Intelligent Clinical Decision Support with Probabilistic ...
1
UC Irvine Heart Disease dataset Intelligent Clinical Decision Support with Probabilistic and Temporal EHR Modeling Simulations over 5,807 IN/TN patients >50% improvement in cost effectiveness >30% improvement in outcomes beyond existing fee-for-service model [Bennett and Hauser, Artificial Intelligence in Medicine 2013] EHR SRL POMDP Domain expert Provides rules & corrective feedback Identifies clinically relevant features Disease progression model Intelligent Clinical Decision Support System (ICDSS) Primary clinician Observations, test results, imaging, interventions Recommended treatment plans w/ human-readable rationale Kris Hauser* (PI), Sriraam Natarajan* (Co-PI), Shaun Grannis† (Co-PI) *Indiana University School of Informatics and Computing † Indiana University School of Medicine, Regenstrief Institute With clinical partners: Overview and Motivation Progress & Expected Outcomes Existing and Ongoing Work POMDP models in chronic depression treatment 0.45 0.55 0.65 0.75 0.85 0.95 J48 SVM AdaBoost Bagging Logis3c Naïve Bayes Single Rela3onal Tree So> Margin boos3ng AUCROC SRL for medical prediction tasks Cardiology domain Circulation; 92(8), 2157-62, 1995 JACC; 43, 842-7, 2004 Plaque in the left Coronary artery Given - age, sex, cholesterol, bmi, glucose, hdl level, ldl level, exercise, trig level, systolic bp and diastolic bp for all years 0 and 20 Predict – high Coronary Artery Calcification (CAC) levels at year 20 Atherosclerosis is the cause of the majority of Acute Myocardial Infarctions (heart attacks) Model: mixtures of relational probabilistic decision trees Learning: Relational Functional Gradient Boosting (RFGB) New Soft Margin relational learning algorithm improves recall Tunable penalties for false positives vs false negatives State-of-the-art performance on UC Irvine Heart Disease dataset (at right) and others Predic’ve model: Dynamic Bayesian Network Predic’on horizon:8 Decision variable: treat / not treat Outcome variables: selfreported survey (CDOI), treatment cost Objec’ve func’on: tradeoff between CDOI and $$$ (clinicianselected weight) [Yang et al, submitted to ICDM] Overview of envisioned CDSS Clinical decision-support systems (CDSS) have potential to exploit the wealth of clinical data in EHRs in addition to expert recommendations New AI techniques needed to plan chronic and multi-stage treatments: compute patient-specific, temporal, statistically- justified treatment plans Balance costs vs patient outcomes, reason with uncertainty Hypothesis: CDSS can improve state of current clinical practice by providing outcome-driven and cost-driven optimized decisions Two promising AI techniques: SRL and POMDP Sta’s’cal Rela’onal Learning (SRL): learning probabilis3c models from datasets with rela3onal structure Handles linked datasets, incomplete/missing data, noise Excellent for EHR data Par’ally Observable Markov Decision Processes (POMDP): learning probabilis3c models from datasets with rela3onal structure Handles linked datasets, incomplete/missing data, noise Excellent for EHR data 1800 1600 1400 1200 1000 800 600 400 200 0 Trivial Rand. Forest Log. Reg. Handmade BN Learned BN LOGLIKELIHOOD MODEL Final Outcome, Probabilis[c Predic[on, 10fold cross valida[on Higher is be\er (0 is perfect predic[on) Developing SRL methods to learn likelihoods of future adverse medical events • CARDIA 20 year longitudinal dataset (N=5115) • Preliminary results suggest high predictive power @ year 20 Future: use POMDP to suggest lifestyle changes (smoking, exercise), medications ER domain Int’l Stroke Trial dataset N=19,435 patients (1991-1996) with acute stroke symptoms 3 observation, 2 decision points: • Admission (t=0): demographics, symptoms observed. Medicine administered • Discharge (t=2 weeks): test results, diagnosis. Possible change of treatment, long-term prescription • Follow up (t=6 mo): death, long term dependency • Current: Learning probabilistic conditional models from partial & noisy data • Future: optimize patient- specific plans Regenstrief PHESS: 65 million records across Indiana Identify high utilizers: significant source of waste Stroke domain
Transcript of Intelligent Clinical Decision Support with Probabilistic ...
UC Irvine Heart Disease dataset
Intelligent Clinical Decision Support with Probabilistic and Temporal EHR Modeling
Simulations over 5,807 IN/TN patients >50% improvement in cost effectiveness >30% improvement in outcomes
beyond existing fee-for-service model
[Bennett and Hauser, Artificial
Intelligence in Medicine 2013]
EHR
SRL POMDP
Domain expert
Provides rules & corrective feedback
Identifies clinically relevant features
Disease progression model
Intelligent Clinical Decision Support System (ICDSS)
Primary clinician Observations, test results, imaging, interventions
Recommended treatment plans w/ human-readable rationale
Kris Hauser* (PI), Sriraam Natarajan* (Co-PI), Shaun Grannis† (Co-PI) *Indiana University School of Informatics and Computing † Indiana University School of Medicine, Regenstrief Institute
With clinical partners:
Overview and Motivation Progress & Expected Outcomes Existing and Ongoing Work
POMDP models in chronic depression treatment
0.45
0.55
0.65
0.75
0.85
0.95
J48 SVM AdaBoost Bagging Logis3c Naïve Bayes Single Rela3onal
Tree
So> Margin boos3ng
AUCR
OC
SRL for medical prediction tasks Cardiology domain
Circulation; 92(8), 2157-62, 1995
JACC; 43, 842-7, 2004
Plaque in the left Coronary artery
Given - age, sex, cholesterol, bmi, glucose, hdl level, ldl level, exercise, trig level, systolic bp and diastolic bp for all years 0 and 20 Predict– high Coronary Artery Calcification (CAC) levels at year 20
Atherosclerosis is the cause of the majority of Acute Myocardial Infarctions (heart attacks)
Model: mixtures of relational probabilistic decision trees Learning: Relational Functional Gradient Boosting (RFGB)
§ New Soft Margin relational learning algorithm improves recall
§ Tunable penalties for false positives vs false negatives
§ State-of-the-art performance on UC Irvine Heart Disease dataset (at right) and others
§ Predic've model: Dynamic Bayesian Network § Predic'on horizon: 8 § Decision variable: treat / not treat § Outcome variables: self-‐reported survey (CDOI), treatment cost § Objec've func'on: tradeoff between CDOI and $$$ (clinician-‐selected weight)
[Yang et al, submitted to ICD
M]
Overview of envisioned CDSS
§ Clinical decision-support systems (CDSS) have potential to exploit the wealth of clinical data in EHRs in addition to expert recommendations
§ New AI techniques needed to plan chronic and multi-stage treatments: compute patient-specific, temporal, statistically-justified treatment plans
§ Balance costs vs patient outcomes, reason with uncertainty § Hypothesis: CDSS can improve state of current clinical practice by
providing outcome-driven and cost-driven optimized decisions
Two promising AI techniques: SRL and POMDP Sta's'cal Rela'onal Learning (SRL): learning probabilis3c models from datasets with rela3onal structure § Handles linked datasets, incomplete/missing data, noise § Excellent for EHR data
Par'ally Observable Markov Decision Processes (POMDP): learning probabilis3c models from datasets with rela3onal structure § Handles linked datasets, incomplete/missing data, noise § Excellent for EHR data
-‐1800
-‐1600
-‐1400
-‐1200
-‐1000
-‐800
-‐600
-‐400
-‐200
0
Trivial Rand. Forest Log. Reg. Handmade BN Learned BN
LOG-‐LIKELIHO
OD
MODEL
Final Outcome, Probabilis[c Predic[on,
10-‐fold cross valida[on Higher is be\er (0 is perfect predic[on)
Developing SRL methods to learn likelihoods of future adverse medical events • CARDIA 20 year longitudinal
dataset (N=5115) • Preliminary results suggest
high predictive power @ year 20
Future: use POMDP to suggest lifestyle changes (smoking, exercise), medications
ER domain
Int’l Stroke Trial dataset N=19,435 patients (1991-1996) with acute stroke symptoms 3 observation, 2 decision points: • Admission (t=0): demographics,
symptoms observed. Medicine administered
• Discharge (t=2 weeks): test results, diagnosis. Possible change of treatment, long-term prescription
• Follow up (t=6 mo): death, long term dependency
• Current: Learning probabilistic conditional models from partial & noisy data
• Future: optimize patient-specific plans
Regenstrief PHESS: 65 million records across Indiana Identify high utilizers: significant source of waste
Stroke domain
![Intelligent Decision Support Systemsmiquel/idss/IDSS-Part 2-MAI-1112.pdf · Intelligent Decision Support Systems (IDSS) [90s] ... An IEDSS is an intelligent information system that](https://static.fdocuments.us/doc/165x107/5f0913f67e708231d4252044/intelligent-decision-support-systems-miquelidssidss-part-2-mai-1112pdf-intelligent.jpg)
