Machine learning-based diagnosis for disseminated intravascular coagulation (DIC): Development, external validation, and comparison to scoring systems

• Published in: PloS one 2018-05, Vol.13 (5), p.e0195861-e0195861
• Main Authors: Yoon, Jihoon G, Heo, JoonNyung, Kim, Minkyu, Park, Yu Jin, Choi, Min Hyuk, Song, Jaewoo, Wyi, Kangsan, Kim, Hakbeen, Duchenne, Olivier, Eom, Soowon, Tsoy, Yury
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial intelligence; Artificial neural networks; Bioinformatics; Biology and Life Sciences; Biomarkers; Coagulation; Computer and Information Sciences; Diabetes; Diabetic retinopathy; Diagnosis; Diagnostic systems; Disseminated intravascular coagulation; Fibrin; Gene expression; Heart attacks; Hospitals; Laboratories; Learning algorithms; Learning theory; Machine learning; Mathematical models; Medical diagnosis; Medical personnel; Medicine; Medicine and Health Sciences; Neural networks; Optimization; Parameter robustness; Pattern recognition; Physicians; Sepsis
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0195861

The major challenge in the diagnosis of disseminated intravascular coagulation (DIC) comes from the lack of specific biomarkers, leading to developing composite scoring systems. DIC scores are simple and rapidly applicable. However, optimal fibrin-related markers and their cut-off values remain to be defined, requiring optimization for use. The aim of this study is to optimize the use of DIC-related parameters through machine learning (ML)-approach. Further, we evaluated whether this approach could provide a diagnostic value in DIC diagnosis. For this, 46 DIC-related parameters were investigated for both clinical findings and laboratory results. We retrospectively reviewed 656 DIC-suspected cases at an initial order for full DIC profile and labeled their evaluation results (Set 1; DIC, n = 228; non-DIC, n = 428). Several ML algorithms were tested, and an artificial neural network (ANN) model was established via independent training and testing using 32 selected parameters. This model was externally validated from a different hospital with 217 DIC-suspected cases (Set 2; DIC, n = 80; non-DIC, n = 137). The ANN model represented higher AUC values than the three scoring systems in both set 1 (ANN 0.981; ISTH 0.945; JMHW 0.943; and JAAM 0.928) and set 2 (AUC ANN 0.968; ISTH 0.946). Additionally, the relative importance of the 32 parameters was evaluated. Most parameters had contextual importance, however, their importance in ML-approach was different from the traditional scoring system. Our study demonstrates that ML could optimize the use of clinical parameters with robustness for DIC diagnosis. We believe that this approach could play a supportive role in physicians' medical decision by integrated into electrical health record system. Further prospective validation is required to assess the clinical consequence of ML-approach and their clinical benefit.