Phenotyping is the process of distinguishing groups of patients to identify different types of disease progression. A recent trend employs low-rank matrix and tensor factorization methods for their capability of dealing with multi-modal, heterogeneous, and missing data. Symptom quantification is crucial for understanding patient experiences in inflammatory bowel disease, especially in conditions such as ulcerative colitis (UC). However, patient-reported symptoms are typically noisy, subjective, and significantly more sparse than other data types. For this reason, they are usually not included in phenotyping and other machine learning methods. This paper explores the application of computational phenotyping to leverage Patient-Reported Outcomes (PROs) using a novel supervised coupled matrix-tensor factorization (SCMTF) method, which integrates temporal PROs and temporal labs with static features to predict medication persistence in ulcerative colitis. This is the first tensor-based method that is both supervised and coupled, it is the first application to the UC domain, and the first application to PROs. We use a deep learning framework that makes the model flexible and easy to train. The proposed method allows us to handle the large amount of missing data in the PROs. The best model predicts changes in medication 8 and 20 months in the future with AUCs of 0.853 and 0.803 on the test set respectively. We derive interpretable phenotypes consisting of static features and temporal features (including their temporal patterns). We show that low-rank matrix and tensor based phenotyping can be successfully applied to the UC domain and to highly missing PRO data. We identify phenotypes useful to predict medication persistence - these phenotypes include several symptom variables, showing that PROs contain relevant infromation that is usually discarded.
Non-negative tensor factorization has been shown a practical solution to automatically discover phenotypes from the electronic health records (EHR) with minimal human supervision. Such methods generally require an input tensor describing the inter-modal interactions to be pre-established; however, the correspondence between different modalities (e.g., correspondence between medications and diagnoses) can often be missing in practice. Although heuristic methods can be applied to estimate them, they inevitably introduce errors, and leads to sub-optimal phenotype quality. This is particularly important for patients with complex health conditions (e.g., in critical care) as multiple diagnoses and medications are simultaneously present in the records. To alleviate this problem and discover phenotypes from EHR with unobserved inter-modal correspondence, we propose the collective hidden interaction tensor factorization (cHITF) to infer the correspondence between multiple modalities jointly with the phenotype discovery. We assume that the observed matrix for each modality is marginalization of the unobserved inter-modal correspondence, which are reconstructed by maximizing the likelihood of the observed matrices. Extensive experiments conducted on the real-world MIMIC-III dataset demonstrate that cHITF effectively infers clinically meaningful inter-modal correspondence, discovers phenotypes that are more clinically relevant and diverse, and achieves better predictive performance compared with a number of state-of-the-art computational phenotyping models.
With large volumes of health care data comes the research area of computational phenotyping, making use of techniques such as machine learning to describe illnesses and other clinical concepts from the data itself. The "traditional" approach of using supervised learning relies on a domain expert, and has two main limitations: requiring skilled humans to supply correct labels limits its scalability and accuracy, and relying on existing clinical descriptions limits the sorts of patterns that can be found. For instance, it may fail to acknowledge that a disease treated as a single condition may really have several subtypes with different phenotypes, as seems to be the case with asthma and heart disease. Some recent papers cite successes instead using unsupervised learning. This shows great potential for finding patterns in Electronic Health Records that would otherwise be hidden and that can lead to greater understanding of conditions and treatments. This work implements a method derived strongly from Lasko et al., but implements it in Apache Spark and Python and generalizes it to laboratory time-series data in MIMIC-III. It is released as an open-source tool for exploration, analysis, and visualization, available at https://github.com/Hodapp87/mimic3_phenotyping
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We propose strong linear and neural baselines for all four tasks and evaluate the effect of deep supervision, multitask training and data-specific architectural modifications on the performance of neural models.