Bio of Dr. Lipika Dey
Lipika Dey is a Professor of Computer Science at Ashoka University, India. She was earlier a
Chief Scientist at Tata Consultancy Services (TCS), Research, where she headed researchin the theme of Economic and Financial Intelligence. Her research interests are in the areasof Artiﬁcial Intelligence, Natural Language Processing and Data Analytics. She has a keeninterest in the ﬁelds of healthcare and sustainability analytics based on multi-structured
data. She loves to focus on building predictive models based on Augmented Intelligencegathered from heterogeneous data accrued from multiple diLerent sources. She has
several international publications and patents. Prior to joining TCS, Lipika was a member offaculty at the Department of Mathematics, IIT Delhi. Lipika was elected to the Fellowship ofIndian National Academy of Engineering in 2021. She is also a Fellow of Asia-Paciﬁc
Artiﬁcial Intelligence Association (AAIA). She was also awarded the Distinguished Scientist
award by TCS in 2012.
Lipika has a PhD in Computer Science and Engineering from IIT Kharagpur, where she had
also done her graduation and post-graduation in Mathematics and Computer
Science. As a member of Association for Computing Machinery (ACM), the world's largest
educational and scientiﬁc computing society engaged in the advancement of computing asa science and a profession, Lipika is actively involved in mentorship programs for PhD
scholars and activities to promote Women in STEM.

Analysis of Healthcare Data - Explainability using rough sets and LLMs
Lipika Dey
The success of deep-neural networks for tasks like prediction as well as contentgeneration, has raised expectations for their use in various application areas. TheHealthcare sector, with an ever-increasing availability of data, is one of the mostprolific areas focused on using these technologies for a multitude of applications.
Healthcare data is available in many different forms which include structured
reports as well as images and texts in the forms of test reports, nursing notes,
prescriptions and so on. Healthcare analytics also make extensive use of biomedical literature. A wide range of tasks are focused towards prediction of future
events for hospital logistics management, like predicting length of stay of apatient in hospital or Intensive Care Unit, patient re-admission probability,
predicting whether the patient would need a particular procedure and so on.
Another line of research is focused towards personalised healthcare. These are
aimed at deriving insights about individual patients or small patient cohorts, inorder to deliver more targeted treatment as well as hospital experience to eachpatient. Predicting adverse effects of drugs for a particular patient is an important
problem in the area. Designing personalised treatment plans, based on insights
obtained from patient cohorts of similar patients, is also an important task.
While all the above areas have benefited with the application of deep-neuralmodels, especially while working with unstructured data like text and images, oneof the critical aspects essential for healthcare applications is explainability of themodel. A significant amount of research is focused on interpretability of the models
and generating post-hoc explanations, to make the models more acceptable [1, 2].
However, the problem is very complex, as applications need explainability fromdifferent perspectives, depending on the end-users.
In this talk we will be presenting the utility of model-agnostic explanationgeneration techniques like SHAP and LIME in explaining predictions [3, 4, 5]. We
also show that using these in conjunction with multiple representations for thesame data point, helps in generating alternative view-points about the same entity,
which helps in interpretation and decision making. While we have worked
primarily with text data in conjunction with a few physiological parameters, thecore idea can be extended to include other forms of data also. Specifically, roughset concepts, which are ideally suited to interpret data that has inherent
uncertainties and incompleteness, can add on to this capability [6]. Though at avery nascent stage, there are attempts to integrate rough-set concepts with deepneural networks to enhance the interpretability of the models. At the simplest level,rough sets have been applied for feature selection and dimensionality reduction
before applying deep-learning models. However, the rough sets can be used at amuch more complex level as techniques for approximations as well as fordesigning hybrid model architectures.

References[1]. Allgaier, J., Mulansky, L., Draelos, R. L., & Pryss, R. (2023). How does the model make predictions? A
systematic literature review on the explainability power of machine learning in healthcare. Artiﬁcial
Intelligence in Medicine, 143, 102616.

[2]. Bienefeld, N., Boss, J. M., Lüthy, R., Brodbeck, D., Azzati, J., Blaser, M., ... & Keller, E. (2023). Solving theexplainable AI conundrum by bridging clinicians’ needs and developers’ goals. npj Digital Medicine, 6(1), 94.
[3]. Jana, S., Dasgupta, T., & Dey, L. (2022). Predicting medical events and ICU requirements using a
multimodal multiobjective transformer network. Experimental Biology and Medicine, 247(22), 1988-2002.
[4]. Dey, L., Jana, S., Dasgupta, T., & Gupta, T. (2023, September). Deciphering Clinical Narratives-Augmented
Intelligence for Decision Making in Healthcare Sector. In 2023 18th Conference on Computer Science andIntelligence Systems (FedCSIS) (pp. 11-24). IEEE.
[5]. Krishnamoorthy, T. V., Venkataiah, C., Rao, Y. M., Prasad, D. R., Chowdary, K. U., Jayamma, M., & Sireesha,
R. (2024). A novel NASNet model with LIME explanability for lung disease classiﬁcation. Biomedical Signal
Processing and Control, 93, 106114.

[6]. 1Gaeta, A., Loia, V., & Orciuoli, F. (2024). An explainable prediction method based on Fuzzy Rough Sets,TOPSIS and hexagons of opposition: Applications to the analysis of Information Disorder. Information
Sciences, 659, 120050.