Clinical Adverse Event (AE) Prediction System

A Clinical Adverse Event (AE) Prediction System is a health event prediction system that can solve a Clinical Adverse Event Prediction Task (for clinical adverse events).

• See: Clinical Adverse Event Prediction Algorithm.

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References

2022

• (Loreaux et al., 2022) ⇒ Eric Loreaux, Ke Yu, Jonas Kemp, Martin Seneviratne, Christina Chen, Subhrajit Roy, Ivan Protsyuk et al. (2022). “Boosting the Interpretability of Clinical Risk Scores with Intervention Predictions.” arXiv preprint arXiv:2207.02941
  • ABSTRACT: Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future interventions that the patient is likely to receive, based on the intervention policy present in the training data. Without this important context, predictions from such systems are less interpretable for clinicians. We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions. We develop such an intervention policy model on MIMIC-III, a real world de-identified ICU dataset, and discuss some use cases that highlight the utility of this approach. We show how combining typical risk scores, such as the likelihood of mortality, with future intervention probability scores leads to more interpretable clinical predictions.

2012

• (Deftereos et al., 2011) ⇒ Spyros N. Deftereos, Christos Andronis, Ellen J. Friedla, Aris Persidis, and Andreas Persidis. (2011). “Drug Repurposing and Adverse Event Prediction Using High‐throughput Literature Analysis.” Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 3(3).
  • ABSTRACT: Drug repurposing is the process of using existing drugs in indications other than the ones they were originally designed for. It is an area of significant recent activity due to the mounting costs of traditional drug development and scarcity of new chemical entities brought to the market by bio-pharmaceutical companies. By selecting drugs that already satisfy basic toxicity, ADME and related criteria, drug repurposing promises to deliver significant value at reduced cost and in dramatically shorter time frames than is normally the case for the drug development process. The same process that results in drug repurposing can also be used for the prediction of adverse events of known or novel drugs. The analytics method is based on the description of the mechanism of action of a drug, which is then compared to the molecular mechanisms underlying all known adverse events. This review will focus on those approaches to drug repurposing and adverse event prediction that are based on the biomedical literature. Such approaches typically begin with an analysis of the literature and aim to reveal indirect relationships among seemingly unconnected biomedical entities such as genes, signaling pathways, physiological processes, and diseases. Networks of associations of these entities allow the uncovering of the molecular mechanisms underlying a disease, better understanding of the biological effects of a drug and the evaluation of its benefit/risk profile. In silico results can be tested in relevant cellular and animal models and, eventually, in clinical trials.

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