Deep Learning-Based Sentiment Analysis for the Prediction of Alzheimer's Drugs
Abstract: A growing public health concern, Alzheimer's disease (AD) affects millions of people globally and has a yearly economic impact of billions of dollars. We examine the pipeline of pharmaceuticals and biologics undergoing AD clinical studies. The majority of the time and money spent on clinical trials of potential therapies for Alzheimer's disease (AD) have yielded disappointing results. The Alzheimer’s research community is continually looking for new biomarkers and other biological indicators to describe the course of the illness or serve as clinical trial outcome indicators. One upshot of these efforts has been a substantial body of literature presenting sample size estimates and power calculations for future cohort studies and clinical trials with the longitudinal rate of change outcome measures. To be as useful as possible, statistical methodologies, model assumptions, and parameter estimations used in power calculations are frequently not disclosed in sufficient depth. Most dementia cases (60–70%) are caused by Alzheimer’s disease (AD). The need for discovering effective medicines to treat AD has increased due to the severity of the condition and the ongoing growth in patient numbers. The medications now used to treat AD can only temporarily reduce the symptoms of dementia; they cannot halt or reverse the course of the illness. Many international pharmaceutical companies have tried numerous times to develop an amyloid-clearing medication based on the amyloid hypothesis but without success. To offer a comprehensive understanding of clinical trials and medication development for AD, we looked at some new impacts to categorize the medication with the help of deep learning techniques for a better and innovative result to reduce the rate of changes of severity. Using a deep learning framework and big data analytics, we developed a strategy called "drug repurposing in Alzheimer's disease" that quantifies the connection between a list of medicine names and the stage of AD as assessed by sentiment analysis.
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Instituto Politécnico Nacional, Centro de Investigación en Computación
2023
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oai:scielo:S1405-554620230004009792024-05-17Deep Learning-Based Sentiment Analysis for the Prediction of Alzheimer's DrugsMansingh,PadminiPattanayak,Binod KumarPati,Bibudhendu Anti-amyloid anti-tau clinical medication trials neuroinflammation neuroprotection Alzheimer’s disease Abstract: A growing public health concern, Alzheimer's disease (AD) affects millions of people globally and has a yearly economic impact of billions of dollars. We examine the pipeline of pharmaceuticals and biologics undergoing AD clinical studies. The majority of the time and money spent on clinical trials of potential therapies for Alzheimer's disease (AD) have yielded disappointing results. The Alzheimer’s research community is continually looking for new biomarkers and other biological indicators to describe the course of the illness or serve as clinical trial outcome indicators. One upshot of these efforts has been a substantial body of literature presenting sample size estimates and power calculations for future cohort studies and clinical trials with the longitudinal rate of change outcome measures. To be as useful as possible, statistical methodologies, model assumptions, and parameter estimations used in power calculations are frequently not disclosed in sufficient depth. Most dementia cases (60–70%) are caused by Alzheimer’s disease (AD). The need for discovering effective medicines to treat AD has increased due to the severity of the condition and the ongoing growth in patient numbers. The medications now used to treat AD can only temporarily reduce the symptoms of dementia; they cannot halt or reverse the course of the illness. Many international pharmaceutical companies have tried numerous times to develop an amyloid-clearing medication based on the amyloid hypothesis but without success. To offer a comprehensive understanding of clinical trials and medication development for AD, we looked at some new impacts to categorize the medication with the help of deep learning techniques for a better and innovative result to reduce the rate of changes of severity. Using a deep learning framework and big data analytics, we developed a strategy called "drug repurposing in Alzheimer's disease" that quantifies the connection between a list of medicine names and the stage of AD as assessed by sentiment analysis.info:eu-repo/semantics/openAccessInstituto Politécnico Nacional, Centro de Investigación en ComputaciónComputación y Sistemas v.27 n.4 20232023-12-01info:eu-repo/semantics/articletext/htmlhttp://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462023000400979en10.13053/cys-27-4-4634 |
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English |
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Digital |
| author |
Mansingh,Padmini Pattanayak,Binod Kumar Pati,Bibudhendu |
| spellingShingle |
Mansingh,Padmini Pattanayak,Binod Kumar Pati,Bibudhendu Deep Learning-Based Sentiment Analysis for the Prediction of Alzheimer's Drugs |
| author_facet |
Mansingh,Padmini Pattanayak,Binod Kumar Pati,Bibudhendu |
| author_sort |
Mansingh,Padmini |
| title |
Deep Learning-Based Sentiment Analysis for the Prediction of Alzheimer's Drugs |
| title_short |
Deep Learning-Based Sentiment Analysis for the Prediction of Alzheimer's Drugs |
| title_full |
Deep Learning-Based Sentiment Analysis for the Prediction of Alzheimer's Drugs |
| title_fullStr |
Deep Learning-Based Sentiment Analysis for the Prediction of Alzheimer's Drugs |
| title_full_unstemmed |
Deep Learning-Based Sentiment Analysis for the Prediction of Alzheimer's Drugs |
| title_sort |
deep learning-based sentiment analysis for the prediction of alzheimer's drugs |
| description |
Abstract: A growing public health concern, Alzheimer's disease (AD) affects millions of people globally and has a yearly economic impact of billions of dollars. We examine the pipeline of pharmaceuticals and biologics undergoing AD clinical studies. The majority of the time and money spent on clinical trials of potential therapies for Alzheimer's disease (AD) have yielded disappointing results. The Alzheimer’s research community is continually looking for new biomarkers and other biological indicators to describe the course of the illness or serve as clinical trial outcome indicators. One upshot of these efforts has been a substantial body of literature presenting sample size estimates and power calculations for future cohort studies and clinical trials with the longitudinal rate of change outcome measures. To be as useful as possible, statistical methodologies, model assumptions, and parameter estimations used in power calculations are frequently not disclosed in sufficient depth. Most dementia cases (60–70%) are caused by Alzheimer’s disease (AD). The need for discovering effective medicines to treat AD has increased due to the severity of the condition and the ongoing growth in patient numbers. The medications now used to treat AD can only temporarily reduce the symptoms of dementia; they cannot halt or reverse the course of the illness. Many international pharmaceutical companies have tried numerous times to develop an amyloid-clearing medication based on the amyloid hypothesis but without success. To offer a comprehensive understanding of clinical trials and medication development for AD, we looked at some new impacts to categorize the medication with the help of deep learning techniques for a better and innovative result to reduce the rate of changes of severity. Using a deep learning framework and big data analytics, we developed a strategy called "drug repurposing in Alzheimer's disease" that quantifies the connection between a list of medicine names and the stage of AD as assessed by sentiment analysis. |
| publisher |
Instituto Politécnico Nacional, Centro de Investigación en Computación |
| publishDate |
2023 |
| url |
http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462023000400979 |
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