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Towards Understanding ICU Procedures Using Similarities In Diplom-Ingenieur (Dipl.-Ing(FH)) MASTER THESIS Towards Understanding ICU Procedures using Similarities in Patient Trajectories An exploratory study on the MIMIC-III intensive care database Alexander Galozy Embedded and Intelligent Systems, 30 credits Halmstad University, June 29, 2018 Alexander Galozy: Towards Understanding ICU Procedures using Simi- larities in Patient Trajectories, c May 2018 supervisors: Slawomir Nowaczyk Anita Sant’Anna examiners: Antanas Verikas Slawomir Nowaczyk location: Halmstad, Sweden ABSTRACT Recent advancements in Artificial Intelligence has prompted a shear explosion of new research initiatives and applications, improving not only existing technologies, but also opening up opportunities for new and exiting applications. One field that has seen a serge in activity in recent years is the health care sector, where Artificial Intelligence promises data driven decision support, efficiency gains and improved health outcomes for patients through individually customized treat- ment. One problem commonly encountered in health care and espe- cially in the Intensive Care Unit (ICU) is overtreatment or mistreat- ment of the patients resulting in increased morbidity and mortality. This thesis explores the MIMIC-III intensive care unit database and conducts experiment on an interpretable feature space based on sever- ity scores, defining a patient health state, commonly used to predict mortality in an ICU setting. Patient health state trajectories are clus- tered and correlated with administered medication and performed procedures to get a better understanding on the potential usefulness to evaluate treatments on their effect on said health state, where com- monalities and deviations in treatment can be understood. Clustering of administered medications and procedures context based on a se- mantic representation has been performed to find commonalities in treatment among patients. Furthermore, medication and procedure classification is carried out to explore their predictability using the severity sub-score feature space. iii "Signals always point to something. In this sense, a signal is not a thing but a relationship. Data becomes useful knowledge of something that matters when it builds a bridge between a question and an answer. This connection is the signal." — Stephen Few[ 1] ACKNOWLEDGEMENTS I would like to thank my thesis supervisors Anita Sant’Anna and Slawomir Nowaczyk for many fruitful discussions and suggestions, enabling me to find my way through the copious amounts of data. Thank you. Alexander Galozy v CONTENTS List of Figures viii List of Tables xiv Acronyms xvii 1 introduction1 1.1 Goal and Approach . 2 1.2 Contribution . 4 1.3 Thesis Outline . 4 2 related work7 2.1 EHR and Patient Representations . 7 2.2 Datamining in the Intensive Care Unit . 8 2.3 Patient Outcome Prediction using Severity Scores . 9 2.4 Utilization of Time Series Data . 10 2.5 Summary . 11 3 mimic-iii intensive care database 13 3.1 Database Structure and Statistics . 13 3.2 Data Classes . 14 3.3 Data for Severity Score Computations . 15 3.3.1 Simplified Acute Physiology Score III (SAPS-III) 15 3.3.2 Oxford Acute Severity of Illness Score (OASIS). 16 3.4 Patient Cohorts under Investigation . 17 4 tools and methods for data analysis 19 4.1 Dimensionality Reduction . 19 4.2 Clustering Methods . 20 4.3 Classification Models . 22 4.4 Representing Patient Trajectories, Medication and Pro- cedures . 24 4.5 SAPS-III and OASIS Sub-scores . 27 4.6 Patient Cohort Selection Process . 28 5 results 33 5.1 Visualization of the Severity Sub-score Feature Space . 33 5.2 Patient Health state trajectories and probability of mor- tality . 36 5.3 Trajectory Clustering using the Time Series Cluster Ker- nel............................... 38 5.4 Diverging Patient Trajectories in 2D-Principal Compo- nent Space . 44 5.5 Patient Trajectory Clustering Via Simple Two-day Sub- score Deltas . 46 5.5.1 Experiments on the All-patient Cohort . 47 5.5.2 Experiments on the CHF-patient Cohort . 64 5.6 Medication and Procedure Classification . 78 5.6.1 Classification using the First-day Sub-scores . 80 vii viii contents 5.6.2 Classification using Two-day Sub-scores and Sub- scores Deltas . 81 5.6.3 Feature Importance for Mediation and Proce- dure Classification . 83 6 conclusion and future work 87 6.1 Future Work . 88 a appendix 91 a.1 ROC-curves of Medication Classification . 91 a.2 ROC-curves of Procedure Classification . 95 bibliography 99 LISTOFFIGURES Figure 3.1 Schematic overview of the MIMIC-III intensive care database. Patient data is deindentified to preserve the privacy of the patients. From [2]. 14 Figure 4.1 A basic Multilayer Perceptron (MLP) with one hidden layer and one output neuron. 24 Figure 4.2 CBOW and Skip-gram architectures. CBOW(left) predicts the word based on the context, while Skip-gram(right) predicts surrounding words given the current word. Reproduced from [3]. 25 Figure 4.3 SAPS-III scoring for vital signs and laboratory abnormalities. Reproduced from [4]. 26 Figure 4.4 SAPS-III scoring for acid-base disturbances. Re- produced from [4]. 28 Figure 4.5 SAPS-III scoring for neurologic abnormalities according to presence of eye opening. Shaded areas with scores are extrapolated values, since these events are unusual and unlikely clinical combinations. Reproduced from [4]. 29 Figure 4.6 SAPS-III scoring for neurologic abnormalities according to absence of eye opening. Shaded areas with scores similar to Figure 4.5. Shaded areas without scores lacked enough data to ex- trapolate. Reproduced from [4]. 29 Figure 4.7 Oasis score computation for physiological, neu- rological and procedural variables. 30 Figure 4.8 Cohort selection process for sub-score explo- ration and trajectory analysis. Patients under the age of 16 at admission are excluded. 31 Figure 5.1 Visualization of the sub-score feature space and in-ICU mortality using Principal Component Analysis (PCA) for the all-patient cohort. 34 Figure 5.2 Visualization of the sub-score feature space and in-ICU mortality using PCA for the CHF-patient cohort. 35 Figure 5.3 Visualization of TSCK embedding of two-day trajectories of all-patient cohort. 39 Figure 5.4 Clustering of the Time Series Cluster Kernel (TSCK) embedding using Spectral Clustering with five clusters. Visualized using Multidimensional Scaling (MDS). ................... 40 ix x List of Figures Figure 5.5 Visualization of TSCK clustering. CHF-patients with a length of stay of two-days only. 41 Figure 5.6 Relative frequencies of age-score , elective surgery score and Pre-ICU-LOS score show the largest differences, while heart rate score is the most similar. 42 Figure 5.7 TSCK trajectory embedding after feature selec- tion. 43 Figure 5.8 An example of 23 CHF-patients that start out with similar probability of death, correspond- ing to "moderate" severity (first day in black, second day in gray), but diverge on the sec- ond day of stay (red lines "get worse", blue lines "get better"), with probability of mortal- ity color-coded to indicate "better" or "worse" areas (based on the full-population health sta- tus). 44 Figure 5.9 The difference between patient group trajecto- ries: drugs prescribed on the first day of stay. 45 Figure 5.10 The difference between patient group trajecto- ries: procedures performed on the first day of stay. 46 Figure 5.11 First and second day health state for a small patient sub-sample of the all-patient cohort. The blue square is in fact individual patients lo- cated in a small grid. 47 Figure 5.12 Probability of mortality distribution for the small patient sub-sample of the all-patient cohort. 48 Figure 5.13 20 nearest neighbors of random patients with similar 2-D principal component trajectory. 49 Figure 5.14 20 nearest neighbors of random patients with similar 25-D sub-score trajectory, projected into 2-D principal components. 49 Figure 5.15 Clustering of 25-sub-score deltas between first and second day using eight clusters. 50 Figure 5.16 Clustering of 25-sub-score deltas between first and second day using 7 clusters and sub-sample of CHF-patient cohort. 51 Figure 5.17 Distribution of euclidean distances between pa- tients’ sub-score deltas. Distances for several percentiles are shown. 52 Figure 5.18 Clustering of sub-score deltas between first and second day using Density-Based Spatial Clus- tering of Applications with Noise (DBSCAN). 53 Figure 5.19 Clustering of first day administered medications. 55 List of Figures xi Figure 5.20 Sub sample of patients from all-patient cohort split into the four categories. 56 Figure 5.21 Cosine distances distribution for 128-D docu- ment embedding of medications administered within the first 24 hour of ICU-stay. 57 Figure 5.22 DBSCAN clustering for a range of percentile us- ing cosine distances. 59 Figure 5.23 Sub-sample of patients from all-patient cohort split into the four categories. The Cosine dis- tance is used for similarity computation. 60 Figure 5.24 Number of patients and % of patient with im- proved health state in the four categories over a range of percentiles using the cosine distance. Colored numbers indicate the number of pa- tients in the category. 61 Figure 5.25 Clustering of first day performed procedures . 62 Figure 5.26 Sub-sample of patients from all-patient cohort split into the four categories. 5th percentile tra- jectory and 10th percentile procedures as clus- tering thresholds for DBSCAN using the Euclidean distance. 63 Figure 5.27 Cosine distances distribution for 128-D docu- ment embedding of medications administered within the first 24 hour of ICU-stay. 64 Figure 5.28 DBSCAN clustering for a range of percentile us- ing cosine distances. 65 Figure 5.29 sub-sample of patients from all-patient cohort split into the four categories.
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