An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification.

While deep learning methods have demonstrated performance comparable to human readers in tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by end users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant.

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data.

Background: Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration.

A Bayesian model for estimating multi-state disease progression.

A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy.

Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network.

Introduction: Identifying high-risk lung cancer individuals at an early disease stage is the most effective way of improving survival. The landmark National Lung Screening Trial (NLST) demonstrated the utility of low-dose computed tomography (LDCT) imaging to reduce mortality (relative to X-ray screening). As a result of the NLST and other studies, imaging-based lung cancer screening programs are now being implemented.

Improving Biomedical Signal Search Results in Big Data Case-Based Reasoning Environments.

Time series subsequence matching has importance in a variety of areas in healthcare informatics. These include case-based diagnosis and treatment as well as discovery of trends among patients. However, few medical systems employ subsequence matching due to high computational and memory complexities.

Consumers' Patient Portal Preferences and Health Literacy: A Survey Using Crowdsourcing.

Background: eHealth apps have the potential to meet the information needs of patient populations and improve health literacy rates. However, little work has been done to document perceived usability of portals and health literacy of specific topics.

Objective: Our aim was to establish a baseline of lung cancer health literacy and perceived portal usability.

A data-driven approach for quality assessment of radiologic interpretations.

Given the increasing emphasis on delivering high-quality, cost-efficient healthcare, improved methodologies are needed to measure the accuracy and utility of ordered diagnostic examinations in achieving the appropriate diagnosis. Here, we present a data-driven approach for performing automated quality assessment of radiologic interpretations using other clinical information (e.g.

Patient Portal Preferences: Perspectives on Imaging Information.

Patient portals have the potential to provide content that is specifically tailored to a patient's information needs based on diagnoses and other factors. In this work, we conducted a survey of 41 lung cancer patients at an outpatient lung cancer clinic at the medical center of the University of California Los Angeles, to gain insight into these perceived information needs and opinions on the design of a portal to fulfill them. We found that patients requested access to information related to diagnosis and imaging, with more than half of the patients reporting that they did not anticipate an increase in anxiety due to access to medical record information via a portal.

An integrated, ontology-driven approach to constructing observational databases for research.

The electronic health record (EHR) contains a diverse set of clinical observations that are captured as part of routine care, but the incomplete, inconsistent, and sometimes incorrect nature of clinical data poses significant impediments for its secondary use in retrospective studies or comparative effectiveness research. In this work, we describe an ontology-driven approach for extracting and analyzing data from the patient record in a longitudinal and continuous manner. We demonstrate how the ontology helps enforce consistent data representation, integrates phenotypes generated through analyses of available clinical data sources, and facilitates subsequent studies to identify clinical predictors for an outcome of interest.

Representing and extracting lung cancer study metadata: study objective and study design.

This paper describes the information retrieval step in Casama (Contextualized Semantic Maps), a project that summarizes and contextualizes current research papers on driver mutations in non-small cell lung cancer. Casama׳s representation of lung cancer studies aims to capture elements that will assist an end-user in retrieving studies and, importantly, judging their strength. This paper focuses on two types of study metadata: study objective and study design.

An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy.

Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists׳ diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules.

Leveraging domain knowledge to facilitate visual exploration of large population datasets.

Observational patient data provides an unprecedented opportunity to gleam new insights into diseases and assess patient quality of care, but a challenge lies in matching our ability to collect data with a comparable ability to understand and apply this information. Visual analytic techniques are promising as they permit the exploration and manipulation of complex datasets through a graphical user interface. Nevertheless, current visualization tools rely on users to manually configure which aspects of the dataset are shown and how they are presented.

Motivating the additional use of external validity: examining transportability in a model of glioblastoma multiforme.

Despite the growing ubiquity of data in the medical domain, it remains difficult to apply results from experimental and observational studies to additional populations suffering from the same disease. Many methods are employed for testing internal validity; yet limited effort is made in testing generalizability, or external validity. The development of disease models often suffers from this lack of validity testing and trained models frequently have worse performance on different populations, rendering them ineffective.

Data model for personalized patient health guidelines: an exploratory study.

Practitioner guidelines simultaneously provide broad overviews and in-depth details of disease. Written for experts, they are difficult for patients to understand, yet patients often use these guidelines as a source of information to help them to learn about their health. Using practitioner guidelines along with patient information needs and preferences, we created a method to design an information model for providing patients access to their personal health information, linked to individualized, relevant supporting information from guidelines within a patient portal.

Predicting discharge mortality after acute ischemic stroke using balanced data.

Several models have been developed to predict stroke outcomes (e.g., stroke mortality, patient dependence, etc.

Unifying acute stroke treatment guidelines for a Bayesian belief network.

With the large number of clinical practice guidelines available, there is an increasing need for a comprehensive unified model for acute ischemic stroke treatment to assist in clinical decision making. We present a unified treatment model derived through review of existing clinical practice guidelines, meta-analyses, and clinical trials. Using logic from the treatment model, a Bayesian belief network was defined and fitted to data from our institution's observational quality improvement database for acute stroke patients.

Imaging-based observational databases for clinical problem solving: the role of informatics.

Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving--from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed.

Imaging informatics for consumer health: towards a radiology patient portal.

Objective: With the increased routine use of advanced imaging in clinical diagnosis and treatment, it has become imperative to provide patients with a means to view and understand their imaging studies. We illustrate the feasibility of a patient portal that automatically structures and integrates radiology reports with corresponding imaging studies according to several information orientations tailored for the layperson.

Methods: The imaging patient portal is composed of an image processing module for the creation of a timeline that illustrates the progression of disease, a natural language processing module to extract salient concepts from radiology reports (73% accuracy, F1 score of 0.

High performance biomedical time series indexes using salient segmentation.

The advent of remote and wearable medical sensing has created a dire need for efficient medical time series databases. Wearable medical sensing devices provide continuous patient monitoring by various types of sensors and have the potential to create massive amounts of data. Therefore, time series databases must utilize highly optimized indexes in order to efficiently search and analyze stored data.

Comparing predictive models of glioblastoma multiforme built using multi-institutional and local data sources.

The growing amount of electronic data collected from patient care and clinical trials is motivating the creation of national repositories where multiple institutions share data about their patient cohorts. Such efforts aim to provide sufficient sample sizes for data mining and predictive modeling, ultimately improving treatment recommendations and patient outcome prediction. While these repositories offer the potential to improve our understanding of a disease, potential issues need to be addressed to ensure that multi-site data and resultant predictive models are useful to non-contributing institutions.

A Monte Carlo Approach to Biomedical Time Series Search.

Time series subsequence matching (or signal searching) has importance in a variety of areas in health care informatics. These areas include case-based diagnosis and treatment as well as the discovery of trends and correlations between data. Much of the traditional research in signal searching has focused on high dimensional -NN matching.

An Approach for Incorporating Context in Building Probabilistic Predictive Models.

With the increasing amount of information collected through clinical practice and scientific experimentation, a growing challenge is how to utilize available resources to construct predictive models to facilitate clinical decision making. Clinicians often have questions related to the treatment and outcome of a medical problem for individual patients; however, few tools exist that leverage the large collection of patient data and scientific knowledge to answer these questions. Without appropriate context, existing data that have been collected for a specific task may not be suitable for creating new models that answer different questions.

Aggregated Indexing of Biomedical Time Series Data.

Remote and wearable medical sensing has the potential to create very large and high dimensional datasets. Medical time series databases must be able to efficiently store, index, and mine these datasets to enable medical professionals to effectively analyze data collected from their patients. Conventional high dimensional indexing methods are a two stage process.

Energy-efficient context classification with dynamic sensor control.

Energy efficiency has been a longstanding design challenge for wearable sensor systems. It is especially crucial in continuous subject state monitoring due to the ongoing need for compact sizes and better sensors. This paper presents an energy-efficient classification algorithm, based on partially observable Markov decision process (POMDP).