Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )最新文献

Clinical event sequences consist of thousands of clinical events that represent records of patient care in time. Developing accurate prediction models for such sequences is of a great importance for defining representations of a patient state and for improving patient care. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. Based on underlying clinical complications, each patient's sequence may consist of different sets of clinical events. However, population-based models learned from such sequences may not accurately predict patient-specific dynamics of event sequences. To address the problem, we develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update.

Central venous pressure (CVP) is the blood pressure in the venae cavae, near the right atrium of the heart. This signal waveform is commonly collected in clinical settings, and yet there has been limited discussion of using this data for detecting arrhythmia and other cardiac events. In this paper, we develop a signal processing and feature engineering pipeline for CVP waveform analysis. Through a case study on pediatric junctional ectopic tachycardia (JET), we show that our extracted CVP features reliably detect JET with comparable results to the more commonly used electrocardiogram (ECG) features. This machine learning pipeline can thus improve the clinical diagnosis and ICU monitoring of arrhythmia. It also corroborates and complements the ECG-based diagnosis, especially when the ECG measurements are unavailable or corrupted.

Low-prior targets are common among many important clinical events, which introduces the challenge of having enough data to support learning of their predictive models. Many prior works have addressed this problem by first building a general patient-state representation model, and then adapting it to a new low-prior prediction target. In this schema, there is potential for the predictive performance to be hindered by the misalignment between the general patient-state model and the target task. To overcome this challenge, we propose a new method that simultaneously optimizes a shared model through multi-task learning of both the low-prior supervised target and general purpose patient-state representation (GPSR). More specifically, our method improves prediction performance of a low-prior task by jointly optimizing a shared model that combines the loss of the target event and a broad range of generic clinical events. We study the approach in the context of Recurrent Neural Networks (RNNs). Through extensive experiments on multiple clinical event targets using MIMIC-III [8] data, we show that the inclusion of general patient-state representation tasks during model training improves the prediction of individual low-prior targets.

Qualitative influence statements are often provided a priori to guide learning; we answer a challenging reverse task and automatically extract them from a learned probabilistic model. We apply our Qualitative Knowledge Extraction method toward early prediction of gestational diabetes on clinical study data. Our empirical results demonstrate that the extracted rules are both interpretable and valid.

Electronic health records (EHRs) contain both ordered and unordered chronologies of clinical events that occur during a patient encounter. However, during data preprocessing steps, many predictive models impose a predefined order on unordered clinical events sets (e.g., alphabetical, natural order from the chart, etc.), which is potentially incompatible with the temporal nature of the sequence and predictive task. To address this issue, we propose DPSS, which seeks to capture each patient's clinical event records as sequences of event sets. For each clinical event set, we assume that the predictive model should be invariant to the order of concurrent events and thus employ a novel permutation sampling mechanism. This paper evaluates the use of this permuted sampling method given different data-driven models for predicting a heart failure (HF) diagnosis in subsequent patient visits. Experimental results using the MIMIC-III dataset show that the permutation sampling mechanism offers improved discriminative power based on the area under the receiver operating curve (AUROC) and precision-recall curve (pr-AUC) metrics as HF diagnosis prediction becomes more robust to different data ordering schemes.

In this paper we develop and study machine learning based models based on latent semantic indexing capable of automatically assigning diagnoses and diagnostic categories to patients based on structured clinical data in their Electronic Health record (EHR). These models can be either used for automatic coding of patient's diagnoses from structured EHR data at the time of discharge, or for supporting dynamic diagnosis and summarization of the patient condition. We study the performance of our diagnostic models on MIMIC-III EHR data.