23,019 research outputs found
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language Models
Large-scale clinical data is invaluable to driving many computational
scientific advances today. However, understandable concerns regarding patient
privacy hinder the open dissemination of such data and give rise to suboptimal
siloed research. De-identification methods attempt to address these concerns
but were shown to be susceptible to adversarial attacks. In this work, we focus
on the vast amounts of unstructured natural language data stored in clinical
notes and propose to automatically generate synthetic clinical notes that are
more amenable to sharing using generative models trained on real de-identified
records. To evaluate the merit of such notes, we measure both their privacy
preservation properties as well as utility in training clinical NLP models.
Experiments using neural language models yield notes whose utility is close to
that of the real ones in some clinical NLP tasks, yet leave ample room for
future improvements.Comment: Clinical NLP Workshop 201
Cognitive Computing supported Medical Decision Support System for Patient’s Driving Assessment
To smartly utilize a huge and constantly growing volume of data, improve productivity and increase competitiveness in various fields of life; human requires decision making support systems that efficiently process and analyze the data, and, as a result, significantly speed up the process. Similarly to all other areas of human life, healthcare domain also is lacking Artificial Intelligence (AI) based solution. A number of supervised and unsupervised Machine Learning and Data Mining techniques exist to help us to deal with structured data. However, in a real life, we pretty much deal with unstructured data that hides useful knowledge and valuable information inside human-readable plain texts, images, audio and video. Therefore, such IT giants as IBM, Google, Microsoft, Intel, Facebook, etc., as well as variety of SMEs are actively elaborating different Cognitive Computing services and tools to get a value from unstructured data. Thus, the paper presents feasibility study of IBM Watson cognitive computing services and tools to address the issue of automated health records processing to support doctor’s decision for patient’s driving assessment
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