Medical Data Mining

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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.

Jensen et al., Nature Reviews Genetics, 2012

Male

Emergency room

Out-patient

In-patient

Female

Jensen et al., submitted, 2013

Text mined codes

Assigned codes

4947 3825

32626

Roque et al., PLOS Computational Biology, 2011

Roque et al., PLOS Computational Biology, 2011

Drug substance
ADE
Chlordiazepoxide Nystagmus
Simvastatin
Personality
changes
Dipyridamole
Visual impairment
Citalopram
Psychosis
Bendroflumethiazi Apoplexy
de

p-value
4.0e-8
8.4e-8
4.4e-4
8.8e-4
8.5e-3

Eriksson et al., submitted, 2013

Acknowledgments
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

Disease trajectories
Anders Bøck Jensen
Tudor Oprea
Pope Moseley
Søren Brunak

Adverse drug reactions
Robert Eriksson
Thomas Werge
Søren Brunak

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.

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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.

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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

Medical Data Mining

Lars Juhl Jensen

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.

Data and text mining of Danish electronic health records

Lars Juhl Jensen

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.

Data and text mining of electronic health records

Lars Juhl Jensen

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

Lars Juhl Jensen

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

Lars Juhl Jensen

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.

Medical data and text mining: Linking diseases, drugs, and adverse reactions

Lars Juhl Jensen

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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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.

Medical data and text mining: Linking diseases, drugs, and adverse reactions

Lars Juhl Jensen

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

Lars Juhl Jensen

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.

Medical data and text mining: Linking diseases, drugs, and adverse reactions

Lars Juhl Jensen

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

Lars Juhl Jensen

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.

Medical data and text mining: Linking diseases, drugs, and adverse reactions

Lars Juhl Jensen

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

Lars Juhl Jensen

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

Lars Juhl Jensen

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

Lars Juhl Jensen

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 text mining: Linking diseases, drugs, and adverse reactions

Lars Juhl Jensen

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.

Medical data and text mining: Linking diseases, drugs, and adverse reactions

Lars Juhl Jensen

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 data and text mining: Linking diseases, drugs, and adverse reactions

Lars Juhl Jensen

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.

Medical data and text mining - Linking diseases, drugs, and adverse reactions

Lars Juhl Jensen

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.

One tagger, many uses: Illustrating the power of dictionary-based named entit...

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This document summarizes a Twitter thread discussing the uses of a dictionary-based named entity recognition tool called Tagger. Tagger can recognize genes, proteins, diseases and other biomedical entities. It is open source, runs quickly processing over 1000 abstracts per second, and achieves 70-80% recall and 80-90% precision. Tagger has been applied to tasks like identifying drug-disease associations, adverse drug events, and protein-protein interactions. It is available as a Docker container or web service.

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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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