Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods.

OBJECTIVE: In this work, we have developed a learning system capable of exploiting information conveyed by longitudinal Electronic Health Records (EHRs) for the prediction of a common postoperative complication, Anastomosis Leakage (AL), in a data-driven way and by fusing temporal population data from different and heterogeneous sources in the EHRs. MATERIAL AND METHODS: We used linear and non-linear kernel methods individually for each data source, and leveraging the powerful multiple kernels for their effective combination. To validate the system, we used data from the EHR of the gastrointestinal department at a university hospital. RESULTS: We first investigated the early prediction performance from each data source separately, by computing Area Under the Curve values for processed free text (0.83), blood tests (0.74), and vital signs (0.65), respectively. When exploiting the heterogeneous data sources combined using the composite kernel framework, the prediction capabilities increased considerably (0.92). Finally, posterior probabilities were evaluated for risk assessment of patients as an aid for clinicians to raise alertness at an early stage, in order to act promptly for avoiding AL complications. DISCUSSION: Machine-learning statistical model from EHR data can be useful to predict surgical complications. The combination of EHR extracted free text, blood samples values, and patient vital signs, improves the model performance. These results can be used as a framework for preoperative clinical decision support.

Data-driven approach for assessing utility of medical tests using electronic medical records.

OBJECTIVE: To precisely define the utility of tests in a clinical pathway through data-driven analysis of the electronic medical record (EMR). MATERIALS AND METHODS: The information content was defined in terms of the entropy of the expected value of the test related to a given outcome. A kernel density classifier was used to estimate the necessary distributions. To validate the method, we used data from the EMR of the gastrointestinal department at a university hospital. Blood tests from patients undergoing surgery for gastrointestinal surgery were analyzed with respect to second surgery within 30 days of the index surgery. RESULTS: The information content is clearly reflected in the patient pathway for certain combinations of tests and outcomes. C-reactive protein tests coupled to anastomosis leakage, a severe complication show a clear pattern of information gain through the patient trajectory, where the greatest gain from the test is 3-4 days post index surgery. DISCUSSION: We have defined the information content in a data-driven and information theoretic way such that the utility of a test can be precisely defined. The results reflect clinical knowledge. In the case we used the tests carry little negative impact. The general approach can be expanded to cases that carry a substantial negative impact, such as in certain radiological techniques.

Consistency and standardization of color in medical imaging: a consensus report.

This article summarizes the consensus reached at the Summit on Color in Medical Imaging held at the Food and Drug Administration (FDA) on May 8-9, 2013, co-sponsored by the FDA and ICC (International Color Consortium). The purpose of the meeting was to gather information on how color is currently handled by medical imaging systems to identify areas where there is a need for improvement, to define objective requirements, and to facilitate consensus development of best practices. Participants were asked to identify areas of concern and unmet needs. This summary documents the topics that were discussed at the meeting and recommendations that were made by the participants. Key areas identified where improvements in color would provide immediate tangible benefits were those of digital microscopy, telemedicine, medical photography (particularly ophthalmic and dental photography), and display calibration. Work in these and other related areas has been started within several professional groups, including the creation of the ICC Medical Imaging Working Group.

A semiautomatic tool for prostate segmentation in radiotherapy treatment planning.

BACKGROUND: Delineation of the target volume is a time-consuming task in radiotherapy treatment planning, yet essential for a successful treatment of cancers such as prostate cancer. To facilitate the delineation procedure, the paper proposes an intuitive approach for 3D modeling of the prostate by slice-wise best fitting ellipses. METHODS: The proposed estimate is initialized by the definition of a few control points in a new patient. The method is not restricted to particular image modalities but assumes a smooth shape with elliptic cross sections of the object. A training data set of 23 patients was used to calculate a prior shape model. The mean shape model was evaluated based on the manual contour of 10 test patients. The patient records of training and test data are based on axial T1-weighted 3D fast-field echo (FFE) sequences. The manual contours were considered as the reference model. Volume overlap (Vo), accuracy (Ac) (both ratio, range 0-1, optimal value 1) and Hausdorff distance (HD) (mm, optimal value 0) were calculated as evaluation parameters. RESULTS: The median and median absolute deviation (MAD) between manual delineation and deformed mean best fitting ellipses (MBFE) was Vo (0.9 ± 0.02), Ac (0.81 ± 0.03) and HD (4.05 ± 1.3)mm and between manual delineation and best fitting ellipses (BFE) was Vo (0.96 ± 0.01), Ac (0.92 ± 0.01) and HD (1.6 ± 0.27)mm. Additional results show a moderate improvement of the MBFE results after Monte Carlo Markov Chain (MCMC) method. CONCLUSIONS: The results emphasize the potential of the proposed method of modeling the prostate by best fitting ellipses. It shows the robustness and reproducibility of the model. A small sample test on 8 patients suggest possible time saving using the model.

Power law approximations of movement network data for modeling infectious disease spread.

Globalization and increased mobility of individuals enable person-to-person transmitted infectious diseases to spread faster to distant places around the world, making good models for the spread increasingly important. We study the spatiotemporal pattern of spread in the remotely located and sparsely populated region of North Norway in various models with fixed, seasonal, and random effects. The models are applied to influenza A counts using data from positive microbiology laboratory tests as proxy for the underlying disease incidence. Human travel patterns with local air, road, and sea traffic data are incorporated as well as power law approximations thereof, both with quasi-Poisson regression and based on the adjacency structure of the relevant municipalities. We investigate model extensions using information about the proportion of positive laboratory tests, data on immigration from outside North Norway and by connecting population to the movement network. Furthermore, we perform two separate analyses for nonadults and adults as children are an important driver for influenza A. Comparisons of one-step-ahead predictions generally yield better or comparable results using power law approximations.

Using noninferiority tests to evaluate telemedicine and e-health services: systematic review.

BACKGROUND: An increasing number of studies within the field of telemedicine and e-health are designed as noninferiority studies, aiming to show that the telemedicine/e-health solution is not inferior to the traditional way of treating patients. OBJECTIVE: The objective is to review and sum up the status of noninferiority studies within this field, describing advantages and pitfalls of this approach. METHODS: PubMed was searched according to defined criteria, and 16 relevant articles were identified from the period 2008-June 2011. RESULTS: Most of the studies were related to the fields of psychiatry and emergency medicine, and most were published in journals relating to these fields or in general scientific or general medicine journals. All the studies claimed to be noninferiority studies, but 7 out of 16 tested for statistical differences as a proxy of noninferiority. CONCLUSIONS: The methodological quality of the studies varied. We discuss optimal procedures for future noninferiority studies within the field of telemedicine and e-health and situations in which this approach is most appropriate.

Scale-space methods for live processing of sensor data.

A temporal scale-space is a vector space spanned by time and a scale parameter, and by constructing the scale-space correctly a causal structure can be imposed on the scale-space. This enables early warning of significant changes in sensor data at an early time, and on any scale. We describe a feasibility study on how to use these ideas for live surveillance of monitoring processes such that important features can be visualized and users warned about changes an early stage. Sensor data from motion sensors on patients with chronic obstructive pulmonary disease are used as the example of such system, where important pattern are found and visualized using significance plots.

Model driven mobile care for patients with type 1 diabetes.

We gathered a data set from 30 patients with type 1 diabetes by giving the patients a mobile phone application, where they recorded blood glucose measurements, insulin injections, meals, and physical activity. Using these data as a learning data set, we describe a new approach of building a mobile feedback system for these patients based on periodicities, pattern recognition, and scale-space trends. Most patients have important patterns for periodicities and trends, though better resolution of input variables is needed to provide useful feedback using pattern recognition.

Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data.

This study describes a novel approach to solve the surgical site infection (SSI) classification problem. Feature engineering has traditionally been one of the most important steps in solving complex classification problems, especially in cases with temporal data. The described novel approach is based on abstraction of temporal data recorded in three temporal windows. Maximum likelihood L1-norm (lasso) regularization was used in penalized logistic regression to predict the onset of surgical site infection occurrence based on available patient blood testing results up to the day of surgery. Prior knowledge of predictors (blood tests) was integrated in the modelling by introduction of penalty factors depending on blood test prices and an early stopping parameter limiting the maximum number of selected features used in predictive modelling. Finally, solutions resulting in higher interpretability and cost-effectiveness were demonstrated. Using repeated holdout cross-validation, the baseline C-reactive protein (CRP) classifier achieved a mean AUC of 0.801, whereas our best full lasso model achieved a mean AUC of 0.956. Best model testing results were achieved for full lasso model with maximum number of features limited at 20 features with an AUC of 0.967. Presented models showed the potential to not only support domain experts in their decision making but could also prove invaluable for improvement in prediction of SSI occurrence, which may even help setting new guidelines in the field of preoperative SSI prevention and surveillance.

Correction: Does proximity of women to facilities with better choice of contraceptives affect their contraceptive utilization in rural Ethiopia?

[This corrects the article DOI: 10.1371/journal.pone.0187311.].

Does proximity of women to facilities with better choice of contraceptives affect their contraceptive utilization in rural Ethiopia?

BACKGROUND: There is limited evidence of the linkage between contraceptive use, the range of methods available and level of contraceptive stocks at health facilities and distance to facility in developing countries. The present analysis aims at examining the influence of contraceptive method availability and distance to the nearby facilities on modern contraceptive utilization among married women in rural areas in Ethiopia using geo-referenced data. METHODS: We used data from the first round of surveys of the Performance Monitoring & Accountability 2020 project in Ethiopia (PMA2020/Ethiopia-2014). The survey was conducted in a sample of 200 enumeration areas (EAs) where for each EA, 35 households and up to 3 public or private health service delivery points (SDPs) were selected. The main outcome variable was individual use of a contraceptive method for married women in rural Ethiopia. Correlates of interest include distance to nearby health facilities, range of contraceptives available in facilities, household wealth index, and the woman's educational status, age, and parity and whether she recently visited a health facility. This analysis primarily focuses on stock provision at public SDPs. RESULTS: Overall complete information was collected from 1763 married rural women ages 15-49 years and 198 SDPs in rural areas (97.1% public). Most rural women (93.9%) live within 5 kilometers of their nearest health post while a much lower proportion (52.2%) live within the same distance to the nearest health centers and hospital (0.8%), respectively. The main sources of modern contraceptive methods for married rural women were health posts (48.8%) and health centers (39.0%). The mean number of the types of contraceptive methods offered by hospitals, health centers and health posts was 6.2, 5.4 and 3.7 respectively. Modern contraceptive use (mCPR) among rural married women was 27.3% (95% CI: 25.3, 29.5). The percentage of rural married women who use modern contraceptives decreased as distance from the nearest SDP increased; 41.2%, 27.5%, 22.0%, and 22.6% of women living less than 2 kilometers, 2 to 3.9kilometers, 4 to 5.9 kilometers and 6 or more kilometers, respectively (p-value<0.01). Additionally, women who live close to facilities that offer a wider range of contraceptive methods were significantly more likely to use modern contraceptives. The mCPR ranged from 42.3% among women who live within 2 kilometers of facilities offering 3 or more methods to 22.5% among women living more than 6 kilometers away from the nearest facility with the same number (3 or more methods) available after adjusting for observed covariates. CONCLUSIONS: Although the majority of the Ethiopian population lives within a relatively close distance to lower level facilities (health posts), the number and range of methods available (method choice) and proximity are independently associated with contraceptive utilization. By demonstrating the extent to which objective measures of distance (of relatively small magnitude) explain variation in contraceptive use among rural women, the study fills an important planning gap for family planning programs operating in resource limited settings.

Using anchors from free text in electronic health records to diagnose postoperative delirium.

OBJECTIVES: Postoperative delirium is a common complication after major surgery among the elderly. Despite its potentially serious consequences, the complication often goes undetected and undiagnosed. In order to provide diagnosis support one could potentially exploit the information hidden in free text documents from electronic health records using data-driven clinical decision support tools. However, these tools depend on labeled training data and can be both time consuming and expensive to create. METHODS: The recent learning with anchors framework resolves this problem by transforming key observations (anchors) into labels. This is a promising framework, but it is heavily reliant on clinicians knowledge for specifying good anchor choices in order to perform well. In this paper we propose a novel method for specifying anchors from free text documents, following an exploratory data analysis approach based on clustering and data visualization techniques. We investigate the use of the new framework as a way to detect postoperative delirium. RESULTS: By applying the proposed method to medical data gathered from a Norwegian university hospital, we increase the area under the precision-recall curve from 0.51 to 0.96 compared to baselines. CONCLUSIONS: The proposed approach can be used as a framework for clinical decision support for postoperative delirium.

Analysis of free text in electronic health records for identification of cancer patient trajectories.

With an aging patient population and increasing complexity in patient disease trajectories, physicians are often met with complex patient histories from which clinical decisions must be made. Due to the increasing rate of adverse events and hospitals facing financial penalties for readmission, there has never been a greater need to enforce evidence-led medical decision-making using available health care data. In the present work, we studied a cohort of 7,741 patients, of whom 4,080 were diagnosed with cancer, surgically treated at a University Hospital in the years 2004-2012. We have developed a methodology that allows disease trajectories of the cancer patients to be estimated from free text in electronic health records (EHRs). By using these disease trajectories, we predict 80% of patient events ahead in time. By control of confounders from 8326 quantified events, we identified 557 events that constitute high subsequent risks (risk > 20%), including six events for cancer and seven events for metastasis. We believe that the presented methodology and findings could be used to improve clinical decision support and personalize trajectories, thereby decreasing adverse events and optimizing cancer treatment.

Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records.

The free text in electronic health records (EHRs) conveys a huge amount of clinical information about health state and patient history. Despite a rapidly growing literature on the use of machine learning techniques for extracting this information, little effort has been invested toward feature selection and the features' corresponding medical interpretation. In this study, we focus on the task of early detection of anastomosis leakage (AL), a severe complication after elective surgery for colorectal cancer (CRC) surgery, using free text extracted from EHRs. We use a bag-of-words model to investigate the potential for feature selection strategies. The purpose is earlier detection of AL and prediction of AL with data generated in the EHR before the actual complication occur. Due to the high dimensionality of the data, we derive feature selection strategies using the robust support vector machine linear maximum margin classifier, by investigating: 1) a simple statistical criterion (leave-one-out-based test); 2) an intensive-computation statistical criterion (Bootstrap resampling); and 3) an advanced statistical criterion (kernel entropy). Results reveal a discriminatory power for early detection of complications after CRC (sensitivity 100%; specificity 72%). These results can be used to develop prediction models, based on EHR data, that can support surgeons and patients in the preoperative decision making phase.

Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial.

BACKGROUND: A mobile phone-based application can be useful for patients with type 1 diabetes in managing their disease. This results in large datasets accumulated on the patient's devices, which can be used for individualized feedback. The effect of such feedback is investigated in this article. MATERIALS AND METHODS: We developed an application that included a data-driven feedback module known as Diastat for patients on self-measured blood glucose regimens. Using a stepped-wedge design, both groups initially received an application without Diastat. Group 1 activated Diastat after 4 weeks, whereas Group 2 activated Diastat 12 weeks after startup (T1). End points were glycated hemoglobin (HbA1c) level and number of out-of-range (OOR) measurements (i.e., outside the range 72-270 mg/dL). RESULTS: Thirty patients were recruited to the study, and 15 were assigned to each group after the initial meeting. There were no significant differences between groups at T1 in HbA1c or OOR events. Overall, all patients had a decrease of 0.6 percentage points in mean HbA1c (P < 0.001) and 14.5 in median OOR events over 2 weeks (P < 0.001). CONCLUSIONS: The study does not provide evidence that data-driven feedback improves glycemic control. The decrease in HbA1c was sizeable and significant, even though the study was not powered to detect this. The overall improvement in glycemic control suggests that, in general, mobile phone-based interventions can be useful in diabetes self-management.

Data-driven Temporal Prediction of Surgical Site Infection.

Analysis of data from Electronic Health Records (EHR) presents unique challenges, in particular regarding nonuniform temporal resolution of longitudinal variables. A considerable amount of patient information is available in the EHR - including blood tests that are performed routinely during inpatient follow-up. These data are useful for the design of advanced machine learning-based methods and prediction models. Using a matched cohort of patients undergoing gastrointestinal surgery (101 cases and 904 controls), we built a prediction model for post-operative surgical site infections (SSIs) using Gaussian process (GP) regression, time warping and imputation methods to manage the sparsity of the data source, and support vector machines for classification. For most blood tests, wider confidence intervals after imputation were obtained in patients with SSI. Predictive performance with individual blood tests was maintained or improved by joint model prediction, and non-linear classifiers performed consistently better than linear models.

Implementation of an electronic surgical referral service. Collaboration, consensus and cost of the surgeon - general practitioner Delphi approach.

BACKGROUND: Poor coordination between levels of care plays a central role in determining the quality and cost of health care. To improve patient coordination, systematic structures, guidelines, and processes for creating, transferring, and recognizing information are needed to facilitate referral routines. METHODS: Prospective observational survey of implementation of electronic medical record (EMR)-supported guidelines for surgical treatment. RESULTS: One university clinic, two local hospitals, 31 municipalities, and three EMR vendors participated in the implementation project. Surgical referral guidelines were developed using the Delphi method; 22 surgeons and seven general practitioners (GPs) needed 109 hours to reach consensus. Based on consensus guidelines, an electronic referral service supported by a clinical decision support system, fully integrated into the GPs' EMR, was developed. Fifty-five information technology personnel and 563 hours were needed (total cost 67,000 £) to implement a guideline supported system in the EMR for 139 GPs. Economical analyses from a hospital and societal perspective, showed that 504 (range 401-670) and 37 (range 29-49) referred patients, respectively, were needed to provide a cost-effective service. CONCLUSION: A considerable amount of resources were needed to reach consensus on the surgical referral guidelines. A structured approach by the Delphi method and close collaboration between IT personnel, surgeons and primary care physicians were needed to reach consensus.

Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists.

BACKGROUND: It is often difficult to differentiate early melanomas from benign melanocytic nevi even by expert dermatologists, and the task is even more challenging for primary care physicians untrained in dermatology and dermoscopy. A computer system can provide an objective and quantitative evaluation of skin lesions, reducing subjectivity in the diagnosis. OBJECTIVE: Our objective is to make a low-cost computer aided diagnostic tool applicable in primary care based on a consumer grade camera with attached dermatoscope, and compare its performance to that of experienced dermatologists. METHODS AND MATERIALS: We propose several new image-derived features computed from automatically segmented dermoscopic pictures. These are related to the asymmetry, color, border, geometry, and texture of skin lesions. The diagnostic accuracy of the system is compared with that of three dermatologists. RESULTS: With a data set of 206 skin lesions, 169 benign and 37 melanomas, the classifier was able to provide competitive sensitivity (86%) and specificity (52%) scores compared with the sensitivity (85%) and specificity (48%) of the most accurate dermatologist using only dermoscopic images. CONCLUSION: We show that simple statistical classifiers can be trained to provide a recommendation on whether a pigmented skin lesion requires biopsy to exclude skin cancer with a performance that is comparable to and exceeds that of experienced dermatologists.