A5035060987- Biomedical Text Mining and Ontologies
- Computational Drug Discovery Methods
- Advanced Text Analysis Techniques
- Topic Modeling
- Semantic Web and Ontologies
- Pharmacovigilance and Adverse Drug Reactions
- Social Media in Health Education
- Machine Learning in Bioinformatics
- Mental Health Research Topics
- Bioinformatics and Genomic Networks
- Academic integrity and plagiarism
- Mental Health via Writing
- Data Quality and Management
- Natural Language Processing Techniques
- Health Literacy and Information Accessibility
- Electronic Health Records Systems
- Text and Document Classification Technologies
- Pharmaceutical Economics and Policy
- Mobile Health and mHealth Applications
- Meta-analysis and systematic reviews
- Digital Mental Health Interventions
- Rough Sets and Fuzzy Logic
- Web Data Mining and Analysis
- Patient-Provider Communication in Healthcare
- Ethics in Clinical Research
Merck (India)
2023-2024
Merck (Germany)
2012-2022
Fraunhofer Institute for Algorithms and Scientific Computing
2009-2013
Bonn Aachen International Center for Information Technology
2009-2012
Fraunhofer Society
2009-2011
Abstract The sheer amount of information about potential adverse drug events publishedin medical case reports pose major challenges for safety experts toperform timely monitoring. Efficient strategies identification andextraction fromfree‐text resources are needed to support pharmacovigilance researchand pharmaceutical decision making. Therefore, this work focusses on theadaptation a machine learning‐based system the identificationand extraction event relations from MEDLINE casereports. It...
Speculative statements communicating experimental findings are frequently found in scientific articles, and their purpose is to provide an impetus for further investigations into the given topic. Automated recognition of speculative text has gained interest recent years as systematic analysis such could transform thoughts testable hypotheses. We describe here a pattern matching approach detection that uses dictionary patterns classify sentences hypothetical. To demonstrate practical utility...
ABSTRACT Purpose The aim of this study was to assess the impact automatically detected adverse event signals from text and open‐source data on prediction drug label changes. Methods Open‐source effect were collected FAERS, Yellow Cards SIDER databases. A shallow linguistic relation extraction system (JSRE) applied for effects MEDLINE case reports. Statistical approach extracted datasets signal detection subsequent changes issued 29 drugs by UK Regulatory Authority in 2009. Results 76%...
Chemical information extracted from the literature is of immense value for pharmaceutical and chemical industries in many areas, including supporting drug discovery, manufacturing processes, or intellectual property protection. However, exponential growth has made it increasingly difficult researchers to find they need within a reasonable time-frame. In order address this issue, large number text mining approaches have been developed that can extract different types literature. But lack...
The anatomical therapeutic chemical (ATC) classification system maintained by the World Health Organization provides a global standard for of medical substances and serves as source drug repurposing research. Nevertheless, it lacks several drugs that are major players in market. In order to establish classifications yet unclassified drugs, this paper presents newly developed approach based on combination information extraction (IE) machine learning (ML) techniques. Most about is published...
Patients' increasing digital participation provides an opportunity to pursue patient-centric research and drug development by understanding their needs. Social media has proven be one of the most useful data sources when it comes a company's potential audience drive more targeted impact. Navigating through ocean information is tedious task where techniques such as artificial intelligence text analytics have effective in identifying relevant posts for healthcare business questions. Here, we...
Labeling corpora constitutes a bottleneck to create models for new tasks or domains. Large language mitigate the issue with automatic corpus labeling methods, particularly categorical annotations. Some NLP such as emotion intensity prediction, however, require text regression, but there is no work on automating annotations continuous label assignments. Regression considered more challenging than classification: The fact that humans perform worse when tasked choose values from rating scale...
Background Pharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials. However, identifying quality is challenging due to the increasing volume heterogeneity of relevant data describing biological networks that underlie disease mechanisms. A pathway network typically includes entities (e.g. genes, proteins chemicals/drugs) as well relationships between these curated or mined from structured databases textual...
Ontology enrichment is a process of embedding metadata associated with concepts described in the ontology. Manual information retrieval and labor-intensive time consuming as each concept unique has domain specific meanings. An approach to deal this problem have unified resource an automated solution. Different approaches been used automate varying success. Here, we describe our combining manual retrieved results. Unified Medical Language System implemented on MySQL server was for ontology...
With the growing unstructured data in healthcare and pharmaceutical, there has been a drastic adoption of natural language processing for generating actionable insights from text sources. One key areas our exploration is Medical Information function within organization. We receive significant amount medical information inquires form text. An enterprise-level solution must deal with interactions via multiple communication channels which are always nuanced variety keywords emotions that unique...
Identification of chemical named entities in text and subsequent linkage information to biological events is immense value fulfill the knowledge needs pharmaceutical R&D. A significant amount investigation has been carried out since a decade for identifying at morphological level. However, barrier still remains terms proposition scientists chemistry Therefore, work described here aims circumvent by adaptation Conditional Random Fields-based approach various levels namely generic level,...
This article examines the opportunities and benefits of artificial intelligence (AI)-enabled social media listening (SML) in assisting successful patient-focused drug development (PFDD). PFDD aims to incorporate patient perspective improve quality, relevance, safety, efficiency evaluation. Gathering perspectives support is aided by participation groups communicating their treatment experiences, needs, preferences, priorities through online platforms. SML a method gathering feedback directly...
<sec> <title>UNSTRUCTURED</title> Patient-focused drug development (PFDD) aims to incorporate the patient perspective improve quality, relevance, safety, and efficiency of inform evaluation. Gathering perspectives support PFDD has become more feasible with increased digital presence participation groups that communicate their treatment experiences, needs, preferences, priorities through online forums. Social media listening (SML) is a method gathering substantial amount feedback directly...