PMID- 28494618
OWN - NLM
STAT- MEDLINE
DCOM- 20190610
LR  - 20190613
IS  - 1932-2968 (Electronic)
IS  - 1932-2968 (Linking)
VI  - 12
IP  - 2
DP  - 2018 Mar
TI  - Machine Learning Methods to Predict Diabetes Complications.
PG  - 295-302
LID - 10.1177/1932296817706375 [doi]
AB  - One of the areas where Artificial Intelligence is having more impact is machine 
      learning, which develops algorithms able to learn patterns and decision rules 
      from data. Machine learning algorithms have been embedded into data mining 
      pipelines, which can combine them with classical statistical strategies, to 
      extract knowledge from data. Within the EU-funded MOSAIC project, a data mining 
      pipeline has been used to derive a set of predictive models of type 2 diabetes 
      mellitus (T2DM) complications based on electronic health record data of nearly 
      one thousand patients. Such pipeline comprises clinical center profiling, 
      predictive model targeting, predictive model construction and model validation. 
      After having dealt with missing data by means of random forest (RF) and having 
      applied suitable strategies to handle class imbalance, we have used Logistic 
      Regression with stepwise feature selection to predict the onset of retinopathy, 
      neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years 
      from the first visit at the Hospital Center for Diabetes (not from the 
      diagnosis). Considered variables are gender, age, time from diagnosis, body mass 
      index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final 
      models, tailored in accordance with the complications, provided an accuracy up to 
      0.838. Different variables were selected for each complication and time scenario, 
      leading to specialized models easy to translate to the clinical practice.
FAU - Dagliati, Arianna
AU  - Dagliati A
AD  - 1 Department of Electrical, Computer and Biomedical Engineering, University of 
      Pavia, Pavia, Italy.
AD  - 2 Centre for Health Technologies, University of Pavia, Pavia, Italy.
AD  - 3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
FAU - Marini, Simone
AU  - Marini S
AD  - 1 Department of Electrical, Computer and Biomedical Engineering, University of 
      Pavia, Pavia, Italy.
AD  - 2 Centre for Health Technologies, University of Pavia, Pavia, Italy.
AD  - 3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
FAU - Sacchi, Lucia
AU  - Sacchi L
AD  - 1 Department of Electrical, Computer and Biomedical Engineering, University of 
      Pavia, Pavia, Italy.
AD  - 2 Centre for Health Technologies, University of Pavia, Pavia, Italy.
FAU - Cogni, Giulia
AU  - Cogni G
AD  - 3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
FAU - Teliti, Marsida
AU  - Teliti M
AD  - 3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
FAU - Tibollo, Valentina
AU  - Tibollo V
AD  - 3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
FAU - De Cata, Pasquale
AU  - De Cata P
AD  - 3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
FAU - Chiovato, Luca
AU  - Chiovato L
AD  - 3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
FAU - Bellazzi, Riccardo
AU  - Bellazzi R
AD  - 1 Department of Electrical, Computer and Biomedical Engineering, University of 
      Pavia, Pavia, Italy.
AD  - 2 Centre for Health Technologies, University of Pavia, Pavia, Italy.
AD  - 3 IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Pavia, Italy.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20170512
PL  - United States
TA  - J Diabetes Sci Technol
JT  - Journal of diabetes science and technology
JID - 101306166
SB  - IM
MH  - *Algorithms
MH  - Data Mining/*methods
MH  - *Diabetes Complications
MH  - Diabetes Mellitus, Type 2/*complications
MH  - Humans
MH  - *Machine Learning
PMC - PMC5851210
OTO - NOTNLM
OT  - Data Mining
OT  - Machine Learning
OT  - Microvascular Complications
OT  - Risk Predictions
OT  - Type 2 Diabetes
COIS- Declaration of Conflicting Interests: The author(s) declared the following 
      potential conflicts of interest with respect to the research, authorship, and/or 
      publication of this article: RB is a shareholder in Biomeris s.r.l., which 
      designs software to support clinical research.
EDAT- 2017/05/13 06:00
MHDA- 2019/06/14 06:00
PMCR- 2018/05/12
CRDT- 2017/05/13 06:00
PHST- 2017/05/13 06:00 [pubmed]
PHST- 2019/06/14 06:00 [medline]
PHST- 2017/05/13 06:00 [entrez]
PHST- 2018/05/12 00:00 [pmc-release]
AID - 10.1177_1932296817706375 [pii]
AID - 10.1177/1932296817706375 [doi]
PST - ppublish
SO  - J Diabetes Sci Technol. 2018 Mar;12(2):295-302. doi: 10.1177/1932296817706375. 
      Epub 2017 May 12.