Readily accessible risk model to predict in-hospital major adverse cardiac events in patients with acute myocardial infarction: a retrospective study of Chinese patients.

Objective: Rapid, accurate identification of patients with acute myocardial infarction (AMI) at high risk of in-hospital major adverse cardiac events (MACE) is critical for risk stratification and prompt management. This study aimed to develop a simple, accessible tool for predicting in-hospital MACE in Chinese patients with AMI.

Design: Retrospective review of deidentified medical records.

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.

Identification and external validation of IgA nephropathy patients benefiting from immunosuppression therapy.

Background: Although IgA nephropathy (IgAN), an immune-mediated disease with heterogeneous clinical and pathological phenotypes, is the most common glomerulonephritis worldwide, it remains unclear which IgAN patients benefit from immunosuppression (IS) therapy.

Methods: Clinical and pathological data from 4047 biopsy-proven IgAN patients from 24 renal centres in China were included. The derivation and validation cohorts were composed of 2058 and 1989 patients, respectively.

Network-Based Prediction of Major Adverse Cardiac Events in Acute Coronary Syndromes from Imbalanced EMR Data.

The low proportion and the rapid evolvement of major adverse cardiac events (MACE) present challenges for predicting MACE by machine learning models. In this paper, we propose a method to predict MACE from large-scale imbalanced EMR data by using a network-based one-class classifier. It only used the reliably known MACE samples to establish the hyperspherical model.

Facilitating Clinical Trial Recruitment by Recommending Cost-Efficient Medical Exams.

Clinical trials are key and essential processes for researchers to develop new treatments as well as evaluate their effectiveness and safety, whilst more than half of all clinical trials experience delays, which leads to a considerable amount of cost. In this paper, we present a cost-effective framework to reduce the time and monetary cost in the stage of recruiting and screening eligible clinical trial participants. By leveraging patients' observed conditions and the cost of medical examinations, the proposed framework uses collaborative filtering techniques to predict the utilized cost for the to-do medical examinations and then rank patients and medical examinations.

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.

Decentralized Privacy-Preserving Platform for Clinical Data Sharing and Analysis.

Collection and management of clinical data for administration and analysis is a time-consuming and complex task, especially when multiple data providers been involved. Even if people are willing to take on the burden for it, there is still no mature solution to protect data privacy for distributed data providers. Distributed ledger is an emerging technology that supports decentralized data sharing and management.

Outcome-Driven Clustering of Acute Coronary Syndrome Patients Using Multi-Task Neural Network with Attention.

Cluster analysis aims at separating patients into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. It is an important approach in data-driven disease classification and subtyping. Acute coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery blood flow, where disease classification would help to inform therapeutic strategies and provide prognostic insights.

Knowledge Learning Symbiosis for Developing Risk Prediction Models from Regional EHR Repositories.

Secondary use of regional EHR data suffers several problems, including data selection bias and limited data size caused by data incompleteness. Here, we propose knowledge learning symbiosis (KLS) as a framework to incorporate domain knowledge to address the problems and make better secondary use of EHR data. Under the framework, we introduce three main categories of methods: knowledge injection to input features, objective functions, and output labels, where knowledge-enhanced neural network (KENN) was first introduced to inject knowledge into objective functions.

Prediction and Risk Stratification of Kidney Outcomes in IgA Nephropathy.

Rationale & Objective: Immunoglobulin A nephropathy (IgAN) is common worldwide and has heterogeneous phenotypes. Predicting long-term outcomes and stratifying risk are important for clinical decision making and designing future clinical trials.

Study Design: Multicenter retrospective cohort study of 2,047 patients with IgAN.

Mining Disease-Symptom Relation from Massive Biomedical Literature and Its Application in Severe Disease Diagnosis.

Disease-symptom relation is an important biomedical relation that can be used for clinical decision support including building medical diagnostic systems. Here we present a study on mining disease-symptom relation from massive biomedical literature and constructing biomedical knowledge graph from the relation. From 15,970,134 MEDLINE/PubMed citation records, occurrences of 8,514 disease concepts from the Human Disease Ontology and 842 symptom concepts from the Symptom Ontology and their relation were analyzed and characterized.

Learning Doctors' Medicine Prescription Pattern for Chronic Disease Treatment by Mining Electronic Health Records: A Multi-Task Learning Approach.

Increasing learning ability from massive medical data and building learning methods robust to data quality issues are key factors toward building data-driven clinical decision support systems for medicine prescription decision support. Here, we attempted accordingly to address the factors using a multi-task neural network approach, benefiting from multi-task learning's advantage in modeling commonalities to increase learning performance and neural network's robustness to imprecise data. By mining electronic health record data, we learned medicine prescription patterns of multiple correlated antidiabetic agents in blood glucose control and antihypertensive drugs in blood pressure control scenarios.

Using Machine Learning Approaches for Emergency Room Visit Prediction Based on Electronic Health Record Data.

Emergency room(ER) visit prediction, especially whether visit ER or not and ER visit count, is crucial for hospitals to reasonably adapt resource allocation and` for patients to know future health state. Some existing studies have explored to use machine learning methods especially kinds of general linear model to settle down the task. But, in the clinical problems, there exist complex correlation between targets and features.

Comparative whole-genome sequence analysis of Mycobacterium tuberculosis isolated from tuberculous meningitis and pulmonary tuberculosis patients.

Tuberculous meningitis (TBM) is a severe form of tuberculosis with a high mortality rate. The factors associated with TBM pathogenesis are still unclear. Using comparative whole-genome sequence analysis we compared Mycobacterium tuberculosis (Mtb) isolates from cerebrospinal fluid of TBM cases (n = 73) with those from sputum of pulmonary tuberculosis (PulTB) patients (n = 220) from Thailand.

Gathering Real World Evidence with Cluster Analysis for Clinical Decision Support.

Clinical decision support systems are information technology systems that assist clinical decision-making tasks, which have been shown to enhance clinical performance. Cluster analysis, which groups similar patients together, aims to separate patient cases into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. Useful as it is, the application of cluster analysis in clinical decision support systems is less reported.

Applying Risk Models on Patients with Unknown Predictor Values: An Incremental Learning Approach.

In clinical practice, many patients may have unknown or missing values for some predictors, causing that the developed risk models cannot be directly applied on these patients. In this paper, we propose an incremental learning approach to apply a developed risk model on new patients with unknown predictor values, which imputes a patient's unknown values based on his/her k-nearest neighbors (k-NN) from the incremental population. We perform a real world case study by developing a risk prediction model of stroke for patients with Type 2 diabetes mellitus from EHR data, and incrementally applying the risk model on a sequence of new patients.

In silico region of difference (RD) analysis of Mycobacterium tuberculosis complex from sequence reads using RD-Analyzer.

Background: Whole-genome sequencing is increasingly used in clinical diagnosis of tuberculosis and study of Mycobacterium tuberculosis complex (MTC). MTC consists of several genetically homogenous mycobacteria species which can cause tuberculosis in humans and animals. Regions of difference (RDs) are commonly regarded as gold standard genetic markers for MTC classification.

Whole-Genome Sequencing Analysis of Serially Isolated Multi-Drug and Extensively Drug Resistant Mycobacterium tuberculosis from Thai Patients.

Multi-drug and extensively drug-resistant tuberculosis (MDR and XDR-TB) are problems that threaten public health worldwide. Only some genetic markers associated with drug-resistant TB are known. Whole-genome sequencing (WGS) is a promising tool for distinguishing between re-infection and persistent infection in isolates taken at different times from a single patient, but has not yet been applied in MDR and XDR-TB.

Tracking inter-institutional spread of NDM and identification of a novel NDM-positive plasmid, pSg1-NDM, using next-generation sequencing approaches.

Objectives: Owing to gene transposition and plasmid conjugation, New Delhi metallo-β-lactamase (NDM) is typically identified among varied Enterobacteriaceae species and STs. We used WGS to characterize the chromosomal and plasmid molecular epidemiology of NDM transmission involving four institutions in Singapore.

Methods: Thirty-three Enterobacteriaceae isolates (collection years 2010-14) were sequenced using short-read sequencing-by-synthesis and analysed.

Local transmission and global dissemination of New Delhi Metallo-Beta-Lactamase (NDM): a whole genome analysis.

Background: New Delhi metallo-β-lactamase (bla NDM), a plasmid-borne carbapenemase gene associated with significant mortality and severely limited treatment options, is of global public health concern as it is found in extremely diverse Gram-negative bacterial strains. This study thus aims to genetically characterize local and global spread of bla NDM.

Methods: To investigate local transmission patterns in the context of a single hospital, whole genome sequencing data of the first 11 bla NDM-positive bacteria isolated in a local hospital were analyzed to: (1) identify and compare bla NDM-positive plasmids; and (2) study the phylogenetic relationship of the bacteria chromosomes.

Reactive Infection Control Strategy for Control of New Delhi Metallo-β-Lactamase (NDM)-Producing Enterobacteriaceae Analyzed Using Whole-Genome Sequencing: Hits and Misses.

Genetically distinct isolates of New Delhi metallo-β-lactamase (NDM)-producing Enterobacteriaceae were identified from the clinical cultures of 6 patients. Screening of shared-ward contacts identified 2 additional NDM-positive patients. Phylogenetic analysis proved that 1 contact was a direct transmission while the other was unrelated to the index, suggesting hidden routes of transmission.

SpoTyping: fast and accurate in silico Mycobacterium spoligotyping from sequence reads.

SpoTyping is a fast and accurate program for in silico spoligotyping of Mycobacterium tuberculosis isolates from next-generation sequencing reads. This novel method achieves high accuracy for reads of both uniform and varying lengths, and is about 20 to 40 times faster than SpolPred. SpoTyping also integrates the function of producing a report summarizing associated epidemiological data from a global database of all isolates having the same spoligotype.

Draft Genome Sequence of a Multidrug-Resistant New Delhi Metallo-β-Lactamase-1 (NDM-1)-Producing Escherichia coli Isolate Obtained in Singapore.

We report the draft genome sequence of a New Delhi metallo-β-lactamase-1 (NDM-1)-positive Escherichia coli isolate obtained from a surgical patient. The assembled data indicate the presence of 3 multidrug resistance plasmids, 1 of which shares 100% identity with an NDM-1 plasmid isolated previously from a nearby hospital, suggesting possible local transmission.