Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks

Abstract

We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issue the most appropriate recommendation in terms of best point of care and time to treat. We use an attention-based convolutional neural network architecture trained on 600,000 doctor notes in German. We compare two approaches, one that uses the full text of the medical notes and one that uses only a selected list of medical entities extracted from the text. These approaches achieve 79% and 66% precision, respectively, but on a confidence threshold of 0.6, precision increases to 85% and 75%, respectively. In addition, a method to detect warning symptoms is implemented to render the classification task transparent from a medical perspective. The method is based on the learning of attention scores and a method of automatic validation using the same data.

Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs

“…The most similar works to ours are in (Yang et al, 2018;Li et al, 2017) which trains an endto-end convolutional network model to predict di-agnosis based on EMRs. Besides, Girardi et al (2018) improves the CNN model with the attention mechanism in automatic diagnosis. Moreover, Mullenbach et al (2018) studies a label-wise attention model to further improve the accuracy of diagnosis at the cost of more computation time.…”

“…CNN (Yang et al, 2018) concatenates CC, HPI and TR together before sending to the multi-channel CNN model. ACNN (Girardi et al, 2018)…”

“…There have been attempts to study automatic text-based diagnosis with Electronic Medical Record (EMR) documents integrated in the Hospital Information System (Mullenbach et al, 2018;Yang et al, 2018;Girardi et al, 2018). Basically, an EMR document is written by a doctor and consists of several sections that describe the illness of the patient.…”

“…1 Specifically, we propose to predict the main diagnosis based on the patient's current illness. Different from the previous works (Yang et al, 2018;Sha and Wang, 2017;Li et al, 2017;Girardi et al, 2018;Mullenbach et al, 2018) that solely rely on the end-to-end neural models, we propose to stack the Bayesian Network (BN) ensembles on top of Entity-aware Convolutional Neural Networks (ECNN) in automatic diagnosis, where ECNN improves the accuracy of the prediction and BN ensembles explain the prediction. The proposed framework attempts to bring some interpretability of the predictions by incorporating the knowledge encoded in the BN ensembles.…”

Ease.ml/ci and Ease.ml/meter in action

“…(Motivating Examples) This work is motivated by our experience of supporting developers, most of whom are not ML experts, in building a range of ML applications [4,7,14,15,16,17] over the last three years. Specifically, we are motivated by the struggles that our users face even after they have access to an AutoML system [11] with a scalable and efficient training engine [18] when training an ML model becomes easier, users start to seek systematic guidelines, which, if followed step by step, can lead to the right model.…”