PMID- 35158071
OWN - NLM
STAT- MEDLINE
DCOM- 20220316
LR  - 20220316
IS  - 1532-0480 (Electronic)
IS  - 1532-0464 (Linking)
VI  - 127
DP  - 2022 Mar
TI  - Three-stage intelligent support of clinical decision making for higher trust, 
      validity, and explainability.
PG  - 104013
LID - S1532-0464(22)00029-6 [pii]
LID - 10.1016/j.jbi.2022.104013 [doi]
AB  - The paper presents a conceptual framework for building practically applicable 
      clinical decision support systems (CDSSs) using data-driven (DD) predictive 
      modelling. With the proposed framework we have tried to fill the gap between 
      experimental CDSS implementations widely covered in the literature and solutions 
      acceptable by physicians in daily practice. The framework is based on a 
      three-stage approach where DD model definition is accomplished with practical 
      norms referencing (scales, clinical recommendations, etc.) and explanation of the 
      prediction results and recommendations. The approach is aimed at increasing the 
      applicability of CDSSs based on DD models through better integration into 
      decision context and higher explainability. The approach has been implemented in 
      software solutions and tested within a case study in type 2 diabetes mellitus 
      (T2DM) prediction, enabling us to improve known clinical scales (such as 
      FINDRISK) while keeping the problem-specific reasoning interface similar to 
      existing applications. A survey was performed to assess and investigate the 
      acceptance level and provide insights on the influences of the introduced 
      framework's element on physicians' behavior.
CI  - Copyright (c) 2022 Elsevier Inc. All rights reserved.
FAU - Kovalchuk, Sergey V
AU  - Kovalchuk SV
AD  - ITMO University, Saint Petersburg, Russia. Electronic address: kovalchuk@itmo.ru.
FAU - Kopanitsa, Georgy D
AU  - Kopanitsa GD
AD  - ITMO University, Saint Petersburg, Russia. Electronic address: kopanitsa@itmo.ru.
FAU - Derevitskii, Ilia V
AU  - Derevitskii IV
AD  - ITMO University, Saint Petersburg, Russia. Electronic address: 
      ivderevitckii@itmo.ru.
FAU - Matveev, Georgy A
AU  - Matveev GA
AD  - Almazov National Medical Research Centre, Institute of Endocrinology, Saint 
      Petersburg, Russia. Electronic address: matveev_ga@almazovcentre.ru.
FAU - Savitskaya, Daria A
AU  - Savitskaya DA
AD  - Almazov National Medical Research Centre, Institute of Endocrinology, Saint 
      Petersburg, Russia. Electronic address: savitskaya_da@almazovcentre.ru.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220212
PL  - United States
TA  - J Biomed Inform
JT  - Journal of biomedical informatics
JID - 100970413
SB  - IM
MH  - Clinical Decision-Making
MH  - *Decision Support Systems, Clinical
MH  - *Diabetes Mellitus, Type 2/diagnosis
MH  - Humans
MH  - *Physicians
MH  - Trust
OTO - NOTNLM
OT  - Clinical decision support
OT  - Diabetes mellitus
OT  - Interpretable machine learning
OT  - Machine learning
OT  - Personalized medicine
OT  - Predictive modeling
EDAT- 2022/02/15 06:00
MHDA- 2022/03/17 06:00
CRDT- 2022/02/14 20:11
PHST- 2021/03/04 00:00 [received]
PHST- 2022/01/03 00:00 [revised]
PHST- 2022/02/02 00:00 [accepted]
PHST- 2022/02/15 06:00 [pubmed]
PHST- 2022/03/17 06:00 [medline]
PHST- 2022/02/14 20:11 [entrez]
AID - S1532-0464(22)00029-6 [pii]
AID - 10.1016/j.jbi.2022.104013 [doi]
PST - ppublish
SO  - J Biomed Inform. 2022 Mar;127:104013. doi: 10.1016/j.jbi.2022.104013. Epub 2022 
      Feb 12.