Physicians' Perception of Alternative Displays of Clinical Research Evidence for Clinical Decision Support - A Study with Case Vignettes.
J Biomed Inform. 2017 Jan 13;:
Authors: Slager SL, Weir CR, Kim H, Mostafa J, Del Fiol G
OBJECTIVE: To design alternate information displays that present summaries of clinical trial results to clinicians to support decision-making; and to compare the displays according to efficacy and acceptability.
METHODS: A 6-between (information display presentation order) by 3-within (display type) factorial design. Two alternate displays were designed based on Information Foraging theory: a narrative summary that reduces the content to a few sentences; and a table format that structures the display according to the PICO (Population, Intervention, Comparison, Outcome) framework. The designs were compared with the summary display format available in PubMed. Physicians were asked to review five clinical studies retrieved for a case vignette; and were presented with the three display formats. Participants were asked to rate their experience with each of the information displays according to a Likert scale questionnaire.
RESULTS: Twenty physicians completed the study. Overall, participants rated the table display more highly than either the text summary or PubMed's summary format (5.9 vs. 5.4 vs. 3.9 on a scale between 1 [strongly disagree] and 7 [strongly agree]). Usefulness ratings of seven pieces of information, i.e. patient population, patient age range, sample size, study arm, primary outcome, results of primary outcome, and conclusion, were high (average across all items = 4.71 on a 1 to 5 scale, with 1=not at all useful and 5=very useful). Study arm, primary outcome, and conclusion scored the highest (4.9, 4.85, and 4.85 respectively). Participants suggested additional details such as rate of adverse effects.
CONCLUSION: The table format reduced physicians' perceived cognitive effort when quickly reviewing clinical trial information and was more favorably received by physicians than the narrative summary or PubMed's summary format display.
PMID: 28089913 [PubMed - as supplied by publisher]
Hiding clinical information in medical images: A new high capacity and reversible data hiding technique.
J Biomed Inform. 2017 Jan 12;:
Authors: Parah SA, Ahad F, Sheikh JA, Bhat GM
A new high capacity and reversible data hiding scheme for e-healthcare applications has been presented in this paper. Pixel to Block (PTB) conversion technique has been used as an effective and computationally efficient alternative to interpolation for the cover image generation to ensure reversibility of medical images. A fragile watermark and Block Checksum (computed for each 4 × 4 block) have been embedded in the cover image for facilitating tamper detection and tamper localization, and hence content authentication at receiver. The EPR, watermark data and checksum data has been embedded using Intermediate Significant Bit Substitution (ISBS) to avoid commonly used LSB removal/replacement attack. Non-Linear dynamics of chaos have been put to use for encrypting the Electronic Patient record (EPR)/clinical data and watermark data for improving the security of data embedded. The scheme has been evaluated for perceptual imperceptibility and tamper detection capability by subjecting it to various image processing and geometric attacks. Experimental results reveal that the proposed system besides being completely reversible is capable of providing high quality watermarked images for fairly high payload. Further, it has been observed that the proposed technique is able to detect and localise the tamper. A comparison of the observed results with that of some state-of-art schemes show that our scheme performs better.
PMID: 28089912 [PubMed - as supplied by publisher]
Navigation in the Electronic Health Record: A Review of the Safety and Usability Literature.
J Biomed Inform. 2017 Jan 11;:
Authors: Roman LC, Ancker JS, Johnson SB, Senathirajah Y
OBJECTIVE: Inefficient navigation in electronic health records has been shown to increase users' cognitive load, which may increase potential for errors, reduce efficiency, and increase fatigue. However, navigation has received insufficient recognition and attention in the electronic health record (EHR) literature as an independent construct and contributor to overall usability. Our aims in this literature review were to (1) assess the prevalence of navigation-related topics within the EHR usability and safety research literature, (2) categorize types of navigation actions within the EHR, (3) capture relationships between these navigation actions and usability principles, and (4) collect terms and concepts related to EHR navigation. Our goal was to improve access to navigation-related research in usability.
MATERIALS AND METHODS: We applied scoping literature review search methods with the assistance of a reference librarian to identify articles published since 1996 that reported evaluation of the usability or safety of an EHR user interface via user test, analytic methods, or inspection methods. The 4,336 references collected from MEDLINE, EMBASE, Engineering Village, and expert referrals were de-duplicated and screened for relevance, and navigation-related concepts were abstracted from the 21 articles eligible for review using a standard abstraction form.
RESULTS: Of the 21 eligible articles, 20 (95%) mentioned navigation in results and discussion of usability evaluations. Navigation between pages of the EHR was the more frequently documented type of navigation (86%) compared to navigation within a single page (14%). Navigation actions (e.g., scrolling through a medication list) were frequently linked to specific usability heuristic violations, among which flexibility and efficiency of use, recognition rather than recall, and error prevention were most common.
DISCUSSION: Discussion of navigation was prevalent in results across all types of evaluation methods among the articles reviewed. Navigating between multiple screens was frequently identified as a usability barrier. The lack of standard terminology created some challenges to identifying and comparing articles.
CONCLUSION: We observed that usability researchers are frequently capturing navigation-related issues even in articles that did not explicitly state navigation as a focus. Capturing and synthesizing the literature on navigation is challenging because of the lack of uniform vocabulary. Navigation is a potential target for normative recommendations for improved interaction design for safer systems. Future research in this domain, including development of normative recommendations for usability design and evaluation, will be facilitated by development of a standard terminology for describing EHR navigation.
PMID: 28088527 [PubMed - as supplied by publisher]
Accuracy is in the Eyes of the Pathologist: The Visual Interpretive Process and Diagnostic Accuracy with Digital Whole Slide Images.
J Biomed Inform. 2017 Jan 10;:
Authors: Brunyé TT, Mercan E, Weaver DL, Elmore JG
Digital whole slide imaging is an increasingly common medium in pathology, with application to education, telemedicine, and rendering second opinions. It has also made it possible to use eye tracking devices to explore the dynamic visual inspection and interpretation of histopathological features of tissue while pathologists review cases. Using whole slide images, the present study examined how a pathologist's diagnosis is influenced by fixed case-level factors, their prior clinical experience, and their patterns of visual inspection. Participating pathologists interpreted one of two test sets, each containing 12 digital whole slide images of breast biopsy specimens. Cases represented four diagnostic categories as determined via expert consensus: benign without atypia, atypia, ductal carcinoma in situ (DCIS), and invasive cancer. Each case included one or more regions of interest (ROIs) previously determined as of critical diagnostic importance. During pathologist interpretation we tracked eye movements, viewer tool behavior (zooming, panning), and interpretation time. Models were built using logistic and linear regression with generalized estimating equations, testing whether variables at the level of the pathologists, cases, and visual interpretive behavior would independently and/or interactively predict diagnostic accuracy and efficiency. Diagnostic accuracy varied as a function of case consensus diagnosis, replicating earlier research. As would be expected, benign cases tended to elicit false positives, and atypia, DCIS, and invasive cases tended to elicit false negatives. Pathologist experience levels, case consensus diagnosis, case difficulty, eye fixation durations, and the extent to which pathologists' eyes fixated within versus outside of diagnostic ROIs, all independently or interactively predicted diagnostic accuracy. Higher zooming behavior predicted a tendency to over-interpret benign and atypia cases, but not DCIS cases. Efficiency was not predicted by pathologist- or visual search-level variables. Results provide new insights into the medical interpretive process and demonstrate the complex interactions between pathologists and cases that guide diagnostic decision-making. Implications for training, clinical practice, and computer-aided decision aids are considered.
PMID: 28087402 [PubMed - as supplied by publisher]
A new method of content based medical image retrieval and its applications to CT imaging sign retrieval.
J Biomed Inform. 2017 Jan 06;:
Authors: Ma L, Liu X, Gao Y, Zhao Y, Zhao X, Zhou C
This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse the semantic and visual similarities between the query image and each image in the database as their pairwise similarities. Then, we construct a weighted graph whose nodes represent the images and edges measure their pairwise similarities. By using the shortest path algorithm over the weighted graph, we obtain a new similarity measure, context-sensitive similarity measure, between the query image and each database image to complete the retrieval process. Actually, we use the fused pairwise similarity to narrow down the semantic gap for obtaining a more accurate pairwise similarity measure, and spread it on the intrinsic data manifold to achieve the context-sensitive similarity for a better retrieval performance. The proposed method has been evaluated on the retrieval of the Common CT Imaging Signs of Lung Diseases (CISLs) and achieved not only better retrieval results but also the satisfactory computation efficiency.
PMID: 28069515 [PubMed - as supplied by publisher]
MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records.
J Biomed Inform. 2017 Jan 05;:
Authors: Huang Z, Chan TM, Dong W
OBJECTIVES: Major adverse cardiac events (MACE) of acute coronary syndrome (ACS) often occur suddenly resulting in high mortality and morbidity. Recently, the rapid development of electronic medical records (EMR) provides the opportunity to utilize the potential of EMR to improve the performance of MACE prediction. In this study, we present a novel data-mining based approach specialized for MACE prediction from a large volume of EMR data.
METHODS: The proposed approach presents a new classification algorithm by applying both over-sampling and under-sampling on minority-class and majority-class samples, respectively, and integrating the resampling strategy into a boosting framework so that it can effectively handle imbalance of MACE of ACS patients analogous to domain practice. The method learns a new and stronger MACE prediction model each iteration from a more difficult subset of EMR data with wrongly predicted MACEs of ACS patients by a previous weak model.
RESULTS: We verify the effectiveness of the proposed approach on a clinical dataset containing 2,930 ACS patient samples with 268 feature types. While the imbalanced ratio does not seem extreme (22%), MACE prediction targets pose great challenge to traditional methods. As these methods degenerate dramatically with increasing imbalanced ratios, the performance of our approach for predicting MACE remains robust and reaches 0.672 in terms of AUC. On average, the proposed approach improves the performance of MACE prediction by 4.8%, 4.5%, 8.6% and 4.8% over the standard SVM, Adaboost, SMOTE, and the conventional GRACE risk scoring system for MACE prediction, respectively.
CONCLUSIONS: We consider that the proposed iterative boosting approach has demonstrated great potential to meet the challenge of MACE prediction for ACS patients using a large volume of EMR.
PMID: 28065840 [PubMed - as supplied by publisher]
SemanticSCo: a Platform to Support the Semantic Composition of Services for Gene Expression Analysis.
J Biomed Inform. 2017 Jan 02;:
Authors: Guardia GD, Pires LF, da Silva EG, de Farias CR
Gene expression studies often require the combined use of a number of analysis tools. However, manual integration of analysis tools can be cumbersome and error prone. To support a higher level of automation in the integration process, efforts have been made in the biomedical domain towards the development of semantic web services and supporting composition environments. Yet, most environments consider only the execution of simple service behaviours and requires users to focus on technical details of the composition process. We propose a novel approach to the semantic composition of gene expression analysis services that addresses the shortcomings of the existing solutions. Our approach includes an architecture designed to support the service composition process for gene expression analysis, and a flexible strategy for the (semi) automatic composition of semantic web services. Finally, we implement a supporting platform called SemanticSCo to realize the proposed composition approach and demonstrate its functionality by successfully reproducing a microarray study documented in the literature. The SemanticSCo platform provides support for the composition of RESTful web services semantically annotated using SAWSDL. Our platform also supports the definition of constraints/conditions regarding the order in which service operations should be invoked, thus enabling the definition of complex service behaviours. Our proposed solution for semantic web service composition takes into account the requirements of different stakeholders and addresses all phases of the service composition process. It also provides support for the definition of analysis workflows at a high-level of abstraction, thus enabling users to focus on biological research issues rather than on the technical details of the composition process. The SemanticSCo source code is available at https://github.com/usplssb/SemanticSCo.
PMID: 28057566 [PubMed - as supplied by publisher]
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Development and Validation of a Continuously Age-Adjusted Measure of Patient Condition for Hospitalized Children Using the Electronic Medical Record.
J Biomed Inform. 2017 Jan 02;:
Authors: Rothman MJ, Tepas JJ, Nowalk AJ, Levin JE, Rimar JM, Marchetti A, Hsiao AL
Awareness of a patient's clinical status during hospitalization is a primary responsibility for hospital providers. One tool to assess status is the Rothman Index (RI), a validated measure of patient condition for adults, based on empirically derived relationships between 1-year post-discharge mortality and each of 26 clinical measurements available in the electronic medical record. However, such an approach cannot be used for pediatrics, where the relationships between risk and clinical variables are distinct functions of patient age, and sufficient 1-year mortality data for each age group simply does not exist. We report the development and validation of a new methodology to use adult mortality data to generate continuously age-adjusted acuity scores for pediatrics. Clinical data were extracted from EMRs at three pediatric hospitals covering 105,470 inpatient visits over a 3-year period. The RI input variable set was used as a starting point for the development of the pediatric Rothman Index (pRI). Age-dependence of continuous variables was determined by plotting mean values versus age. For variables determined to be age-dependent, polynomial functions of mean value and mean standard deviation versus age were constructed. Mean values and standard deviations for adult RI excess risk curves were separately estimated. Based on the "find the center of the channel" hypothesis, univariate pediatric risk was then computed by applying a z-score transform to adult mean and standard deviation values based on polynomial pediatric mean and standard deviation functions. Multivariate pediatric risk is estimated as the sum of univariate risk. Other age adjustments for categorical variables were also employed. Age-specific pediatric excess risk functions were compared to age-specific expert-derived functions and to in-hospital mortality. AUC for 24-hour mortality and pRI scores prior to unplanned ICU transfers were computed. Age-adjusted risk functions correlated well with similar functions in Bedside PEWS and PAWS. Pediatric nursing data correlated well with risk as measured by mortality odds ratios. AUC for pRI for 24-hour mortality was 0.93 (0.92, 0.94), 0.93 (0.93, 0.93) and 0.95 (0.95, 0.95) at the three pediatric hospitals. Unplanned ICU transfers correlated with lower pRI scores. Moreover, pRI scores declined prior to such events. A new methodology to continuously age-adjust patient acuity provides a tool to facilitate timely identification of physiologic deterioration in hospitalized children.
PMID: 28057565 [PubMed - as supplied by publisher]
Temporal Electronic phenotyping by mining Careflows of Breast Cancer Patients.
J Biomed Inform. 2017 Jan 02;:
Authors: Dagliati A, Sacchi L, Zambelli A, Tibollo V, Pavesi L, Holmes JH, Bellazzi R
In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent healthcare histories (careflows) in a population of patients. This approach was applied to an integrated data repository containing clinical and administrative data of more than 4,000 breast cancer patients. We used the mined histories to identify sub-cohorts of patients grouped according to healthcare activities pathways, then we characterized these sub-cohorts with clinical data. In this way, we were able to perform temporal electronic phenotyping of electronic health records (EHR) data.
PMID: 28057564 [PubMed - as supplied by publisher]
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Machine-learned cluster identification in high-dimensional data.
J Biomed Inform. 2016 Dec 28;:
Authors: Ultsch A, Lötsch J
BACKGROUND: High-dimensional biomedical data are frequently clustered to identify subgroup structures pointing at distinct disease subtypes. It is crucial, that the used cluster algorithm works correctly. However, by imposing a predefined shape on the clusters, classical algorithms occasionally suggest a cluster structure in homogenously distributed data or assign data points to incorrect clusters. We analyzed whether this can be avoided when using Emergent Self-organizing feature maps (ESOM).
METHODS: Data sets with different degrees of complexity were submitted to ESOM analysis using large numbers of neurons using interactive R-based bioinformatics tool. On top of the trained ESOM the distance structure in the high dimensional feature space was visualized in the form of a so-called U-matrix. Clustering results were compared with those provided by classical common cluster algorithms including single linkage, Ward and k-means.
RESULTS: Ward clustering imposed cluster structures on cluster-less golf ball, cuboid and S-shaped data sets that contained no structure at all (random data). Ward clustering also imposed structures on permuted real world data. By contrast, the ESOM/U-matrix approach correctly found that these data contain no cluster structure. However, ESOM/U-matrix was correct in identifying clusters in biomedical data truly containing subgroups. It was always correct in cluster structure identification in further canonical artificial data. Using intentionally simple data sets, it is shown, that popular clustering algorithms typically used for biomedical data sets may fail to cluster data correctly, suggesting that they are also likely to perform erroneously on high dimensional data.
CONCLUSIONS: The present analyses emphasized, that generally established classical hierarchical clustering algorithms carry a considerable tendency to produce erroneous results. By contrast, unsupervised machine-learned analysis of cluster structures, applied using the ESOM/U-matrix method, is a viable, unbiased method to identify true clusters in the high-dimensional space of complex data.
PMID: 28040499 [PubMed - as supplied by publisher]
An unsupervised machine learning model for discovering latent infectious diseases using social media data.
J Biomed Inform. 2016 Dec 26;:
Authors: Lim S, Tucker CS, Kumara S
INTRODUCTION: The authors of this work propose an unsupervised machine learning model that has the ability to identify real-world latent infectious diseases by mining social media data. In this study, a latent infectious disease is defined as a communicable disease that has not yet been formalized by national public health institutes and explicitly communicated to the general public. Most existing approaches to modeling infectious-disease-related knowledge discovery through social media networks are top-down approaches that are based on already known information, such as the names of diseases and their symptoms. In existing top-down approaches, necessary but unknown information, such as disease names and symptoms, is mostly unidentified in social media data until national public health institutes have formalized that disease. Most of the formalizing processes for latent infectious diseases are time consuming. Therefore, this study presents a bottom-up approach for latent infectious disease discovery in a given location without prior information, such as disease names and related symptoms.
METHODS: Social media messages with user and temporal information are extracted during the data preprocessing stage. An unsupervised sentiment analysis model is then presented. Users' expressions about symptoms, body parts, and pain locations are also identified from social media data. Then, symptom weighting vectors for each individual and time period are created, based on their sentiment and social media expressions. Finally, latent-infectious-disease-related information is retrieved from individuals' symptom weighting vectors.
DATASETS AND RESULTS: Twitter data from August 2012 to May 2013 are used to validate this study. Real electronic medical records for 104 individuals, who were diagnosed with influenza in the same period, are used to serve as ground truth validation. The results are promising, with the highest precision, recall, and F1 score values of 0.773, 0.680, and 0.724, respectively.
CONCLUSION: This work uses individuals' social media messages to identify latent infectious diseases, without prior information, quicker than when the disease(s) is formalized by national public health institutes. In particular, the unsupervised machine learning model using user, textual, and temporal information in social media data, along with sentiment analysis, identifies latent infectious diseases in a given location.
PMID: 28034788 [PubMed - as supplied by publisher]
Septic Shock Prediction for ICU Patients via Coupled HMM Walking on Sequential Contrast Patterns.
J Biomed Inform. 2016 Dec 20;:
Authors: Ghosh S, Li J, Cao L, Ramamohanarao K
BACKGROUND AND OBJECTIVE: Critical care patient events like sepsis or septic shock in intensive care units (ICUs) are dangerous complications which can cause multiple organ failures and eventual death. Preventive prediction of such events will allow clinicians to stage effective interventions for averting these critical complications.
METHODS: It is widely understood that physiological conditions of patients on variables such as blood pressure and heart rate are suggestive to gradual changes over a certain period of time, prior to the occurrence of a septic shock. This work investigates the performance of a novel machine learning approach for the early prediction of septic shock. The approach combines highly informative sequential patterns extracted from multiple physiological variables and captures the interactions among these patterns via coupled hidden Markov models (CHMM). In particular, the patterns are extracted from three non-invasive waveform measurements: the mean arterial pressure levels, the heart rates and respiratory rates of septic shock patients from a large clinical ICU dataset called MIMIC-II.
EVALUATION AND RESULTS: For baseline estimations, SVM and HMM models on the continuous time series data for the given patients, using MAP (mean arterial pressure), HR (heart rate), and RR (respiratory rate) are employed. Single channel patterns based HMM (SCP-HMM) and multi-channel patterns based coupled HMM (MCP-HMM) are compared against baseline models using 5 fold cross validation accuracies over multiple rounds. Particularly, the results of MCP-HMM are statistically significant having a p-value of 0.0014, in comparison to baseline models. Our experiments demonstrate a strong competitive accuracy in the prediction of septic shock, especially when the interactions between the multiple variables are coupled by the learning model.
CONCLUSIONS: It can be concluded that the novelty of the approach, stems from the integration of sequence-based physiological pattern markers with the sequential CHMM model to learn dynamic physiological behavior, as well as from the coupling of such patterns to build powerful risk stratification models for septic shock patients.
PMID: 28011233 [PubMed - as supplied by publisher]
Towards a Privacy Preserving Cohort Discovery Framework for Clinical Research Networks.
J Biomed Inform. 2016 Dec 19;:
Authors: Yuan J, Malin B, Modave F, Guo Y, Hogan WR, Shenkman E, Bian J
BACKGROUND: The last few years have witnessed an increasing number of clinical research networks (CRNs) focused on building large collections of data from electronic health records (EHRs), claims, and patient-reported outcomes (PROs). Many of these CRNs provide a service for the discovery of research cohorts with various health conditions, which is especially useful for rare diseases. Supporting patient privacy can enhance the scalability and efficiency of such processes; however, current practice mainly relies on policy, such as guidelines defined in the Health Insurance Portability and Accountability Act (HIPAA), which are insufficient for CRNs (e.g., HIPAA does not require encryption of data - which can mitigate insider threats). By combining policy with privacy enhancing technologies we can enhance the trustworthiness of CRNs. The goal of this research is to determine if searchable encryption can instill privacy in CRNs without sacrificing their usability.
METHODS: We developed a technique, implemented in working software to enable privacy-preserving cohort discovery (PPCD) services in large distributed CRNs based on elliptic curve cryptography (ECC). This technique also incorporates a block indexing strategy to improve the performance (in terms of computational running time) of PPCD. We evaluated the PPCD service with three real cohort definitions: 1) elderly cervical cancer patients who underwent radical hysterectomy, 2) oropharyngeal and tongue cancer patients who underwent robotic transoral surgery, and 3) female breast cancer patients who underwent mastectomy) with varied query complexity. These definitions were tested in an encrypted database of 7.1 million records derived from the publically available Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS). We assessed the performance of the PPCD service in terms of 1) accuracy in cohort discovery, 2) computational running time, and 3) privacy afforded to the underlying records during PPCD.
RESULTS: The empirical results indicate that the proposed PPCD can execute cohort discovery queries in a reasonable amount of time, with query runtime in the range of 165 to 262 seconds for the 3 use cases, with zero compromise in accuracy. We further show that the search performance is practical because it supports a highly parallelized design for secure evaluation over encrypted records. Additionally, our security analysis shows that the proposed construction is resilient to standard adversaries.
CONCLUSIONS: PPCD services can be designed for clinical research networks. The security construction presented in this work specifically achieves high privacy guarantees by preventing both threats originating from within and beyond the network.
PMID: 28007583 [PubMed - as supplied by publisher]
kMEn: analyzing noisy and bidirectional transcriptional pathway responses in single subjects.
J Biomed Inform. 2016 Dec 19;:
Authors: Li Q, Grant Schissler A, Gardeux V, Berghout J, Achour I, Kenost C, Li H, Helen Zhang H, Lussier YA
MOTIVATION: Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs).
METHODS: We propose a new N-of-1-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus.
RESULTS: In ∼9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: i) improved precision-recall at various levels of bidirectional response and ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment-specific pathways identified by kMEn correlate with therapeutic response (p-value<0.01).
CONCLUSION: Through improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers.
PMID: 28007582 [PubMed - as supplied by publisher]
Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using hot spot detection problem.
J Biomed Inform. 2016 Dec 18;:
Authors: Gurcan MN, Tomaszewski J, Overton JA, Doyle S, Ruttenberg A, Smith B
Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology - QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts.
PMID: 28003147 [PubMed - as supplied by publisher]
Coordinating Clinic and Surgery Appointments to Meet Access Service Levels for Elective Surgery.
J Biomed Inform. 2016 Dec 16;:
Authors: Kazemian P, Sir MY, Van Oyen MP, Lovely JK, Larson DW, Pasupathy KS
Providing timely access to surgery is crucial for patients with high acuity diseases like cancer. We present a methodological framework to make efficient use of scarce resources including surgeons, operating rooms, and clinic appointment slots with a goal of coordinating clinic and surgery appointments so that patients with different acuity levels can see a surgeon in the clinic and schedule their surgery within a maximum wait time target that is clinically safe for them. We propose six heuristic scheduling policies with two underlying ideas behind them: (1) proactively book a tentative surgery day along with the clinic appointment at the time an appointment request is received, and (2) intelligently space out clinic and surgery appointments such that if the patient does not need his/her surgery appointment there is sufficient time to offer it to another patient. A 2-stage stochastic discrete-event simulation approach is employed to evaluate the six scheduling policies. In the first stage of the simulation, the heuristic policies are compared in terms of the average operating room (OR) overtime per day. The second stage involves fine-tuning the most-effective policy. A case study of the division of colorectal surgery (CRS) at the Mayo Clinic confirms that all six policies outperform the current scheduling protocol by a large margin. Numerical results demonstrate that the final policy, which we refer to as Coordinated Appointment Scheduling Policy considering Indication and Resources (CASPIR), performs 52 percent better than the current scheduling policy in terms of the average OR overtime per day under the same access service level. In conclusion, surgical divisions desiring stratified patient urgency classes should consider using scheduling policies that take the surgical availability of surgeons, patients' demographics and indication of disease into consideration when scheduling a clinic consultation appointment.
PMID: 27993748 [PubMed - as supplied by publisher]
Accuracy of an Automated Knowledge Base for Identifying Drug Adverse Reactions.
J Biomed Inform. 2016 Dec 16;:
Authors: Voss EA, Boyce RD, Ryan PB, van der Lei J, Rijnbeek PR, Schuemie MJ
INTRODUCTION: Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disseminate medical product safety evidence including spontaneous reports, published peer-reviewed literature, and product labels. Automated data processing and classification using these evidence sources can greatly reduce the manual curation currently required to develop reference sets of positive and negative controls (i.e. drugs that cause adverse drug events and those that do not) to be used in drug safety research.
METHODS: In this paper we explore a method for automatically aggregating disparate sources of information together into a single repository, developing a predictive model to classify drug-adverse event relationships, and applying those predictions to a real world problem of identifying negative controls for statistical method calibration.
RESULTS: Our results showed high predictive accuracy for the models combining all available evidence, with an area under the receiver-operator curve of ⩾ 0.92 when tested on three manually generated lists of drugs and conditions that are known to either have or not have an association with an adverse drug event.
CONCLUSIONS: Results from a pilot implementation of the method suggests that it is feasible to develop a scalable alternative to the time-and-resource-intensive, manual curation exercise previously applied to develop reference sets of positive and negative controls to be used in drug safety research.
PMID: 27993747 [PubMed - as supplied by publisher]
Evaluating common data models for use with a longitudinal community registry.
J Biomed Inform. 2016 Oct 28;:
Authors: Garza M, Del Fiol G, Tenenbaum J, Walden A, Zozus M
OBJECTIVE: To evaluate common data models (CDMs) to determine which is best suited for sharing data from a large, longitudinal, electronic health record (EHR)-based community registry.
MATERIALS AND METHODS: Four CDMs were chosen from models in use for clinical research data: Sentinel v5.0 (referred to as the Mini-Sentinel CDM in previous versions), PCORnet v3.0 (an extension of the Mini-Sentinel CDM), OMOP v5.0, and CDISC SDTM v1.4. Each model was evaluated against 11 criteria adapted from previous research. The criteria fell into six categories: content coverage, integrity, flexibility, ease of querying, standards compatibility, and ease and extent of implementation.
RESULTS: The OMOP CDM accommodated the highest percentage of our data elements (76%), fared well on other requirements, and had broader terminology coverage than the other models. Sentinel and PCORnet fell short in content coverage with 37% and 48% matches respectively. Although SDTM accommodated a significant percentage of data elements (55% true matches), 45% of the data elements mapped to SDTM's extension mechanism, known as Supplemental Qualifiers, increasing the number of joins required to query the data.
CONCLUSION: The OMOP CDM best met the criteria for supporting data sharing from longitudinal EHR-based studies. Conclusions may differ for other uses and associated data element sets, but the methodology reported here is easily adaptable to common data model evaluation for other uses.
PMID: 27989817 [PubMed - as supplied by publisher]
Extractive text summarization system to aid data extraction from full text in systematic review development.
J Biomed Inform. 2016 Oct 27;:
Authors: Duc An Bui D, Del Fiol G, Hurdle JF, Jonnalagadda S
OBJECTIVES: Extracting data from publication reports is a standard process in systematic review (SR) development. However, the data extraction process still relies too much on manual effort which is slow, costly, and subject to human error. In this study, we developed a text summarization system aimed at enhancing productivity and reducing errors in the traditional data extraction process.
METHODS: We developed a computer system that used machine learning and natural language processing approaches to automatically generate summaries of full-text scientific publications. The summaries at the sentence and fragment levels were evaluated in finding common clinical SR data elements such as sample size, group size, and PICO values. We compared the computer-generated summaries with human written summaries (title and abstract) in terms of the presence of necessary information for the data extraction as presented in the Cochrane review's study characteristics tables.
RESULTS: At the sentence level, the computer-generated summaries covered more information than humans do for systematic reviews (recall 91.2% vs. 83.8%, p<0.001). They also had a better density of relevant sentences (precision 59% vs. 39%, p<0.001). At the fragment level, the ensemble approach combining rule-based, concept mapping, and dictionary-based methods performed better than individual methods alone, achieving an 84.7% F-measure.
CONCLUSION: Computer-generated summaries are potential alternative information sources for data extraction in systematic review development. Machine learning and natural language processing are promising approaches to the development of such an extractive summarization system.
PMID: 27989816 [PubMed - as supplied by publisher]
What's Ideal? A Case Study Exploring Handoff Routines in Practice.
J Biomed Inform. 2016 Dec 09;:
Authors: Haque SN, Oesterlund CS, Fagan LM
BACKGROUND: Handoffs of care in the healthcare system between responsible providers have traditionally been conceptualized and studied at the point of patient transfer. Thus, clinical practice and associated information systems are designed with the concept of the handoff as a solitary event. This viewpoint does not consider the routine activities necessary for a successful handoff. We propose expanding the analysis of the handoff beyond the single point of transfer to include a routine of interrelated activities leading up to the transfer of responsibility. We used this expanded definition of handoffs to identify exceptions from standard practice as identified by ideal-type handoff routines.
METHOD: We used an ethnographic case method to study handoffs in an interventional cardiology unit in a Midwestern community hospital. This involved examining handoffs and their supporting routines. We conducted thematic analysis of the handoffs using NVivo, a qualitative software analysis program. These analyses include categorization of the types and causes of differences in practice and exceptions from ideal-type handoffs.
RESULTS: Observed handoffs that took place within the clinical unit did not consistently align with the ideal-type routine, yet this variation did not necessarily lead to exceptions. However, for handoffs between clinical units, although more likely to follow the ideal-type routine, differences from the standardized routine more often led to exceptions. We found that problems with performing the routine activities leading up to the handoff and the context in which the handoff occurred affected whether the handoff was successful.
CONCLUSIONS: Considering the handoff as a routine rather than simply the point of transition gives broader insight about how care transitions function. Such consideration helps clinicians better understand how variations occur and how differences from ideal-type handoffs can lead to potential exceptions such as missing information. This analysis can be used to develop information systems that better support handoffs.
PMID: 27956266 [PubMed - as supplied by publisher]
Single-Reviewer Electronic Phenotyping Validation in Operational Settings: Comparison of Strategies and Recommendations.
J Biomed Inform. 2016 Dec 09;:
Authors: Kukhareva P, Staes C, Noonan KW, Mueller HL, Warner P, Shields DE, Weeks H, Kawamoto K
OBJECTIVE: Develop evidence-based recommendations for single-reviewer validation of electronic phenotyping results in operational settings.
MATERIAL AND METHODS: We conducted a randomized controlled study to evaluate whether electronic phenotyping results should be used to support manual chart review during single-reviewer electronic phenotyping validation (N=3,104). We evaluated the accuracy, duration and cost of manual chart review with and without the availability of electronic phenotyping results, including relevant patient-specific details. The cost of identification of an erroneous electronic phenotyping result was calculated based on the personnel time required for the initial chart review and subsequent adjudication of discrepancies between manual chart review results and electronic phenotype determinations.
RESULTS: Providing electronic phenotyping results (vs not providing those results) was associated with improved overall accuracy of manual chart review (98.90% vs 92.46%, p<.001), decreased review duration per test case (62.43 vs 76.78 seconds, p<.001), and insignificantly reduced estimated marginal costs of identification of an erroneous electronic phenotyping result ($48.54 vs $63.56, p=.16). The agreement between chart review and electronic phenotyping results was higher when the phenotyping results were provided (Cohen's kappa 0.98 vs 0.88, p<.001). As a result, while accuracy improved when initial electronic phenotyping results were correct (99.74% vs 92.67%, N=3049, p<.001), there was a trend towards decreased accuracy when initial electronic phenotyping results were erroneous (56.67% vs 80.00%, N=55, p=.07). Electronic phenotyping results provided the greatest benefit for the accurate identification of rare exclusion criteria.
DISCUSSION: Single-reviewer chart review of electronic phenotyping can be conducted more accurately, quickly, and at lower cost when supported by electronic phenotyping results. However, human reviewers tend to agree with electronic phenotyping results even when those results are wrong. Thus, the value of providing electronic phenotyping results depends on the accuracy of the underlying electronic phenotyping algorithm.
CONCLUSION: We recommend using a mix of phenotyping validation strategies, with the balance of strategies based on the anticipated electronic phenotyping error rate, the tolerance for missed electronic phenotyping errors, as well as the expertise, cost, and availability of personnel involved in chart review and discrepancy adjudication.
PMID: 27956265 [PubMed - as supplied by publisher]
Comprehensive Mitigation Framework for Concurrent Application of Multiple Clinical Practice Guidelines.
J Biomed Inform. 2016 Dec 07;:
Authors: Wilk S, Michalowski M, Michalowski W, Rosu D, Carrier M, Kezadri-Hamiaz M
In this work we propose a comprehensive framework based on first-order logic (FOL) for mitigating (identifying and addressing) interactions between multiple clinical practice guidelines (CPGs) applied to a multi-morbid patient while also considering patient preferences related to the prescribed treatment. With this framework we respond to two fundamental challenges associated with clinical decision support: (1) concurrent application of multiple CPGs and (2) incorporation of patient preferences into the decision making process. We significantly expand our earlier research by (1) proposing a revised and improved mitigation-oriented representation of CPGs and secondary medical knowledge for addressing adverse interactions and incorporating patient preferences and (2) introducing a new mitigation algorithm. Specifically, actionable graphs representing CPGs allow for parallel and temporal activities (decisions and actions). Revision operators representing secondary medical knowledge support temporal interactions and complex revisions across multiple actionable graphs. The mitigation algorithm uses the actionable graphs, revision operators and available (and possibly incomplete) patient information represented in FOL. It relies on a depth-first search strategy to find a valid sequence of revisions and uses theorem proving and model finding techniques to identify applicable revision operators and to establish a management scenario for a given patient if one exists. The management scenario defines a safe (interaction-free) and preferred set of activities together with possible patient states. We illustrate the use of our framework with a clinical case study describing two patients who suffer from chronic kidney disease, hypertension, and atrial fibrillation and who are managed according to CPGs for these diseases. While in this paper we are primarily concerned with the methodological aspects of mitigation, we also briefly discuss a high-level proof of concept implementation of the proposed framework in the form of a clinical decision support system (CDSS). The proposed mitigation CDSS "insulates" clinicians from the complexities of the FOL representations and provides semantically meaningful summaries of mitigation results. Ultimately we plan to implement the mitigation CDSS within our MET (Mobile Emergency Triage) decision support environment.
PMID: 27939413 [PubMed - as supplied by publisher]
Using a contextualized sensemaking model for interaction design: A case study of tumor contouring.
J Biomed Inform. 2016 Dec 05;:
Authors: Aselmaa A, van Herk M, Laprie A, Nestle U, Götz I, Wiedenmann N, Schimek-Jasch T, Picaud F, Syrykh C, Cagetti LV, Jolnerovski M, Song Y, Goossens RH
Sensemaking theories help designers understand the cognitive processes of a user when he/she performs a complicated task. This paper introduces a two-step approach of incorporating sensemaking support within the design of health information systems by: 1) modeling the sensemaking process of physicians while performing a task, and 2) identifying software interaction design requirements that support sensemaking based on this model. The two-step approach is presented based on a case study of the tumor contouring clinical task for radiotherapy planning. In the first step of the approach, a contextualized sensemaking model was developed to describe the sensemaking process based on the goal, the workflow and the context of the task. In the second step, based on a research software prototype, an experiment was conducted where three contouring tasks were performed by eight physicians respectively. Four types of navigation interactions and five types of interaction sequence patterns were identified by analyzing the gathered interaction log data from those twenty-four cases. Further in-depth study on each of the navigation interactions and interaction sequence patterns in relation to the contextualized sensemaking model revealed five main areas for design improvements to increase sensemaking support. Outcomes of the case study indicate that the proposed two-step approach was beneficial for gaining a deeper understanding of the sensemaking process during the task, as well as for identifying design requirements for better sensemaking support.
PMID: 27932222 [PubMed - as supplied by publisher]
Variable neighborhood search for reverse engineering of gene regulatory networks.
J Biomed Inform. 2016 Dec 02;:
Authors: Nicholson C, Goodwin L, Clark C
A new search heuristic, Divided Neighborhood Exploration Search, designed to be used with inference algorithms such as Bayesian networks to improve on the reverse engineering of gene regulatory networks is presented. The approach systematically moves through the search space to find topologies representative of gene regulatory networks that are more likely to explain microarray data. In empirical testing it is demonstrated that the novel method is superior to the widely employed greedy search techniques in both the quality of the inferred networks and computational time.
PMID: 27919733 [PubMed - as supplied by publisher]
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Learning from Heterogeneous Temporal Data in Electronic Health Records.
J Biomed Inform. 2016 Dec 02;:
Authors: Zhao J, Papapetrou P, Asker L, Boström H
Electronic health records contain large amounts of longitudinal data that are valuable for biomedical informatics research. The application of machine learning is a promising alternative to manual analysis of such data. However, the complex structure of the data, which includes clinical events that are unevenly distributed over time, poses a challenge for standard learning algorithms. Some approaches to modeling temporal data rely on extracting single values from time series; however, this leads to the loss of potentially valuable sequential information. How to better account for the temporality of clinical data, hence, remains an important research question. In this study, novel representations of temporal data in electronic health records are explored. These representations retain the sequential information, and are directly compatible with standard machine learning algorithms. The explored methods are based on symbolic sequence representations of time series data, which are utilized in a number of different ways. An empirical investigation, using 19 datasets comprising clinical measurements observed over time from a real database of electronic health records, shows that using a distance measure to random subsequences leads to substantial improvements in predictive performance compared to using the original sequences or clustering the sequences. Evidence is moreover provided on the quality of the symbolic sequence representation by comparing it to sequences that are generated using domain knowledge by clinical experts. The proposed method creates representations that better account for the temporality of clinical events, which is often key to prediction tasks in the biomedical domain.
PMID: 27919732 [PubMed - as supplied by publisher]