Thesis

Machine Learning for Medical Decision Support and Individualized Treatment Assignment
Kuusisto F
University of Wisconsin-Madison department of Computer Sciences, 2015.
(paper, slides)

Preprints

Word Embedding Mining for SARS-CoV-2 and COVID-19 Drug Repurposing
Kuusisto F, Page D, Stewart R
F1000Research - Awaiting Review
(preprint, code and data)

Peer Reviewed

KinderMiner Web: A Simple Web Tool for Ranking Pairwise Associations in Biomedical Applications
Kuusisto F, Ng D, Steill J, Ross I, Livny M, Thomson J, Page D, Stewart R
F1000Research, 2021
(paper, web application, indexing code, webapp code)

Machine Learning to Predict Developmental Neurotoxicity with High-Throughput Data from 2D Bio-Engineered Tissues
Kuusisto F, Santos Costa V, Hou Z, Thomson J, Page D, Stewart R
IEEE International Conference on Machine Learning and Applications, 2019
(paper, code and data, raw data, arxiv version)

Data-Driven Phenotype Discovery of FMR1 Premutation Carriers in a Population-Based Sample
Movaghar A, Page D, Brilliant M, Baker M, Greenberg J, Hong J, DaWalt L, Saha K, Kuusisto F, Stewart R, Berry-Kravis E, Mailick M
Science Advances, 2019.
(paper)

Machine Learning Assisted Discovery of Novel Predictive Lab Tests Using Electronic Health Record Data
Kleiman R*, Kuusisto F*, Ross I, Peissig P, Stewart R, Page D, Weiss J
AMIA Informatics Summit, San Francisco, USA, 2019.
* These authors contributed equally
(paper)

A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications
Kuusisto F, Steill J, Kuang Z, Thomson J, Page D, Stewart R
AMIA Joint Summits on Translational Science, San Francisco, USA, 2017.
(paper, slides)

Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution
Weiss J, Kuusisto F, Boyd K, Liu J, Page D
AMIA Annual Symposium, San Francisco, USA, 2015.
(paper)

Leveraging Expert Knowledge to Improve Machine-Learned Decision Support Systems
Kuusisto F, Dutra I, Elezaby M, Mendonca E, Shavlik J, and Burnside ES
AMIA Joint Summits on Translational Science, San Francisco, USA, 2015.
(paper, slides)

Support Vector Machines for Differential Prediction
Kuusisto F, Santos Costa V, Nassif H, Burnside ES, Page D, and Shavlik J
European Conference on Machine Learning (ECML’14), Nancy, France, 2014.
(paper, poster, slides, synthetic data, code)

Using Machine Learning to Identify Benign Cases with Non-Definitive Biopsy
Kuusisto F, Dutra I, Nassif H, Wu Y, Klein ME, Neuman HB, Shavlik J, and Burnside ES
IEEE 15^th International Conference on e-Health Networking, Applications & Services (Healthcom’13), Lisbon, Portugal, 2013.
(paper)

Score As You Lift (SAYL): A Statistical Relational Learning Approach to Uplift Modeling
Nassif H, Kuusisto F, Burnside ES, Page D, Shavlik J, and Santos Costa V
European Conference on Machine Learning (ECML’13), Prague, Czech Republic, 2013.
(paper)

Uplift Modeling with ROC: An SRL Case Study
Nassif H, Kuusisto F, Burnside ES, and Shavlik J
International Conference on Inductive Logic Programming (ILP’13), Rio de Janeiro, Brazil, 2013.
(paper)

Abstracts

Core Needle Biopsies: A Predictive Model that Identifies Low Probability (≤2%) Lesions to Safely Avoid Surgical Excision
Elezaby M, Kuusisto F, Shavlik J, Wu Y, Gegios A, Neuman H, DeMartini WB, Burnside ES. Radiological Society of North America (RSNA) 101st Scientific Assembly and Annual Meeting, Chicago, IL, 2015. (Accepted for oral presentation).

Differential Upgrade Rates for Non-Definitive Image-Guided Core Needle Breast Biopsies Based on BI-RADS Features
Gegios A, Elezaby M, DeMartini WB, Cox J, Montemayor-Garcia C, Neuman H, Kuusisto F, Hampton JM, Burnside ES. Radiological Society of North America (RSNA) 101st Scientific Assembly and Annual Meeting, Chicago, IL, 2015. (Accepted for poster presentation).