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Diabetes mellitus, commonly known as diabetes, is a chronic disease that often results in multiple complications. Risk prediction of diabetes complications is critical for healthcare professionals to design personalized treatment plans for patients in diabetes care for improved outcomes. In this paper, focusing on Type 2 diabetes mellitus (T2DM), we study the risk of developing complications after the initial T2DM diagnosis from longitudinal patient records. We propose a novel multi-task learning approach to simultaneously model multiple complications where each task corresponds to the risk modeling of one complication. Specifically, the proposed method strategically captures the relationships (1) between the risks of multiple T2DM complications, (2) between different risk factors, and (3) between the risk factor selection patterns, which assumes similar complications have similar contributing risk factors. The method uses coefficient shrinkage to identify an informative subset of risk factors from high-dimensional data, and uses a hierarchical Bayesian framework to allow domain knowledge to be incorporated as priors. The proposed method is favorable for healthcare applications because in addition to improved prediction performance, relationships among the different risks and among risk factors are also identified. Extensive experimental results on a large electronic medical claims database show that the proposed method outperforms state-of-the-art models by a significant margin. Furthermore, we show that the risk associations learned and the risk factors identified lead to meaningful clinical insights.

The ability to identify patients who are likely to have an adverse outcome is an essential component of good clinical care. Therefore, predictive risk stratification models play an important role in clinical decision making. Determining whether a given predictive model is suitable for clinical use usually involves evaluating the model’s performance on large patient datasets using standard statistical measures of success (e.g., accuracy, discriminatory ability). However, as these metrics correspond to averages over patients who have a range of different characteristics, it is difficult to discern whether an individual prediction on a given patient should be trusted using these measures alone. In this paper, we introduce a new method for identifying patient subgroups where a predictive model is expected to be poor, thereby highlighting when a given prediction is misleading and should not be trusted. The resulting “unreliability score” can be computed for any clinical risk model and is suitable in the setting of large class imbalance, a situation often encountered in healthcare settings. Using data from more than 40,000 patients in the Global Registry of Acute Coronary Events (GRACE), we demonstrate that patients with high unreliability scores form a subgroup in which the predictive model has both decreased accuracy and decreased discriminatory ability.

Diabetes is a chronic diseases that affects nearly half a billion people around the globe, and is almost always associated with a number of complications, including kidney failure, blindness, stroke, and heart attack. An important step towards improved diabetes care is to accurately predict the risk of diabetes complications and to identify the corresponding risk factors associated with the onset of each complication. In this paper, we study the problem of risk prediction and outcome-specific risk factor identification from readily available patient medical record data. We adopt a private-shared multi-task learning (MTL) model, which jointly models multiple complications with each task corresponding to the risk modeling of one complication. The MTL formulation not only boosts prediction performance but also enables identification of outcome-specific risk factors. Specifically, we decompose the coefficient matrix, in which each column (vector) corresponds to the coefficient of one complication risk model, into a shared component and an outcome-specific private component. The shared component is assumed to be low-rank to capture the relationships among complications in terms of overall diabetes health condition. The private component is assumed to be non-overlapping and sparse so that they are discriminative among the different complication outcomes. Further, the shared component and the private component for the same complication are assumed to be orthogonal. Extensive experimental results on a type 2 diabetes cohort extracted from a large electronic medical claims database show that the proposed method outperforms baseline models by a significant margin. Also the identified outcome-specific risk factors provide meaningful clinical insights. The results demonstrate that simultaneously modeling multiple risks through MTL not only improves prediction performance but also enables identification of outcome-specific risk factors.

In recent years, the rapid advances in Artificial Intelligence (AI) techniques along with an ever-increasing availability of healthcare data have made many novel analyses possible. Significant successes have been observed in a wide range of tasks such as next diagnosis prediction, AKI prediction, adverse event predictions including mortality and unexpected hospital re-admissions. However, there has been limited adoption and use in the clinical practice of these methods due to their black-box nature. A significant amount of research is currently focused on making such methods more interpretable or to make post-hoc explanations more accessible. However, most of this work is done at a very low level and as a result, may not have a direct impact at the point-of-care. This tutorial will provide an overview of the landscape of different approaches that have been developed for explainability in healthcare. Specifically, we will present the problem of explainability as it pertains to various personas involved in healthcare viz. data scientists, clinical researchers, and clinicians. We will chart out the requirements for such personas and present an overview of the different approaches that can address such needs. We will also walk-through several use-cases for such approaches. In this process, we will provide a brief introduction to explainability, charting its different dimensions as well as covering some relevant interpretability methods spanning such dimensions. We will touch upon some practical guides for explainability and provide a brief survey of open source tools such as the IBM AI Explainability 360 Open Source Toolkit.

Disease progression models are important computational tools in healthcare and are used for tasks such as improving disease understanding, informing drug discovery, and aiding in patient management. Although many algorithms for time series modeling exist, healthcare applications face particular challenges such as small datasets, medication effects, disease heterogeneity, and a desire for personalized predictions. In this work, we present a disease progression model that addresses these needs by proposing a probabilistic time-series model that captures individualized disease states, personalized medication effects, disease-state medication effects, or any combination thereof. The model builds on the framework of an input-output hidden Markov model where the parameters are learned using a structured variational approximation. To demonstrate the utility of the algorithm, we apply it to both synthetic and real-world datasets. In the synthetic case, we demonstrate the benefits afforded by the proposed model as compared to standard techniques. In the real-world cases, we use two Parkinson’s disease datasets to show improved predictive performance when ground truth is available and clinically relevant insights that are not revealed via classic Markov models when ground truth is not available.

Electronic Health Records (EHRs) have been widely used in healthcare studies recently, such as the analyses for patient diagnostic outcome and understanding of disease progression. EHR is a treasure for researchers who conduct the Real-World study to discovering Real-World Evidence (RWE). In this paper, we design an end-to-end learning system for disease states discovery based on a data-driven strategy. A large-scale proprietary EHR data mart containing about 55 million patients with over 20 billion data records is used for data extraction and analysis. Given a disease of interest, researchers could easily obtain the hidden disease states. Once our system were operational, biomedical researchers could get the results for downstream analyses such as disease prediction, drug design and outcome analyses.

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision , a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

© 2019 IEEE. Although oversampling methods are widely used to deal with class imbalance problems, most only utilize observed samples in the minority class and ignore the rich information available in the majority class. In this work, we use an oversampling method that leverages information in both the majority and minority classes to mitigate the class imbalance problem. Experimental results on two clinical datasets with highly imbalanced outcomes demonstrate that prediction models can be significantly improved using data obtained from this oversampling method when the number of minority class samples is very small.

In contrast to the one-size-fits-all approach to medicine, precision medicine will allow targeted prescriptions based on the specific profile of the patient thereby avoiding adverse reactions and ineffective but expensive treatments. Longitudinal observational data such as Electronic Health Records (EHRs) have become an emerging data source for personalized medicine. In this paper, we propose a unified computational framework, called PerDREP, to predict the unique response patterns of each individual patient from EHR data. PerDREP models individual responses of each patient to the drug exposure by introducing a linear system to account for patients' heterogeneity, and incorporates a patient similarity graph as a network regularization. We formulate PerDREP as a convex optimization problem and develop an iterative gradient descent method to solve it. In the experiments, we identify the effect of drugs on Glycated hemoglobin test results. The experimental results provide evidence that the proposed method is not only more accurate than state-of-the-art methods, but is also able to automatically cluster patients into multiple coherent groups, thus paving the way for personalized medicine.

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups., Comment: to appear at IEEE Transactions on Visualization and Computer Graphics

In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings. However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.

Objective Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: (1) cohort construction, (2) feature construction, (3) cross-validation, (4) feature selection, and (5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data. Methods To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which (1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, (2) schedules the tasks in a topological ordering of the graph, and (3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported. Results We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3 h in parallel compared to 9 days if running sequentially. Conclusion This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines that are specialized for health data researchers.

Understanding predictive models, in terms of interpreting and identifying actionable insights, is a challenging task. Often the importance of a feature in a model is only a rough estimate condensed into one number. However, our research goes beyond these naive estimates through the design and implementation of an interactive visual analytics system, Prospector. By providing interactive partial dependence diagnostics, data scientists can understand how features affect the prediction overall. In addition, our support for localized inspection allows data scientists to understand how and why specific datapoints are predicted as they are, as well as support for tweaking feature values and seeing how the prediction responds. Our system is then evaluated using a case study involving a team of data scientists improving predictive models for detecting the onset of diabetes from electronic medical records.

In this paper, we examine the application of various grouping techniques to help improve the efficiency and reduce the costs involved in an electronic discovery process. Specifically, we create coherent groups of email documents which characterize either a syntactic theme, a semantic theme or an email thread. All such grouped documents can be reviewed together leading to a faster and more consistent review of documents. Syntactic grouping of emails is based on near duplicate detection whereas semantic grouping is based on identifying concepts in the email content using information extraction. Email thread detection is achieved using a combination of segmentation and near duplicate detection. We present experimental results on the Enron corpus that suggest that these approaches can significantly reduce the review time and show that high precision and recall in identifying the groups can be achieved. We also describe how these techniques are integrated into the IBM eDiscovery Analyzer product offering.

As the volume and variety of healthcare-related data continue to grow, the analysis and use of this data will increasingly depend on the ability to appropriately collect, curate, and integrate disparate data from many different sources including user-generated health data. We describe our approach to, and highlight our experiences with, the development of a robust data curation process that supports healthcare analytics. The process consists of the following steps: collection, understanding, validation, cleaning, integration, enrichment, and storage. It has been successfully applied to the processing of a variety of data types including clinical data from electronic health records and observational studies, genomic data, microbiome data, self-reported data from surveys, and self-tracked data from wearables from more than 600 subjects. The curated data have been used to support a number of healthcare analytic applications, including descriptive analytics, data visualization, patient stratification, and predictive modeling.

This paper explores approaches to the problem of spoken document retrieval (SDR), which is the task of automatically indexing and then retrieving relevant items from a large collection of recorded speech messages in response to a user specified natural language text query. We investigate the use of subword unit representations for SDR as an alternative to words generated by either keyword spotting or continuous speech recognition. In this study, we explore the space of possible subword units to determine the complexity of the subword units needed for SDR; describe the development and application of a phonetic recognition system to extract subword units from the speech signal; examine the behavior and sensitivity of the subword units to speech recognition errors; measure the effect of speech recognition performance on retrieval performance; and investigate a number of robust indexing and retrieval methods in an effort to improve retrieval performance in the presence of speech recognition errors. We find that with the appropriate subword units, it is possible to achieve performance comparable to that of text-based word units if the underlying phonetic units are recognized correctly. In the presence of speech recognition errors, retrieval performance degrades to 60% of the clean reference level. This performance can be improved by 23% (to 74% of the clean reference) with use of the robust methods.

In this paper we present a novel probabilistic information retrieval model that scores documents based on the relative change in the document likelihoods, expressed as the ratio of the conditional probability of the document given the query and the prior probability of the document before the query is specified. The document likelihoods are computed using statistical language modeling techniques and the model parameters are estimated automatically and dynamically for each query to optimize well-specified (maximum likelihood) objective functions. We derive the basic retrieval model, describe the details of the model, and present some extensions to the model including a method to perform automatic feedback. Development experiments are performed using the TREC-6 ad hoc text retrieval task and performance is measured using the TREC-7 ad hoc task. Official evaluation results on the 1999 TREC-8 ad hoc task are also reported. The performance results demonstrate that the model is competitive with current state-of-the-art retrieval approaches.

Topic identification (TID) is the automatic classification of speech messages into one of a known set of possible topics. The TID task can be view as having three principal components: 1) event generation, 2) keyword event selection, and 3) topic modeling. Using data from the Switchboard corpus, the authors present experimental results for various approaches to the TID problem and compare the relative effectiveness of each. In addition, they examine the effect of keyword set size on identification accuracy and gauge the loss in performance when mismatched topic modeling and keyword selection schemes are used. >

This paper describes the development and application of a phonetic recognition system to the task of spoken document retrieval. The recognizer is used to generate phonetic transcriptions of the speech messages which are then processed to produce subword unit representations for indexing and retrieval. Subword units are used as an alternative to words units generated by either keyword spotting or word recognition. We first investigate the use of different acoustic and language models in the speech recognizer in an effort to improve phonetic recognition performance. Then we examine a variety of subword unit indexing terms and measure their ability to perform effective spoken document retrieval. Finally, we look at some simple robust indexing and retrieval methods that take into account the characteristics of the recognition errors in an attempt to improve retrieval performance.

Speech recognition of conversational speech is a difficult task. The performance levels on the Switchboard corpus had been in the vicinity of 70% word error rate. In this paper, we describe the results of applying a variety of modifications to our speech recognition system and we show their impact on improving the performance on conversational speech. These modifications include the use of more complex models, trigram language models, and cross-word triphone models. We also show the effect of using additional acoustic training on the recognition performance. Finally, we present an approach to dealing with the abundance of short words, and examine how the variable speaking rate found in conversational speech impacts on the performance. Currently, the level of performance is at the vicinity of 50% error, a significant improvement over recent levels.

Investigates the fusion of different information sources, with the goal of improving performance on spoken document retrieval (SDR) tasks. In particular, we explore the use of multiple transcriptions from different automatic speech recognizers, the combination of different types of subword unit indexing terms, and the combination of word- and subword-based units. To perform the retrieval, we use a novel probabilistic information retrieval model which retrieves documents based on maximum likelihood ratio scores. Experiments on the 1998 TREC-7 SDR task show that the use of these different information fusion approaches can result in significantly improved retrieval performance.

The basic motivation for employing trajectory models for speech recognition is that sequences of speech features are statistically dependent and that the effective and efficient modeling of the speech process will incorporate this dependency. In our previous work we presented an approach to modeling the speech process with trajectories. In this paper we continue our development of parametric trajectory models for speech recognition. We extend our models to include time-varying covariances and describe our approach for defining a metric between speech segments based on trajectory models; it is important in developing mixture models of trajectories.

The authors present a segmental speech model that explicitly models the dynamics in a variable-duration speech segment by using a time-varying trajectory model of the speech features in the segment. Each speech segment is represented by a set of statistics which includes a time-varying trajectory, a residual error covariance around the trajectory, and the number of frames in the segment. These statistics replace the frames in the segment and become the data that are modeled by either HMMs (hidden Markov models) or mixture models. This segment model is used to develop a secondary processing algorithm that rescores putative events hypothesized by a primary HMM word spotter to try to improve performance by discriminating true keywords from false alarms. This algorithm is evaluated on a keyword spotting task using the Road Rally Database, and performance is shown to improve significantly over that of the primary word spotter. The segmental model is also used on a TIMIT vowel classification task to evaluate its modeling capability. >

It is demonstrated that using the proposed probabilistic vector mapping algorithm as a feature preprocessor results in robust performance levels across a wide range of signal-to-noise (SNR) levels. The authors evaluate the algorithm using an HMM word spotting system trained with clean cepstral features and tested with vector mapped noisy cepstra. In addition to robust behavior, it is shown that using the vector mapper results in performance that equals or exceeds that of using matched training and testing. For example, with 10-dB SNR testing speech, word spotting performance with the vector mapping preprocessor and clean training is 15% better than matching training with 10-dB SNR speech. A mapping algorithm based on the method of radial basis functions (RBFs) for mapping noisy speech features into the space of clean features is presented. Performance using this RBF mapper is shown to be comparable to that of the vector mapper. >