Investigating the Use Of Novel Data Mining And Machine Learning Methods in Healthcare Data Sources Of Multiple Nature
- 1. Thesis Defense Investigating the Use Of Novel Data Mining And Machine Learning Methods in Healthcare Data Sources Of Multiple Nature Roberto Batista
- 2. Content • Introduction • Part I - Survey Data • Overview • Literature review • Methods • Exploratory Data Analysis • Data Transformation • Conclusions • Part II - Electronic Health Record • Overview • Literature review • Methods • Exploratory Data Analysis • Data Transformation • Conclusions 2
- 4. 2008 2015 9.4% 84% Introduction • Non-federal hospitals with basic systems. The Office of the National Coordinator for Health Information Technology (ONC) 4 gender ethnicity religion age social finance exams
- 5. Health Data Sources National Library of Medicine (NIH) EMR $ $ $ $ $ $ SpO2 EHR 5 Survey Electronic Medical Record Claim Data Vital Signs Data Electronic Health Record
- 6. PART I PART II Thesis Components EHR 6 Survey Electronic Health Record
- 7. Part I Survey Data -How to identify personality traits groups in the Health and Retirement Study survey data? 7
- 8. Health and Retirement Study (HRS) 8
- 9. HRS Overview 3 surveys 6 aspects 5 aspects 5 aspects 22,000 >50 yo 9 aspects 4 Derived Datasets 58.54% Medical Ethics Training 9
- 10. Literature Review Gould et al., 2015: Verifies the symptoms of anxiety and depression in veterans and non-veterans using CES-D and BAI. Seligman et Al., 2018: Machine Learning improves the understanding of social determinants of health. Hülür et al., 2015: Investigates association between subjective memory, subjective age and personality traits. Fehrman et al., 2015: Personality correlation with the consumption of eight psychoactive drugs and its consumption by individuals. Aschwanden et al., 2019: Personality traits associations with the probability of having a preventive screening for cancer. Five personality Traits (OCEAN): • Openness • Conscientiousness • Extraversion • Agreeableness • Neuroticism 10 Machine Learning Studies
- 11. HRS Datasets Overview 11 HRS - RAND HRS Core HRS Exit HRS Post-Exit • Adult ADHD • Financial • Material Hardship • Long-term Care • Medication Non- Adherence • Religious • Proxy informant • Health • Family • Finance • Proxy informant • Unresolved financial situations 1992 | 2016 1992 | 2016 1992 | 2016 1992 | 2016
- 12. HRS Datasets of Interest 12 HRS - RAND HRS Core HRS Exit HRS Post-Exit
- 13. HRS Datasets of Interest 2006, 2008, 2010, 2012 HRS - RAND HRS Core - Section LB - Left-Behind Subjective well-being, lifestyle and experience of stress, quality of Social ties, personality traits, work-related beliefs, and self- related beliefs. HRS Core - Section D - Cognition Immediate and delayed free recall, working memory and mental processing, vocabulary, mental status, and self-rated memory. 13 HRS - RAND HRS Core HRS Exit HRS Post-Exit 2006, 2008, 2010, 2012
- 15. Data Transformation HRS: • RAND • Core D • Core LB 15
- 16. Methods Cloud of Individuals: Stars represents individuals Cloud of Variables: Points represents variables A B C 1 a1 b2 c1 ⋮ ⋮ ⋮ ⋮ i a2 b2 c3 i’ a1 b1 c1 ⋮ ⋮ ⋮ ⋮ N a4 b2 c2 • Unsupervised Machine Learning • Multiple Correspondence Analysis (MCA) • Clustering 16
- 17. sophist_A lot sophist_Some bminded_A lot curious_A lot intellig_A lot imagina_A lot creative_A lot sympath_A lot softheart_A lot caring_A lot warm_A lot helpful_A lot talkactive_A lot active_A lot lively_A lot friendly_A lot outgoing_A lot careless_A lot careless_Not at all thorough_A lot hardworker_A lot responsible_A lot organized_A lot calm_A lot nervous_Not at all worry_Not at all moody_Not at all −0.5 0.0 0.5 1.0 1.5 −1.00 −0.75 −0.50 −0.25 0.00 Dim1 (8.1%) Dim2 (4.7%) Region 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● sophist_A little bminded_Some curious_Some intellig_Some imagina_A little creative_A little creative_Some sympath_Some softheart_Some caring_Some warm_Some helpful_Some talkactive_A little talkactive_Some active_Some lively_Some friendly_Some outgoing_Some careless_A little careless_Some thorough_Some hardworker_Some organized_Some calm_Some nervous_A little nervous_Some worry_A little worry_Some moody_A little moody_Some −0.50 −0.25 0.00 0.25 0.50 0.0 0.5 Dim1 (8.1%) Dim2 (4.7%) Region 2 sophist_A little sophist_Not at all bminded_A little bminded_Not at all curious_A little intellig_A little imagina_A little imagina_Not at all creative_Not at all sympath_A little softheart_A little caring_A little caring_Some warm_A little helpful_A little talkactive_A little talkactive_Not at all active_A little lively_A little friendly_A little friendly_Some outgoing_A little outgoing_Not at all thorough_A little hardworker_A little responsible_A little responsible_Some organized_A little organized_Not at all calm_A little calm_Some nervous_A lot worry_A lot moody_A lot −1 0 1 2 −0.5 0.0 0.5 1.0 1.5 2.0 Dim1 (8.1%) Dim2 (4.7%) Region 3 bminded_Not at all curious_Not at all intellig_Not at all imagina_Not at all sympath_Not at all softheart_Not at all warm_Not at all helpful_Not at all active_Not at all lively_Not at all friendly_Not at all thorough_Not at all hardworker_Not at all calm_Not at all 1 2 3 4 5 0.5 1.0 1.5 2.0 Dim1 (8.1%) Dim2 (4.7%) Region 4 17
- 19. Conclusions • The hierarchical clustering technique applied to the low dimensional representation of participants, provided by the MCA method, suggested a reasonable separation of the respondent profile characterized by a personality scale. • This can be applied to survey design and sampling procedures. • This can support correlation studies with other physical and mental health indicators. 19
- 20. Paper Presented and Published 18th IEEE International Conference on Machine Learning and Applications - ICMLA 2019 December 16-19, Boca Raton, Florida, USA 20
- 21. Part II Electronic Health Record - How to predict Intensive Care Unit (ICU) Length of Stay (LOS) using Machine Learning models? 21
- 22. Medical Information Mart for Intensive Care - III (MIMIC-III) 22
- 23. MIMIC-III Overview NB, 15 > 2.1 days 7.76% 380 meas. 11.5% 44.1% 53,423 adm 6.9 days EHR 7,870 38,597 23
- 24. Beth Israel Deaconess Medical Center CareVue DB MetaVision DB MIMIC-III 24
- 25. Literature Review Azari et al., 2012: Approached the LOS prediction identifying similar groups. Reached accuracy of 74.3%. Van Houdenhoven et al., 2007: LOS prediction elective esophagectomy with reconstruction for carcinoma, with presence of gastroesophageal reflux disease, and respiratory minute volume transthoracic. R2 of 45%. Clark & Ryan, 2002: Tested with demographics younger than 55 years old reach the highest accuracy of 69%, individuals in the range of 55 and 70 yo reached 13%, and the group older than 70 years old 17%. Gustafson, 1968: Uses five different methodologies for predicting the LOS of inguinal herniotomy patients. Afrin et al., 2019: Predict LOS using three classifications, focused on the age and death outcome of the patients. Accuracy 54.8% (RF and LR). Intensive Care Unit (ICU) Length of Stay (LOS) 25 Wait time for ICU Admission ICU Management Important predictor for Death Rate ICU Cost
- 26. Data Accessing Data Specimens only Research Training - CITI Program: 1. Belmont Report and Its Principles (ID 1127) 2. History and Ethics of Human Subjects Research (ID 498) 3. Basic Institutional Review Board (IRB) Regulations and 4. Review Process (ID 2) 5. Records-Based Research (ID 5) 6. Genetic Research in Human Populations (ID 6) 7. Populations in Research Requiring Additional Considerations and/or Protections (ID16680) 8. Conflicts of Interest in Human Subjects Research (ID 17464) 26
- 27. Exploratory Data Analysis 26 CSV Files SQLite CSV to SQLite Conversion 27
- 28. Data Transformation 28 CSV STAYS CSV PATIENTS 1. ROW_ID 2. SUBJECT_ID 3. GENDER 4. DOB 5. DOD 6. DOD_HOSP 7. DOD_SSN 8. EXPIRE_FLAG 1. ROW_ID 2. SUBJECT_ID 3. HADM_ID 4. ICUSTAY_ID 5. DBSOURCE 6. FIRST_CAREUNIT 7. LAST_CAREUNIT 8. FIRST_WARDID 9. LAST_WARDID 10.INTIME 11.OUTTIME 12.LOS CSV DIAGNOSIS 1. ROW_ID 2. SUBJECT_ID 3. HADM_ID 4. SEQ_NUM 5. ICD9_CODE CSV ADMISSIONS 1. ROW_ID 2. SUBJECT_ID 3. HADM_ID 4. ADMITTIME 5. DISCHTIME 6. DEATHTIME 7. ADMISSION_TYPE 8. ADMISSION_LOCATION 9. DISCHARGE_LOCATION 10.INSURANCE 11.LANGUAGE 12.RELIGION 13.MARITAL_STATUS 14.ETHNICITY 15.EDREGTIME 16.EDOUTTIME 17.DIAGNOSIS 18.HOSPITAL_EXPIRE_FLAG 19.HAS_CHARTEVENTS_DATA
- 30. Methods Tidymodels framework: • rsample (data sampling) • recipes (data preprocess) • parsnip (machine learning modeling) • yardstick (performance evaluation) • Algorithm families: Decision Trees, Random Forest, Boosted Trees, SVM, and Linear Regression x x x x 30
- 31. Methods Predictors • Ethnicity • Respiratory diagnosis Subset • ICU: SICU • Admission Type: Urgency Linear Regression • R2 Adj.: 63.75% • RMSE: 9.56 Classifier • Accuracy: 92.7% 31 x x x x
- 32. Conclusions • LOS prediction is a very specific prediction task, case oriented and is unlikely that one model can generalize for any case. • It was possible to create a specific prediction model for: • Surgical Intensive Care Unit • Admitted from Emergency • Patients with respiratory disease diagnosed • The use of the novel R library tidymodels enables the use of multiple ML libraries, under a unifying collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the modern data science tools in the tidyverse. 32
- 33. Next Steps • Format a paper and submit to machine learning conferences/ journals. • ACM-BCB ’20: 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics • Apply unsupervised learning technics used in the part I to the MIMIC-III dataset. • Create MIMIC-III subsets with Lab Exams for further investigation. 33
- 34. Thanks to 34
- 36. Thanks to Icons: http://www.flaticons.com 36 Friends at