The 34th IEEE International Conference on Tools with Artificial Intelligence was held virtually from 31s of October to the 2nd of November. With Aron Henriksson and in collaboration with Karolinska Institutet we presented our paper “Improving the Timeliness of Early Prediction Models for Sepsis through Utility Optimization” and we are very happy to announce that we received the best paper award. In the paper that will be published in the proceedings of the conference, we explore the capabilities of using custom objective functions to develop a machine learning model that can perform sepsis prediction over time in a manner that will be useful for practitioners in assisting them to perform timely intervention and initiate treatment early, which is key to survival.
The paper was received well during the CBMS 2022 symposium presentation time. I am delighted to inform you that the paper received the ‘best student paper award’. The award was provided by the IEEE Technical Committee on Computational Life Science (TCCLS).
Want to know more about the paper? Please check out the following presentation video I made!
An excerpt of the paper
Best Student Paper Award Certificate
Abstract:
The area of interpretable deep neural networks has received increased attention in recent years due to the need for transparency in various fields, including medicine, healthcare, stock market analysis, compliance with legislation, and law. Layer-wise Relevance Propagation (LRP) and Gradient-weighted Class Activation Mapping (Grad-CAM) are two widely used algorithms to interpret deep neural networks. In this work, we investigated the applicability of these two algorithms in the sensitive application area of interpreting chest radiography images. In order to get a more nuanced and balanced outcome, we use a multi-label classification-based dataset and analyze the model prediction by visualizing the outcome of LRP and Grad-CAM on the chest radiography images. The results show that LRP provides more granular heatmaps than Grad-CAM when applied to the CheXpert dataset classification model. We posit that this is due to the inherent construction difference of these algorithms (LRP is layer-wise accumulation, whereas Grad-CAM focuses primarily on the final sections in the model’s architecture). Both can be useful for understanding the classification from a micro or macro level to get a superior and interpretable clinical decision support system.
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The 13th Language Resources and Evaluation Conference (LREC 2022) was held in Marseille, France with over 1000 participants. Four of us from DSV were there to present our recent findings and learn about the state of the NLP field. Anastasios Lamproudis, Aron Henriksson, Hercules Dalianis and I (Thomas Vakili) had a total of four papers for the conference and its workshops.
All four of us presented a paper about continued pre-training BERT models using automatically de-identified clinical data. We showed that pre-training with safer de-identified clinical data works just as well as using sensitive data. During the conference, we also received ethical approval to share one of the models with academic researchers.
I also presented two workshop papers co-written with researchers from Linköping University, Linköping University Hospital and RISE. The first paper was about using a clinical BERT model to conduct terminology extraction to find terms associated with medical implants in electronic health records. The other paper investigated how well the de-identification system developed at DSV using the Health Bank performs on data from clinics not present in our datasets.
Anastasios, Aron and Hercules presented a paper in which they evaluated various strategies for creating clinical BERT models. They compared initializing the model from a general-domain model versus pre-training from scratch, and whether adapting the general-domain vocabulary to the clinical domain helps or not. They found that all strategies lead to improvements on clinical tasks, but that all strategies ultimately lead to similarly performing models. However, initializing from a general-domain model decreased the amount of training needed.
We had many fruitful discussions and returned home full of ideas to try out. If you are interested in seeing our posters, then you can find them here and here.
A paper written by Rahmat Mulyana, Lazar Rusu, and Erik Perjons and entitled: “IT Governance Mechanisms that Influence Digital Transformation: A Delphi Study in Indonesian Banking and Insurance” has been published in PACIS 2022 Proceedings, Paper 267, Association for Information Systems (Nominated for the Best Paper (Paper 1160) in PACIS 2022 Detailed Program: https://pacis2022.aisconferences.org/schedule-program/conference-program/)
A paper written by Parisa Aasi, Sebastian Atug, Lorenzo Cermeno, and Lazar Rusu, and entitled: “Digital Transformation Success Through Aligning the Organizational Structure: Case Study of Swedish Public Organizations” has been accepted for publication in AMCIS 2022 Proceedings, Association for Information Systems
A paper written by Gideon Mekonnen Jonathan, Lazar Rusu, and Erik Perjons and entitled: “Digital Transformation in Public Organisations: IT Alignment-Related Success Factors” has been accepted for publication in ISD 2022 Proceedings, Association for Information Systems
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I had the pleasure of presenting a poster of a paper by Hercules Dalianis and me: Utility Preservation of Clinical Text After De-Identification. The paper investigates how automatic de-identification, a necessarily imperfect process, impacts the quality of the resulting texts. When a de-identification system incorrectly class a word as sensitive, the data will be slightly corrupted. Many researchers have been worried that this would make the data less useful, and we investigate this issue.
The impact of automatic de-identification on quality is evaluated using both qualitative and quantitative (machine learning) methods. We find no losses in utility for clinical NLP on three downstream clinical tasks. In fact, the machine learning models trained using automatic de-identification seem to work just as well as those trained using sensitive data. We also find that the experts in our study think the de-identification works well.
Participating in the 60th ACL conference was a great experience. I learned a lot from our global NLP community and met many researchers interested in our work at DSV. You can find the paper here, and the poster I presented here.
