activity
20162020
collaborators

5 papers

cs.HC2020

StudyU: a platform for designing and conducting innovative digital N-of-1 trials

Stefan Konigorski, Sarah Wernicke, Tamara Slosarek +13

N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate…

q-bio.QM2020

Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale

Isotta Landi, Benjamin S. Glicksberg, Hao-Chih Lee +6

Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data pr…

q-bio.GN2019

Scaling structural learning with NO-BEARS to infer causal transcriptome networks

Hao-Chih Lee, Matteo Danieletto, Riccardo Miotto +2

Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimati…

cs.CY2018

Processing of Electronic Health Records using Deep Learning: A review

Venet Osmani, Li Li, Matteo Danieletto +3

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of heal…

cs.NI2016

Estimating the number of receiving nodes in 802.11 networks via machine learning techniques

Davide Del Desta, Matteo Danieletto, Giorgio Maria Di Nunzio +1

Nowadays, most mobile devices are equipped with multiple wireless interfaces, causing an emerging research interest in device to device (D2D) communication: the idea behind the D2D…