Medical data and text mining: Linking diseases, drugs, and adverse reactions
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The document discusses using Danish medical registries and electronic health records to conduct medical text and data mining. It aims to link diseases, drugs, and adverse reactions by analyzing structured and unstructured data. Key details include Denmark's long-established central registries containing data on prescriptions, hospital contacts, and diagnoses for its entire population. Researchers have used this data to study disease trajectories, adverse drug reactions, and discover new relationships by analyzing temporal correlations and text mining free-form clinical notes. Challenges include dealing with missing or incorrect data as well as underreporting of certain conditions.
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Medical data and text mining: Linking diseases, drugs, and adverse reactions
This document discusses using medical data and text mining to link diseases, drugs, and adverse reactions. It describes how structured and unstructured data from Danish medical registries dating back to 1968, containing records on millions of patients, admissions, diagnoses and medications, can be mined to discover disease trajectories, comorbidities, known and unknown factors. It also discusses how text mining of unstructured free-text clinical notes in Danish, after developing comprehensive dictionaries and rules, can help identify adverse drug reactions that were not reported or known previously. This combined approach of mining structured and unstructured data helps gain new medical insights and has direct implications for patient care.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques to link diseases, drugs, and adverse reactions using Danish healthcare registries. It describes how Danish registries contain data on disease diagnoses, medications, and hospital admissions for the entire Danish population dating back to 1968. The registries contain both structured data from medical records and unstructured free-text data. The author discusses how natural language processing and text mining techniques are used to extract additional information on adverse drug reactions from the unstructured free-text data to supplement the structured data from the registries. This combined structured and unstructured data can then be analyzed using techniques like trajectory networks and statistical modeling to discover relationships between drugs, diseases and adverse reactions.
Medical informatics - Linking diseases, drugs, and adverse reactions
This document discusses Denmark's medical informatics capabilities for linking patient data like diseases, drugs, and adverse reactions. Denmark has nationwide registries containing structured data from central registries and unstructured data from hospital electronic health records. The registries include the Civil Registration System established in 1968, the National Patient Registry containing data on hospital admissions dating back 14 years, and the Danish National Prescription Registry. Researchers use this data linked through personal identification numbers to study disease trajectories, adverse drug reactions, and discover new relationships without direct patient consent by law through opt-out provisions. Challenges include analyzing unstructured free-text clinical notes in Danish for relationships between medications and adverse events.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using Danish healthcare registries. It describes how Danish registries contain data from central registries and individual hospitals dating back to 1968. These registries contain information on patients' diagnoses, medications, and hospital admissions. The document also discusses how natural language processing is used to extract structured information from unstructured free-text clinical notes to identify diseases, drugs, adverse reactions and relationships between them. This information can then be used to discover new adverse drug reactions and their frequencies.
Medical data and text mining - Linking diseases, drugs, and adverse reactions
This document summarizes Lars Juhl Jensen's work on linking medical data like diseases, drugs, and adverse reactions through text and data mining. It discusses Jensen's research using Danish healthcare registries containing records on millions of patients to analyze diagnosis trajectories and discover unknown adverse drug reactions. The document also outlines Jensen's development of computer systems that can mine both structured medical data and unstructured free-text to automatically identify relationships between diseases, medications, and side effects.
Medical data and text mining - Linking diseases, drugs, and adverse reactions
This document discusses using medical data and text mining to link diseases, drugs, and adverse reactions. It describes analyzing structured data from Danish registries containing diagnosis trajectories of 6.2 million patients over 14 years. It also discusses mining unstructured free-text data from medical records using a dictionary and rule-based approach to identify mentions of diseases, drugs, and adverse reactions. The goal is to discover new adverse drug reactions and estimate their frequencies to help with pharmacovigilance and clinical trials.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using structured and unstructured data from Danish healthcare registries and electronic health records. It describes analyzing registry data covering 14 years of patient records to study diagnosis trajectories, comorbidities, and other outcomes. It also discusses challenges in analyzing free-text clinical records, such as complex terminology, abbreviations, and misspellings, and techniques used to recognize named entities and identify adverse drug reactions mentioned in the text. Finally, it acknowledges contributions from researchers involved in studying disease trajectories, adverse drug reactions, and electronic health record text mining.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
This document discusses medical data and text mining techniques for linking diseases, drugs, and adverse reactions. It describes using Danish healthcare registries containing data on patients, diagnoses, medications, and other structured clinical information. It also discusses mining unstructured clinical text using named entity recognition and dictionaries to extract disease, drug, and adverse reaction mentions and identify relationships between them to discover known and unknown drug-adverse reaction associations. Rule-based algorithms are used to classify mentions as actual adverse drug reactions.
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Medical data and text mining: Linking diseases, drugs, and adverse reactions
This document discusses using medical data and text mining to link diseases, drugs, and adverse reactions. It describes how structured and unstructured data from Danish medical registries dating back to 1968, containing records on millions of patients, admissions, diagnoses and medications, can be mined to discover disease trajectories, comorbidities, known and unknown factors. It also discusses how text mining of unstructured free-text clinical notes in Danish, after developing comprehensive dictionaries and rules, can help identify adverse drug reactions that were not reported or known previously. This combined approach of mining structured and unstructured data helps gain new medical insights and has direct implications for patient care.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques to link diseases, drugs, and adverse reactions using Danish healthcare registries. It describes how Danish registries contain data on disease diagnoses, medications, and hospital admissions for the entire Danish population dating back to 1968. The registries contain both structured data from medical records and unstructured free-text data. The author discusses how natural language processing and text mining techniques are used to extract additional information on adverse drug reactions from the unstructured free-text data to supplement the structured data from the registries. This combined structured and unstructured data can then be analyzed using techniques like trajectory networks and statistical modeling to discover relationships between drugs, diseases and adverse reactions.
Medical informatics - Linking diseases, drugs, and adverse reactions
This document discusses Denmark's medical informatics capabilities for linking patient data like diseases, drugs, and adverse reactions. Denmark has nationwide registries containing structured data from central registries and unstructured data from hospital electronic health records. The registries include the Civil Registration System established in 1968, the National Patient Registry containing data on hospital admissions dating back 14 years, and the Danish National Prescription Registry. Researchers use this data linked through personal identification numbers to study disease trajectories, adverse drug reactions, and discover new relationships without direct patient consent by law through opt-out provisions. Challenges include analyzing unstructured free-text clinical notes in Danish for relationships between medications and adverse events.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using Danish healthcare registries. It describes how Danish registries contain data from central registries and individual hospitals dating back to 1968. These registries contain information on patients' diagnoses, medications, and hospital admissions. The document also discusses how natural language processing is used to extract structured information from unstructured free-text clinical notes to identify diseases, drugs, adverse reactions and relationships between them. This information can then be used to discover new adverse drug reactions and their frequencies.
Medical data and text mining - Linking diseases, drugs, and adverse reactions
This document summarizes Lars Juhl Jensen's work on linking medical data like diseases, drugs, and adverse reactions through text and data mining. It discusses Jensen's research using Danish healthcare registries containing records on millions of patients to analyze diagnosis trajectories and discover unknown adverse drug reactions. The document also outlines Jensen's development of computer systems that can mine both structured medical data and unstructured free-text to automatically identify relationships between diseases, medications, and side effects.
Medical data and text mining - Linking diseases, drugs, and adverse reactions
This document discusses using medical data and text mining to link diseases, drugs, and adverse reactions. It describes analyzing structured data from Danish registries containing diagnosis trajectories of 6.2 million patients over 14 years. It also discusses mining unstructured free-text data from medical records using a dictionary and rule-based approach to identify mentions of diseases, drugs, and adverse reactions. The goal is to discover new adverse drug reactions and estimate their frequencies to help with pharmacovigilance and clinical trials.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using structured and unstructured data from Danish healthcare registries and electronic health records. It describes analyzing registry data covering 14 years of patient records to study diagnosis trajectories, comorbidities, and other outcomes. It also discusses challenges in analyzing free-text clinical records, such as complex terminology, abbreviations, and misspellings, and techniques used to recognize named entities and identify adverse drug reactions mentioned in the text. Finally, it acknowledges contributions from researchers involved in studying disease trajectories, adverse drug reactions, and electronic health record text mining.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
This document discusses medical data and text mining techniques for linking diseases, drugs, and adverse reactions. It describes using Danish healthcare registries containing data on patients, diagnoses, medications, and other structured clinical information. It also discusses mining unstructured clinical text using named entity recognition and dictionaries to extract disease, drug, and adverse reaction mentions and identify relationships between them to discover known and unknown drug-adverse reaction associations. Rule-based algorithms are used to classify mentions as actual adverse drug reactions.
Medical data and text mining - Linking diseases, drugs, and adverse reactions
This document discusses medical data and text mining to link diseases, drugs, and adverse reactions. It describes using Danish registries containing structured medical data and unstructured free-text data from hospital records to study disease trajectories, adverse drug events, and discover new adverse drug reactions and their frequencies through data and text mining. The challenges of analyzing medical text in Danish from busy doctors is also addressed.
Data and text mining of Danish electronic health records
This document discusses data and text mining of Danish electronic health records. It notes that Denmark has established centralized medical registries since 1968. These registries contain data on 6.2 million patients, 45 million admissions, and 119 million diagnoses. Both structured data from registries and unstructured free-text data from medical records are analyzed. Text mining is used to extract information from the free-text data through techniques like dictionary-based coding. Studies have used this data to develop detailed disease profiles, identify adverse drug events, and study diagnosis trajectories and networks. Challenges include dealing with confounding factors and missing or unreported information.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses using Danish medical registries containing data on over 6.2 million patients and 45 million hospital admissions over 14 years to study disease trajectories and adverse drug reactions through medical data and text mining. It describes how structured data in the registries on diagnoses, treatments and prescriptions can be linked to study relationships between diseases and drugs. It also explains how natural language processing of unstructured free-text data from medical records can help identify adverse drug reactions mentioned in notes. The goal is to gain insights into diagnosis trajectories, discover new adverse drug reactions, and better estimate frequencies of adverse reactions.
Data and text mining of electronic health records
The document discusses data and text mining of electronic health records from Denmark to study disease trajectories, comorbidities, adverse drug reactions, and other topics. It describes the Danish registries which contain data on over 6 million patients and over 68 million records spanning 14 years. Both structured data and unstructured free-text data are analyzed to discover patterns and relationships. Text mining is used to extract information from clinical notes written in Danish. The studies have provided new insights into topics like diagnosis trajectories, drug safety, and precision medicine.
Medical Data Mining
This document discusses medical data mining of Danish healthcare registries containing structured and unstructured data from electronic health records. It describes how researchers have used the Danish civil registration system and national registries containing diagnosis and hospital admission data spanning decades to study disease trajectories, comorbidities, and confounding factors. It also explains how natural language processing of free-text clinical notes has allowed identification of adverse drug reactions and new disease associations by linking medications, diagnoses, and symptoms over time despite challenges from unstructured data like typos and physician notetaking styles. Combining structured registry data with text mining of clinical notes enables public health monitoring through pharmacovigilance and discovery of new adverse drug reactions.
Medical Data Mining
Medical data mining can analyze both structured and unstructured data from Danish healthcare registries and electronic health records. These extensive sources contain data on patients' diagnoses, hospital visits, medications, and free-text clinical notes. Researchers have used this data to study disease trajectories, comorbidities, and potential adverse drug reactions through statistical analysis and text mining techniques. However, accounting for confounding factors, reporting biases, and "unknown unknowns" poses challenges to obtaining accurate insights from such complex real-world data.
Medical data and text mining - Linking diseases, drugs, and adverse reactions
The document discusses using Danish medical registries and electronic health records to link diseases, drugs, and adverse reactions through data and text mining. It describes how Danish registries contain data on 6.2 million patients and their healthcare encounters over 14 years. While this data was originally collected for reimbursement purposes, it can be used for research by identifying diagnosis trajectories and comorbidities. The document also discusses how text mining of free-text clinical notes, which describe approximately 30% of the data, can help identify adverse drug reactions that may not be captured in structured data fields alone. By developing dictionaries of diseases, drugs, and their variations, along with hand-crafted rules, researchers have been able to detect known and potentially new adverse
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This document provides health data from the Field Ganj Area-C in Ludhiana, India collected between October 2012-April 2013. It summarizes population statistics, family planning methods used, vital events like births and deaths, immunization rates of children, nutritional status of children under 5, maternal health services, tuberculosis treatment, common illnesses, and deaths in the community. Diabetes is a major health issue, with efforts made to educate people on managing it through lifestyle changes and medication adherence.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
This document discusses medical data and text mining to link diseases, drugs, and adverse reactions. It describes using structured data from Danish central registries and unstructured data from hospital electronic health records. Named entity recognition is used to extract diseases, drugs, and adverse reactions from free text clinical notes written in Danish. Hand-crafted rules are developed to identify relationships between extracted entities like adverse drug reactions. This allows estimating frequencies of known adverse drug reactions and discovering new adverse drug reactions by analyzing diagnosis trajectories and medication information.
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1) The document discusses several scenarios involving adverse drug reactions and reporting. It emphasizes the importance of reporting all suspected adverse reactions to the appropriate authorities, even if the patient recovers, to further drug safety.
2) In one scenario, a resident witnesses a child develop loose motions after amoxicillin, but the consultant dismisses reporting it. The document says the resident should still report the reaction.
3) In another scenario, a person faints after a COVID vaccine but recovers. The doctor considers not reporting due to fears of negative publicity, but the document says all suspected reactions should be reported to identify potential new adverse reactions.
A SEEMINGLY BENIGN DRUG IN THE SPOTLIGHT: AN EDUCATIONAL INTERVENTION TO REDU...
INTRODUCTION
Overutilization of Proton pump inhibitors (PPI) in hospitalized patients is a well-recognized problem. In our previous study at St. Luke’s Hospital, we found that the lack of published guidelines for PPI use in non-critically ill patients results in overutilization of PPI in hospitalized patients. We conducted an educational intervention program to address this problem.
DISCUSSION
Overutilization of PPI in non-critically ill hospitalized patients is a known problem in medical practice. Use of PPI has been implicated in number of adverse consequences including but not limited to Clostridium difficile infection, pneumonia, and osteoporosis. In recent studies, PPI was found to be associated with increased in-hospital mortality, increased risk of cardiovascular events, dementia and chronic renal failure (CRF). We studied the magnitude of the problem in our hospital and employed an educational intervention program, which resulted in a significant reduction of inappropriate use of PPI in the hospital.
There were several limitations to our study. This was a retrospective study where we noted a lack of proper documentation for outpatient use of PPI. We also were unable to precisely determine for how long the patients were taking PPI as outpatients. Our intervention included only medical residents (a total of 41), so we excluded data from NPs, hospitalists or PCPs after intervention. As a result, we are unable to determine the effect of the educational intervention on their practice.
CONCLUSION
PPIs have long been considered as relatively harmless drugs. In recent years, it has been implicated in several adverse effects that increases patient morbidity and mortality along with increased healthcare expenditure. Our data shows that proper education and ongoing surveillance, especially during admission and discharge can significantly reduce overutilization of PPI.
1138-4368-3-PB.pdf
This study aimed to determine the prevalence of adherence to antiretroviral therapy (ART) among 78 pediatric HIV patients in Mumbai, India over 18 months. The overall adherence was 82.1% after one year. Non-adherence was significantly associated with the caretaker's education, employment, and knowledge of medications. The most common reason for non-adherence was forgetting to take the medication (37.1% of patients). Adherent patients had significantly higher average CD4 counts after one year compared to their baseline, while non-adherent patients' CD4 counts did not change significantly.
ISPOR podium presentation
This study examined the risk of developing diabetes among children and adolescents aged 6 to 20 years old who were prescribed single atypical antipsychotic drugs using Texas Medicaid data. The study found no significant increase in risk of diabetes for children prescribed atypical antipsychotics compared to non-users. However, the results may not be generalizable due to limitations such as not accounting for dosage, BMI, glucose levels, or family history of diabetes which could influence risk. Larger and longer term studies are still needed to better understand the metabolic risks of atypical antipsychotics in pediatric populations.
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1) The study examined the role of personal protective measures in preventing the spread of COVID-19 among physicians in Bangladesh.
2) It found that physicians who were unaware of contact with COVID-19 patients or the status of patients during aerosol-generating procedures had higher odds of testing positive.
3) Wearing an N95 mask during aerosol-generating procedures and face shields/goggles during usual patient care were associated with lower odds of infection, but most associations were not statistically significant due to the small sample size.Medical error Case Study
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1- Learn step-by-step what to do when medical error occurs and how to report it
2- Learn how to identify root cause of a medical error and how to prevent its recurrence
3- Motivate your colleagues to foster a patient safety culture
SCHS Topic6: Medical Errors
PEER (Professionalism and Ethics Education for Residents) Project sponsored and organized by the Saudi Commission for Health Specialties (SCHS).
Definitions of terminology related to Medical Error (ME)
Levels of severity of medical error
Types & Examples of medical errors
Causes of ME
Disclosure of ME
Prevention of Medical Error
Asthma
Cluster analysis identified 4 clusters of childhood asthma in a study of 161 children with severe asthma. Cluster 1 had relatively normal lung function and less atopy. Cluster 2 had slightly lower lung function, more atopy, and increased symptoms and medication use. Cluster 3 had greater comorbidity, increased bronchial responsiveness, and lower lung function. Cluster 4 had the lowest lung function and greatest symptoms and medication use. The clusters did not correspond to current definitions of asthma severity. Larger longitudinal studies are needed to validate the clusters.
Medical data and text mining - Linking diseases, drugs, and adverse reactions
This document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using Danish healthcare databases. It describes analyzing both structured registry data covering 14 years of hospital records and unstructured free-text clinical notes. Named entity recognition and advanced matching techniques are used to extract diagnoses, medications, and adverse events from the free-text. Statistical and network models are then applied to discover previously unknown adverse drug reactions and understand diagnosis trajectories and comorbidities.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using structured and unstructured data from Danish healthcare registries and electronic health records. It describes analyzing registry data containing information on 6.2 million patients and 119 million diagnoses to study diagnosis trajectories and comorbidities. It also discusses using named entity recognition and rule-based systems to extract information on diseases, drugs, adverse reactions from free-text clinical notes written in Danish to better understand adverse drug events mentioned in the notes. The work aims to help discover new adverse drug reactions and estimate their frequencies.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using structured and unstructured data from Danish healthcare registries and electronic health records. It describes analyzing registry data containing information on 6.2 million patients and 119 million diagnoses to study diagnosis trajectories, comorbidities, and confounding factors. It also discusses using named entity recognition, dictionaries of medical terms, and rule-based systems to extract information from free-text clinical notes written in Danish to identify adverse drug reactions and new relationships between drugs and medical conditions. The goal is to advance pharmacovigilance by supplementing spontaneous reports of adverse events with information extracted from extensive real-world healthcare data sources.
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Medical data and text mining - Linking diseases, drugs, and adverse reactions
This document discusses medical data and text mining to link diseases, drugs, and adverse reactions. It describes using Danish registries containing structured medical data and unstructured free-text data from hospital records to study disease trajectories, adverse drug events, and discover new adverse drug reactions and their frequencies through data and text mining. The challenges of analyzing medical text in Danish from busy doctors is also addressed.
Data and text mining of Danish electronic health records
This document discusses data and text mining of Danish electronic health records. It notes that Denmark has established centralized medical registries since 1968. These registries contain data on 6.2 million patients, 45 million admissions, and 119 million diagnoses. Both structured data from registries and unstructured free-text data from medical records are analyzed. Text mining is used to extract information from the free-text data through techniques like dictionary-based coding. Studies have used this data to develop detailed disease profiles, identify adverse drug events, and study diagnosis trajectories and networks. Challenges include dealing with confounding factors and missing or unreported information.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses using Danish medical registries containing data on over 6.2 million patients and 45 million hospital admissions over 14 years to study disease trajectories and adverse drug reactions through medical data and text mining. It describes how structured data in the registries on diagnoses, treatments and prescriptions can be linked to study relationships between diseases and drugs. It also explains how natural language processing of unstructured free-text data from medical records can help identify adverse drug reactions mentioned in notes. The goal is to gain insights into diagnosis trajectories, discover new adverse drug reactions, and better estimate frequencies of adverse reactions.
Data and text mining of electronic health records
The document discusses data and text mining of electronic health records from Denmark to study disease trajectories, comorbidities, adverse drug reactions, and other topics. It describes the Danish registries which contain data on over 6 million patients and over 68 million records spanning 14 years. Both structured data and unstructured free-text data are analyzed to discover patterns and relationships. Text mining is used to extract information from clinical notes written in Danish. The studies have provided new insights into topics like diagnosis trajectories, drug safety, and precision medicine.
Medical Data Mining
This document discusses medical data mining of Danish healthcare registries containing structured and unstructured data from electronic health records. It describes how researchers have used the Danish civil registration system and national registries containing diagnosis and hospital admission data spanning decades to study disease trajectories, comorbidities, and confounding factors. It also explains how natural language processing of free-text clinical notes has allowed identification of adverse drug reactions and new disease associations by linking medications, diagnoses, and symptoms over time despite challenges from unstructured data like typos and physician notetaking styles. Combining structured registry data with text mining of clinical notes enables public health monitoring through pharmacovigilance and discovery of new adverse drug reactions.
Medical Data Mining
Medical data mining can analyze both structured and unstructured data from Danish healthcare registries and electronic health records. These extensive sources contain data on patients' diagnoses, hospital visits, medications, and free-text clinical notes. Researchers have used this data to study disease trajectories, comorbidities, and potential adverse drug reactions through statistical analysis and text mining techniques. However, accounting for confounding factors, reporting biases, and "unknown unknowns" poses challenges to obtaining accurate insights from such complex real-world data.
Medical data and text mining - Linking diseases, drugs, and adverse reactions
The document discusses using Danish medical registries and electronic health records to link diseases, drugs, and adverse reactions through data and text mining. It describes how Danish registries contain data on 6.2 million patients and their healthcare encounters over 14 years. While this data was originally collected for reimbursement purposes, it can be used for research by identifying diagnosis trajectories and comorbidities. The document also discusses how text mining of free-text clinical notes, which describe approximately 30% of the data, can help identify adverse drug reactions that may not be captured in structured data fields alone. By developing dictionaries of diseases, drugs, and their variations, along with hand-crafted rules, researchers have been able to detect known and potentially new adverse
Statistical analysis of the health profile of Field ganj (Area C),Ludhiana ,I...
This document provides health data from the Field Ganj Area-C in Ludhiana, India collected between October 2012-April 2013. It summarizes population statistics, family planning methods used, vital events like births and deaths, immunization rates of children, nutritional status of children under 5, maternal health services, tuberculosis treatment, common illnesses, and deaths in the community. Diabetes is a major health issue, with efforts made to educate people on managing it through lifestyle changes and medication adherence.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
This document discusses medical data and text mining to link diseases, drugs, and adverse reactions. It describes using structured data from Danish central registries and unstructured data from hospital electronic health records. Named entity recognition is used to extract diseases, drugs, and adverse reactions from free text clinical notes written in Danish. Hand-crafted rules are developed to identify relationships between extracted entities like adverse drug reactions. This allows estimating frequencies of known adverse drug reactions and discovering new adverse drug reactions by analyzing diagnosis trajectories and medication information.
IMA perspective of pharmacovigilance
1) The document discusses several scenarios involving adverse drug reactions and reporting. It emphasizes the importance of reporting all suspected adverse reactions to the appropriate authorities, even if the patient recovers, to further drug safety.
2) In one scenario, a resident witnesses a child develop loose motions after amoxicillin, but the consultant dismisses reporting it. The document says the resident should still report the reaction.
3) In another scenario, a person faints after a COVID vaccine but recovers. The doctor considers not reporting due to fears of negative publicity, but the document says all suspected reactions should be reported to identify potential new adverse reactions.
A SEEMINGLY BENIGN DRUG IN THE SPOTLIGHT: AN EDUCATIONAL INTERVENTION TO REDU...
INTRODUCTION
Overutilization of Proton pump inhibitors (PPI) in hospitalized patients is a well-recognized problem. In our previous study at St. Luke’s Hospital, we found that the lack of published guidelines for PPI use in non-critically ill patients results in overutilization of PPI in hospitalized patients. We conducted an educational intervention program to address this problem.
DISCUSSION
Overutilization of PPI in non-critically ill hospitalized patients is a known problem in medical practice. Use of PPI has been implicated in number of adverse consequences including but not limited to Clostridium difficile infection, pneumonia, and osteoporosis. In recent studies, PPI was found to be associated with increased in-hospital mortality, increased risk of cardiovascular events, dementia and chronic renal failure (CRF). We studied the magnitude of the problem in our hospital and employed an educational intervention program, which resulted in a significant reduction of inappropriate use of PPI in the hospital.
There were several limitations to our study. This was a retrospective study where we noted a lack of proper documentation for outpatient use of PPI. We also were unable to precisely determine for how long the patients were taking PPI as outpatients. Our intervention included only medical residents (a total of 41), so we excluded data from NPs, hospitalists or PCPs after intervention. As a result, we are unable to determine the effect of the educational intervention on their practice.
CONCLUSION
PPIs have long been considered as relatively harmless drugs. In recent years, it has been implicated in several adverse effects that increases patient morbidity and mortality along with increased healthcare expenditure. Our data shows that proper education and ongoing surveillance, especially during admission and discharge can significantly reduce overutilization of PPI.
1138-4368-3-PB.pdf
This study aimed to determine the prevalence of adherence to antiretroviral therapy (ART) among 78 pediatric HIV patients in Mumbai, India over 18 months. The overall adherence was 82.1% after one year. Non-adherence was significantly associated with the caretaker's education, employment, and knowledge of medications. The most common reason for non-adherence was forgetting to take the medication (37.1% of patients). Adherent patients had significantly higher average CD4 counts after one year compared to their baseline, while non-adherent patients' CD4 counts did not change significantly.
ISPOR podium presentation
This study examined the risk of developing diabetes among children and adolescents aged 6 to 20 years old who were prescribed single atypical antipsychotic drugs using Texas Medicaid data. The study found no significant increase in risk of diabetes for children prescribed atypical antipsychotics compared to non-users. However, the results may not be generalizable due to limitations such as not accounting for dosage, BMI, glucose levels, or family history of diabetes which could influence risk. Larger and longer term studies are still needed to better understand the metabolic risks of atypical antipsychotics in pediatric populations.
Role of Personal Protective Measures in Prevention of COVID-19 Spread Among P...
Role of Personal Protective Measures in Prevention of COVID-19 Spread Among P...NurFathihaTahiatSeeu
1) The study examined the role of personal protective measures in preventing the spread of COVID-19 among physicians in Bangladesh.
2) It found that physicians who were unaware of contact with COVID-19 patients or the status of patients during aerosol-generating procedures had higher odds of testing positive.
3) Wearing an N95 mask during aerosol-generating procedures and face shields/goggles during usual patient care were associated with lower odds of infection, but most associations were not statistically significant due to the small sample size.Medical error Case Study
This is a presentation about medical error with the following Objectives:
1- Learn step-by-step what to do when medical error occurs and how to report it
2- Learn how to identify root cause of a medical error and how to prevent its recurrence
3- Motivate your colleagues to foster a patient safety culture
SCHS Topic6: Medical Errors
PEER (Professionalism and Ethics Education for Residents) Project sponsored and organized by the Saudi Commission for Health Specialties (SCHS).
Definitions of terminology related to Medical Error (ME)
Levels of severity of medical error
Types & Examples of medical errors
Causes of ME
Disclosure of ME
Prevention of Medical Error
Asthma
Cluster analysis identified 4 clusters of childhood asthma in a study of 161 children with severe asthma. Cluster 1 had relatively normal lung function and less atopy. Cluster 2 had slightly lower lung function, more atopy, and increased symptoms and medication use. Cluster 3 had greater comorbidity, increased bronchial responsiveness, and lower lung function. Cluster 4 had the lowest lung function and greatest symptoms and medication use. The clusters did not correspond to current definitions of asthma severity. Larger longitudinal studies are needed to validate the clusters.
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Medical data and text mining - Linking diseases, drugs, and adverse reactions
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Medical data and text mining: Linking diseases, drugs, and adverse reactions
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Medical data and text mining - Linking diseases, drugs, and adverse reactions
This document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using Danish healthcare databases. It describes analyzing both structured registry data covering 14 years of hospital records and unstructured free-text clinical notes. Named entity recognition and advanced matching techniques are used to extract diagnoses, medications, and adverse events from the free-text. Statistical and network models are then applied to discover previously unknown adverse drug reactions and understand diagnosis trajectories and comorbidities.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using structured and unstructured data from Danish healthcare registries and electronic health records. It describes analyzing registry data containing information on 6.2 million patients and 119 million diagnoses to study diagnosis trajectories and comorbidities. It also discusses using named entity recognition and rule-based systems to extract information on diseases, drugs, adverse reactions from free-text clinical notes written in Danish to better understand adverse drug events mentioned in the notes. The work aims to help discover new adverse drug reactions and estimate their frequencies.
Medical data and text mining: Linking diseases, drugs, and adverse reactions
The document discusses medical data and text mining techniques used to link diseases, drugs, and adverse reactions using structured and unstructured data from Danish healthcare registries and electronic health records. It describes analyzing registry data containing information on 6.2 million patients and 119 million diagnoses to study diagnosis trajectories, comorbidities, and confounding factors. It also discusses using named entity recognition, dictionaries of medical terms, and rule-based systems to extract information from free-text clinical notes written in Danish to identify adverse drug reactions and new relationships between drugs and medical conditions. The goal is to advance pharmacovigilance by supplementing spontaneous reports of adverse events with information extracted from extensive real-world healthcare data sources.
Hvordan bør vi udnytte mulighederne for analyse af registre og sundhedsdata i...
This document discusses opportunities for analyzing structured and unstructured health data and registers in the future. It mentions using text mining and machine learning techniques to identify new adverse drug reactions and monitor drug safety using statistical analysis of large amounts of health data. Integrating and standardizing data from different health IT systems while maintaining ongoing data updates would help address challenges in studying disease trajectories and identifying new correlations.
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Medical data mining
This document discusses how medical data mining of Danish healthcare registries can be used to link diseases, drugs, and adverse reactions. It describes how the Danish registries contain structured and unstructured data from hospitals and central registries dating back to 1968. Text mining of unstructured clinical notes allows identification of diseases, drugs, and possible adverse drug reactions. Analysis of this data through methods like comorbidity, diagnosis trajectories, and temporal correlations can provide new insights into disease relationships and help discover new adverse drug reactions and their frequencies.
Medical text mining: Linking diseases, drugs, and adverse reactions
This document discusses medical text mining and linking diseases, drugs, and adverse reactions. It describes using text mining on clinical narratives in Danish to recognize named entities like drugs and diseases, identify relationships between them like adverse drug reactions, and discover new ADRs. The goal is to generate structured data on topics like comorbidities, diagnosis trajectories, and reimbursement to supplement limited structured data and help busy doctors by analyzing large amounts of unstructured text.
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This document discusses large-scale integration of biological data and text through several databases and resources created by the author's lab, including STRING and STITCH, which integrate protein-protein interaction data from multiple sources. It describes using text mining to extract protein interactions from literature through named entity recognition, relationship extraction, and integration of the results with interaction data from experimental and computational sources. Clinical applications including using Danish health registries and text mining of medical records to discover new drug-drug interactions and adverse drug reactions are also summarized.
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This document summarizes research on medical data mining of Danish healthcare registries and electronic health records. It discusses how structured data from national registries containing diagnosis codes can be used to analyze diagnosis trajectories and comorbidities at a population level. It also describes how unstructured free-text data from medical records can be mined using natural language processing techniques like named entity recognition to extract disease and drug information that is then used to study adverse drug reactions and generate detailed patient profiles. The authors have analyzed large Danish datasets in this way to gain new insights into conditions like epilepsy and gout as well as identify unknown adverse drug reactions.
Medical Data Mining
This document discusses medical data mining of Danish healthcare registries. It describes how Denmark has national healthcare registries containing data on all hospital contacts, prescriptions, and more for its entire population since 1968. This structured data can be mined to study diagnosis trajectories, comorbidities, and adverse drug reactions at a population level while accounting for confounding factors. The document also discusses mining of unstructured free-text data using natural language processing to identify mentions of diseases, drugs, and adverse events, and how this can provide additional insights when combined with structured data analyses.
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Medical network analysis: Linking diseases and genes through data and text mi...
The document summarizes the work of Lars Juhl Jensen and others on medical network analysis and linking diseases and genes through data and text mining of electronic health records. It discusses how they have used Danish national health registries containing data on over 6 million patients and 119 million diagnoses over 14 years to study disease trajectories and comorbidities. It also describes how they have developed methods to integrate data from various sources to generate networks linking diseases and genes.
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lead to chase drug discovery in anti-retroviral drug towards zero
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Forensic pharmacovigilance: Newer dimension of pharmacovigilance
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Similar to Medical data and text mining: Linking diseases, drugs, and adverse reactions (20)
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining - Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Medical data and text mining: Linking diseases, drugs, and adverse reactions
Hvordan bør vi udnytte mulighederne for analyse af registre og sundhedsdata i...
Hvordan bør vi udnytte mulighederne for analyse af registre og sundhedsdata i...
Medical text mining: Linking diseases, drugs, and adverse reactions
Medical text mining: Linking diseases, drugs, and adverse reactions
Open data and open access - A biomedical data- and text-mining perspective
Open data and open access - A biomedical data- and text-mining perspective
Medical network analysis: Linking diseases and genes through data and text mi...
Medical network analysis: Linking diseases and genes through data and text mi...
Spontaneous Reporting and Prescription Event Monitoring.pptx
Spontaneous Reporting and Prescription Event Monitoring.pptx
Forensic pharmacovigilance: Newer dimension of pharmacovigilance
Forensic pharmacovigilance: Newer dimension of pharmacovigilance
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One tagger, many uses: Simple text-mining strategies for biomedicine
The document summarizes a text mining tool called a tagger that can be used for named entity recognition in biomedical texts. It recognizes genes, proteins, chemicals, diseases, and other entities. The tagger is open source, runs quickly at over 1000 abstracts per second, and has 70-80% recall and 80-90% precision. It comes with Python and Docker implementations and can be accessed via a web service. It is useful for tasks like extracting functional associations from literature and electronic health records.
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Network visualization: A crash course on using Cytoscape
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The document discusses STRING and STITCH, two online databases that integrate data on protein-protein interactions, pathways, and functional associations from various sources. STRING collects data on over 9.6 million proteins and 430 thousand chemicals from sources like text mining, experimental assays, and co-expression analyses. It aims to provide a comprehensive global view of known and predicted protein associations. STITCH also integrates interaction data but focuses more on chemical-protein interactions. Both databases provide user-friendly web interfaces for browsing and visualizing interaction networks.
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1) Lars Juhl Jensen discusses biomedical text mining and automatic processing of unstructured text such as patent literature, grant proposals, FDA product labels, and electronic medical records.
2) Named entity recognition is used to identify genes/proteins, chemical compounds, diseases, and other entities in text through comprehensive dictionaries and flexible matching rules that account for variations.
3) Relation extraction uses natural language processing techniques like part-of-speech tagging and sentence parsing along with manually crafted rules and machine learning to identify implicit relations between entities in text such as transcription factor targets, kinase substrates, and protein-protein interactions.
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This document provides an overview of STRING, a protein-protein association database, and Cytoscape, a network visualization tool. It describes how STRING contains functional associations between proteins derived from genomic context, co-expression and curated databases. Cytoscape can import STRING networks and external data to map onto nodes. It offers visualization of networks through layouts and attributes, and analysis through clustering, selection filters and enrichment. The document recommends using these tools together to explore protein association networks.
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This document discusses different approaches to visualizing cellular networks and the molecular interactions between proteins. It notes that there are many different types of data that could be shown, such as protein names, functions, localization, expression, modifications, and interaction types. However, it is impossible to show all this information at once. The document recommends using different visualizations like force-directed layouts to distribute proteins in 2D or lining up interactions in 1D. It acknowledges open challenges like showing time-course data and modification sites. In the end, the document thanks several researchers who have contributed to mapping and visualizing cellular networks.
Cellular Network Biology: Large-scale integration of data and text
The document discusses various community resources and software tools for integrating large-scale data and text, including STRING for protein networks, STITCH for chemical networks, COMPARTMENTS for subcellular localization, TISSUES for tissue expression, and DISEASES for disease associations. It provides an overview of text mining techniques used to extract information from literature to build networks in these resources. The presenter demonstrates the Cytoscape App which can import and analyze networks from STRING, perform queries, and analyze subcellular localization, tissue expression, and disease enrichment.
Statistics on big biomedical data: Methods and pitfalls when analyzing high-t...
This document discusses statistical methods for analyzing high-throughput biomedical screens and common pitfalls. It introduces several statistical tests such as t-tests, ANOVA, Fisher's exact test, and the Mann-Whitney U test. It also discusses challenges like multiple testing, resampling techniques, and biases that can occur like studiedness bias and abundance bias in big data analyses. Controlling false discovery rates and considering effect sizes are recommended over solely relying on p-values to determine biological significance.
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Lars Juhl Jensen leads a group that conducts large-scale integration of biological and medical data using proteomics, text mining, and medical data mining. The group develops protein interaction networks, disease networks, and association networks. They collaborate internationally on projects involving over 9.6 million proteins and 2000 genomes. The group works to integrate data from many sources in different formats to build comprehensive networks and knowledgebases, and also mines biomedical text to link genes and proteins with diseases.
Network biology: Large-scale integration of data and text
The document discusses network biology and large-scale data integration. It describes protein-protein interaction networks like STRING that integrate data from curated knowledge, experiments, and predictions. It provides exercises to explore the human insulin receptor (INSR) in STRING, examining the types of evidence that support its interaction with IRS1. It also introduces other integrated networks like STITCH for chemicals and COMPARTMENTS for subcellular localization. Natural language processing techniques like named entity recognition, information extraction, and semantic tagging are used to integrate text data from the literature into these interaction networks.
Cellular Network Biology
This document discusses cellular network biology and summarizes several key papers on topics like proteome analysis using mass spectrometry, integrating protein network and experimental data, challenges with different biological databases having varying formats and quality, and using natural language processing techniques like named entity recognition and relation extraction to analyze medical text for information like diagnosis trajectories and adverse drug reactions.
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Biomarker bioinformatics: Network-based candidate prioritization
The document discusses three parts of biomarker bioinformatics: data integration from multiple databases, text mining of scientific literature, and using that integrated data to prioritize biomarker candidates. It describes combining data on 9.6 million proteins from curated databases, using text mining to extract named entities from over 10,000 papers, and then using network and heat diffusion approaches to rank candidates based on evidence in the integrated data. The goal is to help identify new biomarker candidates from large amounts of biological data.
The Art of Counting: Scoring and ranking co-occurrences in literature
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Text-mining-based retrieval of protein networks
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+
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−
27.82088
with a host spectroscopic redshift of
2.903
±
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) despite a host galaxy with low-extinction and has a high Ca II velocity (
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cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
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CDM. Therefore unlike low-
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Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
�
truly diverge from their low-
�
counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDS
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
Methods of grain storage Structures in India.pdf
•Post-harvestlossesaccountforabout10%oftotalfoodgrainsduetounscientificstorage,insects,rodents,micro-organismsetc.,
•Totalfoodgrainproductioninindiais311milliontonnesandstorageis145mt.InIndia,annualstoragelosseshavebeenestimated14mtworthofRs.7,000croreinwhichinsectsaloneaccountfornearlyRs.1,300crores.
•InIndiaoutofthetotalproduction,about30%ismarketablesurplus
•Remaining70%isretainedandstoredbyfarmersforconsumption,seed,feed.Hence,growerneedstoragefacilitytoholdaportionofproducetosellwhenthemarketingpriceisfavourable
•TradersandCo-operativesatmarketcentresneedstoragestructurestoholdgrainswhenthetransportfacilityisinadequate
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Medical data and text mining: Linking diseases, drugs, and adverse reactions
- 1. Medical data and text mining Linking diseases, drugs, and adverse reactions Lars Juhl Jensen
- 3. Jensen et al., Nature Reviews Genetics, 2012
- 9. opt-out
- 10. opt-in
- 13. CPR number
- 15. Jensen et al., Nature Reviews Genetics, 2012
- 17. 14 years
- 22. ICD-10
- 23. Jensen et al., Nature Reviews Genetics, 2012
- 24. not research
- 25. reimbursement
- 27. naïve approach
- 28. comorbidity
- 29. Jensen et al., Nature Reviews Genetics, 2012
- 31. “known knowns”
- 32. gender
- 33. age
- 34. type of hospital encounter
- 35. Jensen et al., Nature Communications, 2014
- 36. “known unknowns”
- 37. smoking
- 38. diet
- 40. reporting biases
- 41. matched controls
- 43. multiple testing
- 44. trajectories
- 45. Jensen et al., Nature Communications, 2014
- 47. Jensen et al., Nature Communications, 2014
- 48. key diagnoses
- 49. Jensen et al., Nature Communications, 2014
- 52. structured data
- 53. Jensen et al., Nature Reviews Genetics, 2012
- 56. free text
- 57. Danish
- 58. busy doctors
- 59. typos
- 61. text mining
- 63. drugs
- 65. expansion rules
- 66. Clozapine
- 70. renal failure
- 72. clinical trials
- 75. underreporting
- 76. data mining
- 77. structured data
- 78. medication
- 80. drug indications
- 81. known ADRs
- 86. Eriksson et al., Drug Safety, 2014 Drug introduction Drug discontinuationAdverse event Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction ADR of additional drug
- 87. Eriksson et al., Drug Safety, 2014 Drug introduction Drug discontinuationAdverse eventIdentification start Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction ADR of additional drug
- 88. Eriksson et al., Drug Safety, 2014 Drug introduction Drug discontinuation Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction Adverse event ADR of additional drug Identification start
- 89. Eriksson et al., Drug Safety, 2014 Drug introduction Drug discontinuation Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction Adverse event ADR of additional drug Identification start
- 92. Eriksson et al., Drug Safety, 2014
- 94. Drug substance ADE p-value Chlordiazepoxide Nystagmus 4.0e-8 Simvastatin Personality changes 8.4e-8 Dipyridamole Visual impairment 4.4e-4 Citalopram Psychosis 8.8e-4 Bendroflumethiazi de Apoplexy 8.5e-3 Eriksson et al., Drug Safety, 2014
- 95. Acknowledgments Disease trajectories Anders Bøck Jensen Tudor Oprea Pope Moseley Søren Brunak Adverse drug reactions Robert Eriksson Thomas Werge Søren Brunak EHR text mining Peter Bjødstrup Jensen Robert Eriksson Henriette Schmock Francisco S. Roque Anders Juul Marlene Dalgaard Massimo Andreatta Sune Frankild Eva Roitmann Thomas Hansen Karen Søeby Søren Bredkjær Thomas Werge Søren Brunak