Recommended
More Related Content
What's hot
What's hot (20)
Similar to The Life-Changing Impact of AI in Healthcare
Similar to The Life-Changing Impact of AI in Healthcare (20)
Recently uploaded
Recently uploaded (20)
The Life-Changing Impact of AI in Healthcare
- 7. • • • •
- 17. • • • • • • •
- 18. • •
- 21. • • • •
- 22. Drug Design Translational Medicine High Content Screening
- 23. Translational Medicine Clinical Outcomes Prediction - Insight into Disease Progression High throughput platforms like sequencing provide millions of features on molecular states • Small number have clinical significance Accurate predictions of clinical outcomes from high-dimensional molecular data • Important and major challenge ML / AI approaches current focus Alberto Pascual AI and Analytics Innovations, Perkin Elmer Cox survival model … … … … … … … … … … … … … … … … Risk backpropagation: sensitivity of predicted risk for each input feature and subject • Partial derivatives of risk with respect to each input Selecting features with highest rated features, we can identify common biomarkers.
- 24. Clinical Outcomes Prediction Insight into Disease Progression • Risk scores calculated across large populations • Click and see survival differences • Discover promising treatments, factors
- 25. High Content Screening Automated microscopes capture millions of images of cells • Labelled with fluorescent stains, probes Experiments profile effects of many conditions • Disturb cells – compounds, concentrations • Quantify the visual response of cells Screens are run with clear positive and negative controls to ID ‘hits’ • Characterize a biological system and/or assay Alberto Pascual AI and Analytics Innovations, Perkin Elmer
- 26. Today’s data analysis workflow Images Preprocessing Features • thousands Hits • compounds close to positive control • (effective drug)
- 27. Import Normalization QC Feature Selection Dimensionality Reduction Hit Stratification Classification High Content Screening
- 28. Import Normalization QC Feature Selection Dimensionality Reduction Hit Stratification Classification High Content Screening Feature Selection • Univariate • Multivariate • Unsupervised eg PCA • Supervised ML eg Forests Hits • ML Classification • ML Ensembles
- 29. High Content Screening Import Normalization QC Feature Selection Dimensionality Reduction Hit Stratification Classification SAR siRNA Validation Biological Interpretation
- 30. High Content Screening Normalization Data exploration Batch effect detection PCA class detection Select training cases Hit stratification and QC Feature importance
- 31. AI-Automated High Content Screening Deep-learning
- 32. • Model trained on whole image, not individual objects, no segmentation needed • Label already defined in plate map, no annotation required • Returns prediction over whole image, e.g. here this image contains the number 5 • Provides visualization of which cells / subcellular regions differ between controls AI-Automated High Content Screening
- 33. AI-Automated High Content Screening AI-Automation Benefits Addresses bottleneck in high- content screenings • Avoids custom segmentation and feature extraction for each experiment • Avoids complex assays, such as organoids and co-cultures Avoids feature-engineering focus on subset of phenotypes • May only measure nuclear translocation of a probe; but in one condition it might translocate to the membrane. Hit Predictions • Well, image Image display • Overlay • Transparency Filters • Wells, plates
- 34. Predictions & Efficiency Surgical Site Infections OR Efficiencies
- 35. Cost of hospital acquired infections in US is in excess of $45 billion Readmissions lead to $41.3 billion in costs SSI #1 cause of HAI - 21.8% SSI #1 cause of readmission in surgical patients - 38.8% 1. Surgical Site Infection
- 36. 58% reduction in SSI in 2 years 74% reduction in SSI in 3 years Estimated cost reduction during hospitalization: $2.2 million for every 300 patients undergoing surgery Gbegnon, A., et al. (2014). Predicting Surgical Site Infections in Real-Time. KDD Proceedings Cromwell, J. (2018). Machine Learning and High Definition Care: More Powerful than a Pill. TIBCO NOW Proceedings, community.tibco.com Outcomes — Better than a Pill
- 37. Data Flow Data Entry / OR Merge Variables R- Predictive model TIBCO Data Scienc e GI Data Mart EMR Report Results Web / Intranet Real Time Variables Results + Variables EMR Variables Daily ETL
- 38. Important Variables, Model Summary Variable K- NMI P-value SSIs 1.000 < 0.001 *Duration 0.035 < 0.001 *EBL 0.043 < 0.001 *Ostomy 0.055 < 0.001 Procedure category 0.029 < 0.001 Surgeon 0.021 < 0.001 Total procedures 0.028 < 0.001 *Transfusion 0.028 < 0.001 *Wound class 0.014 < 0.001 Zip code 0.032 0.13 Location 0.013 0.11 OUTCOME No SSI SSI No SSI TN=88 FN=5 SSI FP=27 TP=6 * = variable collected in OR in real-time COST SSI $28,000 Intervention $500 Break-even 56 FP = 1 FN
- 39. EMR IntegrationLayer Offered API Offered API Offered API Data Repository Statistical Model Statistical Model Analytics Server Statistical Model Provider Environment Service Environment End-State Architecture
- 40. In the News
- 41. The Operating Room is the most expensive minute in the hospital Every minute that an Operating Room stands idle costs the hospital $625. And one of the best ways to reduce idle time is to identify, communicate, and mitigate reasons for delays. Spotfire analytics enables the optimization of Surgery operations including on-time starts, scheduling accuracy, and block utilization. 2. Optimize Surgical Workflow
- 42. Surgery Insights
- 43. Surgery Analytics Total Cases Average Case Duration vs Scheduled % On Time StartsSurgeon Kai Gordon Jonas Nick 250 130 102 98 29% 15% 23% 34% Surgeon Statistics 100 75 50 0 Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul # cases per month
- 44. Surgery Analytics Total Cases Average Case Duration vs Scheduled % On Time StartsSurgeon Kai Gordon Kent Irons 678 339 504 146 29% 15% 23% 34% Surgeon Statistics 300 200 100 0 Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul # cases per month
- 45. Customer Success: Surgery Insights 32 operating rooms 20,000 OR patients per year ACS NSQIP since 2007 Measurable results: 25% improvement in first case on-time starts 20% increase in surgery block utilization $450K estimated value of surgery time “We use the Block Utilization Scorecards to identify opportunities to optimize surgical capacity. The reports are an invaluable management tool to quantify usage, encourage timely block release, and to help us allocate time to new surgeons.” Surgery Business Manager Saint Francis Hospital and Medical Center 617 bed acute care hospital
- 46. • • • •
- 50. Toilet is fitted with technology that can detect a range of disease markers in stool and urine, including those of some cancers, such as colorectal or urologic cancers. The device may appeal to individuals who are genetically predisposed to conditions such as irritable bowel syndrome, prostate cancer or kidney failure. The toilet includes video cameras and motion sensing, combined with deep learning CNNs; along with physical and molecular analysis; to assess urine and stool physical characteristics. The toilet automatically sends data to a cloud-based system for safekeeping. In the future the system could be integrated into health care provider’s system for assessment. April 6, Nature Biomedical Engineering.
- 52. Surgical site infection is the #1 cause of readmission in surgical patients. By combining data from the electronic medical record, hospital operations and real-time data from in the operating room, this AI/ML model application predicts the risk of SSI in patients – before, during and after surgical procedures. One fascinating aspect of this problem is the very different cost of intervention (~$500) compared with the cost of an SSI (~$28,000). As such, the break-even cost of implementing an AI/ML model in this setting is approximately 56 false positives to 1 false negative. In other words, ML models can return a relatively large number of false positives, and still create significant value. This solution has emerged from the University of Iowa, and is currently in scale-up testing across 6 hospital ORs. In this multi-center setting, OR data is being shared via API-led services in cloud setting, With multi-OR data, this AI/ML application will have ample opportunity to create significant value, by borrowing strength across the facilities’ data.