Harnessing Machine Learning to Improve Healthcare Monitoring with FAERS

This research study investigates the potential of machine learning techniques to improve healthcare monitoring through the utilization of data from the FDA Adverse Event Reporting System (FAERS). The objective is to explore specific applications of machine learning in healthcare monitoring with FAERS and highlight their findings. The study reveals several significant ways in which machine learning can contribute to enhancing healthcare monitoring using FAERS.Machine learning algorithms can detect potential safety signals at an early stage by analyzing FAERS data. By employing anomaly detection and temporal pattern analysis techniques, these models can identify emerging safety concerns that were previously unknown or underreported. This early detection enables timely action to mitigate risks associated with medications or medical products.Machine learning models can assist in pharmacovigilance triage, addressing the challenge posed by the large number of adverse event reports within FAERS. By developing ranking and classification models, adverse events can be prioritized based on severity, novelty, or potential impact. This automation of the triage process enables pharmacovigilance teams to efficiently identify and investigate critical safety concerns.Machine learning models can automate the classification and coding of adverse events, which are often present in unstructured text within FAERS reports. Through the application of Natural Language Processing (NLP) techniques, such as named entity recognition and text classification, relevant information can be extracted, enhancing the efficiency and accuracy of adverse event coding.Machine learning algorithms can refine and validate signals generated from FAERS data by incorporating additional data sources, such as electronic health records, social media, or clinical trials data. This integration provides a more comprehensive understanding of potential risks and helps filter out false positives, facilitating the identification of signals requiring further investigation.Machine learning enables real-time surveillance of FAERS data, allowing for the identification of safety concerns as they occur. Continuous monitoring and real-time analysis of incoming reports enable machine learning models to trigger alerts or notifications to relevant stakeholders, promoting timely intervention to minimize patient harm.The study demonstrates the use of machine learning models to conduct comparative safety analyses by combining FAERS data with other healthcare databases. These models assist in identifying safety differences between medications, patient populations, or dosing regimens, enabling healthcare providers and regulators to make informed decisions regarding treatment choices.While machine learning is a powerful tool in healthcare monitoring, its implementation should be complemented by human expertise and domain knowledge. The interpretation and validation of results generated by machine learning models necessitate the involvement of healthcare professionals and pharmacovigilance experts to ensure accurate and meaningful insights.This research study illustrates the diverse applications of machine learning in improving healthcare monitoring using FAERS data. The findings highlight the potential of machine learning in early safety signal detection, pharmacovigilance triage, adverse event classification and coding, signal refinement and validation, real-time surveillance and alerting, and comparative safety analysis. The study emphasizes the importance of combining machine learning with human expertise to achieve effective and reliable healthcare monitoring

Clinical narrative analytics challenges

Precision medicine or evidence based medicine is based on the extraction of knowledge from medical records to provide individuals with the appropriate treatment in the appropriate moment according to the patient features. Despite the efforts of using clinical narratives for clinical decision support, many challenges have to be faced still today such as multilinguarity, diversity of terms and formats in different services, acronyms, negation, to name but a few. The same problems exist when one wants to analyze narratives in literature whose analysis would provide physicians and researchers with highlights. In this talk we will analyze challenges, solutions and open problems and will analyze several frameworks and tools that are able to perform NLP over free text to extract medical entities by means of Named Entity Recognition process. We will also analyze a framework we have developed to extract and validate medical terms. In particular we present two uses cases: (i) medical entities extraction of a set of infectious diseases description texts provided by MedlinePlus and (ii) scales of stroke identification in clinical narratives written in Spanish

Special issue on bio-ontologies and phenotypes

The bio-ontologies and phenotypes special issue includes eight papers selected from the 11 papers presented at the Bio-Ontologies SIG (Special Interest Group) and the Phenotype Day at ISMB (Intelligent Systems for Molecular Biology) conference in Boston in 2014. The selected papers span a wide range of topics including the automated re-use and update of ontologies, quality assessment of ontological resources, and the systematic description of phenotype variation, driven by manual, semi- and fully automatic means

Implementing electronic scales to support standardized phenotypic data collection - the case of the Scale for the Assessment and Rating of Ataxia (SARA)

The main objective of this doctoral thesis was to facilitate the integration of the semantics required to automatically interpret collections of standardized clinical data. In order to address the objective, we combined the best performances from clinical archetypes, guidelines and ontologies for developing an electronic prototype for the Scale of the Assessment and Rating of Ataxia (SARA), broadly used in neurology. A scaled-down version of the Human Phenotype Ontology was automatically extracted and used as backbone to normalize the content of the SARA through clinical archetypes. The knowledge required to exploit reasoning on the SARA data was modeled as separate information-processing units interconnected via the defined archetypes. Based on this approach, we implemented a prototype named SARA Management System, to be used for both the assessment of cerebellar syndrome and the production of a clinical synopsis. For validation purposes, we used recorded SARA data from 28 anonymous subjects affected by SCA36. Our results reveal a substantial degree of agreement between the results achieved by the prototype and human experts, confirming that the combination of archetypes, ontologies and guidelines is a good solution to automate the extraction of relevant phenotypic knowledge from plain scores of rating scales