Browsing by Author "Krishnan, Anand"

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    Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records
    (2018) Patel, Jay; Mowery, Danielle; Krishnan, Anand; Thyvalikakath, Thankam
    Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it’s unclear to what extent patient-reported CVD information is accurately captured in Electronic Dental Records (EDRs). In this pilot study, we aimed to measure the reliability of patient-reported CVD conditions in EDRs. We assessed information congruence by comparing patients’ self-reported dental histories to their original diagnosis assigned by their medical providers in the Electronic Medical Record (EMR). To enable this comparison, we encoded patients CVD information from the free-text data of EDRs into a structured format using natural language processing (NLP). Overall, our NLP approach achieved promising performance extracting patients’ CVD-related information. We observed disagreement between self-reported EDR data and physician-diagnosed EMR data.
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    Automated pancreatic cyst screening using natural language processing: a new tool in the early detection of pancreatic cancer
    (Elsevier, 2015-05) Roch, Alexandra M.; Mehrabi, Saeed; Krishnan, Anand; Schmidt, Heidi E.; Kesterson, Joseph; Beesley, Chris; Dexter, Paul R.; Palakal, Matthew; Schmidt, C. Max; Department of Surgery, IU School of Medicine
    INTRODUCTION: As many as 3% of computed tomography (CT) scans detect pancreatic cysts. Because pancreatic cysts are incidental, ubiquitous and poorly understood, follow-up is often not performed. Pancreatic cysts may have a significant malignant potential and their identification represents a 'window of opportunity' for the early detection of pancreatic cancer. The purpose of this study was to implement an automated Natural Language Processing (NLP)-based pancreatic cyst identification system. METHOD: A multidisciplinary team was assembled. NLP-based identification algorithms were developed based on key words commonly used by physicians to describe pancreatic cysts and programmed for automated search of electronic medical records. A pilot study was conducted prospectively in a single institution. RESULTS: From March to September 2013, 566,233 reports belonging to 50,669 patients were analysed. The mean number of patients reported with a pancreatic cyst was 88/month (range 78-98). The mean sensitivity and specificity were 99.9% and 98.8%, respectively. CONCLUSION: NLP is an effective tool to automatically identify patients with pancreatic cysts based on electronic medical records (EMR). This highly accurate system can help capture patients 'at-risk' of pancreatic cancer in a registry.
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    Extraction and Evaluation of Medication Data from Electronic Dental Records
    (IOS Press, 2017) Wang, Yue; Siddiqui, Zasim; Krishnan, Anand; Patel, Jay; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of Dentistry
    With an increase in the geriatric population, dental care professionals are presented with older patients who are managing their comorbidities using multiple medications. In this study, we developed a system to extract medication information from electronic dental records (EDRs) and provided patient distribution by the number of medications.
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    Extraction and Evaluation of Medication Data from Electronic Dental Records
    (IOS Press, 2017) Wang, Yue; Siddiqui, Zasim; Krishnan, Anand; Patel, Jay; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of Dentistry
    With an increase in the geriatric population, dental care professionals are presented with older patients who are managing their comorbidities using multiple medications. In this study, we developed a system to extract medication information from electronic dental records (EDRs) and provided patient distribution by the number of medications.
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    Finding the Patient’s Voice Using Big Data: Analysis of Users’ Health-Related Concerns in the ChaCha Question-and-Answer Service (2009–2012)
    (JMIR, 2016) Priest, Chad; Knopf, Amelia; Groves, Doyle; Carpenter, Janet S.; Furrey, Christopher; Krishnan, Anand; Miller, Wendy R.; Otte, Julie L.; Palakal, Mathew; Wiehe, Sarah E.; Wilson, Jeffrey S.; IU School of Nursing
    Background: The development of effective health care and public health interventions requires a comprehensive understanding of the perceptions, concerns, and stated needs of health care consumers and the public at large. Big datasets from social media and question-and-answer services provide insight into the public’s health concerns and priorities without the financial, temporal, and spatial encumbrances of more traditional community-engagement methods and may prove a useful starting point for public-engagement health research (infodemiology). Objective: The objective of our study was to describe user characteristics and health-related queries of the ChaCha question-and-answer platform, and discuss how these data may be used to better understand the perceptions, concerns, and stated needs of health care consumers and the public at large. Methods: We conducted a retrospective automated textual analysis of anonymous user-generated queries submitted to ChaCha between January 2009 and November 2012. A total of 2.004 billion queries were read, of which 3.50% (70,083,796/2,004,243,249) were missing 1 or more data fields, leaving 1.934 billion complete lines of data for these analyses. Results: Males and females submitted roughly equal numbers of health queries, but content differed by sex. Questions from females predominantly focused on pregnancy, menstruation, and vaginal health. Questions from males predominantly focused on body image, drug use, and sexuality. Adolescents aged 12–19 years submitted more queries than any other age group. Their queries were largely centered on sexual and reproductive health, and pregnancy in particular. Conclusions: The private nature of the ChaCha service provided a perfect environment for maximum frankness among users, especially among adolescents posing sensitive health questions. Adolescents’ sexual health queries reveal knowledge gaps with serious, lifelong consequences. The nature of questions to the service provides opportunities for rapid understanding of health concerns and may lead to development of more effective tailored interventions. [J Med Internet Res 2016;18(3):e44]
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    IDENTIFICATION OF CAUSE AND EFFECT IN CAUSAL SENTENCES OF GERIATRIC CARE DOMAIN USING CONDITIONAL RANDOM
    (Office of the Vice Chancellor for Research, 2012-04-13) Mehrabi, Saeed; Krishnan, Anand; Palakal, Mathew
    Event extraction is a key step in many text mining applications. Identified events can be used in various applications such as question-answering systems, information extraction, summarization or building the knowledge base of a clinical decision support system. In this study we used PubMed abstracts of Geriatric care domain that were manually categorized into 42 different subdomains and further divided into causal and non-causal sentences by three domain experts. There are a total of 19,677 sentences in the collected abstracts from PubMed, out of which 2,856 sentences were selected and manually annotated with cause and effect events. We used conditional random fields (CRFs) that are statistical algorithms used to sequentially tag each word in a sentence as a cause or effect event based on some input variables or features. Features used in this study are words, words categories (lowercase, uppercase, mixed of letter and digits, etc.), affixes, part of speech and phrase chunks such as noun or verb phrase. For every word, a window of features before and after each word was also considered. We tested window of size, one to five meaning one to five features before and after each word was included as the input variables. The CRF algorithm was trained and tested on data set with 2,520 sentences in training set, 252 sentences in validation and 84 sentences in test set. Window of four features before and after each word had the best performance with 75.1% accuracy and F-measure of 85% with 84.6% precision and 87% recall.
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    Identification of Patients with Family History of Pancreatic Cancer - Investigation of an NLP System Portability
    (IOS, 2015) Mehrabi, Saeed; Krishnan, Anand; Roch, Alexandra M.; Schmidt, Heidi; Li, DingCheng; Kesterson, Joe; Beesley, Chris; Dexter, Paul; Schmidt, Max; Palakal, Mathew; Liu, Hongfang; Department of BioHealth Informatics, School of Informatics and Computing
    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.
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    Identifying Patients' Smoking Status from Electronic Dental Records Data
    (IOS Press, 2017) Patel, Jay; Siddiqui, Zasim; Krishnan, Anand; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of Dentistry
    Smoking is a significant risk factor for initiation and progression of oral diseases. A patient's current smoking status and tobacco dependency can aid clinical decision making and treatment planning. The free-text nature of this data limits accessibility causing obstacles during the time of care and research utility. No studies exist on extracting patient's smoking status automatically from the Electronic Dental Record. This study reports the development and evaluation of an NLP system for this purpose.
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    Identifying Patients' Smoking Status from Electronic Dental Records Data
    (IOS Press, 2017) Patel, Jay; Siddiqui, Zasim; Krishnan, Anand; Thyvalikakath, Thankam; Cariology, Operative Dentistry and Dental Public Health, School of Dentistry
    Smoking is a significant risk factor for initiation and progression of oral diseases. A patient's current smoking status and tobacco dependency can aid clinical decision making and treatment planning. The free-text nature of this data limits accessibility causing obstacles during the time of care and research utility. No studies exist on extracting patient's smoking status automatically from the Electronic Dental Record. This study reports the development and evaluation of an NLP system for this purpose.
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    MINING CAUSAL ASSOCIATIONS FROM GERIATRIC LITERATURE
    (2013-08-14) Krishnan, Anand; Palakal, Mathew J; Xia, Yuni; Durresi, Arjan; Fang, Shiaofen
    Literature pertaining to geriatric care contains rich information regarding the best practices related to geriatric health care issues. The publication domain of geriatric care is small as compared to other health related areas, however, there are over a million articles pertaining to different cases and case interventions capturing best practice outcomes. If the data found in these articles could be harvested and processed effectively, such knowledge could then be translated from research to practice in a quicker and more efficient manner. Geriatric literature contains multiple domains or practice areas and within these domains is a wealth of information such as interventions, information on care for elderly, case studies, and real life scenarios. These articles are comprised of a variety of causal relationships such as the relationship between interventions and disorders. The goal of this study is to identify these causal relations from published abstracts. Natural language processing and statistical methods were adopted to identify and extract these causal relations. Using the developed methods, causal relations were extracted with precision of 79.54%, recall of 81% while only having a false positive rate 8%.