This case-study examines the patterns, symmetries, associations and causality in a rare but devastating disease, amyotrophic lateral sclerosis (ALS). A major clinically relevant question in t

 

This case-study examines the patterns, symmetries, associations and causality in a rare but devastating disease, amyotrophic lateral sclerosis (ALS). A major clinically relevant question in this biomedical study is: What patient phenotypes can be automatically and reliably identified and used to predict the change of the ALSFRS slope over time?. This problem aims to explore the data set by unsupervised learning (you only need to work on K mean in this assignment).

  • Load and prepare the data.
  • Perform summary and preliminary visualization (i.e. show the clustering with selected features).
  • Train a k-Means model on the data with selected features (3 or more), experiment at least two different k values, and explain which k value is a better choice.
  • Evaluating the model performance by report the center of clusters.
  • Visualize the final clustering result.

Submit Python code, report that explains the k experiment, performance evaluation, and visualizations. 

Case-Study 15

Amyotrophic Lateral Sclerosis (ALS)

Overview: This case-study examines the patterns, symmetries, associations and causality in a rare but devastating disease, amyotrophic lateral sclerosis (ALS). ALS demands conducting clinical trials and collecting big, multi-source and heterogeneous datasets that can be interrogated to derive potential biomarkers. Overcoming many scientific, technical and infrastructure barriers is required to establish complete, efficient, and reproducible protocols (pipelines/workflows) starting with acquiring raw data, preprocessing, aggregation, harmonization, analysis, visualization and result interpretation.

The clinical data shows that the rate of ALS progression varies significantly among patients. Majority of the patients die within 3 to 5 years after ALS onset, however, a few are able survive for over 10 years. This heterogeneity of disease course hinders demonstration of its biological mechanism and development of effective treatment. We need to develop reliable predictive models of ALS progression to understand the pathophysiology of the disease.

Driving Challenges:

· What patient phenotypes can be automatically and reliably determined?

· Predict the change of the ALSFRS slope change using the holistic patient-specific data.

· Predict survival of patients at a given time-point (post diagnosis).

Meta-Data

· There are 2 datasets:

· training (N1=2,223): ALS_TrainingData_2223.csv, and

· testing (N2=78): ALS_TestingData_78.csv

· Each dataset includes the following 131 variables:

ID; Age_mean; Albumin_max; Albumin_median; Albumin_min; Albumin_range; ALSFRS_slope; ALSFRS_Total_max; ALSFRS_Total_median; ALSFRS_Total_min; ALSFRS_Total_range; ALT.SGPT._max; ALT.SGPT._median; ALT.SGPT._min; ALT.SGPT._range; AST.SGOT._max; AST.SGOT._median; AST.SGOT._min; AST.SGOT._range; Basophils_max; Basophils_median; Basophils_min; Basophils_range; Bicarbonate_max; Bicarbonate_median; Bicarbonate_min; Bicarbonate_range; Bilirubin..total._max; Bilirubin..total._median; Bilirubin..total._min; Bilirubin..total._range; Blood.Urea.Nitrogen..BUN._max; Blood.Urea.Nitrogen..BUN._median; Blood.Urea.Nitrogen..BUN._min; Blood.Urea.Nitrogen..BUN._range; BMI_max; bp_diastolic_max; bp_diastolic_median; bp_diastolic_min; bp_diastolic_range; bp_systolic_max; bp_systolic_median; bp_systolic_min; bp_systolic_range; Calcium_max; Calcium_median; Calcium_min; Calcium_range; Chloride_max; Chloride_median; Chloride_min; Chloride_range; Creatinine_max; Creatinine_median; Creatinine_min; Creatinine_range; Eosinophils_max; Eosinophils_median; Eosinophils_min; Eosinophils_range; Gender_mean; Glucose_max; Glucose_median; Glucose_min; Glucose_range; hands_max; hands_median; hands_min; hands_range; Hematocrit_max; Hematocrit_median; Hematocrit_min; Hematocrit_range; Hemoglobin_max; Hemoglobin_median; Hemoglobin_min; Hemoglobin_range; leg_max; leg_median; leg_min; leg_range; Lymphocytes_max; Lymphocytes_median; Lymphocytes_min; Lymphocytes_range; Monocytes_max; Monocytes_median; Monocytes_min; Monocytes_range; mouth_max; mouth_median; mouth_min; mouth_range; onset_delta_mean; onset_site_mean; Platelets_max; Platelets_median; Platelets_min; Potassium_max; Potassium_median; Potassium_min; Potassium_range; pulse_max; pulse_median; pulse_min; pulse_range; Red.Blood.Cells..RBC._max; Red.Blood.Cells..RBC._median; Red.Blood.Cells..RBC._min; Red.Blood.Cells..RBC._range; respiratory_max; respiratory_median; respiratory_min; respiratory_range; Sodium_max; Sodium_median; Sodium_min; Sodium_range; SubjectID; trunk_max; trunk_median; trunk_min; trunk_range; Urine.Ph_max; Urine.Ph_median; Urine.Ph_min; Urine.Ph_range; White.Blood.Cell..WBC._max; White.Blood.Cell..WBC._median; White.Blood.Cell..WBC._min; White.Blood.Cell..WBC._range

References:

· Tang, M., Gao, C, Goutman, SA, Kalinin, A, Mukherjee, B, Guan, Y, and Dinov, ID. (2018) Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering, Neuroinformatics, 1-15, DOI: 10.1007/s12021-018-9406-9.

· https://scholar.google.com/scholar?hl=en&as_sdt=1%2C23&q=%22proact%22+%22als%22&btnG=

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ID Age_mean Albumin_max Albumin_median Albumin_min Albumin_range ALSFRS_slope ALSFRS_Total_max ALSFRS_Total_median ALSFRS_Total_min ALSFRS_Total_range ALT.SGPT._max ALT.SGPT._median ALT.SGPT._min ALT.SGPT._range AST.SGOT._max AST.SGOT._median AST.SGOT._min AST.SGOT._range Basophils_max Basophils_median Basophils_min Basophils_range Bicarbonate_max Bicarbonate_median Bicarbonate_min Bicarbonate_range Bilirubin..total._max Bilirubin..total._median Bilirubin..total._min Bilirubin..total._range Blood.Urea.Nitrogen..BUN._max Blood.Urea.Nitrogen..BUN._median Blood.Urea.Nitrogen..BUN._min Blood.Urea.Nitrogen..BUN._range BMI_max bp_diastolic_max bp_diastolic_median bp_diastolic_min bp_diastolic_range bp_systolic_max bp_systolic_median bp_systolic_min bp_systolic_range Calcium_max Calcium_median Calcium_min Calcium_range Chloride_max Chloride_median Chloride_min Chloride_range Creatinine_max Creatinine_median Creatinine_min Creatinine_range Eosinophils_max Eosinophils_median Eosinophils_min Eosinophils_range Gender_mean Glucose_max Glucose_median Glucose_min Glucose_range hands_max hands_median hands_min hands_range Hematocrit_max Hematocrit_median Hematocrit_min Hematocrit_range Hemoglobin_max Hemoglobin_median Hemoglobin_min Hemoglobin_range leg_max leg_median leg_min leg_range Lymphocytes_max Lymphocytes_median Lymphocytes_min Lymphocytes_range Monocytes_max Monocytes_median Monocytes_min Monocytes_range mouth_max mouth_median mouth_min mouth_range onset_delta_mean onset_site_mean Platelets_max Platelets_median Platelets_min Potassium_max Potassium_median Potassium_min Potassium_range pulse_max pulse_median pulse_min pulse_range Red.Blood.Cells..RBC._max Red.Blood.Cells..RBC._median Red.Blood.Cells..RBC._min Red.Blood.Cells..RBC._range respiratory_max respiratory_median respiratory_min respiratory_range Sodium_max Sodium_median Sodium_min Sodium_range SubjectID trunk_max trunk_median trunk_min trunk_range Urine.Ph_max Urine.Ph_median Urine.Ph_min Urine.Ph_range White.Blood.Cell..WBC._max White.Blood.Cell..WBC._median White.Blood.Cell..WBC._min White.Blood.Cell..WBC._range
3 65.90684932 46 44 43 0.024590164 -1.767329256 33 5 2 0.028518859 93 26 22 0.581967213 72 24 21 0.418032787 0.8 0.5 0.3 0.004098361 27 25 23 0.032786885 9 7 3 0.049180328 7.9 7.1 5.7 0.018032787 0.002969349 91 76 69 0.180327869 134 125 103 0.254098361 2.53 2.45 2.35 0.00147541 103 101 98 0.040983607 71 62 44 0.221311475 5.1 3.9 3.5 0.013114754 2 7.2 4.4 3.6 0.029508197 7 0 0 0.006439742 45.4 44.5 44.1 0.010655738 149 146 141 0.06557377 8 1 0 0.007359706 24.1 19.6 17.4 0.054918033 6.3 5.8 5.4 0.007377049 8 4 2 0.005519779 -617 1 275 275 275 4.3 4.2 3.9 0.003278689 90 77 61 0.237704918 4700 4640 4450 2.049180328 4 0 0 0.003679853 139 138 137 0.016393443 55888 7 0 0 0.006439742 6.5 6 6 0.004098361 8.57 7.68 6.6 0.016147541
4 54 39 36 33 0.013100437 -1.351851852 32 23 14 0.03930131 47 35.5 21 0.056768559 49 33 20 0.063318777 1.2 0.7 0.3 0.001965066 26.7 25 21 0.012445415 19 9.5 5 0.030567686 5.7 4.3 3.4 0.005021834 0.002907331 106 96 75 0.06768559 160 135 110 0.109170306 2.47 2.345 2.26 0.000458515 104 101 96 0.017467249 69 47 21 0.104803493 5.4 2.15 1.1 0.009388646 2 7.2 4.6 3.1 0.008951965 6 4 0 0.013100437 52 47 42 0.021834061 170 158 139 0.06768559 5 1 0 0.010917031 22.2 15.9 11.9 0.022489083 8.1 5.35 3.8 0.009388646 12 12 11 0.002183406 -328 4 349 270 215 4.6 4.2 3.8 0.001746725 104 80 68 0.07860262 5800 5100 4700 2.401746725 4 4 3 0.002183406 144 140.5 135 0.019650655 61505 6 3 0 0.013100437 6.5 5.5 5 0.003275109 8.04 6.62 4.97 0.006703057
5 56.39452055 46 43 39 0.009735744 -0.412429379 15 10 2 0.017173052 42 22 11 0.043115438 37 22 14 0.031988873 1.4 0.7 0.5 0.001251739 27 24 20 0.009735744 5 3 2 0.004172462 8.2 5.4 2.9 0.007371349 0.00228116 85 72.5 65 0.026420079 140 103 90 0.066050198 2.53 2.43 2.33 0.000278164 104 101 97 0.009735744 53 35 18 0.04867872 5.5 3.25 0.4 0.007093185 1 7.3 5.3 4.7 0.003616134 0 0 0 0 48.7 42.35 39.2 0.013212796 143 133.5 122 0.029207232 0 0 0 0 32.7 19.15 14.3 0.025591099 9.8 6.85 3.4 0.008901252 11 6 2 0.011889036 -953 2 391 391 391 5.2 4.5 3.8 0.001947149 123 103.5 70 0.07001321 5130 4590 4190 1.307371349 4 4 0 0.005284016 141 139 136 0.006954103 63255 0 0 0 0 7.5 6.75 6 0.003456221 8.9 7.16 5.01 0.005410292
6 72.61917808 50 42.5 41 0.092783505 -0.383403361 34 24 21 0.033591731 109 39.5 20 0.917525773 83 42 23 0.618556701 0.9 0.7 0.3 0.006185567 27 26 25 0.020618557 9 5 3 0.06185567 7.5 5.2 3.9 0.037113402 0.002408304 67 59 54 0.134020619 148 130 120 0.288659794 2.55 2.43 2.38 0.001752577 108 103.5 102 0.06185567 80 71 71 0.092783505 5.4 3.9 1.3 0.042268041 1 6.9 5.7 5.2 0.017525773 8 7 5 0.007751938 39.8 37.55 35.7 0.042268041 127 119 112 0.154639175 6 4 3 0.007751938 38.4 20.5 11.1 0.281443299 10.8 6.15 4.5 0.064948454 8 4 2 0.015503876 -490 1 383 383 383 4.8 4.5 4.4 0.004123711 76 73 63 0.134020619 4190 3950 3780 4.226804124 4 4 3 0.002583979 143 140 138 0.051546392 70641 8 5.5 5 0.007751938 7.5 7 6 0.024193548 12.38 7.905 4.96 0.076494845
9 65 45 42 36 0.021327014 0 37 37 37 0 48 16.5 13 0.082938389 272 25 22 0.592417062 1.1 0.8 0.6 0.001184834 27.4 22.9 19.3 0.019194313 12 9.5 5 0.016587678 7.5 6.1 4.5 0.007109005 0.002730997 102 85 69 0.078199052 179 140 118 0.144549763 2.57 2.43 2.34 0.000545024 107 105 102 0.011848341 106 91.5 84 0.052132701 3.7 1.6 1.1 0.006161137 2 7.7 5.65 4.5 0.007582938 6 6 6 0 50 45 42 0.018957346 159 149 140 0.045023697 8 8 8 0 39 32.9 25.7 0.031516588 6.9 5.3 4.3 0.006161137 12 12 12 0 -329 4 357 258 229 5.1 4.45 4.1 0.002369668 84 67.5 59 0.059241706 5000 4700 4400 1.421800948 4 4 4 0 146 144 140 0.014218009 108342 7 7 7 0 6 5.5 5 0.002369668 11.53 9.29 7.94 0.008507109
10 67 40 35 25 0.038860104 -1.40717675 32 24 9 0.059585492 22 15 8 0.03626943 25 22 17 0.020725389 1.5 0.85 0.6 0.002331606 34.5 26.85 24.2 0.026683938 10.26 7.8475 5