundação para a Ciência e a Tecnologia, I.P, within the project SNS24.Scout.IA—Aplicação de Metodologias de Inteligência Artificial e Processamento de Linguagem Natural no Serviço de Triagem, Aconselhamento e Encaminhamento do SNS24 (ref. DSAIPA/AI/0040/2019)