Dec 28, 2018 · In this paper, we developed machine learning models including a deep learning framework which can simultaneously predict ADRs and identify the molecular substructures associated with those ADRs without defining the substructures a-priori.

Jan 1, 2023 · A study was conducted to demonstrate how the use of machine learning methods can strengthen the ability to predict ADR in hospitalized patients. Binary classification models were constructed using three class balancing, feature selection and supervised learning approaches known as deep learning, random forest, and gradient boosting trees.

Dec 1, 2023 · This present review offers an in-depth examination of the role of AI and ML in the early detection of ADRs and toxicity, incorporating a wide range of methodologies ranging from data mining to deep learning followed by a list of important databases, modeling algorithms, and software that could be used in modeling and predicting a series of ADRs ...

Jul 1, 2019 · Lexicon-based approach for ADR and drug detection. Shortest dependency-path-based machine learning algorithm for relation extraction: Quantitative. Separate evaluations for entity extraction, ADR detection and classification of patient experiences using 200 manually annotated comments

We designed a DNN model that utilizes the chemical, biological, and biomedical information of drugs to detect ADRs. This model aimed to fulfill two main purposes: identifying the potential ADRs of drugs and predicting the possible ADRs of a new drug.

Nov 21, 2018 · Many studies have developed algorithms for ADR signal detection using electronic health record (EHR) data. In this study, we propose a machine learning (ML) model that enables accurate ADR signal detection by integrating features from existing algorithms based on inpatient EHR laboratory results.

Several machine learning algorithms are employed to analyze the data collected from various sources for ADR detection. Each algorithm has unique strengths that can be leveraged depending on the specific characteristics of the dataset.

In this chapter, we will summarize different resources, types of features and machine learning models that either have been used or have potentials to be used for ADR prediction, especially the latest as well as nascent technology developments.

A machine-learning model using the random forest algorithm was developed on the training set to discriminate between true and false ADR reports. The model was then applied to the test set to assess accuracy using the area under the receiver operating characteristic (AUC).

In this paper, three Machine Learning algorithms SVM (Support Vector Machine), Random Forest, and Gradient boosted trees are implemented on the datasets to predict various disorders caused due to adverse effects, and the performance is evaluated based on performance metrics such as average precision, recall, accuracy, f1 binary, f1 macro, and f1...