Additional file 1 of Identifying and evaluating clinical subtypes of Alzheimer’s disease in care electronic health records using unsupervised machine learning
Creators: name: Alexander, Nonie
name: Alexander, Daniel C.
name: Barkhof, Frederik
name: Denaxas, Spiros
Publisher: name: figshare
Year: 2021
Subjects: Medicine
Biotechnology
Chemical Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified
Cancer

description: Additional file 1: Figure S1. CONSORT flow diagram of patient population from CPRD data showing how patients are excluded from the cohort. AD Alzheimer's Disease, UD Unspecified Dementia. Figure S2. Identification of optimum value of k with k-means run 100 times for each values of k where k = 2-14. Methods of measuring best value of k are a) Baysian Information Score, b) Silhouette score, c) Varience explained per cluster. Figure S3. Prevalence of each variable by cluster using k-means algorithm: A) Alzheimers symptoms, B) Comorbidies associated with AD (Alzheimer's Disease), C) Age, divided into quintiles, D) Gender by, E) Smoking status, F) Drinking Status. Figure S4. Identification of optimum value of k with kernel k-means run 100 times for each values of k where k = 2-8. Methods of measuring best value of k are a) Baysian Information Score, b) Silhouette score. Figure S5. Prevalence of each variable by cluster for kernel k-means: A) Alzheimers symptoms, B) Comorbidies associated with AD (Alzheimer's Disease), C) Age, divided into quintiles, D) Gender by, E) Smoking status, F) Drinking Status. Figure S6. Outcomes of kernel k-means clustering by cluster: a) number of appointments per year post diagnosis with 5% confidence intervals b) number of missed appointments per year post diagnosis with 5% confidence intervals, c) Progression rate based on decline in MMSE score per year with 5% confidence intervals, d) time from onset of AD until AChls are stopped prescribed, with 5% confidence intervals, e) Kaplan-Meier curve from diagnosis to death with log rank error, f) Kaplan-Meier curve for time until the patient moves into assisted living with log rank error bars. Figure S7. Identification of optimum value of k with affinity propergation run 100 times for each values of k 2-7 where the optimum value is found examining the net similarity to find the elbow in the plot. Figure S8. Prevalence of feature by cluster for affinity propagation: A) Alzheimers symptoms, B) Comorbidies associated with AD (Alzheimer's Disease), C) Age, divided into quintiles, D) Gender by, E) Smoking status, F) Drinking Status. Figure S9. Outcomes of affinity propagation clustering by cluster: a) number of appointments per year post diagnosis with 5% confidence intervals b) number of missed appointments per year post diagnosis with 5% confidence intervals, c) Progression rate based on decline in MMSE score per year with 5% confidence intervals, d) time from onset of AD until AChls are stopped prescribed, with 5% confidence intervals, e) Kaplan-Meier curve from diagnosis to death with log rank error, f) Kaplan-Meier curve for time until the patient moves into assisted living with log rank error bars. Figure S10. Identification of optimum value of k with LCA run 100 times for each values of k 2-8 where the optimum value is the minumum value for the baysian information criterion. Figure S11. Prevalence of feature by cluster for LCA: A) Alzheimers symptoms, B) Comorbidies associated with AD (Alzheimer's Disease), C) Age, divided into quintiles, D) Gender by, E) Smoking status, F) Drinking Status. Figure S12. Outcomes of LCA clustering by cluster: a) number of appointments per year post diagnosis with 5% confidence intervals b) number of missed appointments per year post diagnosis with 5% confidence intervals, c) Progression rate based on decline in MMSE score per year with 5% confidence intervals, d) time from onset of AD until AChls are stopped prescribed, with 5% confidence intervals, e) Kaplan-Meier curve from diagnosis to death with log rank error, f) Kaplan-Meier curve for time until the patient moves into assisted living with log rank error bars. Table S1. Sources and references for variables included in the analysis and phenotypes used to extract the variables from the EHR data. Table S2. K-means results % prevalence of each variable by cluster. Table S3. Kernal K-means results % prevalence of each variable by cluster. Table S4. Affinity propagation results % prevalence of each variable by cluster. Table S5. LCA results % prevalence of each variable by cluster. Table S6a. Cluster stability based on jaccard score of cluster membership overlap based on k-means from bootstrapped data run 100 times. Table S6b. Concordance between test set cluster membership determined by cluster method and cluster membership based on a decision tree trained on the original cluster results for each cluster method for the AD and UD test data sets. Table S7a. R squared values and adjusted r squared value for each variable based on a linear regression predicting decline in mmse score. Table S7b. Results of the cox proportional hazard ratio time till death of each variable included in the cluster analysis and cluster membership. Table S7c. Cox proportional hazard ratio time till assisted living of each variable included in the cluster analysis and cluster membership. codelists. codelists used for defining phenotypes for diseases and symptoms.