Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm
Abstract Introduction There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict t...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Alzheimer’s & Dementia: Translational Research & Clinical Interventions |
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| Online Access: | https://doi.org/10.1016/j.trci.2019.07.001 |
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| author | Jack Albright Alzheimer's Disease Neuroimaging Initiative |
| author_facet | Jack Albright Alzheimer's Disease Neuroimaging Initiative |
| author_sort | Jack Albright |
| collection | DOAJ |
| description | Abstract Introduction There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict the progression of AD in future years. Methods Data from 1737 patients were processed using the “All‐Pairs” technique, a novel methodology created for this study involving the comparison of all possible pairs of temporal data points for each patient. Machine learning models were trained on these processed data and evaluated using a separate testing data set (110 patients). Results A neural network model was effective (mAUC = 0.866) at predicting the progression of AD, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. Discussion Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics. |
| format | Article |
| id | doaj-art-49feebfb9f41458bb8227a6ccd8ac9ba |
| institution | OA Journals |
| issn | 2352-8737 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Alzheimer’s & Dementia: Translational Research & Clinical Interventions |
| spelling | doaj-art-49feebfb9f41458bb8227a6ccd8ac9ba2025-08-20T02:09:55ZengWileyAlzheimer’s & Dementia: Translational Research & Clinical Interventions2352-87372019-01-015148349110.1016/j.trci.2019.07.001Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithmJack Albright0Alzheimer's Disease Neuroimaging Initiative1The Nueva SchoolSan MateoCAThe Nueva SchoolSan MateoCAAbstract Introduction There is a 99.6% failure rate of clinical trials for drugs to treat Alzheimer's disease, likely because Alzheimer's disease (AD) patients cannot be easily identified at early stages. This study investigated machine learning approaches to use clinical data to predict the progression of AD in future years. Methods Data from 1737 patients were processed using the “All‐Pairs” technique, a novel methodology created for this study involving the comparison of all possible pairs of temporal data points for each patient. Machine learning models were trained on these processed data and evaluated using a separate testing data set (110 patients). Results A neural network model was effective (mAUC = 0.866) at predicting the progression of AD, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. Discussion Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.https://doi.org/10.1016/j.trci.2019.07.001Machine learningNeural networksDisease progressionAlzheimer's diseaseMild cognitive impairmentDementia |
| spellingShingle | Jack Albright Alzheimer's Disease Neuroimaging Initiative Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm Alzheimer’s & Dementia: Translational Research & Clinical Interventions Machine learning Neural networks Disease progression Alzheimer's disease Mild cognitive impairment Dementia |
| title | Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm |
| title_full | Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm |
| title_fullStr | Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm |
| title_full_unstemmed | Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm |
| title_short | Forecasting the progression of Alzheimer's disease using neural networks and a novel preprocessing algorithm |
| title_sort | forecasting the progression of alzheimer s disease using neural networks and a novel preprocessing algorithm |
| topic | Machine learning Neural networks Disease progression Alzheimer's disease Mild cognitive impairment Dementia |
| url | https://doi.org/10.1016/j.trci.2019.07.001 |
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