RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data

“Extensive attention-based deep models have been developed for images [14, 21, 36, 48], audios and videos [23, 49], machine translations [1, 30, 46].”

“For the multiclass prediction task of forecasting length of stay, we evaluate the models in terms of Cohen’s Kappa [10] and Accuracy.”

“Intervention-based guidance Gt[1] For predicting output yt”

“Extensive attention-based deep models have been developed for images [14, 21, 36, 48], audios and videos [23, 49], machine translations [1, 30, 46].”

“Extensive attention-based deep models have been developed for images [14, 21, 36, 48], audios and videos [23, 49], machine translations [1, 30, 46].”

“We demonstrate our model on the MIMIC-III Waveform Database Matched Subset 2 [22], a publicly available multimodal EHR data released last year on PhysioNet [15].”

“Extensive attention-based deep models have been developed for images [14, 21, 36, 48], audios and videos [23, 49], machine translations [1, 30, 46].”

“…data integrations could be solved by a combination of refining high-density inputs into low-density representations [40] and filling sparse inputs with smoothed missing values [3], advanced methods are still in need for extracting different levels of information from the integrated multimodel data.”

“diagnoses, procedures, medications) to learn vector representations of patients or medical concepts [5, 8, 9, 34]; recurrent neural networks (RNNs) like LSTM and GRU are used to capture the sequential manner of EHR data and predict future disease diagnosis or intervention codes [28, 39], onset of heart disease [4] or…”

“P where W βh , w βa , and b β are the parameters learnt in MLP. 3.2.2 Guidance-based Attention.”

“A more detailed survey of recent deep learning approaches for EHR analysis can be found here [43].”

“Extensive attention-based deep models have been developed for images [14, 21, 36, 48], audios and videos [23, 49], machine translations [1, 30, 46].”

“A reverse time attention mechanism was proposed for identifying influential patient hospital visits and medical codes in predictions of heart failure [7].”

“Prior work [40] has shown CNN can perform well in classifying 30-second ECG signals; for embedding the 1-hour ECG signals in our experiment, we initially trained CNN on 30-second segmentations and train another CNN or RNN on top of the 1-hour concatenated outputs, but these didn’t outperform a…”

“org/physiobank/database/mimic3wdb/matched/ 3The two prediction tasks were originally defined in [16], a recently public multitask benchmark on MIMIC-III Clinical Database; but the data were not matched with the MIMIC-III Waveform Database.”

“There are many recent deep learning successes in modeling such event datasets.[4, 5, 8, 9, 12, 28].”

“There are many recent deep learning successes in modeling such event datasets.[4, 5, 8, 9, 12, 28].”

“Extensive attention-based deep models have been developed for images [14, 21, 36, 48], audios and videos [23, 49], machine translations [1, 30, 46].”

“diagnoses, procedures, medications) to learn vector representations of patients or medical concepts [5, 8, 9, 34]; recurrent neural networks (RNNs) like LSTM and GRU are used to capture the sequential manner of EHR data and predict future disease diagnosis or intervention codes [28, 39], onset of heart disease [4] or…”

“In addition to use discrete structural EHR data, other works target on unstructured data like clinical notes, the main purpose of which are to extract structured medical concepts [13, 19, 20, 29, 31].”

“…data integrations could be solved by a combination of refining high-density inputs into low-density representations [40] and filling sparse inputs with smoothed missing values [3], advanced methods are still in need for extracting different levels of information from the integrated multimodel data.”

“ne learning methods. In addition to use discrete structural EHR data, other works target on unstructured data like clinical notes, the main purpose of which are to extract structured medical concepts [13, 19, 20, 29, 31]. Deep learning on patient monitoring data. Traditional signal processing algorithms were widely us…”

“Traditional signal processing algorithms were widely used for analyzing patient monitoringdata,suchasP-QRS-Tdetection[44]orRRinterval/heart ratevariability(HRV)extraction 1 for ECG signal processing.”

“There are many recent deep learning successes in modeling such event datasets.[4, 5, 8, 9, 12, 28].”

“There are many recent deep learning successes in modeling such event datasets.[4, 5, 8, 9, 12, 28].”

“Later on, graph based attention [6] and hierarchical attention [42] were adapted for incorporating external information like medical ontologies into the deep EHR models; different deep architectures like bidirectional RNN [32], GRU [24] were also applied for improving predictive accuracy.”

“diagnoses, procedures, medications) to learn vector representations of patients or medical concepts [5, 8, 9, 34]; recurrent neural networks (RNNs) like LSTM and GRU are used to capture the sequential manner of EHR data and predict future disease diagnosis or intervention codes [28, 39], onset of heart disease [4] or…”

“In addition to use discrete structural EHR data, other works target on unstructured data like clinical notes, the main purpose of which are to extract structured medical concepts [13, 19, 20, 29, 31].”

“In addition to use discrete structural EHR data, other works target on unstructured data like clinical notes, the main purpose of which are to extract structured medical concepts [13, 19, 20, 29, 31].”

“In addition to use discrete structural EHR data, other works target on unstructured data like clinical notes, the main purpose of which are to extract structured medical concepts [13, 19, 20, 29, 31].”

“diagnoses, procedures, medications) to learn vector representations of patients or medical concepts [5, 8, 9, 34]; recurrent neural networks (RNNs) like LSTM and GRU are used to capture the sequential manner of EHR data and predict future disease diagnosis or intervention codes [28, 39], onset of heart disease [4] or…”

“For example, variations of convolutional neural network (CNN) and restricted boltzmann machine (RBM) are applied on structural clinical data (e.g. diagnoses, procedures, medications) to learn vector representations of patients or medical concepts [5, 8, 9, 34]; recurrent neural networks (RNNs) like LSTM and GRU are us…”

“nchmark codebase, several teams used our dataset generation pipeline (fully or partially). In particular, the pipeline was used for in-hospital mortality prediction [38–43], decompensation prediction [44], length-of-stay prediction [42, 44, 45], phenotyping [38, 39, 46] and readmission prediction [47]. Additionally, a…”

“sification (e.g.,patients with paroxysmal atrial fibrillation or Healthy [55]), multi-class classification (e.g., detection of acute cognitive stress detection [31], decompensation of patients detection [131]) and multi-task classification (e.g., detection of prevalent hypertension, sleep apnea, and diabetes [89]). 2) Ann…”

“This area remains relatively unexplored in clinical ML explainability literature with a few exceptions (Xu et al., 2018; Choi et al., 2016; Yang et al., 2018) focused primarily on attention based and sensitivity analysis mechanisms in deep learning (Vaswani et al.”