1,581 research outputs found
Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
The present methods of diagnosing depression are entirely dependent on self-report
ratings or clinical interviews. Those traditional methods are subjective, where the individual may
or may not be answering genuinely to questions. In this paper, the data has been collected using
self-report ratings and also using electronic smartwatches. This study aims to develop a weighted
average ensemble machine learning model to predict major depressive disorder (MDD) with superior
accuracy. The data has been pre-processed and the essential features have been selected using a
correlation-based feature selection method. With the selected features, machine learning approaches
such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are
applied. Further, for assessing the performance of the proposed model, the Area under the Receiver
Optimization Characteristic Curves has been used. The results demonstrate that the proposed
Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and
the Random Forest approaches
Mental Health Survey Analysis & Prediction Using Deep Learning Algorithms
Mental health is a major concern globally and identifying individuals who require treatment is crucial. This project uses Deep Learning algorithms, specifically DenseNet based on the Convolutional Neural Network (CNN) algorithm, to predict whether an individual requires treatment or not. The dataset used for this analysis contains demographic information and survey responses from individuals across various countries. The preprocessing involved imputing missing values, encoding categorical variables, and normalizing the data. Exploratory Data Analysis (EDA) and visualization were conducted to understand the dataset better. The DenseNet model achieved an accuracy of 88% on the test set. The results of this project can aid in identifying individuals who may require mental health treatment, enabling early intervention and improved outcomes
Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review
Background: Natural Language Processing (NLP) is widely used to extract
clinical insights from Electronic Health Records (EHRs). However, the lack of
annotated data, automated tools, and other challenges hinder the full
utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL)
and NLP techniques are studied and compared to understand the limitations and
opportunities in this space comprehensively.
Methodology: After screening 261 articles from 11 databases, we included 127
papers for full-text review covering seven categories of articles: 1) medical
note classification, 2) clinical entity recognition, 3) text summarisation, 4)
deep learning (DL) and transfer learning architecture, 5) information
extraction, 6) Medical language translation and 7) other NLP applications. This
study follows the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) guidelines.
Result and Discussion: EHR was the most commonly used data type among the
selected articles, and the datasets were primarily unstructured. Various ML and
DL methods were used, with prediction or classification being the most common
application of ML or DL. The most common use cases were: the International
Classification of Diseases, Ninth Revision (ICD-9) classification, clinical
note analysis, and named entity recognition (NER) for clinical descriptions and
research on psychiatric disorders.
Conclusion: We find that the adopted ML models were not adequately assessed.
In addition, the data imbalance problem is quite important, yet we must find
techniques to address this underlining problem. Future studies should address
key limitations in studies, primarily identifying Lupus Nephritis, Suicide
Attempts, perinatal self-harmed and ICD-9 classification
Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier
Structural alterations have been thoroughly investigated in the brain during
the early onset of schizophrenia (SCZ) with the development of neuroimaging
methods. The objective of the paper is an efficient classification of SCZ in 2
different classes: Cognitive Normal (CN), and SCZ using magnetic resonance
imaging (MRI) images. This paper proposed a lightweight 3D convolutional neural
network (CNN) based framework for SCZ diagnosis using MRI images. In the
proposed model, lightweight 3D CNN is used to extract both spatial and spectral
features simultaneously from 3D volume MRI scans, and classification is done
using an ensemble bagging classifier. Ensemble bagging classifier contributes
to preventing overfitting, reduces variance, and improves the model's accuracy.
The proposed algorithm is tested on datasets taken from three benchmark
databases available as open-source: MCICShare, COBRE, and fBRINPhase-II. These
datasets have undergone preprocessing steps to register all the MRI images to
the standard template and reduce the artifacts. The model achieves the highest
accuracy 92.22%, sensitivity 94.44%, specificity 90%, precision 90.43%, recall
94.44%, F1-score 92.39% and G-mean 92.19% as compared to the current
state-of-the-art techniques. The performance metrics evidenced the use of this
model to assist the clinicians for automatic accurate diagnosis of SCZ
Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks
Early diagnosis of mental disorders and intervention can facilitate the
prevention of severe injuries and the improvement of treatment results. Using
social media and pre-trained language models, this study explores how
user-generated data can be used to predict mental disorder symptoms. Our study
compares four different BERT models of Hugging Face with standard machine
learning techniques used in automatic depression diagnosis in recent
literature. The results show that new models outperform the previous approach
with an accuracy rate of up to 97%. Analyzing the results while complementing
past findings, we find that even tiny amounts of data (like users' bio
descriptions) have the potential to predict mental disorders. We conclude that
social media data is an excellent source of mental health screening, and
pre-trained models can effectively automate this critical task.Comment: 19 pages, 5 figure
The Verbal and Non Verbal Signals of Depression -- Combining Acoustics, Text and Visuals for Estimating Depression Level
Depression is a serious medical condition that is suffered by a large number
of people around the world. It significantly affects the way one feels, causing
a persistent lowering of mood. In this paper, we propose a novel
attention-based deep neural network which facilitates the fusion of various
modalities. We use this network to regress the depression level. Acoustic, text
and visual modalities have been used to train our proposed network. Various
experiments have been carried out on the benchmark dataset, namely, Distress
Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). From the results, we
empirically justify that the fusion of all three modalities helps in giving the
most accurate estimation of depression level. Our proposed approach outperforms
the state-of-the-art by 7.17% on root mean squared error (RMSE) and 8.08% on
mean absolute error (MAE).Comment: 10 pages including references, 2 figure
Suicide and self-harm prediction based on social media data using machine learning algorithms
Online social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital biomarkers and assess their external validity with more rigo
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