How to Request and Obtain Feasibility Numbers and Data for Research through the Regenstrief Data Core and the Indiana CTSI Informatics and Data Analysis Core (CIDAC)

poster abstractThis poster presents a one-page, high-level summary view targeted at investigators and other individuals who have need to request numbers for research, explaining the process wherein requests can be made for feasibility and/or research data. Individuals seeking data for feasibility and/or research projects may utilize web based forms to make requests. Requests are tracked and managed by the Regenstrief Data Core. There are separate forms for Feasibility/Preliminary requests and Research Data requests. The purpose of this poster is to familiarize researchers with: Where to locate these forms on the Indiana CTSI website The steps needed to fill out and submit the appropriate request form The events that transpire between making the request and receiving data In addition, a description of available services through CIDAC and the Regenstrief Data Core is provided, included but not limited to expertise in study planning and implementation, assistance with subject recruitment and management and prospective descriptive clinical and demographic data

Development and Preliminary Evaluation of a Spray Deposition Sensing System for Improving Pesticide Application

An electronic, resistance-based sensor array and data acquisition system was developed to measure spray deposition from hydraulic nozzles. The sensor surface consisted of several parallel tin plated copper traces of varying widths with varying gap widths. The system contained an embedded microprocessor to monitor output voltage corresponding to spray deposition every second. In addition, a wireless module was used to transmit the voltage values to a remote laptop. Tests were conducted in two stages to evaluate the performance of the sensor array in an attempt to quantify the spray deposition. Initial tests utilized manual droplet placement on the sensor surface to determine the effects of temperature and droplet size on voltage output. Secondary testing utilized a spray chamber to pass nozzles at different speeds above the sensor surface to determine if output varied based on different application rates or spray droplet classification. Results from this preliminary analysis indicated that manual droplets of 5 and 10 mL resulted in significantly different values from the sensors while temperature did not consistently affect output. Spray chamber test results indicated that different application rates and droplet sizes could be determined using the sensor array

Development and Preliminary Evaluation of a Spray Deposition Sensing System for Improving Pesticide Application

An electronic, resistance-based sensor array and data acquisition system was developed to measure spray deposition from hydraulic nozzles. The sensor surface consisted of several parallel tin plated copper traces of varying widths with varying gap widths. The system contained an embedded microprocessor to monitor output voltage corresponding to spray deposition every second. In addition, a wireless module was used to transmit the voltage values to a remote laptop. Tests were conducted in two stages to evaluate the performance of the sensor array in an attempt to quantify the spray deposition. Initial tests utilized manual droplet placement on the sensor surface to determine the effects of temperature and droplet size on voltage output. Secondary testing utilized a spray chamber to pass nozzles at different speeds above the sensor surface to determine if output varied based on different application rates or spray droplet classification. Results from this preliminary analysis indicated that manual droplets of 5 and 10 μL resulted in significantly different values from the sensors while temperature did not consistently affect output. Spray chamber test results indicated that different application rates and droplet sizes could be determined using the sensor array

Antidepressant Use in the Elderly Is Associated With an Increased Risk of Dementia

A retrospective cohort study was conducted including 3688 patients age 60 years or older without dementia enrolled in a depression screening study in primary care clinics. Information on antidepressant use and incident dementia during follow-up was retrieved from electronic medical records. The Cox proportional hazard models were used to compare the risk for incident dementia among 5 participant groups: selective serotonin re-uptake inhibitors (SSRI) only, non-SSRI only (non-SSRI), mixed group of SSRI and non-SSRI, not on antidepressants but depressed, and not on antidepressants and not depressed. SSRI and non-SSRI users had significantly higher dementia risk than the nondepressed nonusers (hazard ratio [HR]=1.83, P=0.0025 for SSRI users and HR=1.50, P=0.004 for non-SSRI users). In addition, SSRIs users had significantly higher dementia risk than non-users with severe depression (HR=2.26, P=0.0005). Future research is needed to confirm our results in other populations and to explore potential mechanism underlying the observed association

Redefined blood pressure variability measure and its association with mortality in elderly primary care patients

Visit-to-visit blood pressure (BP) variability has received considerable attention recently. The objective of our study is to define a variability measure that is independent of change over time and determine the association between longitudinal summary measures of BP measurements and mortality risk. Data for the study came from a prospective cohort of 2906 adults, aged ≥60 years, in an urban primary care system with ≤15 years of follow-up. Dates of death for deceased participants were retrieved from the National Death Index. Systolic and diastolic BP measurements from outpatient clinic visits were extracted from the Regenstrief Medical Record System. For each patient, the intercept, regression slope, and root mean square error for visit-to-visit variability were derived using linear regression models and used as independent variables in Cox proportional hazards models for both all-cause mortality and mortality attributable to coronary heart disease or stroke. Rate of change was associated with mortality risk in a U-shaped relationship and that participants with little or no change in BP had the lowest mortality risk. BP variability was not an independent predictor of mortality risk. By separating change over time from visit-to-visit variability in studies with relatively long follow-up, we demonstrated in this elderly primary care patient population that BP changes over time, not variability, were associated with greater mortality risk. Future research is needed to confirm our findings in other populations

Pancreatic Cysts Identification Using Unstructured Information Management Architecture

poster abstractPancreatic cancer is one of the deadliest cancers, mostly diagnosed at late stages. Patients with pancreatic cysts are at higher risk of developing cancer and surveillance of these patients can help with early diagnosis. Much information about pancreatic cysts can be found in free text format in various medical narratives. In this retrospective study, a corpus of 1064 records from 44 patients at Indiana University Hospital from 1990 to 2012 was collected. A natural language processing system was developed and used to identify patients with pancreatic cysts. The input goes through series of tasks within the Unstructured Information Management Architecture (UIMA) framework consisting of report separation, metadata detection, sentence detection, concept annotation and writing into the database. Metadata such as medical record number (MRN), report id, report name, report date, report body were extracted from each report. Sentences were detected and concepts within each sentence were extracted using regular expression. Regular expression is a pattern of characters matching specific string of text. Our medical team assembled concepts that are used to identify pancreatic cysts in medical reports and additional keywords were added by searching through literature and Unified Medical Language System (UMLS) knowledge base. The Negex Algorithm was used to find out negation status of concepts. The 1064 reports were divided into sets of train and test sets. Two pancreatic-cyst surgeons created the gold standard data (Inter annotator agreement K=88%). The training set was analyzed to modify the regular expression. The concept identification using the NegEx algorithm resulted in precision and recall of 98.9% and 89% respectively. In order to improve the performance of negation detection, Stanford Dependency parser (SDP) was used. SDP finds out how words are related to each other in a sentence. SDP based negation algorithm improved the recall to 95.7%

Pancreatic Cancer Risk Stratification based on Patient Family History

poster abstractBackground: Pancreatic cancer is the fourth leading cause of cancer-related deaths in the US with an annual death rate approximating the incidence (38,460 and 45,220 respectively according to 2013 American Cancer Society). Due to delayed diagnosis, only 8% of patients are amenable to surgical resection, resulting in a 5-year survival rate of less than 6%. Screening the general population for pancreatic cancer is not feasible because of its low incidence (12.1 per 100,000 per year) and the lack of accurate screening tools. However, patients with an inherited predisposition to pancreatic cancer would benefit from selective screening. Methods: Clinical notes of patients from Indiana University (IU) Hospitals were used in this study. A Natural Language Processing (NLP) system based on the Unstructured Information Management Architecture framework was developed to process the family history data and extract pancreatic cancer information. This was performed through a series of NLP processes including report separation, section separation, sentence detection and keyword extraction. The family members and their corresponding diseases were extracted using regular expressions. The Stanford dependency parser was used to accurately link the family member and their diseases. Negation analysis was done using the NegEx algorithm. PancPro risk-prediction software was used to assess the lifetime risk scores of pancreatic cancer for each patient according to his/her family history. A decision tree was constructed based on these scores. Results: A corpus of 2000 reports of patients at IU Hospitals from 1990 to 2012 was collected. The family history section was present in 249 of these reports containing 463 sentences. The system was able to identify 222 reports (accuracy 87.5%) and 458 sentences (accuracy 91.36%). Conclusion: The family history risk score will be used for patients’ pancreatic cancer risk stratification, thus contributing to selective screening

Identification of Patients with Family History of Pancreatic Cancer - Investigation of an NLP System Portability

In this study we have developed a rule-based natural language processing (NLP) system to identify patients with family history of pancreatic cancer. The algorithm was developed in a Unstructured Information Management Architecture (UIMA) framework and consisted of section segmentation, relation discovery, and negation detection. The system was evaluated on data from two institutions. The family history identification precision was consistent across the institutions shifting from 88.9% on Indiana University (IU) dataset to 87.8% on Mayo Clinic dataset. Customizing the algorithm on the the Mayo Clinic data, increased its precision to 88.1%. The family member relation discovery achieved precision, recall, and F-measure of 75.3%, 91.6% and 82.6% respectively. Negation detection resulted in precision of 99.1%. The results show that rule-based NLP approaches for specific information extraction tasks are portable across institutions; however customization of the algorithm on the new dataset improves its performance

DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx

In Electronic Health Records (EHRs), much of valuable information regarding patients’ conditions is embedded in free text format. Natural language processing (NLP) techniques have been developed to extract clinical information from free text. One challenge faced in clinical NLP is that the meaning of clinical entities is heavily affected by modifiers such as negation. A negation detection algorithm, NegEx, applies a simplistic approach that has been shown to be powerful in clinical NLP. However, due to the failure to consider the contextual relationship between words within a sentence, NegEx fails to correctly capture the negation status of concepts in complex sentences. Incorrect negation assignment could cause inaccurate diagnosis of patients’ condition or contaminated study cohorts. We developed a negation algorithm called DEEPEN to decrease NegEx’s false positives by taking into account the dependency relationship between negation words and concepts within a sentence using Stanford dependency parser. The system was developed and tested using EHR data from Indiana University (IU) and it was further evaluated on Mayo Clinic dataset to assess its generalizability. The evaluation results demonstrate DEEPEN, which incorporates dependency parsing into NegEx, can reduce the number of incorrect negation assignment for patients with positive findings, and therefore improve the identification of patients with the target clinical findings in EHRs

Development and Preliminary Evaluation of a Spray Deposition Sensing System for Improving Pesticide Application

An electronic, resistance-based sensor array and data acquisition system was developed to measure spray deposition from hydraulic nozzles. The sensor surface consisted of several parallel tin plated copper traces of varying widths with varying gap widths. The system contained an embedded microprocessor to monitor output voltage corresponding to spray deposition every second. In addition, a wireless module was used to transmit the voltage values to a remote laptop. Tests were conducted in two stages to evaluate the performance of the sensor array in an attempt to quantify the spray deposition. Initial tests utilized manual droplet placement on the sensor surface to determine the effects of temperature and droplet size on voltage output. Secondary testing utilized a spray chamber to pass nozzles at different speeds above the sensor surface to determine if output varied based on different application rates or spray droplet classification. Results from this preliminary analysis indicated that manual droplets of 5 and 10 mL resulted in significantly different values from the sensors while temperature did not consistently affect output. Spray chamber test results indicated that different application rates and droplet sizes could be determined using the sensor array