Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Md_Muntasir_Zitu_dissertation_final.pdf (1.44 MB)

ETD Abstract Container

Abstract Header

Adverse Drug Event Detection from Clinical Narratives of Electronic Medical Records Using Artificial Intelligence.

Zitu, Md Muntasir
http://rave.ohiolink.edu/etdc/view?acc_num=osu168976942934742

Abstract Details

, Doctor of Philosophy, Ohio State University, Biomedical Sciences.
Electronic Health Records (EHRs) clinical narratives provide longitudinal information about drug-induced adverse events. However, it is time and labor-expensive to manually review those clinical narratives and extract adverse drug events (ADEs). A robust automated system needs to be included in current clinical settings for early detection of ADEs. So, building an automated system that uses Artificial Intelligence (AI) to process those clinical narratives and extract ADEs is in demand. Moreover, a generalized system will work on different types of clinical notes, thus reducing the technical dependencies and associated costs. Natural Language Processing (NLP), a field of AI, can automatically process free texts and extract semantic information. So, the central hypothesis of this research is that NLP models can automatically detect ADEs from unstructured EHRs. The long-term goal is to build an automated system in clinical settings for the early detection of ADEs. This dissertation has three aims that are connected to each other to accomplish the long-term goal. Aim 1 focuses on the generalizability of the NLP model to identify drug-induced ADEs from different EHR sources. The primary objective of Aim 1 is to evaluate the applicability of the NLP model in determining drug-induced ADEs across various EHR systems. To facilitate this goal, we also created a novel gold standard corpus. Aim 2 develops an ADE detection model to identify drug-induced adverse events at the patient level. Aim 3: Identify drug discontinuation information to develop a temporal model for the novel causal drug-ADE relation discovery.
Lang Li (Advisor)
Bioinformatics; Biomedical Research; Oncology
adverse drug events, electronic health records, machine learning, natural language processing, relation extraction

Recommended Citations

Citations

  • Zitu, M. M. (n.d.). Adverse Drug Event Detection from Clinical Narratives of Electronic Medical Records Using Artificial Intelligence. [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu168976942934742

    APA Style (7th edition)

  • Zitu, Md Muntasir. Adverse Drug Event Detection from Clinical Narratives of Electronic Medical Records Using Artificial Intelligence. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu168976942934742.

    MLA Style (8th edition)

  • Zitu, Md Muntasir. "Adverse Drug Event Detection from Clinical Narratives of Electronic Medical Records Using Artificial Intelligence." Doctoral dissertation, Ohio State University. Accessed APRIL 06, 2025. http://rave.ohiolink.edu/etdc/view?acc_num=osu168976942934742

    Chicago Manual of Style (17th edition)

osu168976942934742
135
© , all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.