Blunt Force Trauma to the Ribs: Creating Predictive Models

Forensic anthropologists receive more requests for trauma analysis than any other aspect of the biological profile. Blunt force trauma to the ribs is some of the most common trauma recorded in a medical examiner’s setting, however the structural complexity of ribs make it difficult to move beyond descriptive documentation of injuries. The purpose of this study is to identify common rib fracture patterns, influential variables, and provide probabilistic statements to guide rib fracture interpretations. A sample of 1,415 deceased individuals with known blunt force trauma to the torso were collected from four geographically diverse medical examiner offices. Demographic data and fracture variables were recorded per individual. Frequency distributions, chi-squared tests, Kruskal-Wallis tests of independence, Dunn’s tests, and multiple correspondence analysis were employed to understand variable relationships. Conditional probabilities were calculated to provide probabilistic statements. Additionally, random forest analysis was conducted to classify location and type of fracture based on covariates. A total of 24, 853 fractures were recorded. The most common fractures were displaced and simple fractures on ribs three through seven in the anterolateral and posterolateral locations. The less common fracture patterns revealed significant relationships with demographic data and provided empirical evidence for previously untested statements. BMI had a significant relationship with location, such that fractures were more frequently recorded in lower ribs in individuals with a BMI category of obese. Age had a significant relationship with fracture type and fracture location in all analyses; younger individuals were more likely to have incomplete fractures and incur fractures anteriorly, and older individuals were more likely to have multi-fragmentary fractures. The current study indicates that rib fracture types and location are dependent on the demographics of the individual. Demographics, such as age and health of the individual inform the material properties and structural geometry of bone, which is how bone biomechanics are recommended to be incorporated into trauma analysis. Furthermore, the results from this research can be applied to motor vehicle safety research, experimental research avenues, and bioarcheological trauma analysis. Future rib fracture research should focus on including a more holistic view of an individual during the interpretation of fracture patterns

Extreme multi-label deep neural classification of Spanish health records according to the International Classification of Diseases

111 p.Este trabajo trata sobre la minería de textos clínicos, un campo del Procesamiento del Lenguaje Natural aplicado al dominio biomédico. El objetivo es automatizar la tarea de codificación médica. Los registros electrónicos de salud (EHR) son documentos que contienen información clínica sobre la salud de unpaciente. Los diagnósticos y procedimientos médicos plasmados en la Historia Clínica Electrónica están codificados con respecto a la Clasificación Internacional de Enfermedades (CIE). De hecho, la CIE es la base para identificar estadísticas de salud internacionales y el estándar para informar enfermedades y condiciones de salud. Desde la perspectiva del aprendizaje automático, el objetivo es resolver un problema extremo de clasificación de texto de múltiples etiquetas, ya que a cada registro de salud se le asignan múltiples códigos ICD de un conjunto de más de 70 000 términos de diagnóstico. Una cantidad importante de recursos se dedican a la codificación médica, una laboriosa tarea que actualmente se realiza de forma manual. Los EHR son narraciones extensas, y los codificadores médicos revisan los registros escritos por los médicos y asignan los códigos ICD correspondientes. Los textos son técnicos ya que los médicos emplean una jerga médica especializada, aunque rica en abreviaturas, acrónimos y errores ortográficos, ya que los médicos documentan los registros mientras realizan la práctica clínica real. Paraabordar la clasificación automática de registros de salud, investigamos y desarrollamos un conjunto de técnicas de clasificación de texto de aprendizaje profundo

Improving the Timeliness, Accuracy, and Completeness of Mortality Reporting Using FHIR Apps and Machine Learning

There are approximately 56 million deaths per year world-wide, with millions happening in the United States. Accurate and timely mortality reporting is essential for gathering this important public health data in order to formulate emergency response to epidemics and new disease threats, to prevent communicable diseases such as flu, and to determine vital statistics such as life expectancy, mortality trends, etc. However, accurate collection and aggregation of high-quality mortality data remains an ongoing challenge due to issues such as the average low frequency with which physicians perform death certification, inconsistent training in determining the causes of death, complex data flow between the funeral home, the certifying physician and the registrar, and non-standard practices of data acquisition and transmission. We propose a smart application for medical providers at the point-of-care which will use \glsfirst{fhir} to integrate directly with the medical record, provide the practitioner with context for the death, and use machine learning techniques to enable the reporting of an accurate and complete causal chain of events leading to the death.Ph.D

Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers

As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications

Is Quantitative Ultrasound a Valid Technique for Assessing Bone Quality in Deceased Infants?

There is no quantitative method for evaluating infant bone quality that is non-invasive, portable, brief in scan duration, and does not use ionizing radiation. This study investigates the relationship between components of infant bone quality and a measure of quantitative ultrasound (QUS), speed of sound (SOS), to provide insight into the validity of QUS as a diagnostic tool for evaluating infant bone quality. The study sample was comprised of 78 infants between the age of 30 weeks estimated gestational age and 12 postnatal months receiving an autopsy at the Harris County Institute of Forensic Sciences and Texas Children’s Hospital. Bone SOS measurements, costochondral rib and iliac crest samples, and radiographs of the forearm and leg were prospectively collected over a 9-month period. Demographic information, medical history, autopsy findings, and investigator reports were collected and used to identify chronic illness. Qualitative radiographic evaluation, bone mineral density (BMD), and tibial measurements were obtained from radiographs. Results indicated that SOS measures aspects of bone quality related to bone macrostructure. Prematurity and chronic illness were significantly associated in the current study sample and their detrimental effects could not be separated. Prematurity and, possibly, chronic illness significantly influenced SOS through their adverse effects on growth and bone health. BMD was not significantly associated with tibial or body size measurements, but this may have been due to the small area of bone used to estimate BMD. Although SOS and BMD were not significantly correlated, both showed a postnatal decline and subsequent increase at greater ages. Chronically ill infants had significantly lower BMD and greater qualitative radiographic evaluation scores than infants without chronic illness. Assessing bone quality is complex due to the multitude of factors which compose it. QUS remains a highly promising technology for evaluating infant bone quality, but it cannot be definitively concluded that QUS is a valid technique for evaluating infant bone quality based on this research alone. Research comparing SOS to finer-grained measurements of aspects of bone quality are necessary before the validity of QUS as a diagnostic tool for evaluating infant bone quality and strength can be determined

Who dies where, when and why? Modelling determinants and space-time risk of infant, child and adult mortality in rural South Africa, 1992-2008

Ph.D., University of the Witwatersrand, Faculty of Health Sciences, 201

Data bases and data base systems related to NASA's aerospace program. A bibliography with indexes

This bibliography lists 1778 reports, articles, and other documents introduced into the NASA scientific and technical information system, 1975 through 1980

Technology, Science and Culture

From the success of the first and second volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them