Improving palliative care with deep learning

Abstract

Abstract-Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life . We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.

Scalable Machine Learning for Predicting At-Risk Profiles Upon Hospital Admission

“…Our prediction system is thus much more flexible and general, since it can be used with patients admitted for the first time with no known history. Second, [30] and [31] use deep neural network models whereas the current article presents results obtained with more classical models that: (i) perform faster on CPU machines, and (ii) are more intelligible: they offer more insights into the factors that influence the predictions, as emphasized in [32]. For instance, the weights computed by our models are stable and intelligible, allowing further interpretation (and fix if needed) by clinicians.…”

“…Closely related works in terms of research objectives are the independent works performed simultaneously found in Avati et al [30], Rajkomar et al [31]. Authors develop a model for predicting all-cause mortality of patients from EHR data.…”

“…The research objectives pursued are similar, and the methodology and the data used exhibit some similarities but with several important differences. First, [30] and [31] require "longitudinal" data in the sense that for making a prediction, their systems require historical data for the patient: it relies on historical observation windows and several observation slices: [30] uses 4 observation slices and [31] uses data from 24 hours before hospital admission (and also after admission). Both systems require the knowledge of the patient situation at several moments in time.…”

“…Last but not least, to be able to operate without the need for historical data of patients, our models were trained with much more "transversal" data. [30] studies a population of 221 284 patients (216 221 hospitalizations were studied in [31]), whereas our models are built from the automated analysis of more than 1.2 million of patients.…”

Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning

“…On the other hand, recently, deep networks have attracted widespread attention, mainly by defeating alternative machine learning methods such as support vector machines in numerous critical applications such as classifying Alzheimer's disease [11], classifying AD/MCI patients [12], and improving palliative care [13]. While support vector machines are still popular techniques within the machine learning community [4] [14], the family of deep learning techniques are gaining considerable attention [15].…”