Anjani DhrangadhariyaHES-SO Valais-Wallis | HES-SO · Bereich eHealth
Anjani Dhrangadhariya
Master of Science
About
17
Publications
3,529
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28
Citations
Introduction
Natural Language Processing in health and biomedicine
Education
October 2013 - March 2016
University of Bonn
Field of study
- Life Science Informatics
Publications
Publications (17)
PICO recognition is an information extraction task for identifying participant, intervention, comparator, and outcome information from clinical literature. Manually identifying PICO information is the most time-consuming step for conducting systematic reviews (SR), which is already labor-intensive. A lack of diversified and large, annotated corpora...
Objective
The aim of this study was to test the feasibility of PICO (participants, interventions, comparators, outcomes) entity extraction using weak supervision and natural language processing.
Methodology
We re-purpose more than 127 medical and nonmedical ontologies and expert-generated rules to obtain multiple noisy labels for PICO entities in...
Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to conducting systematic reviews. Manual RoB assessment for hundreds of RCTs is a cognitively demanding, lengthy process and is prone to subjective judgment. Supervised machine learning (ML) can help to accelerate this process but requires a hand-labelled corpus. There are...
This paper describes the HEVS-TUW team submission to the SemEval-2023 Task 8: Causal Claims. We participated in two subtasks: (1) causal claims detection and (2) PIO identification. For subtask 1, we experimented with an ensemble of weakly supervised question detection and fine-tuned Transformer-based models. For subtask 2 of PIO frame extraction,...
PICO recognition is an information extraction task for detecting parts of text describing Participant (P), Intervention (I), Comparator (C), and Outcome (O) (PICO elements) in clinical trial literature. Each PICO description is further decomposed into finer semantic units. For example, in the sentence ‘The study involved 242 adult men with back pai...
Free-text reporting has been the main approach in clinical pathology practice for decades. Pathology reports are an essential information source to guide the treatment of cancer patients and for cancer registries, which process high volumes of free-text reports annually. Information coding and extraction are usually performed manually and it is an...
![Fig. 2. Screenshot of the OpenI image retrieval system [8].](profile/Henning-Mueller-3/publication/337110201/figure/fig2/AS:822949238030337@1573217796397/Screenshot-of-the-OpenI-image-retrieval-system-8_Q320.jpg)
Medical imaging research has long suffered problems getting access to large collections of images due to privacy constraints and to high costs that annotating images by physicians causes. With public scientific challenges and funding agencies fostering data sharing, repositories, particularly on cancer research in the US, are becoming available. St...
Radiology reports describe the findings of a radiologist in an imaging examination, produced for another clinician in order to answer to a clinical indication. Sometimes, the report does not fully answer the question asked, despite guidelines for the radiologist. In this article, a system that controls the quality of reports automatically is descri...
Evidence-based practice is highly dependent upon up-to-date systematic reviews (SR) for decision making. However, conducting and updating systematic reviews, especially the citation screening for identification of relevant studies, requires much human work and is therefore expensive. Automating citation screening using machine learning (ML) based a...
The overall lower survival rate of patients with rare cancers can be explained, among other factors, by the limitations resulting from the scarce available information about them. Large biomedical data repositories, such as PubMed Central Open Access (PMC-OA), have been made freely available to the scientific community and could be exploited to adv...
Radiology reports describe the findings of a radiologist in an imaging examination, produced for another clinician in order to answer to a clinical indication. Sometimes, the report does not fully answer the question asked, despite guidelines for the radiologist. In this article, a system that controls the quality of reports automatically is descri...
The Republic of India, with a population of 135.26 billion, flaunts the largest educational network and is the 3 rd largest force of scientists, engineers, and doctors. However, Indian Life Science journals face numerous adversities when placed in the international arena. Journal Citation Reports (JCR) indexes about 2.16%, Science Citation Index (S...
Medical imaging research has long suffered problems getting access to large collections of images due to privacy constraints and to high costs that annotating images by physicians causes. With public scientific challenges and funding agencies fostering data sharing, repositories, particularly on cancer research in the US, are becoming available. St...
Neurodegenerative diseases are chronic debilitating conditions, characterized by progressive loss of neurons that represent a significant health care burden as the global elderly population continues to grow. Over the past decade, high-throughput technologies such as the Affymetrix GeneChip microarrays have provided new perspectives into the pathom...
Questions
Question (1)
Medical text corpus annotation is a tough task because it requires expert annotations. Oftentimes, it is really expensive and time-consuming. It is also important that the corpus is annotated by more than one expert and Inter Annotator Agreement is measured (IAA). (IAA = inter-annotator agreement is a measure of how well two (or more) annotators can make the same annotation decision for a certain category.) IAA measures how trustworthy the annotations are and how easy was it to delineate the categories being annotated.
Since annotation is really time-consuming, is it really necessary that an entire corpus is annotated by two separate annotators? Could a midway scenario like this be actually possible? The midway scenario - About 20% of the corpus documents are annotated by two experts and IAA is measured. If IAA is above a certain threshold, the rest of the corpus (80%) is only annotated by one expert annotator.
