Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and ... more Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity. Identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients' electronic health records (EHR) is difficult because there is no International Classification of Disease (ICD) code and the only place IDU information can be indicated is unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods: To address this gap in clinical information, we design and demonstrate a questionanswering (QA) framework to extract information on IDU from clinical notes. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing 1 arXiv:2305.08777v2 [cs.AI] 28 Dec 2023 the QA model. We utilize 2323 clinical notes of 1145 patients sourced from the VA Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model's ability to extract IDU-related information on temporally out-of-distribution data. Results: Here we show that for a strict match between gold-standard and predicted answers, the QA model achieves 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.
Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into... more Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into continuous vector spaces (i.e., vector embeddings) are critical for the application of deep learning in healthcare. Initial work in this area explored the use of variants of the word2vec algorithm to learn embeddings for medical concepts from electronic health records or medical claims datasets. We propose learning embeddings for medical concepts by using graphbased representation learning methods on SNOMED-CT, a widely popular knowledge graph in the healthcare domain with numerous operational and research applications. Current work presents an empirical analysis of various embedding methods, including the evaluation of their performance on multiple tasks of biomedical relevance (node classification, link prediction, and patient state prediction). Our results show that concept embeddings derived from the SNOMED-CT knowledge graph significantly outperform state-of-the-art embeddings, showing 5-6x improvement in "concept similarity" and 6-20% improvement in patient diagnosis.
ImportanceSuicide is a leading cause of death; however, the molecular genetic basis of suicidal t... more ImportanceSuicide is a leading cause of death; however, the molecular genetic basis of suicidal thoughts and behaviors (SITB) remains unknown.ObjectiveTo identify novel, replicable genomic risk loci for SITB.Design, Setting, and ParticipantsThis genome-wide association study included 633 778 US military veterans with and without SITB, as identified through electronic health records. GWAS was performed separately by ancestry, controlling for sex, age, and genetic substructure. Cross-ancestry risk loci were identified through meta-analysis. Study enrollment began in 2011 and is ongoing. Data were analyzed from November 2021 to August 2022.Main Outcome and MeasuresSITB.ResultsA total of 633 778 US military veterans were included in the analysis (57 152 [9%] female; 121 118 [19.1%] African ancestry, 8285 [1.3%] Asian ancestry, 452 767 [71.4%] European ancestry, and 51 608 [8.1%] Hispanic ancestry), including 121 211 individuals with SITB (19.1%). Meta-analysis identified more than 200 G...
Biomedical machine reading comprehension (bio-MRC), a crucial task in natural language processing... more Biomedical machine reading comprehension (bio-MRC), a crucial task in natural language processing, is a vital application of a computer-assisted clinical decision support system. It can help clinicians extract critical information effortlessly for clinical decision-making by comprehending and answering questions from biomedical text data. While recent advances in bio-MRC consider text data from resources such as clinical notes and scholarly articles, the clinical practice guidelines (CPGs) are still unexplored in this regard. CPGs are a pivotal component of clinical decision-making at the point of care as they provide recommendations for patient care based on the most up-to-date information available. Although CPGs are inherently terse compared to a multitude of articles, often, clinicians find them lengthy and complicated to use. In this paper, we define a new problem domain-bio-MRC on CPGs-where the ultimate goal is to assist clinicians in efficiently interpreting the clinical practice guidelines using MRC systems. To that end, we develop a manually annotated and subject-matter expert-validated benchmark dataset for the bio-MRC task on CPGs-cpgQA. This dataset aims to evaluate intelligent systems performing MRC tasks on CPGs. Hence, we employ the state-of-the-art MRC models to present a case study illustrating an extensive evaluation of the proposed dataset. We address the problem of lack of training data in this newly defined domain by applying transfer learning. The results show that while the current state-of-the-art models perform well with 78% exact match scores on the dataset, there is still room for improvement, warranting further research on this problem domain. We release the dataset at https://github.com/mmahbub/cpgQA. INDEX TERMS Biomedical machine reading comprehension, clinical practice guidelines, deep learning, natural language processing, transfer learning, transformers.
ABSTRACTRapid and automated extraction of clinical information from patients’ notes is a desirabl... more ABSTRACTRapid and automated extraction of clinical information from patients’ notes is a desirable though difficult task. Natural language processing (NLP) and machine learning have great potential to automate and accelerate such applications, but developing such models can require a large amount of labeled clinical text, which can be a slow and laborious process. To address this gap, we propose the MedDRA tagger, a fast annotation tool that makes use of industrial level libraries such as spaCy, biomedical ontologies and weak supervision to annotate and extract clinical concepts at scale. The tool can be used to annotate clinical text and obtain labels for training machine learning models and further refine the clinical concept extraction performance, or to extract clinical concepts for observational study purposes. To demonstrate the usability and versatility of our tool, we present three different use cases: we use the tagger to determine patients with a primary brain cancer diagn...
Background It is nearly impossible to predict when someone will die from suicide, but with early ... more Background It is nearly impossible to predict when someone will die from suicide, but with early intervention many deaths are preventable. Clinical trials using machine learning algorithms have been able to use patient language to compute the likelihood that someone is suicidal at a particular point in time. Here we determine if the language characteristics associated with suicide risk are persistent 30 days after discharge. Methods Multiple hospital base emergency departments and outpatient clinics were used to enroll subjects (n=253) into one of two groups: suicidal or control. Their responses to standardized instruments and interviews designed to harvest thought markers were recorded and analyzed with machine learning algorithms. Subjects were re-interviewed approximately 30 days later, and the language in their interviews was analyzed to determine the presence of suicidal ideation. Outcomes The results show that the language characteristics used to classify suicidality at the initial encounter are still present in the participants’ speech 30 days later (AUC = 0·89 (95% CI: 0·85-0·95) with p < 0·0001) and that this also holds in the inverse case; classifiers trained on second interviews could identify the cohort that produced the first interviews (AUC = 0·85 (95% CI: 0·81–0·90) with p < 0·0001). Interpretation This approach explores the stability of suicidal interviews 30 days after it is recorded. It does so with computational innovations and well established computational linguistic methods. The results show that the thoughts exhibited by a patient’s language are still valid for machine learning 30-days after first disclosed, but the initial correlation with the standard measures is not. This can be useful when seeking decision support for follow-up care. Funding Statement: Cincinnati Children’s Hospital Medical Center, Innovation Fund. Declaration of Interests: The authors have no competing interests to declare. Ethics Approval Statement: A prospective clinical trial was conducted between October 2013 and March 2015 (Institutional Review Board (#2013-3770) approved).
Developing prediction models for emerging infectious diseases from relatively small numbers of ca... more Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ($${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over...
Developing prediction models for emerging infectious diseases from relatively small numbers of ca... more Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model (TransMED) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of TransMED's predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. TransMED's superior performance over state-of-the-art methods shows that leveraging patient data across modalities and tr...
UNSTRUCTURED Although NLP techniques support automated information extraction in number of indust... more UNSTRUCTURED Although NLP techniques support automated information extraction in number of industries, the adoption of NLP methods to extract patient level information from Electronic Health Records has been slow. This could be attributed to a disconnect between state-of-the-art systems developed by researchers and their ability to support healthcare decision making that leads to improved outcomes. We enumerate a set of practical considerations for developing NLP system that are scientifically innovative and have potential to improve health outcomes. The key considerations that we propose include determining (1) the readiness of the data and compute resources for NLP, (2) the organizational incentives to use and maintain the NLP systems and (3) the feasibility of implementation and evaluation. They are intended to help to enable a system that is well-positioned to scale to other health systems in the US, and globally.
The onset and persistence of life events (LE) such as housing instability, job instability, and r... more The onset and persistence of life events (LE) such as housing instability, job instability, and reduced social connection have been shown to increase risk of suicide. Predictive models for suicide risk have low sensitivity to many of these factors due to under-reporting in structured electronic health records (EHR) data. In this study, we show how natural language processing (NLP) can help identify LE in clinical notes at higher rates than reported medical codes. We compare domain-specific lexicons formulated from Unified Medical Language System (UMLS) selection, content analysis by subject matter experts (SME) and the Gravity Project, to datadriven expansion through contextual word embedding using Word2Vec. Our analysis covers EHR from the VA Corporate Data Warehouse (CDW) and measures the prevalence of LE across time for patients with known underlying cause of death in the National Death Index (NDI). We found that NLP methods had higher sensitivity of detecting LE relative to stru...
ObjectiveTo accelerate the use of outcome measures in rheumatology, we developed and evaluated a ... more ObjectiveTo accelerate the use of outcome measures in rheumatology, we developed and evaluated a natural language processing (NLP) pipeline for extracting these measures from free‐text outpatient rheumatology notes within the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry.MethodsWe included all patients in RISE (2015–2018). The NLP pipeline extracted scores corresponding to 8 measures of rheumatoid arthritis (RA) disease activity (DA) and functional status (FS) documented in outpatient rheumatology notes. Score extraction performance was evaluated by chart review, and we assessed agreement with scores documented in structured data. We conducted an external validation of our NLP pipeline using data from rheumatology notes from an academic medical center that is not included in the RISE registry.ResultsWe processed over 34 million notes from 854,628 patients, 158 practices, and 24 electronic health record (EHR) systems from RIS...
Objective: To design and develop a prototype expert system to aid physicians in diagnosing migrai... more Objective: To design and develop a prototype expert system to aid physicians in diagnosing migraines.
Opiates are some of the most effective pain relief medications available for patients suffering f... more Opiates are some of the most effective pain relief medications available for patients suffering from cancer and surgery-related pain. Despite the affordability and effectiveness of these medications, access to opiates is highly geographically variable. Pain researchers have attributed geographic variation to various factors including the fear of opioid addiction, diversion of legal opiods to the underground market and pharmaceutical industry influences. However, the extent to which there is inequity in untreated cancer and surgery-related pain is unknown. To help opioid investigators study these questions, we designed a tool, the Opioid Atlas, for exploring data on legal opioid consumption, by country and time, collected by the International Narcotics Control Board. Our design borrows ideas from the data visualization and multivariate statistics communities, especially the principles of linking and dimensionality reduction. Our work is relevant to policymakers and pain researchers w...
The United States is in the midst of an opioid epidemic with recent estimates indicating that mor... more The United States is in the midst of an opioid epidemic with recent estimates indicating that more than 130 people die every day due to drug overdose. The over-prescription and addiction to opioid painkillers, heroin, and synthetic opioids, has led to a public health crisis and created a huge social and economic burden. Statistical learning methods that use data from multiple clinical centers across the US to detect opioid over-prescribing trends and predict possible opioid misuse are required. However, the semantic heterogeneity in the representation of clinical data across different centers makes the development and evaluation of such methods difficult and non-trivial. We create the Opioid Drug Knowledge Graph (ODKG) -- a network of opioid-related drugs, active ingredients, formulations, combinations, and brand names. We use the ODKG to normalize drug strings in a clinical data warehouse consisting of patient data from over 400 healthcare facilities in 42 different states. We show...
Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and ... more Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity. Identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients' electronic health records (EHR) is difficult because there is no International Classification of Disease (ICD) code and the only place IDU information can be indicated is unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods: To address this gap in clinical information, we design and demonstrate a questionanswering (QA) framework to extract information on IDU from clinical notes. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing 1 arXiv:2305.08777v2 [cs.AI] 28 Dec 2023 the QA model. We utilize 2323 clinical notes of 1145 patients sourced from the VA Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model's ability to extract IDU-related information on temporally out-of-distribution data. Results: Here we show that for a strict match between gold-standard and predicted answers, the QA model achieves 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.
Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into... more Representation learning methods that transform encoded data (e.g., diagnosis and drug codes) into continuous vector spaces (i.e., vector embeddings) are critical for the application of deep learning in healthcare. Initial work in this area explored the use of variants of the word2vec algorithm to learn embeddings for medical concepts from electronic health records or medical claims datasets. We propose learning embeddings for medical concepts by using graphbased representation learning methods on SNOMED-CT, a widely popular knowledge graph in the healthcare domain with numerous operational and research applications. Current work presents an empirical analysis of various embedding methods, including the evaluation of their performance on multiple tasks of biomedical relevance (node classification, link prediction, and patient state prediction). Our results show that concept embeddings derived from the SNOMED-CT knowledge graph significantly outperform state-of-the-art embeddings, showing 5-6x improvement in "concept similarity" and 6-20% improvement in patient diagnosis.
ImportanceSuicide is a leading cause of death; however, the molecular genetic basis of suicidal t... more ImportanceSuicide is a leading cause of death; however, the molecular genetic basis of suicidal thoughts and behaviors (SITB) remains unknown.ObjectiveTo identify novel, replicable genomic risk loci for SITB.Design, Setting, and ParticipantsThis genome-wide association study included 633 778 US military veterans with and without SITB, as identified through electronic health records. GWAS was performed separately by ancestry, controlling for sex, age, and genetic substructure. Cross-ancestry risk loci were identified through meta-analysis. Study enrollment began in 2011 and is ongoing. Data were analyzed from November 2021 to August 2022.Main Outcome and MeasuresSITB.ResultsA total of 633 778 US military veterans were included in the analysis (57 152 [9%] female; 121 118 [19.1%] African ancestry, 8285 [1.3%] Asian ancestry, 452 767 [71.4%] European ancestry, and 51 608 [8.1%] Hispanic ancestry), including 121 211 individuals with SITB (19.1%). Meta-analysis identified more than 200 G...
Biomedical machine reading comprehension (bio-MRC), a crucial task in natural language processing... more Biomedical machine reading comprehension (bio-MRC), a crucial task in natural language processing, is a vital application of a computer-assisted clinical decision support system. It can help clinicians extract critical information effortlessly for clinical decision-making by comprehending and answering questions from biomedical text data. While recent advances in bio-MRC consider text data from resources such as clinical notes and scholarly articles, the clinical practice guidelines (CPGs) are still unexplored in this regard. CPGs are a pivotal component of clinical decision-making at the point of care as they provide recommendations for patient care based on the most up-to-date information available. Although CPGs are inherently terse compared to a multitude of articles, often, clinicians find them lengthy and complicated to use. In this paper, we define a new problem domain-bio-MRC on CPGs-where the ultimate goal is to assist clinicians in efficiently interpreting the clinical practice guidelines using MRC systems. To that end, we develop a manually annotated and subject-matter expert-validated benchmark dataset for the bio-MRC task on CPGs-cpgQA. This dataset aims to evaluate intelligent systems performing MRC tasks on CPGs. Hence, we employ the state-of-the-art MRC models to present a case study illustrating an extensive evaluation of the proposed dataset. We address the problem of lack of training data in this newly defined domain by applying transfer learning. The results show that while the current state-of-the-art models perform well with 78% exact match scores on the dataset, there is still room for improvement, warranting further research on this problem domain. We release the dataset at https://github.com/mmahbub/cpgQA. INDEX TERMS Biomedical machine reading comprehension, clinical practice guidelines, deep learning, natural language processing, transfer learning, transformers.
ABSTRACTRapid and automated extraction of clinical information from patients’ notes is a desirabl... more ABSTRACTRapid and automated extraction of clinical information from patients’ notes is a desirable though difficult task. Natural language processing (NLP) and machine learning have great potential to automate and accelerate such applications, but developing such models can require a large amount of labeled clinical text, which can be a slow and laborious process. To address this gap, we propose the MedDRA tagger, a fast annotation tool that makes use of industrial level libraries such as spaCy, biomedical ontologies and weak supervision to annotate and extract clinical concepts at scale. The tool can be used to annotate clinical text and obtain labels for training machine learning models and further refine the clinical concept extraction performance, or to extract clinical concepts for observational study purposes. To demonstrate the usability and versatility of our tool, we present three different use cases: we use the tagger to determine patients with a primary brain cancer diagn...
Background It is nearly impossible to predict when someone will die from suicide, but with early ... more Background It is nearly impossible to predict when someone will die from suicide, but with early intervention many deaths are preventable. Clinical trials using machine learning algorithms have been able to use patient language to compute the likelihood that someone is suicidal at a particular point in time. Here we determine if the language characteristics associated with suicide risk are persistent 30 days after discharge. Methods Multiple hospital base emergency departments and outpatient clinics were used to enroll subjects (n=253) into one of two groups: suicidal or control. Their responses to standardized instruments and interviews designed to harvest thought markers were recorded and analyzed with machine learning algorithms. Subjects were re-interviewed approximately 30 days later, and the language in their interviews was analyzed to determine the presence of suicidal ideation. Outcomes The results show that the language characteristics used to classify suicidality at the initial encounter are still present in the participants’ speech 30 days later (AUC = 0·89 (95% CI: 0·85-0·95) with p < 0·0001) and that this also holds in the inverse case; classifiers trained on second interviews could identify the cohort that produced the first interviews (AUC = 0·85 (95% CI: 0·81–0·90) with p < 0·0001). Interpretation This approach explores the stability of suicidal interviews 30 days after it is recorded. It does so with computational innovations and well established computational linguistic methods. The results show that the thoughts exhibited by a patient’s language are still valid for machine learning 30-days after first disclosed, but the initial correlation with the standard measures is not. This can be useful when seeking decision support for follow-up care. Funding Statement: Cincinnati Children’s Hospital Medical Center, Innovation Fund. Declaration of Interests: The authors have no competing interests to declare. Ethics Approval Statement: A prospective clinical trial was conducted between October 2013 and March 2015 (Institutional Review Board (#2013-3770) approved).
Developing prediction models for emerging infectious diseases from relatively small numbers of ca... more Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ($${\textsc {TransMED}}$$ T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of $${\textsc {TransMED}}$$ T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. $${\textsc {TransMED}}$$ T R A N S MED ’s superior performance over...
Developing prediction models for emerging infectious diseases from relatively small numbers of ca... more Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model (TransMED) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of TransMED's predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. TransMED's superior performance over state-of-the-art methods shows that leveraging patient data across modalities and tr...
UNSTRUCTURED Although NLP techniques support automated information extraction in number of indust... more UNSTRUCTURED Although NLP techniques support automated information extraction in number of industries, the adoption of NLP methods to extract patient level information from Electronic Health Records has been slow. This could be attributed to a disconnect between state-of-the-art systems developed by researchers and their ability to support healthcare decision making that leads to improved outcomes. We enumerate a set of practical considerations for developing NLP system that are scientifically innovative and have potential to improve health outcomes. The key considerations that we propose include determining (1) the readiness of the data and compute resources for NLP, (2) the organizational incentives to use and maintain the NLP systems and (3) the feasibility of implementation and evaluation. They are intended to help to enable a system that is well-positioned to scale to other health systems in the US, and globally.
The onset and persistence of life events (LE) such as housing instability, job instability, and r... more The onset and persistence of life events (LE) such as housing instability, job instability, and reduced social connection have been shown to increase risk of suicide. Predictive models for suicide risk have low sensitivity to many of these factors due to under-reporting in structured electronic health records (EHR) data. In this study, we show how natural language processing (NLP) can help identify LE in clinical notes at higher rates than reported medical codes. We compare domain-specific lexicons formulated from Unified Medical Language System (UMLS) selection, content analysis by subject matter experts (SME) and the Gravity Project, to datadriven expansion through contextual word embedding using Word2Vec. Our analysis covers EHR from the VA Corporate Data Warehouse (CDW) and measures the prevalence of LE across time for patients with known underlying cause of death in the National Death Index (NDI). We found that NLP methods had higher sensitivity of detecting LE relative to stru...
ObjectiveTo accelerate the use of outcome measures in rheumatology, we developed and evaluated a ... more ObjectiveTo accelerate the use of outcome measures in rheumatology, we developed and evaluated a natural language processing (NLP) pipeline for extracting these measures from free‐text outpatient rheumatology notes within the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry.MethodsWe included all patients in RISE (2015–2018). The NLP pipeline extracted scores corresponding to 8 measures of rheumatoid arthritis (RA) disease activity (DA) and functional status (FS) documented in outpatient rheumatology notes. Score extraction performance was evaluated by chart review, and we assessed agreement with scores documented in structured data. We conducted an external validation of our NLP pipeline using data from rheumatology notes from an academic medical center that is not included in the RISE registry.ResultsWe processed over 34 million notes from 854,628 patients, 158 practices, and 24 electronic health record (EHR) systems from RIS...
Objective: To design and develop a prototype expert system to aid physicians in diagnosing migrai... more Objective: To design and develop a prototype expert system to aid physicians in diagnosing migraines.
Opiates are some of the most effective pain relief medications available for patients suffering f... more Opiates are some of the most effective pain relief medications available for patients suffering from cancer and surgery-related pain. Despite the affordability and effectiveness of these medications, access to opiates is highly geographically variable. Pain researchers have attributed geographic variation to various factors including the fear of opioid addiction, diversion of legal opiods to the underground market and pharmaceutical industry influences. However, the extent to which there is inequity in untreated cancer and surgery-related pain is unknown. To help opioid investigators study these questions, we designed a tool, the Opioid Atlas, for exploring data on legal opioid consumption, by country and time, collected by the International Narcotics Control Board. Our design borrows ideas from the data visualization and multivariate statistics communities, especially the principles of linking and dimensionality reduction. Our work is relevant to policymakers and pain researchers w...
The United States is in the midst of an opioid epidemic with recent estimates indicating that mor... more The United States is in the midst of an opioid epidemic with recent estimates indicating that more than 130 people die every day due to drug overdose. The over-prescription and addiction to opioid painkillers, heroin, and synthetic opioids, has led to a public health crisis and created a huge social and economic burden. Statistical learning methods that use data from multiple clinical centers across the US to detect opioid over-prescribing trends and predict possible opioid misuse are required. However, the semantic heterogeneity in the representation of clinical data across different centers makes the development and evaluation of such methods difficult and non-trivial. We create the Opioid Drug Knowledge Graph (ODKG) -- a network of opioid-related drugs, active ingredients, formulations, combinations, and brand names. We use the ODKG to normalize drug strings in a clinical data warehouse consisting of patient data from over 400 healthcare facilities in 42 different states. We show...
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Papers by Suzanne Tamang