Disease network delineates the disease progression profile of cardiovascular diseases.

OBJECTIVE: As Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real world evidence. In this study, a graphical disease network, named progressive cardiovascular disease network (progCDN), was built to delineate the progression profiles of cardiovascular diseases (CVD). MATERIALS AND METHODS: The EHR data of 14.3 million patients with CVD diagnoses were collected for building disease network and further analysis. We applied a new designed method, progression rates (PR), to calculate the progression relationship among different diagnoses. Based on the disease network outcome, 23 disease progression pair were selected to screen for salient features. RESULTS: The network depicted the dominant diseases in CVD development, such as the heart failure and coronary arteriosclerosis. Novel progression relationships were also discovered, such as the progression path from long QT syndrome to major depression. In addition, three age-group progCDNs identified a series of age-associated disease progression paths and important successor diseases with age bias. Furthermore, a list of important features with sufficient abundance and high correlation was extracted for building disease risk models. DISCUSSION: The PR method designed for identifying the progression relationship could be widely applied in any EHR database due to its flexibility and robust functionality. Meanwhile, researchers could use the progCDN network to validate or explore novel disease relationships in real world data. CONCLUSION: The first-time interrogation of such a huge CVD patients cohort enabled us to explore the general and age-specific disease progression patterns in CVD development.

3-Hydroxybutyrate ameliorates sepsis-associated acute lung injury by promoting autophagy through the activation of GPR109α in macrophages.

BACKGROUND: Sepsis is a systemic inflammatory disease caused by multiple pathogens, with the most commonly affected organ being the lung. 3-Hydroxybutyrate plays a protective role in inflammatory diseases through autophagy promotion; however, the exact mechanism remains unexplored. METHOD: Our study used the MIMIC-III database to construct a cohort of ICU sepsis patients and figure out the correlation between the level of ketone bodies and clinical prognosis in septic patients. In vivo and in vitro models of sepsis were used to reveal the role and mechanism of 3-hydroxybutyrate in sepsis-associated acute lung injury (sepsis-associated ALI). RESULT: Herein, we observed a strong correlation between the levels of ketone bodies and clinical prognosis in patients with sepsis identified using the MIMIC- III database. In addition, exogenous 3-hydroxybutyrate supplementation improved the survival rate of CLP-induced sepsis in mice by promoting autophagy. Furthermore, 3-hydroxybutyrate treatment protected against sepsis-induced lung damage. We explored the mechanism underlying these effects. The results indicated that 3-hydroxybutyrate upregulates autophagy levels by promoting the transfer of transcription factor EB (TFEB) to the macrophage nucleus in a G-protein-coupled receptor 109 alpha (GPR109α) dependent manner, upregulating the transcriptional level of ultraviolet radiation resistant associated gene (UVRAG) and increasing the formation of autophagic lysosomes. CONCLUSION: 3-Hydroxybutyrate can serve as a beneficial therapy for sepsis-associated ALI through the upregulation of autophagy. These results may provide a basis for the development of promising therapeutic strategies for sepsis-associated ALI.

A clinical research analytics toolkit for cohort study.

This paper presents a clinical informatics toolkit that can assist physicians to conduct cohort studies effectively and efficiently. The toolkit has three key features: 1) support of procedures defined in epidemiology, 2) recommendation of statistical methods in data analysis, and 3) automatic generation of research reports. On one hand, our system can help physicians control research quality by leveraging the integrated knowledge of epidemiology and medical statistics; on the other hand, it can improve productivity by reducing the complexities for physicians during their cohort studies.

A Word Graph-based Method for Disease Topic Identification in Biomedical Literature.

An important task in biomedical literature precise search is to identify paper describing a certain disease. The tradi- tional topic identification approaches based on neural network can be used to recognize the disease topic of literature. To achieve better performance, we propose a novel word graph-based method for disease topic identification in this paper. Word graphs are constructed from literature title and abstract. Graph features are extracted and used for disease topic classification using a logistic regression or random forest classifier. Experiment results showed the word graph features outperformed disease mention frequency by a large margin. Our approach achieved better perfor- mance in identifying disease topic compared to hierarchical attention networks, which is a deep learning approach for document classification. We also demonstrated the use of the proposed method in identifying the disease topic in an application scenario.

A Method to Accelerate and Visualize Iterative Clinical Paper Searching.

Clinical paper searching is a major task for clinical researchers to collect authoritative and up-to-date evidences to support their research works and clinical practices. Currently, this task needs huge amount of labor work. Researchers usually spend a lot of time searching on the online repository and iterating many times to get the final paper list. Systematic review is a special case, in which the paper searching process is a critical step. To address this challenge, this paper introduces a method to streamline the iterative paper searching process. It automatically selects the most probably matched papers, and then generates new search strategy. All the intermediate results are visualized based on the paper citation graph. It assembles technologies such as PageRank and Topic-based clustering to accelerate the paper searching tasks. The precision, recall, and execution time of the proposed method are then evaluated by comparing with published systematic reviews.

Developing Healthcare Data Analytics APPs with Open Data Science Tools.

Recent advances in big data analytics provide more flexible, efficient, and open tools for researchers to gain insight from healthcare data. Whilst many tools require researchers to develop programs with programming languages like Python, R and so on, which is not a skill set grasped by many researchers in the healthcare data analytics area. To make data science more approachable, we explored existing tools and developed a practice that can help data scientists convert existing analytics pipelines to user-friendly analytics APPs with rich interactions and features of real-time analysis. With this practice, data scientists can develop customized analytics pipelines as APPs in Jupyter Notebook and disseminate them to other researchers easily, and researchers can benefit from the shared notebook to perform analysis tasks or reproduce research results much more easily.

Personal Health Self-Management in a Data Perspective.

Along with the growth of numbers of patients with chronic diseases, personal health self-management becomes critical. The heterogeneity of self-management requirements makes the detail design and implementation of self-management program a non-trivial work. In this paper we address the problem with the Personal Health Advisor (PHA) application by introducing a personal health data flow mechanism, as well as modules including personal health risk assessment, similar patients profiling, and health question answering.

Feature++: Automatic Feature Construction for Clinical Data Analysis.

With the rapid growth of clinical data and knowledge, feature construction for clinical analysis becomes increasingly important and challenging. Given a clinical dataset with up to hundreds or thousands of columns, the traditional manual feature construction process is usually too labour intensive to generate a full spectrum of features with potential values. As a result, advanced large-scale data analysis technologies, such as feature selection for predictive modelling, cannot be fully utilized for clinical data analysis. In this paper, we propose an automatic feature construction framework for clinical data analysis, namely, Feature++. It leverages available public knowledge to understand the semantics of the clinical data, and is able to integrate external data sources to automatically construct new features based on predefined rules and clinical knowledge. We demonstrate the effectiveness of Feature++ in a typical predictive modelling use case with a public clinical dataset, and the results suggest that the proposed approach is able to fulfil typical feature construction tasks with minimal dataset specific configurations, so that more accurate models can be obtained from various clinical datasets in a more efficient way.

Accelerate Healthcare Data Analytics: An Agile Practice to Perform Collaborative and Reproducible Analyses.

Recent advances in cloud computing and machine learning made it more convenient for researchers to gain insights from massive healthcare data, while performing analyses on healthcare data in current practice still lacks efficiency for researchers. What's more, collaborating among different researchers and sharing analysis results are challenging issues. In this paper, we developed a practice to make analytics process collaborative and analysis results reproducible by exploiting and extending Jupyter Notebook. After applying this practice in our use cases, we can perform analyses and deliver results with less efforts in shorter time comparing to our previous practice.

A Decision Fusion Framework for Treatment Recommendation Systems.

Treatment recommendation is a nontrivial task--it requires not only domain knowledge from evidence-based medicine, but also data insights from descriptive, predictive and prescriptive analysis. A single treatment recommendation system is usually trained or modeled with a limited (size or quality) source. This paper proposes a decision fusion framework, combining both knowledge-driven and data-driven decision engines for treatment recommendation. End users (e.g. using the clinician workstation or mobile apps) could have a comprehensive view of various engines' opinions, as well as the final decision after fusion. For implementation, we leverage several well-known fusion algorithms, such as decision templates and meta classifiers (of logistic and SVM, etc.). Using an outcome-driven evaluation metric, we compare the fusion engine with base engines, and our experimental results show that decision fusion is a promising way towards a more valuable treatment recommendation.

Mining Temporal and Data Constraints Associated with Outcomes for Care Pathways.

A care/clinical pathway defines a standardized care process for a specific patient group, which consists of clinical goals, activities, data attributes, and constraints describing temporal dependencies and data preconditions of the activities. The constraints, which are the key elements to represent the best practices, are difficult to define due to the variations in different regions and populations. In this paper, we propose an approach to discover temporal and data constraints that are correlated with clinical outcomes for care pathways. For each activity of interest, we extract a set of associated event-condition-action (ECA) rules from electronic medical records (EMR) to represent the temporal and data preconditions of the activity, by using our modified association rule mining algorithm. Then the best ECA rule that is significantly more likely to lead to a positive outcome is translated into the constraint on the activity. The approach has been applied to real-world EMR, and discovered meaningful constraints for different groups of type 2 diabetes patients, which can be used to provide decision support during individual patient care.

Outcome-driven Evaluation Metrics for Treatment Recommendation Systems.

Treatment recommendation systems aim to providing clinical decision supports, e.g. with integration of Computerized Physician Order Entry (CPOE). One of the most significant issue is the quality of recommendations which needs to be quantified, before getting the acceptance from physicians. In computer science, such evaluations are typically performed by applying appropriate metrics that provides a comparison of different systems. However, a big challenge for evaluating treatment recommendation systems is that ground truth is only partially observed. In this paper, we propose an outcome-driven evaluation methodology, and present five metrics (i.e. precision, recall, accuracy, relative risk and odds ratio) with highlight of their statistic meanings in clinical context. The experimental results are based on the comparison of two well-developed treatment recommendation systems (one is knowledge-driven and based on clinical practice guidelines, while the other is data-driven and based on patient similarity analysis), using our proposed evaluation metrics. As a conclusion, physicians are less prone to comply with clinical guidelines, but once following guideline recommendations, it is much more likely to get good outcomes than not following.

Synthesizing analytic evidence to refine care pathways.

Care pathways play significant roles in delivering evidence-based and coordinated care to patients with specific conditions. In order to put care pathways into practice, clinical institutions always need to adapt them based on local care settings so that the best local practices can be incorporated and used to develop refined pathways. However, it is knowledge-intensive and error-prone to incorporate various analytic insights from local data sets. In order to assist care pathway developers in working effectively and efficiently, we propose to automatically synthesize the analytical evidences derived from multiple analysis methods, and recommend modelling operations accordingly to derive a refined care pathway for a specific patient cohort. We validated our method by adapting a Congestive Heart Failure (CHF) Ambulatory Care Pathway for patients with additional condition of COPD through synthesizing the results of variation analysis and frequent pattern mining against patient records.

Analysis of care pathway variation patterns in patient records.

A care/clinical pathway (CP) is a standardized care process where temporal and data constraints of clinical activities are defined to ensure quality of care. In actual care practice, various situations of compliance and non-compliance with CPs can be observed. Analysis of these CP variation patterns (CPVPs) can help improve care quality and enhance decision support. In this paper, we propose an automatic method to detect CPVPs in electronic medical records (EMR), and statistically examine their correlation with patient outcomes. From each CP constraint, we first derive a CPVP tree, where each pattern is represented using first-order linear temporal logic and translated into a Büchi automaton for pattern detection. Then we identify the CPVPs that are evidently correlated with a patient outcome by examining the odds ratios. The method has been applied to a CP for congestive heart failure and real world EMR to demonstrate the effectiveness.

Embracing case management for computerization of care pathways.

The computerization of care pathways (CPs) has drawn considerable attention, for improving quality of health care and reducing costs. A well-known big challenge of implementing CPs is their flexibility and ad hoc variations in execution of clinical tasks. We observe that case management suits well to address this problem, and this paper proposes a CMMN-based CP model, where CMMN (Case Management Model and Notation) is becoming an industry standard. Via an experimental experience on modelling CHF (congestive heart failure) ambulatory CP, we illustrate that the usage of case management paves the way to popularize CPs, particularly for its quick deployment and execution in industrial products.

Care pathway workbench: evidence harmonization from guideline and data.

Care pathways (CPs) as a means of healthcare quality control are getting increasing attention due to widespread recognition in the healthcare industry of the need for well coordinated, evidence based and personalized care. To keep the promise, CPs require continuous refinement in order to stay up to date with regard to both clinical guidelines and data-driven insights from real world practices. There is therefore a strong demand for a unified platform that allows harmonization of evidence coming from multiple sources. In this paper we describe Care Pathway Workbench, a web-based platform that enables users to build and continuously improve Case Management Model and Notation based CPs by harmonizing evidences from guidelines and patient data. To illustrate the functionalities, we describe how a CHF (Congestive Heart Failure) Ambulatory Care Pathway can be developed using this workbench by first extracting key elements from widely accepted guidelines for CHF management, then incorporating evidence mined from clinical practice data, and finally transforming and exporting the resulting CP model to a care management product.

Automatic variance analysis of multistage care pathways.

A care pathway (CP) is a standardized process that consists of multiple care stages, clinical activities and their relations, aimed at ensuring and enhancing the quality of care. However, actual care may deviate from the planned CP, and analysis of these deviations can help clinicians refine the CP and reduce medical errors. In this paper, we propose a CP variance analysis method to automatically identify the deviations between actual patient traces in electronic medical records (EMR) and a multistage CP. As the care stage information is usually unavailable in EMR, we first align every trace with the CP using a hidden Markov model. From the aligned traces, we report three types of deviations for every care stage: additional activities, absent activities and violated constraints, which are identified by using the techniques of temporal logic and binomial tests. The method has been applied to a CP for the management of congestive heart failure and real world EMR, providing meaningful evidence for the further improvement of care quality.

A novel approach to generating CER hypotheses based on mining clinical data.

Comparative effectiveness research (CER) is a scientific method of investigating the effectiveness of alternative intervention methods. In a CER study, clinical researchers typically start with a CER hypothesis, and aim to evaluate it by applying a series of medical statistical methods. Traditionally, the CER hypotheses are defined manually by clinical researchers. This makes the task of hypothesis generation very time-consuming and the quality of hypothesis heavily dependent on the researchers' skills. Recently, with more electronic medical data being collected, it is highly promising to apply the computerized method for discovering CER hypotheses from clinical data sets. In this poster, we proposes a novel approach to automatically generating CER hypotheses based on mining clinical data, and presents a case study showing that the approach can facilitate clinical researchers to identify potentially valuable hypotheses and eventually define high quality CER studies.