Information and Knowledge for Decision Making

Transcription

1 Information and Knowledge for Decision Making An NSF I/UCRC Planning Grant Workshop Research Concept Presentations Section 2

2 L.I.F.E. Form Access Level of Interest and Feedback Evaluation (LIFE) Forms Please go to: Select Planning Grant Workshop Then select L.I.F.E Form Evaluations PASSWORD: unc2015 ID yourself as IAB

3 Symptom Extraction from the EHR for Epidemiological Studies: The Hybrid NLP Workbench Stephanie W. Haas School of Information and Library Science OBJECTIVES The Atherosclerosis Risk in Communities (ARIC)1 study focuses on identifying symptoms of worsening heart function such as shortness of breath, edema and orthopnea. Currently, records are read by human experts. An NLP system that automatically extracts symptom mentions and presents the results for human review will improve cost- effectiveness, timeliness and accuracy of the process. Better data provision will support epidemiologic surveillance. APPROACH/TECHNIQUES Develop and evaluate performance of rule- based NLP, machine learning, and hybrid algorithms for identifying symptom mentions in the EHR. The need to tailor algorithms to specific symptoms, parts of the EHR, or hospitals will also be explored. Design a workbench that allows a human expert to review and confirm/deny proposed mentions, supporting expert system interaction in a variety of ways DELIVERABLES Rule- based, machine learning, or hybrid system that identifies symptom mentions in all parts of the EHR. Interaction design to facilitate human expert confirmation of mentions. Workbench- style interface for results review. BENEFITS TO INDUSTRY Workbench design and interaction leverages strengths of automatic extraction technologies and expert judgment, with regard to usability requirements. Algorithms and workbench could be extended to other health conditions and to other domains where human expertise must be merged with automatic extraction processes to produce optimal results.

4 Symptom Extraction from the EHR for Epidemiological Studies: The Hybrid NLP Workbench Stephanie W. Haas School of Information and Library Science read record identify symptom mention add symptom list EHR extraction system proposed symptom mentions confirm current proposed symptom list Workbench deny view more context

5 Symptom Extraction from the EHR for Epidemiological Studies: The Hybrid NLP Workbench Stephanie W. Haas School of Information and Library Science Rule- based NLP system vs. gold standard (n = 112 records) 2 ARIC HF Variable Recall Precision based on gold standard (based on post-extraction review) New onset or worsening shortness of breath New onset or worsening edema Paroxysmal nocturnal dyspnea # additional patients identified by system 100% 76% (91%) 13 98% 52% (66%) % 64% (73% 1 Orthopnea 100% 81% (90%) 2 1 The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol 1989; 129(4): Moore C, Shaffer K, Kucharska- Newton A, Haas S, Heiss G (2015) Using natural language processing to facilitate medical record abstraction in epidemiological studies. AMIA 2015

6 Symptom Extraction from the EHR for Epidemiological Studies: The Hybrid NLP Workbench Stephanie W. Haas School of Information and Library Science Aspects of the Research Weighting symptom mentions symptom type frequency form of expression location in EHR Relationship between mentions confirmation contradiction uncertainty change over time Interaction workflow (e.g., group all mentions of a symptom) context of mentions (text, EHR location) include confidence rating default confirm or deny Design & Deployment algorithm: rule- based, machine learning, hybrid tuning for variation across symptom, expert, hospital acceptance by experts, epidemiologists expansion into other conditions

7 PRECISE CARE using MedSIFTER: Depression & Memory Loss Case Studies OBJECTIVES AIM1: Personalization: Leverage highly robust user modeling algorithm to learn and predict precise care information AIM2: Prediction: Develop online diagnosis and screening tools for precise status checks and monitoring ~35 million American adults struggle with depression at somepoint in their lives Alzheimer patients will rise from 5 to 14 million by 2050 in the USA Both conditions are grossly underdiagnosed and require ongoing monitoring and support 87% US adults use the Internet and 72% sought health informaton DELIVERABLES A highly effcient and effective system for data integration and analytics for difficult to diagnose and treat conditions A flexible service oriented platform that can be leveraged for a wide variety of precision care settings Javed Mostafa School of Information and Library Science Biomedical Research Imaging Center APPROACH/TECHNIQUES Personalization & Precision Care High- volume text & image processing and personalization platform Unstructured content can be processed ONLINE to determine key themes & clusters automatically Content can be MAPPED to a user profile (i.e., user model) The model can PREDICT the likelihood of interest / user characteristics Can DETECT changing information and interests BENEFITS TO INDUSTRY Work with seasoned researchers in ML and HCI Access to realistic data and workflow settings

8 PRECISE CARE using MedSIFTER: Depression & Memory Loss Case Studies Javed Mostafa School of Information and Library Science Biomedical Research Imaging Center Patient Patient Portal (Mobile App/Web) Diagnosis Prognosis Progression Care Provider/s Treatment Options User Model for Personalization Medications

9 PRECISE CARE using MedSIFTER: Depression & Memory Loss Case Studies Javed Mostafa School of Information and Library Science Biomedical Research Imaging Center Patient Patient Reported Outcome (PRO) or Other Instruments ( Plus Behavioral Data on Mobile App/Web) Severity Index Mild Slightly Degraded Degraded User Model for Screening / Status-Checks Alarming Condition

10 PRECISE CARE using MedSIFTER: Depression & Memory Loss Case Studies Javed Mostafa School of Information and Library Science Biomedical Research Imaging Center Probability that category 2 is the top-most relevant category Probability that category 1 is relevant PRO Forms t 1 u 1 c 1 Carolina DW UNC EHR data t 2 u 2 c 2 t 3 : : t n Top class u 3 : : u n Relevance of categories User profile/model Acquired by using Robust ML techniques c 3 : : c n Categories (info topics / severity levels) Data Streams/Sources Behavior or Clinical Data Physiological Real-time Data

11 Adapting information extraction as a tool to guide research exploration Charles Schmitt Renaissance Computing Institute OBJECTIVES Research, whether for science, business, or intelligence, is an exploratory process that involves seeking, processing, and structuring information from a variety of sources to form conclusions that must then be supported by evidence This project seeks to improve the research process by extracting and structuring information that is processed during research activities into a research- focused knowledge base (RKB). The RKB provides the basis to: improve subsequent information seeking tasks, provide review of prior exploration, and to provide provenance about conclusions. DELIVERABLES A set of methods for developing RKB Software libraries for extracting research information from common web- based information sources and to serve as templates for additional extractors Software library and API to score new information sources for relevancy to RKBs, allowing users to rank potential new information sources Software library and API to allow for development of additional applications that leverage RKBs, such as tools to provide research summaries. APPROACH/TECHNIQUES Information extraction techniques will be employed to extract key content from web- based information sources Recent advances in statistical embedding will be employed to develop knowledge representations that reduce information dimensionality while providing generalization Knowledge representations will form the basis for research specific knowledge bases that drive subsequent applications Development of new methods for calculating distance from new information sources to RKB BENEFITS TO INDUSTRY New methods and tools to assist R&D programs that rely heavily on integration of knowledge from multiple sources Filter new information, capture provenance, support conclusions Especially relevant for biomedical fields e.g., adjudication of clinical- relevant genomic variants; research into side effects of specific therapeutics; understanding the biology impacts of natural products; reviewing literature to determine environmental impacts of materials.

12 Adapting information extraction as a tool to guide research exploration Charles Schmitt Renaissance Computing Institute Extract & Organize Research Specific Knowledge Base Research Specific Knowledge Base Research Specific Knowledge Base Summarize R&D activities What knowledge is needed next??? Improve exploration Provide provenance

13 Adapting information extraction as a tool to guide research exploration Charles Schmitt Renaissance Computing Institute Current work: Don t solve general AI, focus on usefulness RKB Core techniques are rapidly evolving Latent Semantic Analysis Word embeddings King is to queen as man is to Phrase embedding Provide both: - Structure for RKB - Distance metric kdist kgrowth K_dist = distance of new information source from RKB K_growth = growth in RKB induced by a new information source

14 Adapting information extraction as a tool to guide research exploration Charles Schmitt Renaissance Computing Institute Project Objectives: Assess outside of current test environment Compare techniques for calculating k_dist, k_growth Compare unsupervised, semi- supervised, and supervised training Further develop solution User selection of relevant information and research project User feedback Explore statistical embeddings augmented with domain ontologies Explore use of RKB: Summarizing exploration Providing provenance

15 Using Systems Science Methods to Improve Colorectal Cancer Screening in NC OBJECTIVES Support federal, state, payer, and local community decision making about how to improve colorectal cancer screening rates overall, address disparities, and improve health among the population of North Carolina by simulating the determinants of current care as well as alternate strategies under consideration. Kristen Hassmiller Lich Gillings School of Global Public Health Deptof Health Policy & Mgmt APPROACH/TECHNIQUES Individual- based modeling (IBM) using AnyLogic software was used to integrate census data, multi- level statistical models developed using population- based claims and other data to explain colorectal cancer screening behaviors (compliance and modality), research on the natural history of colorectal cancer, and stakeholder- developed intervention scenarios. DELIVERABLES Simulation- informed policy recommendations were presented to national (Centers for Disease Control and Prevention) and local (NC Dept of Health) decision makers and others through research and policy presentations and peer- reviewed manuscripts. BENEFITS TO INDUSTRY This replicable approach leverages existing (but often fragmented) data and technology to support comparative effectiveness analysis at the population level, and to support local capacity planning (i.e., colonoscopy). Technology could be extended to other populations, diseases, or behaviors.

16 Using Systems Science Methods to Improve Colorectal Cancer Screening in NC Kristen Hassmiller Lich Gillings School of Global Public Health Dept of Health Policy & Mgmt The model integrated rich data, and informed state and federal decision making about howto address gaps in colorectal cancer screening at the population level.

17 Using Systems Science Methods to Improve Colorectal Cancer Screening in NC Kristen Hassmiller Lich Gillings School of Global Public Health Deptof Health Policy & Mgmt We simulate current screening behaviors, in order to compare future intervention options ( counterfactuals ) (Cost- effectiveness efficiency frontier is shown above; and NC projections by county are shown at right)

18 Data- driven decision making in emergency health- care operations Nilay Tanik Argon Statistics and Operations Research OBJECTIVES Support federal, state, and local emergency response planning within emergency departments and beyond hospitals during day- to- day emergencies as well as mass- casualty events by means of mathematical and statistical decision making tools APPROACH/TECHNIQUES Design and control Statistical analysis and machine learning tools Stochastic modeling queueing theory, Markov decision processes, etc. Computer simulation mainly discrete- event simulations (Arena, Simio, Anylogic, etc.) DELIVERABLES Rules of thumbs for the design of emergency response systems: Number and location of trauma centers and transportation resources Dynamic policy recommendations and simple calculators for ambulance routing, surge capacity generation, triage, etc. during mass- casualty events. Dynamic policy recommendations, simulation, and calculation tools at emergency departments: patient flow, staffing, triage, diversion BENEFITS TO INDUSTRY Analytics- based decision making tools that can be used in different hospitals and emergency response systems. Advanced modeling of health- care operations that could be expanded to other parts of health systems Core values: Quality of care; fairness; efficiency; cost effectiveness

19 Data- driven decision making in emergency health- care operations Nilay Tanik Argon Statistics and Operations Research Ambulance dispatching during a disaster (with A. Mills and S. Ziya) Question: Which casualties should be transported to which treatment facilities? Factors: 1. Limited ambulances 2. Travel times 3. Hospital capabilities 4. Changing ED occupancy levels Solution approach: Model as a queuing control problem Develop heuristic policies that are easy to implement Test policies by a realistic simulation model data from national trauma data base Medical Collaborator: James Winslow, MD, NC State EMS Medical Director

20 Data- driven decision making in emergency health- care operations Nilay Tanik Argon Statistics and Operations Research Question: At each casualty location, which patients should be given priority for transportation? Triage! Solution approach: Model as a fluid model and solve Patient Prioritization in Mass Casualty Incidents (with A. Mills and S. Ziya) Test policies by a discrete- event simulator Decision support tool: Available via web (

21 Data- driven decision making in emergency health- care operations Nilay Tanik Argon Statistics and Operations Research Predictive and operational solutions for Emergency Departments (with A. Mehrotra, D. Travers, and S. Ziya) Predict operational characteristics of patients at triage: Admit or not? Complex or not? Develop statistical tools that could be embedded to already existing electronic records system for prediction. Use these tools for more efficient operational design: If a patient is predicted to have a high probability of admission, request a hospital bed earlier to shorten boarding time. Based on the complexity of the patient, treat the patient at fast track or change his/her priority level.