A general text mining method to extract echocardiography measurement results from echocardiography documents.

BACKGROUND: In everyday medical practice, the results of cardiac ultrasound examinations are generally recorded in unstructured text, from which extracting relevant information is an important and challenging task. This paper presents a generally applicable language and corpus-independent text mining method for extracting and structuring numerical measurement results and their descriptions from echocardiography reports. METHOD: The developed method is based on generally applicable text mining preprocessing activities, it automatically identifies and standardizes the descriptions of the cardiac ultrasound measures, and it stores the extracted and standardized measurement descriptions with their measurement results in a structured form for later usage. The method does not contain any regular expression-based search and does not rely on information about the structure of the document. RESULTS: The method has been tested on a document set containing more than 20,000 echocardiographic reports by examining the efficiency of extracting 12 echocardiography parameters considered important by experts. The method extracted and structured the echocardiography parameters under the study with good sensitivity (lowest value: 0.775, highest value: 1.0, average: 0.904) and excellent specificity (for all cases 1.0). The F1 score ranged between 0.873 and 1.0, and its average value was 0.948. CONCLUSION: The presented case study has shown that the proposed method can extract measurement results from echocardiography documents with high confidence without performing a direct search or having detailed information about the data recording habits. Furthermore, it effectively handles spelling errors, abbreviations and the highly varied terminology used in descriptions. As it does not rely on any information related to the structure or the language of the documents or data recording habits, it can be applied for processing any free-text written medical texts.

Prediction for Future Yaw Rate Values of Vehicles Using Long Short-Term Memory Network.

Currently, electric mobility and autonomous vehicles are of top priority from safety, environmental and economic points of view. In the automotive industry, monitoring and processing accurate and plausible sensor signals is a crucial safety-critical task. The vehicle's yaw rate is one of the most important state descriptors of vehicle dynamics, and its prediction can significantly contribute to choosing the correct intervention strategy. In this article, a Long Short-Term Memory network-based neural network model is proposed for predicting the future values of the yaw rate. The training, validating and testing of the neural network was conducted based on experimental data gathered from three different driving scenarios. The proposed model can predict the yaw rate value in 0.2 s in the future with high accuracy, using sensor signals of the vehicle from the last 0.3 s in the past. The R2 values of the proposed network range between 0.8938 and 0.9719 in the different scenarios, and in a mixed driving scenario, it is 0.9624.

Comparison of Cancer Survival Trends in Hungary in the Periods 2001-2005 and 2011-2015 According to a Population-Based Cancer Registry.

Background: Assessment of population-based cancer survival may provide the most valuable feedback about the effectiveness of oncological surveillance and treatment. Aims: Based on the database of the Hungarian National Cancer Registry, standardized incidence rates of lung, breast, colorectal, prostate and cervical cancer were compared to standardized mortality data of the Hungarian Central Statistical Office in the period between 2001 and 2015. Then survival analysis was performed on cleansed database. Results: The incidence of colorectal, breast and prostate cancer increased, while standardized rates of lung and cervical cancer declined. The survival of colorectal, breast and prostate cancer showed improvement. Contrarily, lung cancer exhibited a mild decline, while that of cervical cancer did not change significantly. In earlier stages survival was improved among almost every studied tumor type, while in advanced stages improvement was not observed. Comparison of stage distribution revealed that in the 2011-2015 period colorectal, breast and prostate cancer cases were diagnosed at earlier stages, while lung and cervical cancer patients were typically discovered at more advanced stages. Discussion: The outcome of advanced cancer treatments is better in earlier stages, which highlighted the importance of screening network. However, growth of oncological treatment costs with longer patient survival imposes a constantly increasing burden on society.

Multi-level process mining methodology for exploring disease-specific care processes.

BACKGROUND: Public healthcare is a complex domain with many actors and highly variable protocols, which makes traditional process mining tools less effective and calls for specialized methods. AIM: The objective of the work was to develop a generally applicable process mining methodology to explore care processes related to diseases. METHODS: The proposed methodology called Process Mining Methodology for Exploring Disease-specific Care Processes (MEDCP) is based on a systematic, step-wise refinement of the raw event logs by using such a multi-level expert taxonomy of events that encapsulates the professional concepts of the analysis. A treatment process is defined according to domain-specific rules to identify the starting (index) and closing events. Concepts from various levels of the taxonomy support the final process definition for an analysis that can deliver meaningful conclusions for domain experts. RESULTS: The applicability of the methodology was demonstrated on two case studies in the cardiological and oncological care domains, in the public health care system in Hungary over a period of ten years. Thanks to the multi-level taxonomy, these studies successfully identified the most important high-level event sequence patterns and some key anomalies in the national care system, such as the significantly different behavior of low-volume vs. high volume care providers in the oncology study or the geographically connected, homogeneous clusters of providers with similar care spectra in the cardiology study. DISCUSSION: As the case studies showed, the proposed methodology can improve the efficiency of standard process mining methods, and deliver high level conclusions that are easy to interpret by domain experts. System-level insight into health care processes can serve as a basis for the optimisation and long-term planning of the whole care system.

Preventing cancer therapy-related heart failure: the need for novel studies.

AIMS: After enhancing the survivorship of cancers, the impact of cardiovascular diseases on mortality is increasing among cancer patients. However, anticancer therapies pose a higher cardiovascular risk to patients. As prevention against cancer therapy-induced cardiomyopathy has yet to be explored, the preventive ability of concomitant cardiovascular medications against incident heart failure was assessed. METHODS: A retrospective, population-based study was run using anonymized integration of healthcare databases. All the Hungarian patients diagnosed with breast or colorectal carcinoma and undergoing chemotherapy or biological therapy were analysed. Participants were not treated with any anticancer therapy nor suffered from heart failure/dilated cardiomyopathy during the preceding observational period (≥6.5 years). The heart failure endpoint was established by I50 International Classification of Diseases codes upon discharge from hospital or issuance of an autopsy report. RESULTS: Among the 9575 patients who were enrolled, the cumulative incidence of heart failure over 4 years was 6.9%. The time until the first heart failure event in the propensity score-matched treated and untreated groups was compared using Cox proportional-hazards models. A significant association between lower heart failure risk and concomitant statin therapy was observed (hazard ratio: 0.748, P = 0.038); the preventive ability was more pronounced in the anthracycline/capecitabine/platinum-treated subgroup (hazard ratio: 0.660, P = 0.032). For angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker therapy, a significantly lower heart failure risk was also observed (hazard ratio: 0.809, P = 0.032). Among beta blockers, nebivolol administered to anthracycline/capecitabine-treated patients was associated with a nonsignificant trend to lower heart failure risk (hazard ratio: 0.584, P = 0.069). CONCLUSION: Only concomitant statin and angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker therapies were associated with significantly lower risk of anticancer therapy-related heart failure.

Weighted nearest neighbours-based control group selection method for observational studies.

Although in observational studies, propensity score matching is the most widely used balancing method, it has received much criticism. The main drawback of this method is that the individuals of the case and control groups are paired in the compressed one-dimensional space of propensity scores. In this paper, such a novel multivariate weighted k-nearest neighbours-based control group selection method is proposed which can eliminate this disadvantage of propensity score matching. The proposed method pairs the elements of the case and control groups in the original vector space of the covariates and the dissimilarities of the individuals are calculated as the weighted distances of the subjects. The weight factors are calculated from a logistic regression model fitted on the status of treatment assignment. The efficiency of the proposed method was evaluated by Monte Carlo simulations on different datasets. Experimental results show that the proposed Weighted Nearest Neighbours Control Group Selection with Error Minimization method is able to select a more balanced control group than the most widely applied greedy form of the propensity score matching method, especially for individuals characterized with few descriptive features.

Managing Alternative Patient Appointments Using P-Graph Methodology.

BACKGROUND: Patient appointment scheduling is one of the main challenging tasks in the healthcare administration and is constantly in the focus of theoretical researches. OBJECTIVES: The aim of this study was to investigate the applicability of the P-graph (Process graph) methodology to find the n-best alternative for patient's scheduling. METHODS: The patient appointment scheduling task was formalised as an integer linear programming problem and was considered as a process network synthesis problem. The optimal and n-best alternative solutions were determined by an efficient branch and bound algorithm implemented in a decision support system. RESULTS: Experimental results show, that the P-graph methodology can be effectively applied to produce the optimal scheduling for the examinations and to find the alternatives of the best scheduling.

The Efficiency of Different Distance Metrics for Keyword-Based Search in Medical Documents: A Short Case Study.

BACKGROUND: Processing of free text written medical texts involves many difficulties arising from typographical errors, synonyms, and abbreviations occurring in the texts. METHODS: In this study, the applicability of the most common string similarity measures were analyzed and compared for the keyword-based medical text search. RESULTS: The usefulness of the similarity measures was studied in a set of medical documents containing more than 20,000 echocardiography reports. Experimental results showed that the Jaro-Winkler dissimilarity measure is the most capable measure to explore the content of the medical texts.

Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox.

Network science has become increasingly important in life science over the last decade. The proposed Octave and MATLAB-compatible NOCAD toolbox provides a set of methods which enables the structural controllability and observability analysis of dynamical systems. In this paper, the functionality of the toolbox is presented, and the implemented functions demonstrated.

Application of Named Entity Recognition Methods to Extract Information from Echocardiography Reports.

As there is no consensus about how to store the results of echocardiography examinations, information extraction from them is a non-trivial task. Successful named entity recognition (NER) is key to getting access to the stored information and the process of identification has been recognized as a bottleneck in text mining. Our goal was to develop and compare such NER methods that are capable of achieving this task. Our practical results show that the text mining-based NER method is able to perform at a similar level in finding and identifying terms as the regular expression-based NER method. The paper highlights the advantages and disadvantages of both methods.

Risk prediction model for long-term heart failure incidence after epirubicin chemotherapy for breast cancer - A real-world data-based, nationwide classification analysis.

BACKGROUND: Dilated cardiomyopathy (DCM) incidence during and after anthracycline therapy is highly dependent on anthracycline cumulative dose (CD), but its detailed risk factors remained unexplored. Our aim was to assess heart failure (HF) incidence after epirubicin therapy and construct a HF risk-prediction score. METHODS AND RESULTS: A retrospective study was conducted by anonymized integration of nationwide healthcare databases. All the analysed patients were diagnosed with breast carcinoma confirmed by histology from 2007 to 2016. Participants did not undergo chemo- or radiotherapy or suffer HF/DCM during the preceding 3 years. The HF endpoint was established by assignment of I50 International Classification of Diseases (ICD) codes upon discharge from hospital or issuance of an autopsy report. 8068 patients treated with epirubicin were analysed. The 3-10-year HF cumulative incidence was 6.9%. Using binomial logistic regression the independent predictors were identified. A CD-dependent and significant effect on HF was revealed for epirubicin (threshold dose: 709 mg/m2, odds ratio (OR): 1.76) and docetaxel (CD: >510 mg/m2, OR: 1.59; CD ≤510 mg/m2, OR: 1.28, respectively). HF risk increased with age, even over 40. A risk-prediction score derived from regression coefficients consisting of age, diabetes mellitus, hypertension, coronary artery disease, stroke, epirubicin CD, docetaxel CD, capecitabine, gemcitabine, bevacizumab and cancer stage was able to classify HF risk over a wide range (2-30%). CONCLUSION: Long-term HF risk for patients treated with epirubicin was stratified by our risk-prediction score with a nearly 15-fold difference between the lowest and highest groups.

Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree.

Network science-based analysis of the observability of dynamical systems has been a focus of attention over the past five years. The maximum matching-based approach provides a simple tool to determine the minimum number of sensors and their positions. However, the resulting proportion of sensors is particularly small when compared to the size of the system, and, although structural observability is ensured, the system demands additional sensors to provide the small relative order needed for fast and robust process monitoring and control. In this paper, two clustering and simulated annealing-based methodologies are proposed to assign additional sensors to the dynamical systems. The proposed methodologies simplify the observation of the system and decrease its relative order. The usefulness of the proposed method is justified in a sensor-placement problem of a heat exchanger network. The results show that the relative order of the observability is decreased significantly by an increase in the number of additional sensors.

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

Frequent Treatment Sequence Mining from Medical Databases.

The huge amount of data stored in healthcare databases allows wide range possibilities for data analysis. In this article, we present a novel multilevel analysis methodology to generate and analyze sequential healthcare treatment events. The event sequences can be generated on different abstraction levels automatically from the source data, and so they describe the treatment of patients on different levels of detail. To present applicability of the proposed methodology, we introduce a short case study as well, in which some analysis results are presented arising from the analysis of a group of patients suffering from colorectal cancer.

Comparison of Control Group Generating Methods.

Retrospective studies suffer from drawbacks such as selection bias. As the selection of the control group has a significant impact on the evaluation of the results, it is very important to find the proper method to generate the most appropriate control group. In this paper we suggest two nearest neighbors based control group selection methods that aim to achieve good matching between the individuals of case and control groups. The effectiveness of the proposed methods is evaluated by runtime and accuracy tests and the results are compared to the classical stratified sampling method.

Controllability and observability in complex networks - the effect of connection types.

Network theory based controllability and observability analysis have become widely used techniques. We realized that most applications are not related to dynamical systems, and mainly the physical topologies of the systems are analysed without deeper considerations. Here, we draw attention to the importance of dynamics inside and between state variables by adding functional relationship defined edges to the original topology. The resulting networks differ from physical topologies of the systems and describe more accurately the dynamics of the conservation of mass, momentum and energy. We define the typical connection types and highlight how the reinterpreted topologies change the number of the necessary sensors and actuators in benchmark networks widely studied in the literature. Additionally, we offer a workflow for network science-based dynamical system analysis, and we also introduce a method for generating the minimum number of necessary actuator and sensor points in the system.