Analytics for Situational Awareness in Healthcare

Anupam JoshiCSEE Department, UMBC

http://ebiquity.umbc.edu/

October 2010

Situational Awareness

Being aware of what is happening around you to understand how infor-mation, events, and your own actions will impact your goals and objectives, both now and in the near future

Applies both to people and to systems• SA is a field of study concerned with

perception of the environment critical to decision-makers in complex, dynamic areas. Wikipedia

Situational Awareness (SA)• Common theme in many scenarios as we become

increasingly instrumented and interconnectedHot conflicts, homeland security, cyber-security, cyber-physical systems, disaster relief, health-care, IT services, network operations & management …

• Moving from “react and respond” to “predict and effect”• Spans CSEE and IS – sensors, wireless networks,

embedded systems, streaming data, analytics, image processing, AI, human-computer interfaces, cloud computing, …

• Highly distributed, dynamic and interconnected systems

Some Key Research Challenges• Analysis and integration of unprecedented

volumes of rich media and text to infer context• Representation of complex, uncertain, and

evolving information, e.g., human networks • Fusion into a common operational picture for SA• Mining of patterns and trends to predict events• Planning under uncertainty• Intuitive presentation to the responder in the

field and in the command center to enable decision making

Some recent UMBC research• DHS: Unified Incident Command & Decision Support

(recommended for funding, awaiting contract)• NIST: Image Analysis for Bio and Clinical Informatics (current)• NIST: Personalized Medicine (current)• NSF: Situational Awareness and equipment supplement for

I/UCRC (current)• ONR: Relief Social Media (sub. from LMCO, current)• NSF: Platys: from position to context (current)• DARPA: PbWAN: policy based network control (STTR with

Shared Spectrum Co., 2006-10)• UMMS: Operating Room of the Future (2006-09)• DARPA: Traumapod robotic surgery (2005-06)

Underlying Technologies we work on ….

• Agent based Systems• MANET management and security• Ontologies for Semantic Interoperability • Semantic Policy Languages (especially for

Security/Privacy/Trust)• Social Media Analytics (NER, Community

Detection, …)• Streaming Analytics on KBs• Reasoning with uncertain/incomplete

information

Operating Room Of the Future• ORs will be pervasive computing environments

Devices, sensors, tags, trainers, PDAs, monitors will discover one another and interoperate

• Components will require access to a context model to manage resources effectively

Includes relevant information on people, roles, activities, events, workflow, devices, …

• Intelligent components will recognize events and activities

Even in the presence of noisy, incomplete or contradictory data

System Architecture

Stream Processor(TelegraphCQ)

ContinuousQueries

RFID System

Medicines

Tools

Staff

Patient Monitor

Rule Base

Trend Analyzer

Physiological Data

Assert facts

Assert factsContext Aware Agent

Database

Patient History Medical

Supplies

Staff MedicalEncounterRecord

Events

Video Clipper

Stream Processor(TelegraphCQ)

ContinuousQueries

Patient Monitor

RFID System

MedicinesTools

Staff

Trend Analyzer Physiological

Data

Low-LevelEvent Processor

Database

Patient History

Medical Supplies

Staff

Rule Base

Assert facts

MedicalEncounterRecord

Video Clipper

Assert facts

Event Detection - Level 3

Event Detection - Level 2

Event Detection - Level 1

Events

Events

Simulations and Results

• The Human Patient Simulator (HPS) from METI• Designed to react like a human• Used for training resident doctors• Responds to medical treatment• Physiological data sets from HPS

Scenario and Patient Profile• HPS can run patient profiles• Data logs from simulations used to

evaluate the system• Significant events for a blunt

trauma multiple injuries profile include hypovolemia, tension pneumothorax, decompression and fluid infusions

• Provides data for Medical Encounter Record

• Ran 30 simulations on 7 profiles measuring false positives & negatives and latency in detecting events

Patient Profile

Tables are everywhere and yet they are ignored!!

• 14 nations (including US) share datasets publicly. Most of it is in spreadsheets and CSV

• 154 million high quality relational tables on the web

• Key domains like Medical, Bio-technology store information in spread sheets and tables

• Traditional text processing techniques do not work well with tables

• Key public policy decisions is often based on the information encoded in tables.

• Lack of systems that can understand and infer the intended meaning of tables

Evidence based medicine

Figure: Evidence-Based Medicine - the Essential Role of Systematic Reviews, and the Need for Automated Text Mining Tools, IHI 2010

The idea behind Evidence-based Medicine is to judge the efficacy oftreatments or tests by meta-analyses or reviews of clinical trials. Key information in such trials is encoded in tables.

However, the rate at which meta-analyses are published remains very low … hampers effective health care treatment …

Given a table, we …

Name Team Position Height

Michael Jordan Chicago Shooting guard 1.98

Allen Iverson Philadelphia Point guard 1.83

Yao Ming Houston Center 2.29

Tim Duncan San Antonio Power forward 2.11

http://dbpedia.org/class/yago/NationalBasketballAssociationTeams

http://dbpedia.org/resource/Allen_Iverson Map numbers as values of properties

dbprop:team

Generate a machine – understandable representation

Class label prediction and Entity Linking

Team

ChicagoPhiladelphiaHoustonSan Antonio

1. Chicago Bulls2. Chicago3. Judy Chicago

1. Philadelphia2. Philadelphia 76ers3. Philadelphia (film)

Possible Classes for the column - dbpedia-owl:Placedbpedia-owl:Cityyago:WomenArtistyago:LivingPeopleyago:NationalBasketballAssociationTeamsdbpedia-owl:PopulatedPlacedbpedia-owl:Film… …. …..

Query the Knowledgebase

Generate a set of possible classes

Score the classes

Re-query KB with predicted class

label as additional evidence

An SVM-Rank classifier ranks the result set

A second SVM classifier decides whether to link

to the top-ranked instance or not

A machine learning based approach for entity linking

Relation identification and RDF representation

Name

Michael Jordan

Allen Iverson

Yao Ming

Tim Duncan

Team

Chicago

Philadelphia

Houston

San Antonio

Rel ‘A’

Rel ‘A’

Rel ‘A’, ‘C’

Rel ‘A’, ‘B’, ‘C’

Rel ‘A’, ‘B’

• Query the knowledge base

• Generate a set of possible relations

• Rank the relations

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .@prefix dbpedia: <http://dbpedia.org/resource/> .@prefix dbpedia-owl: <http://dbpedia.org/ontology/> .@prefix yago: <http://dbpedia.org/class/yago/> .

"Name"@en is rdfs:label of dbpedia-owl:BasketballPlayer ."Team"@en is rdfs:label of yago:NationalBasketballAssociationTeams .

"Michael Jordan"@en is rdfs:label of dbpedia:Michael Jordan .dbpedia:Michael Jordan a dbpedia-owl:BasketballPlayer .

"Chicago Bulls"@en is rdfs:label of dbpedia:Chicago Bulls .dbpedia:Chicago Bulls a yago:NationalBasketballAssociationTeams .

• “Linked” RDF representation of data

ResultsNumber of Tables 15

Total Number of rows 199

Total Number of columns 56 (52)

Total Number of entities 639 (611)

* The number in the brackets indicates # excluding columns that contained numbers

Class prediction for column: 76.92 % Entity Linking for table cells: 66. 12 %

Examples of class label prediction results:Column – NationalityPrediction – MilitaryConflict

Column – Birth PlacePrediction – PopulatedPlace

Category wise Entity Linking results

Medical Table – Challenges

More Numbers; less strings ! Numbers expressed in a pattern [e.g. 24 – 3.2 is Mean – Std. deviation]

Row headers (in addition to column headers)

Clues often hidden in captions

Proposed approach

Information Extraction techniques(OMC ->

Omeprazole, Metronidazole, Clarithromycin)

Number preprocessing [Identify patterns such

as Mean – S.D.]

Incorporate caption and text surrounding

table as additional evidence

Graphical Model based framework

for table interpretation

Medical Tables

Knowledge Source

Background knowledge of

Medical Domain

A Relational Learning based framework

C1 C2 C3

R11 R12 R13 R21 R22 R23 R31 R32 R33

Function that captures the interaction between the column headers and row values

Alternative – Markov Logic Network based system

Problem

• Current epidemiologic studies of large cohorts does not take into account individual’s genetic, proteomic and metabolic characteristics.

• For complex diseases, state-of-the-art clinical-genomic based studies do not provide simple means to disseminate new findings into clinical practice.

• Hence gap continues to grow between the knowledge (clinical research, therapeutic guidelines, etc.) accumulated and it’s implementation at the patient’s bedside.

Our focus ->Type 2 diabetes -> Chronic disease that comprises 90% of

people with diabetes around the world

Genetic Vs Non-Genetic Risk Factors

Proposed Solution

• Develop a Web-based Clinical Decision Support System that will integrate genomic, metabolic associations and data mining correlative evidence gathered by computational algorithms for prediction and knowledge discovery and will be invoked on demand at the point of care.

• Study data – GENEVA Diabetes Study data obtained from dbGAP (NCBI-NIH database)

Phenotypes

Type Woman Men

Cases (T2D) 1538 1182

Controls 1826 1354

Uncertain diabetes type

65 68

Genotypes

Type Woman Men

Cases (T2D) 1518 1164

Controls 1810 1338

Uncertain diabetes type

- 68

Approaches

• Identification of dominant phenotypes in controls vs cases. (Primary - Body mass index, family history, cholesterol, physical activity, high blood pressure. Secondary – fat intake, cereal fiber intake, glycemic load, etc.

• Identification of Type II diabetes genes and their corresponding risk alleles.

• Correlating risk SNPs with the phenotypes and other environmental factors.

• Assign genomic risk score to a patient based on presence of risk SNPs in his/her genotype.

Parameters to be used: - % of occurrence in cases vs controls. - Intensity variation in alleles A against allele B using scattered plots of .CEL files.

• Build a prediction model based on clinical and genomic datasets.

• Identify groups of patients based on their clinical and genomic behaviors.

Key Results

• Type II Diabetes risk SNPs identified in the dataset under study: TCF7L2, FTO, MCR4, TSPAN8, VEGFA, BCL11A, HHEX, CDAKL1, MTNR1B.

• Findings: -Risk alleles always present in higher % of cases than controls. -TCF7L2 gene prominently present in cases than controls. -Individuals with FTO (fat mass and obesity association gene) weigh 2kgs more than an average person. -Individuals with TCF7L2 are strongly associated with family inheritance.

• Patients with higher genomic score (more no. of risk SNPs present) have higher chances of occurrence of T2D and vice versa.

• Genomic score along with phenotype features increases prediction accuracy by ~2%.

Laparoscopic Cholecystectomy (Lap Chole)

• Most common laparoscopic procedure performed

• Cholecystectomy – surgical removal of gallbladder

• First-choice treatment for:– gallstones and – inflammation of gall bladder

• 4-5 incisions of 0.5-1.5 cm in diameter

• CO2 – used to inflate abdominal cavity

Complications in Lap Chole

• Hemorrhage• Injury to common bile duct – connects gallbladder

and liver• Bile Leakage – Dangerous infection• Stray burns from electrocauter• Injury to bowel or vascular structures• Abdominal peritoneal adhesions• 5 – 20% conversion to open cholecystectomy

Our Approach

Region of Interest (ROI)

• Cystic artery course the neck of the gallbladder

• It lies towards the lower right side of the neck

Patch-based Template Matching

Relative distance estimation

Reduced Region of Interest (ROI)

SVM classifier training

• Training data – 900 images (470 positive, 430 negative)

• Test data – 213 images (135 positive, 78 negative)

• Linear kernel

Results

26.7 % increase in accuracy

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