AIMC Topic: Deep Learning

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Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it...
Deep Learning Sepsis Humans Reinforcement, Psychology Learning Delivery of Health Care Algorithms Hemodynamics
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Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for deep learning models to...
Deep Learning Rare Diseases Humans Electronic Health Records
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Neural Multi-Task Learning for Adverse Drug Reaction Extraction.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
A reliable and searchable knowledge database of adverse drug reactions (ADRs) is highly important and valuable for improving patient safety at the point of care. In this paper, we proposed a neural multi-task learning system, NeuroADR, to extract ADR...
Humans Data Mining Machine Learning Deep Learning Databases, Factual Drug-Related Side Effects and Adverse Reactions
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RadLex Normalization in Radiology Reports.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Radiology reports have been widely used for extraction of various clinically significant information about patients' imaging studies. However, limited research has focused on standardizing the entities to a common radiology-specific vocabulary. Furth...
Radiology Information Systems Vocabulary, Controlled Radiology Documentation Unified Medical Language System Natural Language Processing Diagnostic Imaging Humans Deep Learning
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Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, ca...
Databases, Factual Electronic Health Records Humans Patient Selection Clinical Trials as Topic Partnership Practice Dietary Supplements Natural Language Processing Feasibility Studies Deep Learning Models, Theoretical
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Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading cause...
Decision Support Systems, Clinical Shock, Septic Clinical Decision Rules Natural Language Processing Severity of Illness Index Deep Learning Sepsis Electronic Health Records Language Humans Algorithms
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DeepCompete : A deep learning approach to competing risks in continuous time domain.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
An increasing number of people survive longer ages leading to a growing population of people 65 years of age or older. A large percentage of this population is afflicted with multiple acute diseases (multi-morbidity). Clinicians need new tools to qua...
Models, Statistical Survival Analysis Risk Aged Deep Learning Humans
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Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association Jan 23, 2021
BACKGROUND: Traditional diagnosis methods for lymph node metastases are labor-intensive and time-consuming. As a result, diagnostic systems based on deep learning (DL) algorithms have become a hot topic. However, current research lacks testing with s...
Lymphatic Metastasis Predictive Value of Tests Image Processing, Computer-Assisted Lymph Nodes Humans Stomach Neoplasms Deep Learning Prospective Studies
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Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.

Journal of pharmaceutical sciences Jan 23, 2021
Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which ...
Drug Discovery Deep Learning Rats Humans Animals Drug Elimination Routes Pharmacokinetics Pharmaceutical Preparations Species Specificity Models, Biological Metabolic Clearance Rate
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Parkinson's disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation.

Neuroradiology Jan 22, 2021
PURPOSE: To investigate whether Parkinson's disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)-based stru...
Humans Parkinson Disease Connectome Diffusion Tensor Imaging Prospective Studies Deep Learning
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