Indiana University-Purdue University Indianapolis (IUPUI) / Since exposure to carcinogens can occur in the environment from various point sources, cancer risk assessment attempts to define and limit potential exposure such that the risk of developing cancer is negligible. While cancer risk assessment is widely used with certain methodologies well accepted in the scientific literature and regulatory guidances, there are still gaps which increase uncertainties when assessing risk including: (1) mixtures of genotoxins, (2) genotoxic metabolites, and (3) nongenotoxic carcinogens. An in silico model was developed to predict the cancer risk of a genotoxin which improved methodology for a single compound and mixtures. Monte Carlo simulations performed with a carcinogenicity potency database to estimate the overall carcinogenic risk of a mixture of genotoxic compounds showed that structural similarity would not likely increase the overall cancer risk. A cancer risk model was developed for genotoxic metabolites using excretion material in both animals and humans to determine the probability not exceeding a 1 in 100,000 excess cancer risk. Two model nongenotoxic compounds (fenofibrate and methapyraline) were tested in short-term microarray studies to develop a framework for cancer risk assessment. It was determined that a threshold for potential key events could be derived using benchmark dose analysis in combination with well developed ontologies (Kegg/GO), which were at or below measured tumorigenic and precursor events. In conclusion, informatics was effective in advancing toxicology-based cancer risk assessment using databases and predictive techniques which fill critical gaps in its methodology.

Indiana University-Purdue University Indianapolis (IUPUI) / Translational research has proven to be a powerful process that bridges the gap between basic science and medical practice. The complexity of translational research is two-fold: integration of vast amount of information in disparate silos, and dissemination of discoveries to stakeholders with different interests. We designed and implemented a hybrid knowledge discovery framework. We developed strategies to leverage both traditional biomedical databases and Health Social Network Communities content in the discovery process. Heuristic and quantitative evaluations were carried out in Colorectal Cancer and Amyotrophic Lateral Sclerosis disease areas. The results demonstrate the potential of our approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases, which may eventually lead to the discovery of novel disease targets, biomarkers and therapies.

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Objetivo: Avaliar a satisfação e expectativas dos pacientes e médicos antes e depois da implantação de um registro médico eletrônico (RME) de pacientes no ambulatório de clínica médica de um hospital universitário. Métodos: Foram realizadas 389 entrevistas com pacientes e 151 com médicos antes e depois da implantação de um RME comercial no ambulatório de Clínica Médica do Hospital das Clínicas da Universidade de São Paulo, Brasil. Os médicos foram categorizados por seus anos de graduação (10 anos ou menos e mais de 10 anos). As respostas ao questionário dadas pelos médicos foram classificadas como favorável ou contra o uso de RME, antes e depois da implantação do sistema, recebendo 1 ou 0 pontos, respectivamente. A soma destes pontos gerou uma pontuação analisada por regressão múltipla para determinar quais os fatores que contribuem para a aceitação de RME pelos médicos. Para se avaliar o comportamento de pacientes e médicos em um período em que a implantação já havia entrado na rotina, realizou-se uma terceira etapa de entrevistas com pacientes e médicos. Resultados: O grau de satisfação do paciente era o mesmo antes e depois da implantação (p > 0,05). O tempo de espera para ser atendido foi maior após a implantação de RME (p < 0,0001), embora a percepção dos pacientes tenha apontado para uma direção diferente (p=0,0186). Médicos formados há menos de 10 anos já tinham usado os registros eletrônicos em outros hospitais e clínicas (p=0,0141). Estes médicos tinham expectativas mais positivas antes da implantação (p=0,0018). Este otimismo foi reduzido após a implantação, devido ao mau funcionamento do sistema durante a fase inicial (p=0,0229). A utilização do RME foi maior pelos médicos mais jovens (p < 0,0001). Na terceira avaliação os pacientes mostraram-se muito satisfeitos com o atendimento (porcentagem de mais de 90%). Percebiam a utilização do computador durante a consulta e valorizavam essa utilização. Os médicos com 10 anos ou menos de graduação, percebiam e valorizavam mais as facilidades do registro eletrônico e o utilizavam mais. Em 11 de 18 questões específicas sobre o desempenho de tarefas clínicas os médicos mais jovens julgaram mais fácil utilizar o RME, do que os médicos mais antigos p < 0,05. Questionados especificamente sobre a satisfação com o RME, os médicos mais jovens responderam \"boa\" e \"excelente\" em maior proporção do que os com mais de 10 anos de formados (p=0,0011) / Objective: To evaluate the satisfaction and expectations of patients and physicians before and after the implementation of an electronic medical record (EMR) in internal medicine outpatient clinic of a university hospital. Methods: We conducted 389 interviews with patients and 151 with doctors before and after the implementation of a commercial RME in internal medicine outpatient clinic of the Hospital das Clinicas, University of São Paulo, Brazil. Doctors were categorized by their graduate years (10 years or less and more than 10 years). The answers to the questionnaire given by doctors were classified as favorable or against the use of EMR, before and after the implementation of the system, receiving 1 or 0 points, respectively. The sum of the points generated scores analyzed by multiple regression to determine the factors that contribute to the acceptance of EMR by doctors. To evaluate the behavior of patients and doctors in a period when the implementation had already entered the routine, a third stage of interviews with patients and doctors was carried out. Results: The degree of patient satisfaction was the same before and after implantation (p > 0.05). The waiting time to be attended was increased after the implementation of EMR (p < 0.0001), although the perception of patients has pointed to a different direction (p=0.0186). Doctors graduated less than 10 years had already used the electronic records in other hospitals and clinics (p=0.0141). These doctors had more positive expectations before implantation (p=0.0018). This optimism was reduced after implantation, due to system malfunction during the initial phase (p=0.0229). Utilization of EMR was higher by younger physicians (p < 0.0001). The third evaluation showed the patients were very satisfied with the service (over 90%). They noticed the use of the computer during the consultation and valued such use. Doctors with 10 or less graduation years, perceived and valued more the facilities of electronic medical records and used more. In 11 of 18 specific questions about the performance of clinical tasks younger doctors deemed it easier to use the electronic medical record, than older physicians (p < 0.05). When asked specifically about satisfaction with EMR, younger physicians responded \"good\" and \"excellent\" in greater proportion than the old physicians (p=0.0011)

Objetivo: Avaliar a satisfação e expectativas dos pacientes e médicos antes e depois da implantação de um registro médico eletrônico (RME) de pacientes no ambulatório de clínica médica de um hospital universitário. Métodos: Foram realizadas 389 entrevistas com pacientes e 151 com médicos antes e depois da implantação de um RME comercial no ambulatório de Clínica Médica do Hospital das Clínicas da Universidade de São Paulo, Brasil. Os médicos foram categorizados por seus anos de graduação (10 anos ou menos e mais de 10 anos). As respostas ao questionário dadas pelos médicos foram classificadas como favorável ou contra o uso de RME, antes e depois da implantação do sistema, recebendo 1 ou 0 pontos, respectivamente. A soma destes pontos gerou uma pontuação analisada por regressão múltipla para determinar quais os fatores que contribuem para a aceitação de RME pelos médicos. Para se avaliar o comportamento de pacientes e médicos em um período em que a implantação já havia entrado na rotina, realizou-se uma terceira etapa de entrevistas com pacientes e médicos. Resultados: O grau de satisfação do paciente era o mesmo antes e depois da implantação (p > 0,05). O tempo de espera para ser atendido foi maior após a implantação de RME (p < 0,0001), embora a percepção dos pacientes tenha apontado para uma direção diferente (p=0,0186). Médicos formados há menos de 10 anos já tinham usado os registros eletrônicos em outros hospitais e clínicas (p=0,0141). Estes médicos tinham expectativas mais positivas antes da implantação (p=0,0018). Este otimismo foi reduzido após a implantação, devido ao mau funcionamento do sistema durante a fase inicial (p=0,0229). A utilização do RME foi maior pelos médicos mais jovens (p < 0,0001). Na terceira avaliação os pacientes mostraram-se muito satisfeitos com o atendimento (porcentagem de mais de 90%). Percebiam a utilização do computador durante a consulta e valorizavam essa utilização. Os médicos com 10 anos ou menos de graduação, percebiam e valorizavam mais as facilidades do registro eletrônico e o utilizavam mais. Em 11 de 18 questões específicas sobre o desempenho de tarefas clínicas os médicos mais jovens julgaram mais fácil utilizar o RME, do que os médicos mais antigos p < 0,05. Questionados especificamente sobre a satisfação com o RME, os médicos mais jovens responderam \"boa\" e \"excelente\" em maior proporção do que os com mais de 10 anos de formados (p=0,0011) / Objective: To evaluate the satisfaction and expectations of patients and physicians before and after the implementation of an electronic medical record (EMR) in internal medicine outpatient clinic of a university hospital. Methods: We conducted 389 interviews with patients and 151 with doctors before and after the implementation of a commercial RME in internal medicine outpatient clinic of the Hospital das Clinicas, University of São Paulo, Brazil. Doctors were categorized by their graduate years (10 years or less and more than 10 years). The answers to the questionnaire given by doctors were classified as favorable or against the use of EMR, before and after the implementation of the system, receiving 1 or 0 points, respectively. The sum of the points generated scores analyzed by multiple regression to determine the factors that contribute to the acceptance of EMR by doctors. To evaluate the behavior of patients and doctors in a period when the implementation had already entered the routine, a third stage of interviews with patients and doctors was carried out. Results: The degree of patient satisfaction was the same before and after implantation (p > 0.05). The waiting time to be attended was increased after the implementation of EMR (p < 0.0001), although the perception of patients has pointed to a different direction (p=0.0186). Doctors graduated less than 10 years had already used the electronic records in other hospitals and clinics (p=0.0141). These doctors had more positive expectations before implantation (p=0.0018). This optimism was reduced after implantation, due to system malfunction during the initial phase (p=0.0229). Utilization of EMR was higher by younger physicians (p < 0.0001). The third evaluation showed the patients were very satisfied with the service (over 90%). They noticed the use of the computer during the consultation and valued such use. Doctors with 10 or less graduation years, perceived and valued more the facilities of electronic medical records and used more. In 11 of 18 specific questions about the performance of clinical tasks younger doctors deemed it easier to use the electronic medical record, than older physicians (p < 0.05). When asked specifically about satisfaction with EMR, younger physicians responded \"good\" and \"excellent\" in greater proportion than the old physicians (p=0.0011)

HIV drug resistance (HIVDR) has emerged as a major clinical and public health challenge in many resource poor countries especially in Africa. HIVDR testing has become increasingly important and is of significant value in the management of HIV. The use of low cost technologies and procedures in testing HIVDR is being recommended. HIVDR computer interpretation algorithms make use of artificial intelligence and other computer technologies to predict HIVDR, and are recommended for use in resource poor countries. However, there is little known about the knowledge, attitude and perception of HIVDR computer algorithms by doctors in developing countries who are supposed to use computer algorithms. This study aimed to determine the knowledge, attitude and perception regarding computer interpretation algorithms of the 4th and 5th year medical students at Nelson R. Mandela School of Medicine, University of KwaZulu Natal in South Africa. Primary data collection was done using a questionnaire administered to a convenience sample of 216 4th and 5th year medical students. The study revealed that 90% of the respondents were aware of HIV drug resistance testing in South Africa but only 4% had knowledge of the computer interpretation algorithms. The study revealed that although the UKZN medical students are not aware of computer interpretation algorithms, majority are willing to use them in the future. / Thesis (M.Med.Sc.)-University of KwaZulu-Natal, Durban, 2013.

Accurate mortality prediction in intensive care units (ICUs) allows for the risk adjustment of study populations, aids in patient care and provides a method for benchmarking overall hospital and ICU performance. ICU risk-adjustment models are primarily comprised of an integer severity of illness score which increases with increasing patient risk of mortality. First published in the 1980s, the improvements to these scores primarily consisted of increasing the dimensionality of the model, and hence also increasing their complexity. This thesis aims to improve upon these models. First, the field is surveyed and the major models for risk-adjusting critically ill patient cohorts are identified including the acute physiology score (APS) and the simplified acute physiology score (SAPS). A key component of model performance is data preprocessing. The effect of preprocessing ICU data is quantified on a dataset of 8,000 ICU patients, and it is shown that after preprocessing to remove extreme values a logistic regression (LR) model performed competitively (AUROC of 0.8633) with the more complex machine learning model; a support vector machine (SVM) which had an AUROC of 0.8653. For validation, model development was repeated in a larger database containing over 80,000 patients admitted to 89 ICUs in the United States. Results were similar (AUROC of 0.8895 for the LR vs 0.8917 for the SVM) but showed the performance gain when using automated outlier rejection is less pronounced in well quality controlled datasets (0.8883 for LR without rejection). It is hypothesised from this that simpler models can perform competitively with more complicated models, while having a greatly reduced burden of data collection. A severity score is developed on the large multi-center database using a Genetic Algorithm and Particle Swarm Optimisation. The severity score, named the Oxford Acute Severity of Illness Score (OASIS), is shown to outperform the APS III (AUROC 0.837 vs 0.822) and perform competitively with APACHE IV when used as a covariate in a regression model (AUROC 0.868 vs 0.881). The severity score requires only 10 variables (58&percnt; as many as APS III), reducing the burden of quality control and data collection. These variables are routinely collected in critical care by continuous monitors and do not include comorbidities, diagnosis or laboratory measurements. The severity score is then externally evaluated in an American hospital and shown to discriminate well (AUROC 0.790 vs. 0.782 for the APS III) with excellent calibration. Finally, the severity score was evaluated in an English hospital and compared to other severity scores. OASIS again had excellent calibration and discrimination (AUROC 0.776 vs 0.750 for APS III) whilst requiring a much smaller number of variables. OASIS has many applications, including both simplifying data collection for studies and improving the risk assessment therein.

The purpose of this study was to describe the attitudes of a group of nurses towards the use of computers in a Southern California Veterans Affairs hospital.

Developed with the intention to make the patient data storage system in the clinical outpatient area more efficient, this system stores all pertinent and relevant patient data such as lab results, patient history and X-ray images. The system is accessible via the internet as well as operable over a local area network (LAN). The intended audience for this program is essentially the clinical staff (e.g., physicians, nursing staff, secretarial staff). The computer program was developed using Java Server Pages (JSP) and utilizes the Oracle 9i database.

Pain is a personal subjective experience with physiological and psychological components and involves many complex processes. In 1965 Melzack and Wall proposed the influential gate control theory (GCT) of pain and, in general, this has been supported by subsequent research. This theory postulates that cells in the substantia gelatinosa, located within the spinal cord, act like a gate mechanism that modulates the flow of information through the spinal cord to the brain and thus impacts on the pain experience. The abundance of literature and experimental data that is available from pain research supports the development and testing of computational models for the simulation and exploration of the pain process. Despite the fact that pain is an ideal candidate for modeling, it is an area that has received little attention. One of the few published models (Britton and Skevington, 1989; Britton et al., 1996) translated the explicitness of the GCT and its well-defined architecture into a basic mathematical model. The aim of this research is to develop a biologically appropriate computational model of pain, capable of modelling both acute and chronic pain states, and describe applications and simulations appropriate to such a model. Therefore this research firstly replicates a mathematical model of pain (Britton and Skevington, 1989; Britton et al., 1996) to explore its adequacy and to assess its potential for further development. The original model is then developed and extended to produce a more biologically plausible representation of the pain processes involved in the Gate Control mechanism. The improvements in the computational model have enabled a clinically plausible simulation of a pain modulatory technique, transcutaneous electrical nerve stimulation (TENS), which validates the model’s representation of the GCT and provides insight into how pain modulation can occur. Other developments to this model show its unique ability to represent symptoms of chronic pain, such as allodynia and hyperalgesia, which are associated with pathological pain states developed through the loss of inhibition and glial cell activation