Location: San Diego, CA, USA
Workshop Date: August 24, 2020
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Healthcare is, traditionally, a knowledge-driven enterprise with an enormous amount of data - both structured and unstructured. These data can impact positively on the development of data-driven health care including precision medicine and precision public health. However, in the era of big data, the mining of such data in a manner that leads to clinically actionable outcomes remains a challenge. In recent years, large scale medical/clinical datasets, such as “omics” data and radiology reports. are increasingly available. We have also witnessed an increasing number of successful AI/ML applications using such datasets to address problems such as drug repurposing and preliminary screening of radiology reports. In this deep learning era, what are the challenges and opportunities to deploy such solutions in practice? What is the current status of AI/ML applications in healthcare? Can the different facets of trust and explanations drive adoption of such methods in practice? Can knowledge-backed AI lead to more interpretable models? How do data scientists and physicians apply this knowledge in collaboration to further the field and improve healthcare? After witnessing so many great achievements from deep learning lately, we propose to invite world-leading experts from both data science and healthcare to discuss and debate the path forward for practical applications of AI/ML in healthcare, including demos, early work, and critiques on various aspects of actionable and trustworthy AI. More specifically, we plan to attract high-quality original research from emerging areas with significant implications in healthcare and invite open discussions on controversial yet crucial topics regarding healthcare transformation.
We have accepted 17 papers for presentation at the workshop. Out of these 5 papers have been selected for oral and 12 for spotlight presentations, respectively. The list will be updated with the final camera ready links for the papers (authors should have received instructions on how to submit the camera ready papers).
|#14 "Transfer Learning for Activity Recognition in Mobile Health", Yuchao Ma, Andrew Campbell, Diane Cook, John Lach, Shwetak Patel, Thomas Ploetz, Majid Sarrafzadeh, Donna Spruijt-Metz and Hassan Ghasemzadeh|
|#15 "Impact of Medical Data Imprecision on Learning Results", Mei Wang, Haiqin Lu and Jianwen Su|
|#17 "Customize Deep Learning-based De-Identification Systems Using Local Clinical Notes - A Study of Sample Size", Xi Yang, Jiang Bian and Yonghui Wu|
|#18 "Information Extraction of Clinical Trial Eligibility Criteria", Yitong Tseo, M. I. Salkola, Ahmed Mohamed, Anuj Kumar and Freddy Abnousi|
|#21 "Deeppseudo: A Deep Learning Approach Based on Pseudo Values for Competing Risk Analysis", Md Mahmudur Rahman and Sanjay Purushotham|
|#2 "Challenging Common Bolus Advisor for Self-Monitoring Type-I Diabetes Patients Using Reinforcement Learning", Frédéric Logé, Erwan Le Pennec and Habiboulaye Amadou-Boubacar|
|#3 "Mcu-Net: A Framework Towards Uncertainty Representations For Decision Support System Patient Referrals In Healthcare Contexts", Nabeel Seedat|
|#4 "User-driven Analysis of Longitudinal Health Data with Hidden Markov Models for Clinical Insights", Bum Chul Kwon|
|#5 "Clinical Recommender System: Predicting Medical Specialty Diagnostic Choices with Neural Network Ensembles", Morteza Noshad, Ivana Jankovic and Jonathan Chen|
|#7 "Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test", Simon Kocbek, Primoz Kocbek, Leona Cilar and Gregor Stiglic|
|#11 "Automatic Deep Learning-based Histopathologic Image Classification", Mingyang Pu, Mengjun Tao, Xiaolu Zheng and Chang Yin|
|#12 "Explicit-Blurred Memory Network for Analyzing Patient Electronic Health Records", Prithwish Chakraborty, Fei Wang, Jianying Hu and Daby Sow|
|#13 "Identifying Patterns in Cystic Fibrosis Physiotherapy using Unsupervised Clustering", Tempest A. van Schaik, Olga Liakhovich, Bianca Furtuna, Mihaela Curmei, Emma Raywood, Helen Douglas, Kunal Kapoor, Nicole Filipow, and Eleanor Main|
|#16 "Visualizing Deep Graph Generative Models for Drug Discovery", Karan Yang, Chengxi Zang and Fei Wang|
|#19 "A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records", Parthasarathy Suryanarayanan, Bhavani Iyer, Prithwish Chakraborty, Shannon Harrer, Sarah Miller, Gigi Yuen-Reed and Daby Sow|
|#20 "Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations", Alexanda-Ioana Albu, Alina Enescu and Luigi Malagò|
|#22 "An Enhanced Text Classification to Explore Health based Indian Government Policy Tweets", Aarzoo Dhiman and Durga Toshniwal|
We invite full papers, as well as work-in-progress on the application of data science in healthcare. Topics may include, but not limited to, the following topics (For more information see workshop overview) with special focus on techniques that are aimed at addressing the importance of trustable and actionable AI in healthcare.
Papers must be submitted in PDF format to easychair https://easychair.org/conferences/?conf=dshealth2020 and formatted according to the new Standard ACM Conference Proceedings Template . Papers must be a maximum length of 4 pages, including references.
The program committee will select the papers based on originality, presentation, and technical quality for spotlight and/or poster presentation.
All deadlines correspond to 11:59 PM Hawaii Standard Time ( HST).