Objective We sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 selected complications. Accurate prediction of complications could help with more targeted measures that would prevent or slow down their development. Materials and Methods Experiments were conducted on the Healthcare Cost and Utilization Project State Inpatient Databases of California for the period of 2003 to 2011. Recurrent neural network (RNN) long short-term memory (LSTM) and RNN gated recurrent unit (GRU) deep learning methods were designed and compared with random forest and multilayer perceptron traditional models. Prediction accuracy of selected complications were compared on 3 settings corresponding to minimum number of hospitalizations between diabetes diagnosis and the diagnosis of complications. Results The diagnosis domain was used for experiments. The best results were achieved with RNN GRU model, followed by RNN LSTM model. The prediction accuracy achieved with RNN GRU model was between 73% (myocardial infarction) and 83% (chronic ischemic heart disease), while accuracy of traditional models was between 66% – 76%. Discussion The number of hospitalizations was an important factor for the prediction accuracy. Experiments with 4 hospitalizations achieved significantly better accuracy than with 2 hospitalizations. To achieve improved accuracy deep learning models required training on at least 1000 patients and accuracy significantly dropped if training datasets contained 500 patients. The prediction accuracy of complications decreases over time period. Considering individual complications, the best accuracy was achieved on depressive disorder and chronic ischemic heart disease. Conclusions The RNN GRU model was the best choice for electronic medical record type of data, based on the achieved results.

Event detection in electrical grids is a challenging problem for machine learning methods due to spatiotemporally nonstationary systems and the inability to automate event labeling in high-volume data such as PMU measurements. As a result, the existing historical event logs created manually do not correlate well with the corresponding PMU measurements due to scarce and temporally imprecise labels. Trying to overcome this problem by extending event logs to a complete set of labeled events is very costly and often infeasible. We focused on utilizing a transfer learning model to reduce the need for additional data labeling by leveraging some labeled data instances available from a small number of well-defined event detection task. To demonstrate the feasibility, we tested our approach on a large dataset collected by 38 PMUs from the Western Interconnection of the U.S.A. over two years. The model evaluation performed based on varying percentages of labeled source data corresponding to ~20-700 characteristic events on different sizes of time windows ranging from 2-seconds to 1-minute demonstrates that the developed method can significantly improve automated event detection based on PMU measurements when extensive labeling is costly or impossible to obtain. When compared to the state-of-the-art machine learning algorithms (unsupervised, semi-supervised, and supervised), the results show that the transfer learning method has significantly better performances when detecting events by learning from as low as 20 representative labeled data instances.

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In patients with diabetes, current models for predicting the risk of readmission within 30 days of hospital discharge vary in performance. We previously published the Diabetes Early Readmission Risk Indicator (DERRI TM), a logistic regression (LR) model based on 10 simple features with modest predictive performance (C-statistic 0.69). The current study aims to develop a more accurate model using deep learning on electronic health record (EHR) data. We electronically abstracted data from 36,563 patients with diabetes and at least 1 hospitalization at an urban, academic medical center between 7/1/2010 and 12/31/2020. One hospitalization per patient (index hospitalization) was randomly selected for analysis. A deep learning long short-term memory Recurrent Neural Network (RNN) was developed and compared to traditional linear and non-linear models: LR, AdaBoost, and Random Forest (RF). Models to predict unplanned, all-cause readmission were developed using demographics, vital signs, diagnostic and procedure codes, medications, laboratory tests, and administrative data as defined by the National Patient-Centered Clinical Research Network (PCORnet) Common Data Model. Unplanned readmissions were identified according to the Centers for Medicare and Medicaid (CMS) definition. A look-back time of 1 year before the index hospitalization and up to 60 previous ambulatory and hospital visits were used for learning and inference. Data dimensionality was reduced to 3,000 features by Singular Value Decomposition. The RNN model C-statistic is significantly greater than those of the traditional models (RNN 0.78, AdaBoost 0.71, RF 0.73, and LR 0.71, p<0. 0001). Likewise, the F1-score is numerically greater for the RNN model (RNN 0.76, AdaBoost 0.75, RF 0.75, LR 0.72). Direct comparison to the DERRI TM is limited by lack of EHR data on two of the component variables (employment status and zip code). The deep learning RNN model outperforms the DERRI TM and is based on more generalizable EHR data. This new model may provide the basis for a more useful readmission risk prediction tool for patients with diabetes. Deep learning models may outperform traditional models at predicting readmission risk in this population. Presentation: No date and time listed