Goto

Collaborating Authors

Results


CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data

arXiv.org Artificial Intelligence

Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e.g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths. In this paper, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data using an integrated deep learning architecture based on a recurrent neural network (RNN) unit and a continuous-time autoregressive (CAR) model. The proposed model, called CARRNN, uses a generalized discrete-time autoregressive model that is trainable end-to-end using neural networks modulated by time lags to describe the changes caused by the irregularity and asynchronicity. It is applied to multivariate time-series regression tasks using data provided for Alzheimer's disease progression modeling and intensive care unit (ICU) mortality rate prediction, where the proposed model based on a gated recurrent unit (GRU) achieves the lowest prediction errors among the proposed RNN-based models and state-of-the-art methods using GRUs and long short-term memory (LSTM) networks in their architecture.


Deep Learning Used to Detect Earliest Stages of Alzheimer's

#artificialintelligence

The rise of precision medicine is being augmented by greater use of deep learning technologies that provide predictive analytics for earlier diagnosis of a range of debilitating diseases. The latest example comes from researchers at Michigan-based Beaumont Health who used deep learning to analyze genomic DNA. The resulting simple blood test could be used to detect earlier onset of Alzheimer's disease. In a study published this week in the peer-reviewed scientific journal PLOS ONE, the researchers said their analysis discovered 152 "significant" genetic differences among Alzheimer's and healthy patients. Those biomarkers could be used to provide diagnoses before Alzheimer's symptoms develop and a patient's brain is irreversibly damaged.


Integrated Age Estimation Mechanism

arXiv.org Artificial Intelligence

Machine-learning-based age estimation has received lots of attention. Traditional age estimation mechanism focuses estimation age error, but ignores that there is a deviation between the estimated age and real age due to disease. Pathological age estimation mechanism the author proposed before introduces age deviation to solve the above problem and improves classification capability of the estimated age significantly. However,it does not consider the age estimation error of the normal control (NC) group and results in a larger error between the estimated age and real age of NC group. Therefore, an integrated age estimation mechanism based on Decision-Level fusion of error and deviation orientation model is proposed to solve the problem.Firstly, the traditional age estimation and pathological age estimation mechanisms are weighted together.Secondly, their optimal weights are obtained by minimizing mean absolute error (MAE) between the estimated age and real age of normal people. In the experimental section, several representative age-related datasets are used for verification of the proposed method. The results show that the proposed age estimation mechanism achieves a good tradeoff effect of age estimation. It not only improves the classification ability of the estimated age, but also reduces the age estimation error of the NC group. In general, the proposed age estimation mechanism is effective. Additionally, the mechanism is a framework mechanism that can be used to construct different specific age estimation algorithms, contributing to relevant research.


AI uncovers Eli Lilly's rheumatoid arthritis drug Olumiant as potential Alzheimer's treatment

#artificialintelligence

Could janus kinase (JAK) inhibitors like Eli Lilly's rheumatoid arthritis drug Olumiant be repurposed to treat Alzheimer's disease? Researchers at Harvard University and Massachusetts General Hospital have set out to find the answer to that question with a new clinical trial that was born from artificial intelligence. The researchers used a type of AI called machine learning to identify existing drugs that might be able to prevent neuronal death in Alzheimer's. The screen pulled up a list of 15 FDA-approved drugs as candidates for repurposing in Alzheimer's, and five of them were JAK inhibitors, they reported in the journal Nature Communications. JAK proteins fuel inflammation and have long been suspected to play a role in Alzheimer's.


AI Reveals Current Drugs May Help Combat Alzheimer's Disease

#artificialintelligence

Researchers have developed a method based on Artificial Intelligence (AI) that rapidly identifies currently available medications that may treat Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School. "We, therefore, built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.


Artificial Intelligence reveals current drugs may help combat Alzheimer's

#artificialintelligence

Researchers have developed a method based on Artificial Intelligence (AI) that rapidly identifies currently available medications that may treat Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School. "We therefore built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.


AI reveals current drugs that may help combat Alzheimer's

#artificialintelligence

New York, March 7 (IANS) Researchers have developed a method based on Artificial Intelligence (AI) that rapidly identifies currently available medications that may treat Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School. "We therefore built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.


AI-based method used to screen for Alzheimer's disease drugs

#artificialintelligence

Researchers have used artificial intelligence to screen 80 FDA-approved drugs and reveal which could be used as Alzheimer's treatments. A team at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS), both US, has developed an artificial intelligence (AI)-based method to screen currently available medications as possible treatments for Alzheimer's disease. According to the researchers, the method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for the neurodegenerative condition. It could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing US Food and Drug Administration (FDA)-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment – but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," explained Dr Artem Sokolov, at HMS. "We therefore built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones."


Artificial intelligence reveals current drugs that may help combat Alzheimer's disease

#artificialintelligence

Independent validation of the nominated drug targets could provide new insights into the mechanisms behind Alzheimer's disease and lead to novel therapies. BOSTON – New treatments for Alzheimer's disease are desperately needed, but numerous clinical trials of investigational drugs have failed to generate promising options. Now a team at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has developed an artificial intelligence–based method to screen currently available medications as possible treatments for Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action.


Can AI Machine Learning and Genomics Find Alzheimer's Drugs?

#artificialintelligence

What if a new treatment for Alzheimer's disease exists today among existing U.S. Food and Drug Administration (FDA) approved drugs? A new peer-reviewed study published last week in Nature Communications by researchers at Harvard Medical School and Massachusetts General Hospital shows how an AI machine learning framework combined with genomics can help predict drug repurposing candidates for Alzheimer's disease. There are an estimated 50 million people living with Alzheimer's disease, a neurodegenerative disorder, and other forms of dementia globally according to the World Alzheimer Report 2018. In the United States, 5.8 million people are affected by Alzheimer's disease--two-thirds of whom are women. There are over 16 million people in the U.S. caring for those with Alzheimer's according to an article published today in Time by Maria Shriver, founder of the Women's Alzheimer's Movement, and George Vradenburg, co-founder of UsAgainstAlzheimer's.