Convenient Bedfellows: Natural Language Processing and Clinical Decision Support

Transcription

1 Convenient Bedfellows: Natural Language Processing and Clinical Decision Support J. Marc Overhage, MD, PhD Chief Medical Informatics Officer

2 Machine-readable data & knowledge will shift paradigms Unstructured data Disease models Therapy Patient-specific Diagnostic data Imaging In-Vitro diagnostics Physical examinations Genome, Proteome Non-patient-specific Disease causalities Infectious diseases Addiction Cancer Diabetes Diagnosis Therapy selection Treatment Patient record Family data Diseases Physician Education Experience Verification Post-treatment result evaluation Aftercare Data domain Experience domain Action domain Page 2

3 Machine-readable data & knowledge will shift paradigms Structured data Patient Model(s) Individualized Therapy Patient-specific Diagnostic data Imaging In-Vitro diagnostics Physical examinations Genome, Proteome Non-patient-specific Comprehensive models Anatomical m. Physiological m. Biochemical m. Metabolomic m. Diagnosis & Therapy proposals Therapy selection Treatment Patient record Family data Diseases Falsification Post-treatment result evaluation Aftercare Data domain Knowledge domain Action domain Page 3

4 Overview Model Lab EMR HIE PHR Baysian Belief Networks Patient Data Patient Data Inference Engine Question Answering Patient Data Learning Knowledge Base Semantic Modeling / Ontology Evidence Literature Guidelines Textbooks Personalized Models Semantic Integration Page 4

5 Improving Models Page 5

6 Data Class Agents Page 6

7 REMIND Knowledge Platform Images Patient Factors Extraction REMIND Platform Proteomics Extraction Combine Conflicting Local Evidence Probabilistic Inference Over Time Genomics Treatment Plans Extraction Extraction Combination Inference Plug-in Domain Domain Knowledge Knowledge (e.g., CMS Measures) Decision Support / Knowledge Discovery Clinical Decision Support *Not considered a Medical device. Page 7

8 Analyze As You Type Enter symptoms, findings, diagnosis. The user activity (typing, highlighting) defines the current focus and instantaneously triggers data analysis Entered findings can be added to the protocol All information relevant in the context of the current focus are highlighted The sound interpretation of a single data entry requires the reflection of how this information relates to proceeding or succeeding information. Highlighting the semantic relations and contexts between the data entries at different time points paves the way towards efficient and intelligent information access and analysis. Combining the process of entering data and data analysis the user is not slowed down in his work progress. Page 8

9 Analyze As You Type Overview Input Time series of mixed structured data like measurements and textual descriptions Data analytics Processing Information need Process: diagnosis Context: fever Retrieval mode: on the fly component Knowledge base Domain model Semantic knowledge processing component Rendering Patient record 1.Afasfaf adafg er et 2.Sfadfsdg wet esd wetwt 3.Uteert gsdfgf wet e tet et et sdsfg 4.Wtwetwt r gsg were erwert tetwetw 5.Dsafdsafaf wrwrw es dsd rww ewet 6.Asdaf erer er dfdf df fd da ada af 7.Werwe w tw wetwt wetwt w wet t 8.Eqewuut eter er ry yry rsdfs sdfs s 9.Ppwewet ewtew tgsdg wetee 10.Dsa aewre rwtw weette erttet etar wetet wett 11.Sdgsgs we etwdge et e w ttwt tdsgdsdg 12.Dsafdsafaf wrwrw es dsd rww ewet 13.Asdaf erer er dfdf df fd da ada af 14.Werwe w tw wetwt wetwt w wet t 15.Eqewuut eter er ry yry rsdfs sdfs s 16.Ppwewet ewtew tgsdg wetee 17.Dsa aewre rwtw weette erttet safasf wetet wettaf aefa a asgwf 18.Sdgsgs we etwdge et e w ttwt tdsgdsdg 38,5 36,8 38,6 Current context (e.g. recently entered words) Interaction and context detection mechanism Temperature data extracted and highlighted Page 9

10 Providing Contextual Cues Page 10

11 Individualized Models Page 11 Based on data from large study, >4000 patients over 10 years Genetic (SNPs), clinical, lab, imaging, Analyzed 100s of potential predictive variables to identify the most informative subset Learned a Bayesian Network predicts patients who are at high risk to become hypertensive (AUC >0.85). Model also suggests patient specific interventions that can reduce risk for a subset of patients, Undergoing multi-site validation G. Fung, S. Yu, T. Itterman, M. Nauck, R. Rettig, S. B. Felix, H. K. Kroemer, H. Volzke. A combined diagnostics model for the prediction of hypertension development, AMIA 2010 (submitted to Circulation, Dec 2011)

12 Overview Model EMR HIE PHR Baysian Belief Networks Patient Data Patient Data Inference Engine Question Answering Patient Data Learning Knowledge Base Semantic Modeling / Ontology Evidence Literature Guidelines Textbooks Personalized Models Semantic Integration Page 12

13 Knowledge Extraction and Engineering Search Explore Organize Extract Advise Aggregate Usecases Text mining and information retrieval Machine learning Semantic modeling and reasoning Unstructured and textual data Relational data Semantic data Page 13

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15 Semantic Integration Scope Access to heterogeneous knowledge sources by mediating the knowledge models and providing an integrated view of the data Synthesizing the understanding of a subject from different perspectives Incorporation of new information into a body of existing knowledge Applications Horizontal, vertical, and longitudinal integration of patient data, medical information and knowledge Establish mappings between Ontologies of overlapping domains Tasks Identify the piece of knowledge required for an application Discover the interrelation of knowledge pieces in the context of the application Fine-tuning of established mapping results by tracking user behavior Page 15 Service 1 Service 2 DB 1 bmw: Car bmw: Center Console bmw: Audio bp: Sthlm 100 post: Lot type has subcomponent contains subclass of DB 2 has subcomponent type std: CD Drive CAD type type type CAE bmw-i: Car 375 bmw-i: Center Console 29 bp-i: Radio 17 krp-i: Lot 28 has subcomponent contains same as has subcomponent same as ssg-i: CD drive 5 contains PLM ERP SCM same as Service 3 X Y bmw-i: Radio 56 bp-i: CD Drive 19 dep-i: Lot 66

16 FMA Exten. Mappings to external sources Information Element Ontology images, texts, volumes, Clinical Ontology -doctor, nurse, patient -medical case -DICOM Ontology Thesauri & Taxonomies Representational Ontology: OWL Upper Ontology: time, space, organization, person, event FMA annotation Medical Ontologies ICD-10 mapping& merging Visual Charac. extraction Knowledge-based, medical services using semantic annotated data Intelligent Services Semantic Search Quality Control Intelligent Navigation Image Diagnose Multi-modal Interaction Intelligent Diagnose Semantic Processing Semantic Annotation more domain specific more likely to be changed Information Element Ontology images, texts, volumes, Clinical Ontology -doctor, nurse, patient -medical case -DICOM Ontology Thesauri & Taxonomies Representational Ontology: OWL Upper Ontology: time, space, organization, person, event Annotation Ontology FMA annotation Medical Ontologies ICD-10 mapping& merging Mappings to external sources ICD-10 Website Visual Charac. extraction RadLex mid-level ontology low-level ontology Ontology Mapping Reasoning & Inference more domain specific more likely to be changed Annotation Ontology FMA Exten. Mappings to external sources ICD-10 Website Mappings to external sources RadLex mid-level ontology low-level ontology Thorax Abdomen Pelvis Segmentation Text Mining Formalization Text Mining Formalization Heterogeneous data sources Medical Images Patientrecords Treatmentplans Online Knowledge Expert Knowledge Page 16

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18 To wrest from nature the secrets which have perplexed philosophers in all ages, to track to their sources the causes of disease, to correlate the vast stores of knowledge, that they may be quickly available for the prevention and cure of disease these are our ambitions. Sir William Osler, Father of Modern Medicine Page 18