About me

I'm Michele (yes, Michele with just one "L"). I'm currently a Research Data Scientist at Prometeia and Visiting Research Scholar at Massachusetts Institute of Technology in the Clinical Decision Making group.

My research interests span around applications of text analytics: artificial intelligence, natural language processing, text mining, and machine learning are my things. :)

Projects

Research

Publications

New! Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID Shared Tasks Track 2
M. Filannino, A. Stubbs, Ö. Uzuner
Journal of Biomedical Informatics, ELSEVIER (2017)
paper, review, project page, source code.

New! De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID Shared Tasks Track 1
A. Stubbs, M. Filannino, Ö. Uzuner
Journal of Biomedical Informatics, ELSEVIER (2017)
paper, review, project page, source code.

A Survey of Bioinformatics Database and Software Usage through Mining the Literature
G. Duck, G. Nenadic, M. Filannino, A. Brass, D.L. Robertson, R. Stevens
PloS one (2016)
paper, review.

Temporal expression extraction with extensive feature type selection and a posteriori label adjustment
M. Filannino, G. Nenadic
Data & Knowledge Engineering, ELSEVIER (2015)
paper, review, demo, project page, source code.

Using machine learning to predict temporal orientation of search engines' queries in the Temporalia challenge
M. Filannino, G. Nenadic
Proceedings of the Sixth International Workshop on Evaluating Information Access (EVIA 2014) a Satellite Workshop of the NTCIR-11 Conference
paper, poster, slides, review, demo, project page, source code.

Mining temporal footprints from Wikipedia
M. Filannino, G. Nenadic
Proceedings of the 1st AHA! Workshop on Information Discovery in Text (COLING 2014)
paper, poster, reviews, bibtex, project page, source code.

Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives
A. Kovačević , A. Dehghan , M. Filannino , J. A. Keane , G. Nenadic
Journal of the American Medical Informatics Association (JAMIA)
paper, poster, bibtex, software.

ManTIME: Temporal expression identification and normalization in TempEval-3 challenge
M. Filannino, G. Brown, G. Nenadic
Proceedings of the 7th International Workshop on Semantic Evaluation (SemEval 2013)
paper, poster, reviews, bibtex, demo, project page, source code.

Temporal expression normalisation in natural language texts
M. Filannino
CoRR, abs/1206.2010, 2012

DBWorld e-mail classification using a very small corpus
M. Filannino
Unpublished (Machine Learning course project)
paper, data set, raw data.

More on Google Scholar

Talks

22nd Oct 2018: Big data: un nuovo strumento al servizio della società, Orientamento, Scuola Svizzera di Milano, Milano, IT
22nd March 2017: How is artificial intelligence helping medicine, Artificial Intelligence, Simmons College, Boston, USA
14th February 2017: 2016 CEGS N-GRID Shared-Tasks and Workshop on Challenges in Natural Language Processing for Clinical Data, Meeting on Clinical Machine Learning NYU + MIT, MIT, Cambridge, USA
18th November 2016: Chiacchierata tra NERDs (A chat among nerds), TAG Innovation School, TAG Milan, Milan, Italy
11th November 2016: 2016 CEGS N-GRID Shared-Tasks and Workshop on Challenges in Natural Language Processing for Clinical Data, AMIA Annual Symposium, AMIA2016, Chicago, USA
19th October 2016: 2016 CEGS N-GRID Shared-Tasks and Workshop on Challenges in Natural Language Processing for Clinical Data, CEGS Annual Grantee Meeting, CEGS, Stanford, USA
11th December 2014: Using machine learning to predict temporal orientation of search engines’ queries in the Temporalia challenge, The 11th NTCIR Conference, NTCIR-11, Tokyo, Japan
23rd August 2014: Mining temporal footprints from Wikipedia, 1st AHA! Workshop on Information Discovery in Text, COLING 2014, Dublin, Ireland
29th October 2013: Extracting and interpreting temporal information from text (awarded with Honourable Mention from IBM), School Research Symposium, Manchester, UK
1st May 2013: Can computers understand time?, Languages @ Leeds PGR Seminars, University of Leeds, Leeds, UK
2nd November 2012: Extraction of temporal expressions, events and relations from clinical narratives using rules and machine-learning, 6th i2b2 Workshop on Challenges in NLP for Clinical Data, Chicago, USA
30th October 2012: Temporal information extraction in the clinical domain, School Research Symposium, Manchester, UK
5th March 2012: Detecting novel associations in large data sets, GN-TEAM Internal presentation, Manchester, UK
29th February 2012: Temporal expressions identification in biomedical texts, Scientific Methods II, Manchester, UK
15th February 2012: My research taster project: temporal expression extraction, GN-TEAM Internal presentation, Manchester, UK
2011: Nonlinear component analysis as a kernel eigenvalue problem, Modelling and visualization of high-dimensional data, Manchester, UK

Downloads

ManTIME. This is a temporal information extraction pipeline based on CRFs models. It uses NorMA and Clinical NorMA as normalizers. Click here to download it (citation).
Clinical NorMA. This is a rule-based temporal expression normaliser for clinical domain written in Python. It is an extension of NorMA. Click here to download it (citation).
NorMA. This is a rule-based temporal expression normaliser for general domain written in Python. It is an extension of TRIOS from the University of Rochester (USA) originally written by Dr. Naushad UzZaman. Click here to download it (citation).
Temporal Expressions Corpus. This is a corpus containing 2822 unique temporal expressions. Each one contains: natural expression, type, human value annotation and utterance reference. Click here to download it (citation).
Self-organising map. This is a MATLAB implementation of SOM. Click here to download it. You can also watch a video.
In MATLAB 2007 and earlier it doesn't work because of the randi function. Here is the working code for older version of MATLAB. (Thanks to Anuja Bokhare)