Machine learning is commonly described as a “field of study that gives computers the ability to learn without being explicitly programmed” (Simon, 2013). Despite this common claim, practitioners know that designing effective machine learning pipelines is often a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish.
Saja Al-Alawneh abstract: Providing radiologists with feedback has been shown to improve their performance in mammography diagnosis. In 1992, the Mammography Quality Standards Act (MQSA) was enacted to improve the quality of mammography using audit and feedback procedures. However, no standard audit and feedback system for radiologists has been installed in the United States. Instead, auditing typically requires human effort to manually correlate radiology and pathology results.
Rathnam Abstract: Ubiquitin is arguable one of the most important molecules involved in post-translational modifications as it is present in all eukaryotic cells and plays a key role in mediating a wide assortment of biological processes, such as cell cycle regulation, endocytosis of cellular proteins, and transcriptional regulation.
Lee Abstract: The dysregulation of microRNAs (miRNAs) alters expression level of pro-oncogenic or tumor suppressive mRNAs in breast cancer, and in the long run, causes multiple biological abnormalities.
The long-term goal of the proposed work is to develop an effective informatics intervention that prevents harm to nursing home (NH) residents from potential drug-drug interactions (PDDIs) while avoiding known issues with interaction alerting such as alert fatigue. In this talk I will discuss progress toward that goal that examines the feasibility and potential clinical usefulness of actively monitoring patients exposed to psychotropic drugs.
Recent developments in scalable Bayesian inference have enabled fast learning of complex probabilistic models using massive data sets. We will discuss these developments in the context of topic models of discrete grouped data, focusing on text. We will review our recent collaborations on scalable model learning using stochastic variational inference, and discuss new applications to structured hierarchical topic models.
Eye-tracking is a valuable research tool that is used in laboratory and limited field environments. We take steps toward developing methods that enable widespread adoption of eye-tracking and its real-time application in clinical decision support. Eye-tracking will enhance awareness and enable intelligent views, more precise alerts, and other forms of decision support in the Electronic Medical Record (EMR). We evaluated a low-cost eye-tracking device and found the device’s accuracy to be non-inferior to a more expensive device.