1-0020 TITLE : Health-Terrain : Visualizing Large Scale Health Data

The Notifiable Condition Detector (NCD) system is an automated electronic lab reporting (ELR) and casenotification system developed by Regenstrief Institute. It has been used in Indiana for over ten years to report laboratory results for the detection of notifiable conditions such as novel H1N1 influenza, sexually transmitted diseases, lead poisoning, and salmonella 1 . In this paper, we present a knowledge discovery system which integrates database, text mining and data mining on the NCD data. We show that the knowledge discovery system could not only discover new associations among terminologies in health science but also enhance friendly visualization application to show more significant and useful data to users. Introduction The Regenstrief Institute implemented and maintains an HIE-based, automated electronic lab reporting (ELR) and case-notification system for over ten years in Indiana State Marion County. The Notifiable Condition Detector (NCD) System uses a standards-based messaging and vocabulary infrastructure that includes Health Level Seven (HL7) and Logical Observation Identifiers Names and Codes (LOINC). The NCD receives real-time HL7 version 2 clinical transactions daily, including diagnoses, laboratory studies, and transcriptions from hospitals, national labs and local ancillary service organizations. The NCD, now operational in Indiana, automatically detects positive cases of indicated conditions and forwards alerts to local and state health departments for review and possible follow up. These alerts assist public health agencies to perform population health monitoring more efficiently and effectively. Data Summary The dataset we received from Regenstrief NCD system contains 833,710 observations. The dataset has been deidentified, with patient age and zip code pseudonymized. The dataset contains 47 columns including patient pseudo ID, condition name, test result name, test result value, test normal range, patient race, patient gender, etc. The missing data rate for columns varied substantially from 0% for column Patient Pseudo_ID to over 70% for column Test_Abnormal_Flag. Methods The NCD receives clinical data that includes the diagnoses, laboratory studies, and transcriptions from hospitals, national labs and local ancillary service organizations. But much of the information pertaining the patients’ condition is available in the clinical reports. Mining these reports can provide a bigger picture of various other conditions that the patient experienced during his/her treatment. This information can provide valuable insights on the patients’ socioeconomic condition, behavior risk factors, environmental factors and genetic information (family history). Natural Language Processing (NLP) provides a means to augment the NCD data analytics with the information discovered from these clinical reports. Text Mining Process NLP techniques were carried out to process 325791 clinical notes that contain patient discharge summaries, laboratory reports, patient history, etc. Although these records are de-identified due to which the patient specific information are absent, a pseudo-patient Id has been provided to help process the reports. Basic processing of the reports was performed for converting the clinical notes from XML format to simple text format and sentence splitting. Advanced level NLP was applied in the form of named entity recognition (NER) for extracting diseases, symptoms, mental behavior, risky behavior and medication information from the reports. This was done with the help of UMLS database which is a repository of clinical and health related terms. Once the entities were extracted using NER, negation analysis was applied using NEGEX algorithm to remove negated terms. Figure 1 shows the process that was used in extracting this vital information from the reports. The advantage of using UMLS is that all variations of clinical terms get captured that provide a large set of terms available for further analysis. For example, clinical notes that indicate “Hepatitis” contains terms like “Hepatitis”, “Hepatitis B”, “Hep”, “Hep B” etc. The large number of terms extracted contains different occurrences of the same diseases, symptoms, etc. We apply stemming and grouping algorithms to group these terms to reduce the total number of terms. The identified terms are stored in different data tables and joined using the pseudo-patient Id. Figure 1. NLP steps applied on clinical reports Comorbidity Analysis Once the data tables are constructed, we perform deeper analysis to compute the comorbid conditions of the diseases. For this, we use the tf-idf (term frequency – inverse document frequency) vector space model to identify the significantly co-occurring diseases. The tf-idf model is considered to be an effective text mining model that provides the importance of a term/word to a document in a collection of documents. This model uses the concept of relevance and co-occurrence of terms. Equation (1) gives the relevance of a term j w.r. t. a document i,