Predicting adverse drug reactions through interpretable deep learning framework

Dey, Sanjoy; Luo, Heng; Fokoue, Achille; Hu, Jianying; Zhang, Ping

doi:10.1186/s12859-018-2544-0

Cited by 121 publications

(80 citation statements)

References 34 publications

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“…Studies in this category retrieved data from different repositories such as DrugBank, Side Effect Resource, the Food and Drug Administration (FDA)’s adverse event reporting system, University of Massachusetts Medical School, Observational Medical Outcomes Partnership database, and Human Protein-Protein Interaction database to identify adverse drug interactions and reactions that can potentially negatively influence patient health [ 86 - 88 , 101 , 102 , 105 - 107 , 110 ]. Some studies also used AI to predict drug interactions by analyzing EHR data [ 88 ], unstructured discharge notes [ 90 ], and clinical charts [ 99 , 104 ].…”

Section: Resultsmentioning

confidence: 99%

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

2020

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Background Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.

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Section: Resultsmentioning

confidence: 99%

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

2020

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“…XAI will foster the collaboration between medicinal chemists, chemoinformaticians and data scientists 40,41 . In fact, XAI already enables the mechanistic interpretation of drug action 42,43 , and contributes to drug safety enhancement, as well as organic synthesis planning 9,44 . If successful in the long run, XAI will provide fundamental support in the analysis and interpretation of increasingly more complex chemical data, as well as in the formulation of new pharmacological hypotheses, while avoiding human bias 45,46 .…”

Section: Drug Discovery With Explainable Artificial Intelligencementioning

confidence: 99%

Drug discovery with explainable artificial intelligence

2020

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arious concepts of 'artificial intelligence' (AI) have been successfully adopted for computer-assisted drug discovery in the past few years 1-3. This advance is mostly owed to the ability of deep learning algorithms, that is, artificial neural networks with multiple processing layers, to model complex nonlinear inputoutput relationships, and perform pattern recognition and feature extraction from low-level data representations. Certain deep learning models have been shown to match or even exceed the performance of the familiar existing machine learning and quantitative structure-activity relationship (QSAR) methods for drug discovery 4-6. Moreover, deep learning has boosted the potential and broadened the applicability of computer-assisted discovery, for example, in molecular design 7,8 , chemical synthesis planning 9,10 , protein structure prediction 11 and macromolecular target identification 12,13. The ability to capture intricate nonlinear relationships between input data (for example, chemical structure representations) and the associated output (for example, assay readout) often comes at the price of limited comprehensibility of the resulting model. While there have been efforts to explain QSARs in terms of algorithmic insights and molecular descriptor analysis 14-19 , deep neural network models notoriously elude immediate accessibility by the human mind 20. In medicinal chemistry in particular, the availability of 'rules of thumb' correlating biological effects with physicochemical properties underscores the willingness, in certain situations, to sacrifice accuracy in favour of models that better fit human intuition 21-23. Thus, blurring the lines between the 'two QSARs' 24 (that is, mechanistically interpretable versus highly accurate models) may be key to accelerated drug discovery with AI 25. Automated analysis of medical and chemical knowledge to extract and represent features in a human-intelligible format dates back to the 1990s 26,27 , but has been receiving increasing attention due to the re-emergence of neural networks in chemistry and healthcare. Given the current pace of AI in drug discovery and related fields, there will be an increased demand for methods that help us understand and interpret the underlying models. In an effort to mitigate the lack of interpretability of certain machine learning models, and to augment human reasoning and decision-making, 28 , attention has been drawn to explainable AI (XAI) approaches 29,30. Providing informative explanations alongside the mathematical models aims to (1) render the underlying decision-making process transparent ('understandable') 31 , (2) avoid correct predictions for the wrong reasons (the so-called clever Hans effect) 32 , (3) avert unfair biases or unethical discrimination 33 and (4) bridge the gap between the machine learning community and other scientific disciplines. In addition, effective XAI can help scientists navigate 'cognitive valleys' 28 , allowing them to hone their knowledge and beliefs on the investigated process 34. While the e...

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“…Counter-screening for unwanted mechanisms of action and adverse side effects can and should be built early into the discovery process to help identify unsuitable compounds well before they are administered to an animal. An emerging opportunity is the use of machine-learning, analyzing large compound libraries to predict adverse reactions for new combinations of molecular structures, which could help reduce the time involved in producing and testing analogues ( Gao et al, 2017 ; Dey et al, 2018 ).…”

Section: Anthelmintic Discovery and Developmentmentioning

confidence: 99%

Where are all the anthelmintics? Challenges and opportunities on the path to new anthelmintics

Nixon

Welz

Woods

et al. 2020

International Journal for Parasitology: Drugs and Drug Resistan

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Control of helminth parasites is a key challenge for human and veterinary medicine. In the absence of effective vaccines and adequate sanitation, prophylaxis and treatment commonly rely upon anthelmintics. There are concerns about the development of drug resistance, side-effects, lack of efficacy and cost-effectiveness that drive the need for new classes of anthelmintics. Despite this need, only three new drug classes have reached the animal market since 2000 and no new classes of anthelmintic have been approved for human use. So where are all the anthelmintics? What are the barriers to anthelmintic discovery, and what emerging opportunities can be used to address this? This was a discussion group focus at the 2019 8th Consortium for Anthelmintic Resistance and Susceptibility (CARS) in Wisconsin, USA. Here we report the findings of the group in the broader context of the human and veterinary anthelmintic discovery pipeline, highlighting challenges unique to antiparasitic drug discovery. We comment on why the development of novel anthelmintics has been so rare. Further, we discuss potential opportunities for drug development moving into the 21st Century.

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Predicting adverse drug reactions through interpretable deep learning framework

Cited by 121 publications

References 34 publications

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

Drug discovery with explainable artificial intelligence

Where are all the anthelmintics? Challenges and opportunities on the path to new anthelmintics

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