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features_bjoern.R
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features_bjoern.R
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library(tidyverse)
library(uwot)
# Auxilliary functions
# Function to add column
fncols <- function(data, cname, what_entry=0) {
add <-cname[!cname%in%names(data)]
if(length(add)!=0) data[add] <- what_entry
data
}
# Function to calculate geometric mean
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
################################################
# UMAP embedding for indications
################################################
indumap <- function(fulldat){
message("----- Indication UMAP -----")
# Check whether we are on evaluation sever or training server, pre-pend path accordingly
if (Sys.getenv("AICROWD_TRAIN_DATA_PATH")==""){
filepath = paste0("/home/desktop1/files/", "umapind.rds")
} else {
filepath = "umapind.rds"
}
if (file.exists(filepath)){
ctime = file.info(filepath)$ctime
message( paste("Found existing umap-embedding on disk, using that. Created at ", ctime, ", if that is too old, delete it.") )
rval <- read_rds(filepath)
} else {
message("No existing umap embedding on disk, creating a new one - will not reproduce old results (this is random, make sure to read in the umapind.rds to obtain consistent results.")
dis1 <- fulldat %>%
dplyr::select(indicationkey, strDiseaseType) %>% #strtherapeuticarea, outcome
mutate(strDiseaseType=str_remove_all(str_remove_all(str_remove_all(str_remove_all(strDiseaseType, "\\(N/A\\)\\|"),"\\|\\(N/A\\)"),"\\(\\)\\|"),"\\|\\(\\)")) %>%
mutate(distyp = str_split(strDiseaseType, pattern="\\|")) %>%
unnest(cols="distyp") %>%
distinct() %>%
mutate(distyp=str_remove_all(distyp, "[\\[\\]]"),
disno = case_when(
trimws(tolower(distyp)) == trimws(tolower("Allergic Rhinitis")) ~1,
trimws(tolower(distyp)) == trimws(tolower("Asthma")) ~2,
trimws(tolower(distyp)) == trimws(tolower("Chronic Obstructive Pulmonary Disease")) ~3,
trimws(tolower(distyp)) == trimws(tolower("Insomnia")) ~4,
trimws(tolower(distyp)) == trimws(tolower("HBV")) ~5,
trimws(tolower(distyp)) == trimws(tolower("Pain (neuropathic)")) ~6,
trimws(tolower(distyp)) == trimws(tolower("Colorectal")) ~7,
trimws(tolower(distyp)) == trimws(tolower("Soft Tissue Sarcoma")) ~8,
trimws(tolower(distyp)) == trimws(tolower("CNS, Glioblastoma")) ~9,
trimws(tolower(distyp)) == trimws(tolower("Multiple Myeloma")) ~10,
trimws(tolower(distyp)) == trimws(tolower("(N/A)")) ~11,
trimws(tolower(distyp)) == trimws(tolower("Ovarian")) ~12,
trimws(tolower(distyp)) == trimws(tolower("Prostate")) ~13,
trimws(tolower(distyp)) == trimws(tolower("Renal")) ~14,
trimws(tolower(distyp)) == trimws(tolower("Melanoma")) ~15,
trimws(tolower(distyp)) == trimws(tolower("Esophageal")) ~16,
trimws(tolower(distyp)) == trimws(tolower("Breast")) ~17,
trimws(tolower(distyp)) == trimws(tolower("Lung, Non-Small Cell")) ~18,
trimws(tolower(distyp)) == trimws(tolower("Liver")) ~19,
trimws(tolower(distyp)) == trimws(tolower("Pancreas")) ~20,
trimws(tolower(distyp)) == trimws(tolower("Head/Neck")) ~21,
trimws(tolower(distyp)) == trimws(tolower("Endometrial")) ~22,
trimws(tolower(distyp)) == trimws(tolower("Leukemia, Acute Myelogenous")) ~23,
trimws(tolower(distyp)) == trimws(tolower("Lymphoma, Non-Hodgkin's")) ~24,
trimws(tolower(distyp)) == trimws(tolower("Myelodysplastic Syndrome")) ~25,
trimws(tolower(distyp)) == trimws(tolower("Leukemia, Acute Lymphocytic")) ~26,
trimws(tolower(distyp)) == trimws(tolower("Lupus")) ~27,
trimws(tolower(distyp)) == trimws(tolower("Age-Related Macular Degeneration")) ~28,
trimws(tolower(distyp)) == trimws(tolower("Gastric")) ~29,
trimws(tolower(distyp)) == trimws(tolower("Alzheimer's Disease")) ~30,
trimws(tolower(distyp)) == trimws(tolower("Diabetic Complications")) ~31,
trimws(tolower(distyp)) == trimws(tolower("Acute Coronary Syndromes")) ~32,
trimws(tolower(distyp)) == trimws(tolower("Coronary Artery Disease")) ~33,
trimws(tolower(distyp)) == trimws(tolower("Peripheral Arterial Disease")) ~34,
trimws(tolower(distyp)) == trimws(tolower("Psoriasis")) ~35,
trimws(tolower(distyp)) == trimws(tolower("Unspecified Solid Tumor")) ~36,
trimws(tolower(distyp)) == trimws(tolower("Multiple Sclerosis")) ~37,
trimws(tolower(distyp)) == trimws(tolower("HIV")) ~38,
trimws(tolower(distyp)) == trimws(tolower("Other Viral Vaccines")) ~39,
trimws(tolower(distyp)) == trimws(tolower("Congestive Heart Failure")) ~40,
trimws(tolower(distyp)) == trimws(tolower("Hypertension")) ~41,
trimws(tolower(distyp)) == trimws(tolower("Crohn's Disease")) ~42,
trimws(tolower(distyp)) == trimws(tolower("Osteoporosis")) ~43,
trimws(tolower(distyp)) == trimws(tolower("Thyroid")) ~44,
trimws(tolower(distyp)) == trimws(tolower("Mesothelioma")) ~45,
trimws(tolower(distyp)) == trimws(tolower("Rheumatoid Arthritis")) ~46,
trimws(tolower(distyp)) == trimws(tolower("Ulcerative Colitis")) ~47,
trimws(tolower(distyp)) == trimws(tolower("Leukemia, Chronic Lymphocytic")) ~48,
trimws(tolower(distyp)) == trimws(tolower("Thrombotic Disorders")) ~49,
trimws(tolower(distyp)) == trimws(tolower("GERD")) ~50,
trimws(tolower(distyp)) == trimws(tolower("Irritable Bowel Syndrome")) ~51,
trimws(tolower(distyp)) == trimws(tolower("Supportive Care")) ~52,
trimws(tolower(distyp)) == trimws(tolower("Type 2 Diabetes")) ~53,
trimws(tolower(distyp)) == trimws(tolower("Obesity")) ~54,
trimws(tolower(distyp)) == trimws(tolower("HCV")) ~55,
trimws(tolower(distyp)) == trimws(tolower("Anxiety")) ~56,
trimws(tolower(distyp)) == trimws(tolower("Depression")) ~57,
trimws(tolower(distyp)) == trimws(tolower("Bipolar Disorder")) ~58,
trimws(tolower(distyp)) == trimws(tolower("Atopic Dermatitis")) ~59,
trimws(tolower(distyp)) == trimws(tolower("Cystic Fibrosis")) ~60,
trimws(tolower(distyp)) == trimws(tolower("Lymphoma, Hodgkin's")) ~61,
trimws(tolower(distyp)) == trimws(tolower("Bladder")) ~62,
trimws(tolower(distyp)) == trimws(tolower("Leukemia, Chronic Myelogenous")) ~63,
trimws(tolower(distyp)) == trimws(tolower("Lung, Small Cell")) ~64,
trimws(tolower(distyp)) == trimws(tolower("Sepsis")) ~65,
trimws(tolower(distyp)) == trimws(tolower("CNS, Other")) ~66,
trimws(tolower(distyp)) == trimws(tolower("CNS, Medulloblastoma")) ~67,
trimws(tolower(distyp)) == trimws(tolower("Movement Disorders")) ~68,
trimws(tolower(distyp)) == trimws(tolower("Metastatic Cancer")) ~69,
trimws(tolower(distyp)) == trimws(tolower("Uterine fibroids")) ~70,
trimws(tolower(distyp)) == trimws(tolower("Myeloproliferative Neoplasms")) ~71,
trimws(tolower(distyp)) == trimws(tolower("Attention Deficit Hyperactive Disorder")) ~72,
trimws(tolower(distyp)) == trimws(tolower("Constipation")) ~73,
trimws(tolower(distyp)) == trimws(tolower("Dyslipidemia")) ~74,
trimws(tolower(distyp)) == trimws(tolower("Migraine")) ~75,
trimws(tolower(distyp)) == trimws(tolower("Renal Disease")) ~76,
trimws(tolower(distyp)) == trimws(tolower("Sjogren's Syndrome")) ~77,
trimws(tolower(distyp)) == trimws(tolower("Overactive Bladder")) ~78,
trimws(tolower(distyp)) == trimws(tolower("Arrhythmia")) ~79,
trimws(tolower(distyp)) == trimws(tolower("Anti-aging (dermatology)")) ~80,
trimws(tolower(distyp)) == trimws(tolower("Pain (nociceptive)")) ~81,
trimws(tolower(distyp)) == trimws(tolower("Smoking Cessation")) ~82,
trimws(tolower(distyp)) == trimws(tolower("Diabetic Retinopathy")) ~83,
trimws(tolower(distyp)) == trimws(tolower("Female Sexual Dysfunction")) ~84,
trimws(tolower(distyp)) == trimws(tolower("Amyotrophic Lateral Sclerosis")) ~85,
trimws(tolower(distyp)) == trimws(tolower("Parkinson's Disease")) ~86,
trimws(tolower(distyp)) == trimws(tolower("GIST")) ~87,
trimws(tolower(distyp)) == trimws(tolower("Osteoarthritis")) ~88,
trimws(tolower(distyp)) == trimws(tolower("Endometriosis")) ~89,
trimws(tolower(distyp)) == trimws(tolower("Glaucoma")) ~90,
trimws(tolower(distyp)) == trimws(tolower("Other Inflammatory Arthritis")) ~91,
trimws(tolower(distyp)) == trimws(tolower("Transplantation/GVHD")) ~92,
trimws(tolower(distyp)) == trimws(tolower("Hemostasis/Hemophilia")) ~93,
trimws(tolower(distyp)) == trimws(tolower("Schizophrenia")) ~94,
trimws(tolower(distyp)) == trimws(tolower("Osteosarcoma")) ~95,
trimws(tolower(distyp)) == trimws(tolower("Hepatic Fibrosis")) ~96,
trimws(tolower(distyp)) == trimws(tolower("NAFLD")) ~97,
trimws(tolower(distyp)) == trimws(tolower("Cerebral Palsy")) ~98,
trimws(tolower(distyp)) == trimws(tolower("Respiratory Infections")) ~99,
trimws(tolower(distyp)) == trimws(tolower("Thalassemia")) ~100,
trimws(tolower(distyp)) == trimws(tolower("Bacterial Skin Infection")) ~101,
trimws(tolower(distyp)) == trimws(tolower("Benign Prostatic Hyperplasia")) ~102,
trimws(tolower(distyp)) == trimws(tolower("Hepatitis Vaccines")) ~103,
trimws(tolower(distyp)) == trimws(tolower("HPV")) ~104,
trimws(tolower(distyp)) == trimws(tolower("Type 1 Diabetes")) ~105,
trimws(tolower(distyp)) == trimws(tolower("Skin, Basal Cell Carcinoma")) ~106,
trimws(tolower(distyp)) == trimws(tolower("Autism")) ~107,
trimws(tolower(distyp)) == trimws(tolower("Retinitis Pigmentosa")) ~108,
trimws(tolower(distyp)) == trimws(tolower("Unspecified Hematological Cancer")) ~109,
trimws(tolower(distyp)) == trimws(tolower("Otitis Media")) ~110,
trimws(tolower(distyp)) == trimws(tolower("Epilepsy")) ~111,
trimws(tolower(distyp)) == trimws(tolower("Spinal Muscular Atrophies")) ~112,
trimws(tolower(distyp)) == trimws(tolower("Anemia")) ~113,
trimws(tolower(distyp)) == trimws(tolower("Retinal Vein Occlusion")) ~114,
trimws(tolower(distyp)) == trimws(tolower("Hyponatremia")) ~115,
trimws(tolower(distyp)) == trimws(tolower("Urinary Incontinence")) ~116,
trimws(tolower(distyp)) == trimws(tolower("Alcohol Dependence")) ~117,
trimws(tolower(distyp)) == trimws(tolower("Unspecified Cancer")) ~118,
trimws(tolower(distyp)) == trimws(tolower("Restless Legs Syndrome")) ~119,
trimws(tolower(distyp)) == trimws(tolower("Rabies")) ~120,
trimws(tolower(distyp)) == trimws(tolower("Contraception")) ~121,
trimws(tolower(distyp)) == trimws(tolower("Respiratory Vaccines")) ~122,
trimws(tolower(distyp)) == trimws(tolower("Functional Dyspepsia")) ~123,
trimws(tolower(distyp)) == trimws(tolower("Testicular")) ~124,
trimws(tolower(distyp)) == trimws(tolower("Clostridium difficile")) ~125,
trimws(tolower(distyp)) == trimws(tolower("Urinary Tract Infections")) ~126,
trimws(tolower(distyp)) == trimws(tolower("Onychomycosis")) ~127,
trimws(tolower(distyp)) == trimws(tolower("Cervical")) ~128,
trimws(tolower(distyp)) == trimws(tolower("Vector-Borne Disease Vaccines")) ~129,
trimws(tolower(distyp)) == trimws(tolower("West Nile Virus (WNV)")) ~130,
trimws(tolower(distyp)) == trimws(tolower("Pulmonary Fibrosis")) ~131,
trimws(tolower(distyp)) == trimws(tolower("Other Bacterial Vaccines")) ~132,
trimws(tolower(distyp)) == trimws(tolower("Influenza Vaccines")) ~133,
trimws(tolower(distyp)) == trimws(tolower("Growth Disorders")) ~134,
trimws(tolower(distyp)) == trimws(tolower("Hyperuricemia/Gout")) ~135,
trimws(tolower(distyp)) == trimws(tolower("Gastroparesis")) ~136,
trimws(tolower(distyp)) == trimws(tolower("Scleroderma")) ~137,
trimws(tolower(distyp)) == trimws(tolower("Dry Eye Syndrome")) ~138,
trimws(tolower(distyp)) == trimws(tolower("Intra-abdominal Infections")) ~139,
trimws(tolower(distyp)) == trimws(tolower("Menopausal Symptoms")) ~140,
trimws(tolower(distyp)) == trimws(tolower("Huntington's Disease")) ~141,
trimws(tolower(distyp)) == trimws(tolower("Cardiomyopathy")) ~142,
trimws(tolower(distyp)) == trimws(tolower("Sickle Cell Disease")) ~143,
trimws(tolower(distyp)) == trimws(tolower("Neuroendocrine")) ~144,
trimws(tolower(distyp)) == trimws(tolower("Infertility")) ~145,
trimws(tolower(distyp)) == trimws(tolower("Cytomegalovirus Infection (CMV)")) ~146,
trimws(tolower(distyp)) == trimws(tolower("Dementia (non-Alzheimer's)")) ~147,
trimws(tolower(distyp)) == trimws(tolower("Neonatal Brain Injury")) ~148,
trimws(tolower(distyp)) == trimws(tolower("Stroke (neuroprotection)")) ~149,
trimws(tolower(distyp)) == trimws(tolower("Bone Fracture Healing")) ~150,
trimws(tolower(distyp)) == trimws(tolower("Lysosomal Storage Disorders")) ~151,
trimws(tolower(distyp)) == trimws(tolower("Infant Respiratory Distress Syndrome")) ~152,
TRUE ~ 11)) %>%
dplyr::select(indicationkey, disno) %>%
mutate(anytyp=1) %>%
distinct() %>%
group_by(indicationkey) %>%
pivot_wider(names_from = disno, names_prefix = "disease", values_from = anytyp, id_cols = c("indicationkey")) %>%
replace(., is.na(.), 0)
tastuff <- fulldat %>%
dplyr::select(indicationkey, strtherapeuticarea, DrugKey) %>%
group_by(indicationkey) %>%
mutate(sta = str_split(strtherapeuticarea, pattern="\\|"), # Therapeutic areas
number_of_projects=length(unique(DrugKey))) %>%
unnest(cols="sta") %>%
ungroup() %>%
mutate(
intsta = case_when(
sta=="Autoimmune/Inflammation" ~ 1,
sta=="Cardiovascular" ~ 2,
sta=="CNS" ~ 3,
sta=="Genitourinary" ~ 4,
sta=="Infectious Disease" ~ 5,
sta=="Metabolic/Endocrinology" ~ 6,
sta=="Oncology" ~ 7,
sta=="Ophthalmology" ~ 8,
sta=="Vaccines (Infectious Disease)" ~ 9,
TRUE ~ 10),
flg=2) %>%
dplyr::select(indicationkey, intsta, number_of_projects, flg) %>%
distinct() %>%
group_by(indicationkey, number_of_projects) %>%
pivot_wider(names_from=intsta, names_prefix="ta", values_from = flg,
values_fill=list(flg=0)) %>%
ungroup() %>%
fncols(paste0("ta", 1:9)) %>%
mutate(number_of_projects=sqrt(number_of_projects))#,sqrt_sumt_patno=(sumt_patno)^(1/3))
ages <- fulldat %>%
dplyr::select(indicationkey, strMinAgeUnit, strMaxAgeUnit, decMinAge, decMaxAge) %>%
mutate(veryyoung = 1 *(trimws(tolower(strMinAgeUnit)) %in% c("days","weeks","months") |
trimws(tolower(strMaxAgeUnit)) %in% c("days", "weeks", "months")),
peds = 1*(veryyoung==1 | ifelse(is.na(decMinAge), FALSE, (decMinAge<18)) | ifelse(is.na(decMaxAge), FALSE, (decMaxAge<18))),
above65 = 1*( ( (trimws(tolower(strMinAgeUnit))=="years" | is.na(strMinAgeUnit)) & ifelse(is.na(decMinAge), FALSE, (decMinAge>65))) |
((trimws(tolower(strMaxAgeUnit))=="years" | is.na(strMaxAgeUnit)) & ifelse(is.na(decMaxAge), FALSE, (decMaxAge>65))) ),
above75 = 1*( ((trimws(tolower(strMinAgeUnit))=="years" | is.na(strMinAgeUnit)) & ifelse(is.na(decMinAge), FALSE, (decMinAge>75))) |
((trimws(tolower(strMaxAgeUnit))=="years" | is.na(strMaxAgeUnit)) & ifelse(is.na(decMaxAge), FALSE, (decMaxAge>75))) ),
noextreme = 1*(veryyoung==0 & peds==0 & above65==0 & above75==0)
) %>%
group_by(indicationkey) %>%
summarize(veryyoung=mean(veryyoung),
peds=mean(peds),
above65=mean(above65),
above75=mean(above75),
noextreme=mean(noextreme)) %>%
ungroup()
ddrdat <- fulldat %>%
dplyr::select(indicationkey, DrugDeliveryRouteDescription) %>%
mutate(ddrd = str_split(DrugDeliveryRouteDescription, pattern="\\|")) %>%
unnest(cols="ddrd") %>%
mutate(ddrd=str_remove_all(ddrd, "[\\[\\]]"),
ddrdg = case_when(
str_detect(tolower(ddrd),"subcu") | str_detect(tolower(ddrd),"intraven") | str_detect(tolower(ddrd),"intra-articul") |
str_detect(tolower(ddrd),"intradermal") | str_detect(tolower(ddrd),"intra-arterial") | str_detect(tolower(ddrd),"intraarterial") |
str_detect(tolower(ddrd),"intramusc") | str_detect(tolower(ddrd),"subcu") | trimws(tolower(ddrd))=="injectable" | trimws(tolower(ddrd))=="injected" ~ 1,
str_detect(tolower(ddrd),"intravesical") | str_detect(tolower(ddrd),"intralymphatic") | str_detect(tolower(ddrd),"intracardiac") |
str_detect(tolower(ddrd),"intraperitoneal") | str_detect(tolower(ddrd),"intracavity") | str_detect(tolower(ddrd),"intratumoral") ~ 2,
str_detect(tolower(ddrd),"intracerebral") | str_detect(tolower(ddrd),"intraspinal") |
str_detect(tolower(ddrd),"intracranial") | str_detect(tolower(ddrd),"intrathecal") ~ 3,
str_detect(tolower(ddrd),"ocular") | str_detect(tolower(ddrd),"intravitreal") | str_detect(tolower(ddrd),"ophthalmo") ~ 4,
str_detect(tolower(ddrd),"oral") ~ 5,
str_detect(tolower(ddrd),"inhal") ~ 6,
str_detect(tolower(ddrd),"topical") | str_detect(tolower(ddrd),"transderm") |
str_detect(tolower(ddrd),"topical, mucosal") | str_detect(tolower(ddrd),"otic") ~ 7,
str_detect(tolower(ddrd),"depot") | str_detect(tolower(ddrd),"implant") ~ 8,
str_detect(tolower(ddrd),"multiple") ~ 9,
str_detect(tolower(ddrd),"rectal") | str_detect(tolower(ddrd),"vaginal") ~ 10,
TRUE ~ 0
),
flg=1) %>%
group_by(indicationkey, ddrdg) %>%
summarize(flags=sum(flg)) %>%
ungroup() %>%
group_by(indicationkey) %>%
pivot_wider(names_from=ddrdg, values_from = flags, names_prefix = "ddr") %>%
replace(., is.na(.), 0) %>%
fncols(paste0("ddr", 0:10)) %>%
mutate(thesum=sum(ddr0, ddr1, ddr5, ddr7, ddr8, ddr6, ddr3, ddr2, ddr9, ddr10, ddr4),
ddr0=ddr0/thesum,
ddr1=ddr1/thesum,
ddr5=ddr5/thesum,
ddr7=ddr7/thesum,
ddr8=ddr8/thesum,
ddr6=ddr6/thesum,
ddr3=ddr3/thesum,
ddr2=ddr2/thesum,
ddr9=ddr9/thesum,
ddr10=ddr10/thesum,
ddr4=ddr4/thesum) %>%
ungroup() %>%
dplyr::select(-thesum)
eptype <- fulldat %>%
dplyr::select(indicationkey, strPrimaryEndPoint) %>%
mutate(mortality = 1*(str_detect(tolower(strPrimaryEndPoint), "death") | str_detect(tolower(strPrimaryEndPoint), "mortality") | str_detect(tolower(strPrimaryEndPoint), "survival")),
transpl = 1*(str_detect(tolower(strPrimaryEndPoint), "reject") | str_detect(tolower(strPrimaryEndPoint), "graft") |
str_detect(tolower(strPrimaryEndPoint), "bpar") | str_detect(tolower(strPrimaryEndPoint), "transplant")),
safety = 1*(str_detect(tolower(strPrimaryEndPoint), "safety") | str_detect(tolower(strPrimaryEndPoint), "adverse") | str_detect(tolower(strPrimaryEndPoint), "dose-limit") |
str_detect(tolower(strPrimaryEndPoint), "toxicity") | str_detect(tolower(strPrimaryEndPoint), "dlt")
| str_detect(tolower(strPrimaryEndPoint), "grade") | str_detect(tolower(strPrimaryEndPoint), "mtd") | str_detect(tolower(strPrimaryEndPoint), "tolerab") ),
progression = 1*(str_detect(tolower(strPrimaryEndPoint), "progression") | str_detect(tolower(strPrimaryEndPoint), "onset")
| str_detect(tolower(strPrimaryEndPoint), "new diagno") | str_detect(tolower(strPrimaryEndPoint), "(cr)") | str_detect(tolower(strPrimaryEndPoint), "recist") |
str_detect(tolower(strPrimaryEndPoint), "objective resp") | str_detect(tolower(strPrimaryEndPoint), "complete resp")),
visual = 1*(str_detect(tolower(strPrimaryEndPoint), "visual") | str_detect(tolower(strPrimaryEndPoint), "acuity") ),
bp = 1*(str_detect(tolower(strPrimaryEndPoint), "pressure") | str_detect(tolower(strPrimaryEndPoint), "sbp") | str_detect(tolower(strPrimaryEndPoint), "dbp") ),
resp = 1*(str_detect(tolower(strPrimaryEndPoint), "respon") | str_detect(tolower(strPrimaryEndPoint), "threshold") | str_detect(tolower(strPrimaryEndPoint), "achiev") ),
bleed = 1*(str_detect(tolower(strPrimaryEndPoint), "bleed") | str_detect(tolower(strPrimaryEndPoint), "hemorr") | str_detect(tolower(strPrimaryEndPoint), "haemorr") ),
pro = 1*(str_detect(tolower(strPrimaryEndPoint), "patient report") | str_detect(tolower(strPrimaryEndPoint), "patient-report") |
str_detect(tolower(strPrimaryEndPoint),"e-diary") | str_detect(tolower(strPrimaryEndPoint),"ediary") | str_detect(tolower(strPrimaryEndPoint),"qol") |
str_detect(tolower(strPrimaryEndPoint),"quality of") | str_detect(tolower(strPrimaryEndPoint),"quality-of") | str_detect(tolower(strPrimaryEndPoint),"symptom score") |
str_detect(tolower(strPrimaryEndPoint),"questionnair") | str_detect(tolower(strPrimaryEndPoint),"pain") | str_detect(tolower(strPrimaryEndPoint),"rating") ),
occur = 1*(str_detect(tolower(strPrimaryEndPoint), "occur") | str_detect(tolower(strPrimaryEndPoint), "event") | str_detect(tolower(strPrimaryEndPoint), "observ") ),
pain = 1*str_detect(tolower(strPrimaryEndPoint),"pain"),
efficacy = 1*(str_detect(tolower(strPrimaryEndPoint),"efficac") | str_detect(tolower(strPrimaryEndPoint),"effectiv")),
spiro = 1*(str_detect(tolower(strPrimaryEndPoint),"fev1")|str_detect(tolower(strPrimaryEndPoint),"pef") |str_detect(tolower(strPrimaryEndPoint),"fvc") |
str_detect(tolower(strPrimaryEndPoint),"spirometr") |str_detect(tolower(strPrimaryEndPoint),"expira")),
brain = 1*(str_detect(tolower(strPrimaryEndPoint),"lesions")|str_detect(tolower(strPrimaryEndPoint),"brain mri")| str_detect(tolower(strPrimaryEndPoint)," brain ")),
mri = 1*(str_detect(tolower(strPrimaryEndPoint),"magnetic resonan") | str_detect(tolower(strPrimaryEndPoint)," mri") | str_detect(tolower(strPrimaryEndPoint),"\\(mri")),
gluc = 1*(str_detect(tolower(strPrimaryEndPoint), "hba1c") | str_detect(tolower(strPrimaryEndPoint), "glucose") |
str_detect(tolower(strPrimaryEndPoint), "fpg") | str_detect(tolower(strPrimaryEndPoint), "ogtt")
| str_detect(tolower(strPrimaryEndPoint), "hypogly") | str_detect(tolower(strPrimaryEndPoint), "insulin"))) %>%
replace(., is.na(.), 0) %>%
group_by(indicationkey) %>%
summarize(mort=qbeta(p=0.5,1+sum(mortality),1+sum(1-mortality)),
safety=qbeta(p=0.5,1+sum(safety),1+sum(1-safety)),
prog=qbeta(p=0.5,1+sum(progression),1+sum(1-progression)),
visual=qbeta(p=0.5,1+sum(visual),1+sum(1-visual)),
bp=qbeta(p=0.5,1+sum(bp),1+sum(1-bp)),
transpl=qbeta(p=0.5,1+sum(transpl),1+sum(1-transpl)),
resp=qbeta(p=0.5,1+sum(resp),1+sum(1-resp)),
bleed=qbeta(p=0.5,1+sum(bleed),1+sum(1-bleed)),
pro=qbeta(p=0.5,1+sum(pro),1+sum(1-pro)),
occur=qbeta(p=0.5,1+sum(occur),1+sum(1-occur)),
pain=qbeta(p=0.5,1+sum(pain),1+sum(1-pain)),
efficacy=qbeta(p=0.5,1+sum(efficacy),1+sum(1-efficacy)),
spiro=qbeta(p=0.5,1+sum(spiro),1+sum(1-spiro)),
brain=qbeta(p=0.5,1+sum(brain),1+sum(1-brain)),
mri=qbeta(p=0.5,1+sum(mri),1+sum(1-mri)),
gluc=qbeta(p=0.5,1+sum(gluc),1+sum(1-gluc)))
prepro <- fulldat %>%
dplyr::select(indicationkey, strSponsor, strSponsorType, strdesignkeyword) %>%
rowwise() %>%
mutate(nspons = length(str_split(strSponsor, pattern="\\|")[[1]]),
nsponst = length(str_split(strSponsorType, pattern="\\|")[[1]]),
spons = list( str_split(strSponsor, pattern="\\|")[[1]][ c(1:nspons, rep(nspons, max(nspons,nsponst)-nspons)) ] ),
sponst = list( str_split(strSponsorType, pattern="\\|")[[1]][ c(1:nsponst, rep(nsponst, max(nspons,nsponst)-nsponst)) ] ),
rando = 1*str_detect(strdesignkeyword, "random"),
efficacy = 1*str_detect(strdesignkeyword, "efficacy"),
safety = 1*str_detect(strdesignkeyword, "safety"),
pk = 1*str_detect(strdesignkeyword, "pharmacokin"),
pd = 1*str_detect(strdesignkeyword, "pharmacodyn"),
drf = 1*str_detect(strdesignkeyword, "dose resp"),
rubbish = 1*(str_detect(strdesignkeyword, "open")) + 1*(str_detect(strdesignkeyword, "single")) + 1*(str_detect(strdesignkeyword, "ascend")),
sophis = 1*str_detect(strdesignkeyword, "random") + 1*str_detect(strdesignkeyword, "multiple arm") + 1*str_detect(strdesignkeyword, "umbrella") +
1*str_detect(strdesignkeyword, "basket") + 1*str_detect(strdesignkeyword, "efficacy") + 1*str_detect(strdesignkeyword, "super") + 1*str_detect(strdesignkeyword, "infer") +
1*str_detect(strdesignkeyword, "immunogen") + 1*str_detect(strdesignkeyword, "dose resp") + 1*str_detect(strdesignkeyword, "active") +
1*str_detect(strdesignkeyword, "double") + 1*str_detect(strdesignkeyword, "placebo")) %>%
ungroup() %>%
replace(., is.na(.), 0) %>%
group_by(indicationkey) %>%
summarize(nspons=log(mean(nspons)+0.5),
nsponst=log(mean(nsponst)+0.5),
rando=qbeta(0.5, 1+sum(rando), 1+sum(1-rando)),
efficacy=qbeta(0.5, 1+sum(efficacy), 1+sum(1-efficacy)),
safety=qbeta(0.5, 1+sum(safety), 1+sum(1-safety)),
pk=qbeta(0.5, 1+sum(pk), 1+sum(1-pk)),
pd=qbeta(0.5, 1+sum(pd), 1+sum(1-pd)),
drf=qbeta(0.5, 1+sum(drf), 1+sum(1-drf)),
rubbish=log(mean(rubbish)+0.5),
sophis=log(mean(sophis)+0.5)) %>%
ungroup()
inddis <- dis1 %>%
left_join(tastuff, by="indicationkey") %>%
left_join(ages, by="indicationkey") %>%
left_join(eptype, by="indicationkey") %>%
left_join(ddrdat, by="indicationkey") %>%
left_join(prepro, by="indicationkey") %>%
ungroup() %>%
replace(., is.na(.), 0)
# inddis %>% print(n=100)
# colnames(inddis)[colSums(is.na(inddis)) > 0]
indumap <- umap(dplyr::select(inddis, -indicationkey),
n_neighbors = dim(inddis)[1],
n_epochs = 2500,learning_rate = 0.5, init = "random")
rval <- inddis %>%
ungroup() %>%
mutate(ind_umap1 = indumap[,1],
ind_umap2 = indumap[,2]) %>%
dplyr::select(indicationkey, ind_umap1, ind_umap2)
write_rds(x=rval, path=filepath)
}
return( rval )
}
################################################
# UMAP embedding for sponsors
################################################
umap_sponsor <- function(fulldat){
message("----- Sponsor UMAP ----- ")
# Check whether we are on evaluation sever or training server, pre-pend path accordingly
if (Sys.getenv("AICROWD_TRAIN_DATA_PATH")==""){
filepath = paste0("/home/desktop1/files/", "umapspo.rds")
} else {
filepath = "umapspo.rds"
}
if (file.exists(filepath)){
ctime = file.info(filepath)$ctime
message( paste("Found existing umap-embedding on disk, using that. Created at ", ctime, ", if that is too old, delete it.") )
rval <- read_rds(filepath)
} else {
message("No existing umap embedding on disk, creating a new one.")
message("Start generating sponsor embedding")
# Some TA based features for sponsors
tas <- fulldat %>%
dplyr::select(row_id, strtherapeuticarea) %>%
mutate(sta = str_split(strtherapeuticarea, pattern="\\|")) %>%
unnest(cols="sta") %>%
group_by(row_id, sta) %>%
mutate(
intsta = case_when(
sta=="Autoimmune/Inflammation" ~ 1,
sta=="Cardiovascular" ~ 2,
sta=="CNS" ~ 3,
sta=="Genitourinary" ~ 4,
sta=="Infectious Disease" ~ 5,
sta=="Metabolic/Endocrinology" ~ 6,
sta=="Oncology" ~ 7,
sta=="Ophthalmology" ~ 8,
sta=="Vaccines (Infectious Disease)" ~ 9,
TRUE ~ 10),
#mutate(intsta=group_indices(), # Create group incdices - may want to redo this to avoid issues with new groups
flg=1) %>% #group_by(sta, sta) %>%summarize(n=n())
ungroup() %>%
dplyr::select(-strtherapeuticarea, -sta) %>%
distinct() %>%
group_by(row_id) %>%
pivot_wider(names_from=intsta, names_prefix="ta", values_from = flg,
values_fill=list(flg=0)) %>%
ungroup() %>%
fncols(paste0("ta", 1:9))
# Get UMAP embedding for each sponsor
message("Sponsor embedding: start pre-prossing on basic sponsor features")
prepro <- fulldat %>%
left_join(tas, by="row_id") %>%
ungroup() %>%
rowwise() %>%
mutate(nspons = length(str_split(strSponsor, pattern="\\|")[[1]]),
nsponst = length(str_split(strSponsorType, pattern="\\|")[[1]]),
spons = list( str_split(strSponsor, pattern="\\|")[[1]][ c(1:nspons, rep(nspons, max(nspons,nsponst)-nspons)) ] ),
sponst = list( str_split(strSponsorType, pattern="\\|")[[1]][ c(1:nsponst, rep(nsponst, max(nspons,nsponst)-nsponst)) ] ),
rando = 1*str_detect(strdesignkeyword, "random"),
pk = 1*str_detect(strdesignkeyword, "pharmacokin"),
pd = 1*str_detect(strdesignkeyword, "pharmacodyn"),
drf = 1*str_detect(strdesignkeyword, "dose resp"),
rubbish = (str_detect(strdesignkeyword, "open"))*1 + 1*(str_detect(strdesignkeyword, "single")) + 1*(str_detect(strdesignkeyword, "ascend")),
sophis = 1*str_detect(strdesignkeyword, "random") + 1*str_detect(strdesignkeyword, "multiple arm") + 1*str_detect(strdesignkeyword, "umbrella") +
1*str_detect(strdesignkeyword, "basket") + 1*str_detect(strdesignkeyword, "efficacy") + 1*str_detect(strdesignkeyword, "super") + 1*str_detect(strdesignkeyword, "infer") +
1*str_detect(strdesignkeyword, "immunogen") + 1*str_detect(strdesignkeyword, "dose resp") + 1*str_detect(strdesignkeyword, "active") - rubbish +
1*str_detect(strdesignkeyword, "double") + 1*str_detect(strdesignkeyword, "placebo")
) %>%
ungroup() %>%
unnest(cols=c("spons","sponst")) %>%
group_by(spons, sponst) %>%
summarize(log_nrec=log(n()+0.5),
ta1=log(sum(ta1)+0.5) - log_nrec,
ta2=log(sum(ta2)+0.5) - log_nrec,
ta3=log(sum(ta3)+0.5) - log_nrec,
ta4=log(sum(ta4)+0.5) - log_nrec,
ta5=log(sum(ta5)+0.5) - log_nrec,
ta6=log(sum(ta6)+0.5) - log_nrec,
ta7=log(sum(ta7)+0.5) - log_nrec,
ta8=log(sum(ta8)+0.5) - log_nrec,
ta9=log(sum(ta9)+0.5) - log_nrec,
nspons = log( length(nspons) + 0.5),
log_totalpats = log( sum( ifelse(is.na(intactualaccrual), ifelse(is.na(inttargetaccrual), 0, inttargetaccrual), intactualaccrual),na.rm=T) + 0.5),
log_totalpats_pt = log( sum( ifelse(is.na(intactualaccrual), ifelse(is.na(inttargetaccrual), 0, inttargetaccrual), intactualaccrual),na.rm=T) + 0.5) - log_nrec,
log_totalna = log( sum(is.na(inttargetaccrual)) + sum(is.na(intactualaccrual)) + sum(is.na(intduration)) + sum(is.na(strdesignkeyword)) + sum(is.na(strTerminationReason)) + 0.5),
log_totalna_pt = log( sum(is.na(inttargetaccrual)) + sum(is.na(intactualaccrual)) + sum(is.na(intduration)) + sum(is.na(strdesignkeyword)) + sum(is.na(strTerminationReason)) + 0.5) - log_nrec,
lognarate = log_totalna-log_nrec,
meanyear=mean(ifelse(intphaseendyear<1990, 2009, ifelse(intphaseendyear>2014, 2014, intphaseendyear) ))-2009,
log_drugs = log( length(unique(DrugKey)) + 0.5),
log_indications = log( length(unique(indicationkey)) + 0.5),
log_indications_pt = log( length(unique(indicationkey)) + 0.5) - log_nrec,
log_indications_pd = log_indications - log_drugs,
log_stas = log( length(unique(strtherapeuticarea)) + 0.5),
log_stas_pt =log_stas - log_nrec,
log_stas_pt =log_stas - log_drugs) %>%
ungroup() %>%
# mutate(odd = ifelse(tolower(sponst) %in% c("academic","contract research organization","cooperative group","government",
# "industry, all other pharma","industry, generic","industry, top 20 pharma"),0,1),
# toppharma = ifelse(tolower(sponst) == "industry, top 20 pharma", 1, 0),
# nonindustry = ifelse( tolower(sponst) %in% c("academic","cooperative group","government","not for profit funding entity "), 1, 0),
# bucket = 1*str_detect(prepro$spons, "\\(Oth"),
# bucket2 = rnorm(n=n(), bucket*10, 0.5),
# sponstype = 1*(str_detect(tolower(spons),"industry") |
# str_detect(tolower(spons),"contract research")) +
# 2*(str_detect(tolower(spons),"academic") | str_detect(tolower(spons),"cooperative") |
# str_detect(tolower(spons),"government")),
# spons_nobrack = trimws(str_remove_all(spons, "\\{.*\\}")),
# spons_inbrack = trimws(str_extract(paste0(spons,"{none}"), "\\{.*\\}"))) %>%
# ungroup()
mutate(odd = ifelse(tolower(sponst) %in% c("academic","contract research organization","cooperative group","government",
"industry, all other pharma","industry, generic","industry, top 20 pharma"),0,1),
toppharma = ifelse(tolower(sponst) == "industry, top 20 pharma", 1, 0),
nonindustry = ifelse( tolower(sponst) %in% c("academic","cooperative group","government","not for profit funding entity "), 1, 0),
bucket = 1*str_detect(spons, "\\(Oth"),
bucket2 = rnorm(n=n(), bucket*10, 0.5),
sponstype = 1*(str_detect(tolower(spons),"industry") |
str_detect(tolower(spons),"contract research")) +
2*(str_detect(tolower(spons),"academic") | str_detect(tolower(spons),"cooperative") |
str_detect(tolower(spons),"government")),
clean_spons = trimws(str_remove_all(spons, "\\{.*\\}")),
spons_nobrack = str_detect(spons, "\\{.*\\}"),
spons_inbrack = str_remove_all( ifelse(spons_nobrack, trimws(str_extract(spons, "\\{.*\\}")), "none"), "[\\{\\}]"))
message("Sponsor embedding: get country information")
tmp <- fulldat %>%
dplyr::select(row_id, strLocation) %>%
mutate(location = str_split(strLocation, pattern="\\|")) %>%
unnest(location) %>%
mutate(location = str_remove_all(location, "[\\[\\]]"),
country_us = 1*str_detect(tolower(location),"united states"),
country_ge = 1*str_detect(tolower(location),"germany"),
country_uk = 1*str_detect(tolower(location),"united king"),
country_ca = 1*str_detect(tolower(location),"canada"),
country_fr = 1*str_detect(tolower(location),"france"),
country_it = 1*str_detect(tolower(location),"italy"),
country_be = 1*str_detect(tolower(location),"belgium"),
country_sp = 1*str_detect(tolower(location),"spain"),
country_nl = 1*str_detect(tolower(location),"netherland"),
country_pl = 1*str_detect(tolower(location),"poland"),
country_au = 1*str_detect(tolower(location),"australia"),
country_ru = 1*str_detect(tolower(location),"russia"),
country_cz = 1*str_detect(tolower(location),"czech"),
country_se = 1*str_detect(tolower(location),"sweden"),
country_hu = 1*str_detect(tolower(location),"hungary"),
country_dk = 1*str_detect(tolower(location),"denmark"),
country_aa = 1*str_detect(tolower(location),"austria"),
country_ch = 1*str_detect(tolower(location),"switzer"),
country_jp = 1*str_detect(tolower(location),"japan"),
country_ii = 1*str_detect(tolower(location),"israel"),
country_ro = 1*str_detect(tolower(location),"romania"),
country_sa = 1*str_detect(tolower(location),"south africa"),
country_in = 1*str_detect(tolower(location),"india"),
country_mx = 1*str_detect(tolower(location),"mexico"),
country_rk = 1*str_detect(tolower(location),"south korea"),
country_ar = 1*str_detect(tolower(location),"argentin"),
country_fi = 1*str_detect(tolower(location),"finland"),
country_bu = 1*str_detect(tolower(location),"bulga"),
country_ur = 1*str_detect(tolower(location),"ukrai"),
country_sl = 1*str_detect(tolower(location),"slovak"),
country_no = 1*str_detect(tolower(location),"norway"),
country_nz = 1*str_detect(tolower(location),"zealand"),
country_br = 1*str_detect(tolower(location),"brazil"),
country_pr = 1*str_detect(tolower(location),"puerto rico"),
country_tw = 1*str_detect(tolower(location),"taiwan"),
country_pe = 1*str_detect(tolower(location),"peru"),
country_cl = 1*str_detect(tolower(location),"chile"),
country_sp = 1*str_detect(tolower(location),"singap"),
country_cp = 1*str_detect(tolower(location),"china"),
country_hk = 1*str_detect(tolower(location),"kong"),
region_yu = 1*(str_detect(tolower(location),"serbia") | str_detect(tolower(location),"yugosl") | str_detect(tolower(location),"macedon") |
str_detect(tolower(location),"bosnia") | str_detect(tolower(location),"croatia") + str_detect(tolower(location),"montenegro") > 0),
region_eu = 1*(country_ge + country_uk + country_fr + country_be + country_sp + country_nl + country_pl + country_cz + country_se + country_hu + country_dk +
country_aa + country_ch + country_ro + country_fi + country_bu + country_ur + country_sl + country_no + region_yu +
str_detect(tolower(location),"malta") + str_detect(tolower(location),"luxem") + str_detect(tolower(location),"moldov") + str_detect(tolower(location),"iceland") +
+ str_detect(tolower(location),"europe") + str_detect(tolower(location),"portugal") + str_detect(tolower(location),"latvia") +
str_detect(tolower(location),"ireland") + str_detect(tolower(location),"lithuan") + str_detect(tolower(location),"estonia") + str_detect(tolower(location),"greece") +
str_detect(tolower(location),"norway") + str_detect(tolower(location),"malta") >0),
region_ee = 1*(country_ru + country_pl + country_cz + country_hu + country_ro + country_bu + country_ur + country_sl + region_yu + str_detect(tolower(location),"moldov") +
str_detect(tolower(location),"latvia") + str_detect(tolower(location),"lithuan") + str_detect(tolower(location),"estonia") +
str_detect(tolower(location),"belarus") + str_detect(tolower(location),"eastern europe") + str_detect(tolower(location),"armenia") +str_detect(tolower(location),"georgia") >0),
region_we = 1*(country_ge + country_uk + country_fr + country_be + country_sp + country_nl +
country_aa + country_ch + str_detect(tolower(location),"malta") + str_detect(tolower(location),"luxem") +
str_detect(tolower(location),"portugal") + str_detect(tolower(location),"ireland") >0),
region_ne = 1*(country_se + country_dk + country_fi + country_no + str_detect(tolower(location),"iceland") >0),
region_usca = 1*(country_us + country_ca>0),
region_af_nsa = 1*(str_detect(tolower(location),"egypt") + str_detect(tolower(location),"africa") & !str_detect(tolower(location),"south africa") + str_detect(tolower(location),"morocc") +
str_detect(tolower(location),"tunisia") + str_detect(tolower(location),"ghana") + str_detect(tolower(location),"gambia") + str_detect(tolower(location),"mali") +
str_detect(tolower(location),"tanza") + str_detect(tolower(location),"zambia") + str_detect(tolower(location),"kenya") >0),
region_un = 1*(region_af_nsa + str_detect(tolower(location),"arabia") + str_detect(tolower(location),"iran") + str_detect(tolower(location),"middle east") +
str_detect(tolower(location),"arab") + str_detect(tolower(location),"egypt") + str_detect(tolower(location),"cuba") + str_detect(tolower(location),"north korea") >0),
region_as = 1*(str_detect(tolower(location),"asia") + str_detect(tolower(location),"vietna") + str_detect(tolower(location),"indones") +
str_detect(tolower(location),"indones") + str_detect(tolower(location),"thail") + str_detect(tolower(location),"malaysia") + str_detect(tolower(location),"philip") +
country_hk + country_cp + country_rk + country_sp + country_tw >0),
region_is = 1*(str_detect(tolower(location),"sri lanka") + str_detect(tolower(location),"india") + str_detect(tolower(location),"pakista") + str_detect(tolower(location),"bangla") +
str_detect(tolower(location),"afghan") + str_detect(tolower(location),"nepal")>0),
region_ans = 1*(country_nz + country_au + country_sa>0)
) %>%
dplyr::select(-strLocation, -location) %>%
group_by(row_id) %>%
summarize_all(sum)
message("Sponsor embedding: Merge in country")
countries <- fulldat %>%
dplyr::select(row_id, strSponsor) %>%
mutate(spons = str_split(strSponsor, pattern="\\|")) %>%
unnest(cols="spons") %>%
left_join(tmp, by="row_id") %>%
ungroup() %>%
dplyr::select(-row_id, -strSponsor) %>%
group_by(spons) %>%
summarize_all(mean) %>%
ungroup()
message("Sponsor embedding: merge country back in together")
prepro <- prepro %>%
left_join(countries, by="spons") %>%
ungroup()
# message("Summarize missing before UMAP")
# prepro %>%
# summarise_all(funs(sum(is.na(.)))) %>%
# gather(key="variable", value="value") %>%
# filter(value>0) %>%
# print(n=200)
message("Sponsor embedding pre-processing done - start UMAP")
#spons_umap <- umap(, n_neighbors = min(dim(prepro)[1], 500), learning_rate = 0.5, init = "random")
spons_umap <- umap(dplyr::select(prepro, -log_totalna),
n_neighbors = dim(prepro)[1],
n_epochs = 2500,learning_rate = 0.5, init = "random")
message("Sponsor embedding pre-processing done - UMAP done")
prepro2 <- prepro %>%
mutate(spons_umap1=spons_umap[,1],
spons_umap2=spons_umap[,2])
message("Sponsor embedding: Finished, generate return tibble")
rval <- fulldat %>%
dplyr::select(DrugKey, indicationkey, row_id, strSponsor) %>%
mutate(spons = str_split(strSponsor, pattern="\\|")) %>%
unnest(cols="spons") %>%
dplyr::select(DrugKey, indicationkey, row_id,spons) %>%
left_join(dplyr::select(prepro2, spons, sponst, spons_umap1, spons_umap2),
by="spons")
write_rds(x=rval, path=filepath)
}
#%>%
# group_by(DrugKey, indicationkey) %>%
# summarize(n=n()) %>%
# ungroup() %>%
# summarize(min=min(n), max=max(n), mean=mean(n), q3=quantile(n,0.75) , p95=quantile(n,0.95))
# group_by(DrugKey, indicationkey) %>%
# mutate(num_of_trials = length(unique(row_id))) %>%
# ungroup() %>%
# group_by(DrugKey, indicationkey, spons, sponstype) %>%
# summarize(num_of_trials = mean(num_of_trials),
# trial_prop = length(unique(row_id))/num_of_trials) %>%
# ungroup() %>%
#group_by(DrugKey,indicationkey) %>%
# group_by(DrugKey, indicationkey) %>%
# arrange(desc(num_of_trials), DrugKey,indicationkey, desc(trial_prop)) %>%
# mutate(rowno=row_number()) %>%
# filter(rowno==1) %>% print(n=100)
# # %>%
# mutate(rowno=row_number(),
# xn=n()) %>%
# ungroup()
# # %>%
# group_by(row_id) %>%
# summarize(n=n()) %>%
# ungroup() %>%
# group_by(n) %>%
# summarize(hm=n())
return( rval )
}
################################################
# Function to get the features derived by Bjoern
################################################
get_features_bjoern <- function(file1="/shared_data/data/training_data/training_data_2015_split_on_outcome.csv", file2=NA){
# Read in the files, both need to be either character strings or tibbles/dataframes (mixing the two not foreseen)
if (!is.na(file2)){
if (is.character(file1)){
fulldat <- read_csv(file1) %>%
bind_rows(read_csv(file2))
} else {
fulldat <- as_tibble(file1) %>%
bind_rows(as_tibble(file2))
}
} else {
if (is.character(file1)){
fulldat <- read_csv(file1)
} else {
fulldat <- as_tibble(file1)
}
}
if (!("outcome" %in% colnames(fulldat))) {
message("'outcome' is not present adding missing")
fulldat <- fulldat %>%
mutate(outcome=NA_integer_)
}
# Get sponsor information
sponsor <- fulldat %>%
mutate(spons = str_split(strSponsorType, pattern="\\|")) %>%
unnest(cols="spons") %>%
mutate(spons_partners = str_count(strSponsor,"\\|"),
spons_curlybr = str_count(strSponsor,"[\\{\\}]"),
sponstype = 1*(str_detect(tolower(spons),"industry") |
str_detect(tolower(spons),"contract research")) +
2*(str_detect(tolower(spons),"academic") | str_detect(tolower(spons),"cooperative") |
str_detect(tolower(spons),"government")), # General sponsor type
# One-hot-encoded (dummy variables) for specific sponsor types
spons1=1*(str_detect(tolower(spons),"academic")),
spons2=1*(str_detect(tolower(spons),"cooperativ")),
spons3=1*(str_detect(tolower(spons),"government")),
spons4=1*(str_detect(tolower(spons),"top 20")),
spons5=1*(str_detect(tolower(spons),"generic")),
spons6=1*(str_detect(tolower(spons),"diagnostics") | str_detect(tolower(spons),"device")),
spons7=1*(str_detect(tolower(spons),"other pharma")),
spons8=1*(str_detect(tolower(spons),"not for profi")),
spons9=1*(str_detect(tolower(spons),"miscel")),
) %>%
group_by(DrugKey, indicationkey, row_id) %>% # Take maximum for each trial
summarize(sponstype=max(sponstype), # highest "quality" sponsor for each trial
spons_partners = mean(spons_partners),
spons_curlybr = mean(spons_curlybr),
spons1=max(spons1),
spons2=max(spons2),
spons3=max(spons3),
spons4=max(spons4),
spons5=max(spons5),
spons6=max(spons6),
spons7=max(spons7),
spons8=max(spons8)) %>%
ungroup() %>%
group_by(DrugKey, indicationkey) %>% # Take maximum for the drug
summarize(sponstype=max(sponstype),
spons_partners = mean(spons_partners),
spons_curlybr = mean(spons_curlybr),
spons_partners_sd = ifelse(is.na(sd(spons_partners)), 0, sd(spons_partners)),
spons_curlybr_sd = ifelse(is.na(sd(spons_curlybr)), 0, sd(spons_curlybr)),
spons1=max(spons1),
spons2=max(spons2),
spons3=max(spons3),
spons4=max(spons4),
spons5=max(spons5),
spons6=max(spons6),
spons7=max(spons7),
spons8=max(spons8))
indiumap <- indumap(fulldat)
ris <- umap_sponsor(fulldat)
ris2 <- ris %>%
group_by(DrugKey, indicationkey, spons, sponst, spons_umap1, spons_umap2 ) %>%
summarize(count=length(unique(row_id))) %>%
group_by(DrugKey, indicationkey) %>%
mutate(srecords=n(),
industry=str_detect(tolower(sponst), "industry")) %>%
arrange(DrugKey, indicationkey,desc(count)) %>%
mutate(srank=row_number()) %>%
group_by(DrugKey, indicationkey, industry) %>%
arrange(DrugKey, indicationkey, industry, desc(count)) %>%
mutate(srank2=row_number(),
flag1=(srank==1),
flag2=(srank2==1)) %>%
ungroup()
# arrange(DrugKey, indicationkey,desc(count)) %>%
# filter(srecords>5) %>% print(n=30)
ris2oth <- ris2 %>%
filter(!flag1 & !flag2) %>%
group_by(DrugKey, indicationkey) %>%
summarize(avgoth_umap1=mean(spons_umap1),
avgoth_umap2=mean(spons_umap2))
ris3 <- dplyr::select(filter(ris2, flag1), DrugKey, indicationkey, spons_umap1, spons_umap2) %>%
left_join( rename( dplyr::select(filter(ris2, flag2 & industry==T), DrugKey, indicationkey, spons_umap1, spons_umap2),
spons_ind_umap1=spons_umap1, spons_ind_umap2=spons_umap2),
by=c("DrugKey","indicationkey")) %>%
left_join( rename( dplyr::select(filter(ris2, flag2 & industry==F), DrugKey, indicationkey, spons_umap1, spons_umap2),
spons_nind_umap1=spons_umap1, spons_nind_umap2=spons_umap2),
by=c("DrugKey","indicationkey")) %>%
left_join(ris2oth, by=c("DrugKey","indicationkey") ) %>%
ungroup()
# Information for trial completion
trialcompl <- fulldat %>%
mutate(termreason = str_split(strTerminationReason, pattern="\\|")) %>%
unnest(cols="termreason") %>%
mutate(
safety = ifelse(is.na(termreason),0,str_detect(tolower(termreason), "adverse eff")*1), # Was there a study termination due to safety?
bad = case_when( # Rate trial outcomes as far as recorded from 0 = good to 4 = bad
str_detect(tolower(termreason), "positive outcome") ~ 0,
is.na(termreason) ~ 1,
str_detect(tolower(termreason), "terminated, other") ~ 2,
str_detect(tolower(termreason), "indeterminat") ~ 2,
str_detect(tolower(termreason), "enrollment") ~ 2,
str_detect(tolower(termreason), "priorit") ~ 4,
str_detect(tolower(termreason), "business") ~ 3,
str_detect(tolower(termreason), "funding") ~ 3,
str_detect(tolower(termreason), "lack of eff") ~ 4,
str_detect(tolower(termreason), "negative outcome") ~ 4,
str_detect(tolower(termreason), "adverse eff") ~ 4,
TRUE ~ 5,
),
pct_accrual = ifelse( is.na(intactualaccrual) | is.na(inttargetaccrual), # Proportion of target accrual that is achieved
ifelse(bad>2, 0.1,
ifelse(bad==1, 0.75, 1)),
pmin(1,intactualaccrual/inttargetaccrual))
) %>%
group_by(DrugKey, indicationkey, row_id) %>% # Summarize on a trial level
summarize(pct_accrual=min(pct_accrual),
bad=max(bad),
safety=max(safety)) %>%
mutate(accrued90 = (pct_accrual>=0.90)*1,
accrued50 = (pct_accrual>=0.5)*1) %>%
ungroup() %>%
group_by(DrugKey, indicationkey) %>% # summarize on a drug/indication level
summarize(pct_accrual=gm_mean(pct_accrual),
accrued50=mean(accrued50),
accrued90=mean(accrued90),
mean_trialendscore=mean(bad),
max_trialendscore=max(bad),
trialendscore0=mean( (bad==0)),
trialendscore1=mean( (bad==1)),
trialendscore2=mean( (bad==2)),
trialendscore3=mean( (bad==3)),
trialendscore4=mean( (bad==4)),
safety=max(safety)) %>%
ungroup()
# Look for safety stopping of any trial of the drug
drug_compl <- trialcompl %>%
group_by(DrugKey) %>%
summarize(drug_safety=mean(safety),
drug_max_trialendscore=max(max_trialendscore)) %>%
ungroup()
trialcompl <- trialcompl %>%
left_join(drug_compl, by="DrugKey") %>%
ungroup()
# Some further simple features (on disease area):
simpdat1 <- fulldat %>%
mutate(patno = ifelse(is.na(intactualaccrual), ifelse(is.na(inttargetaccrual), # Number of patients
0, inttargetaccrual), intactualaccrual)) %>%
group_by(DrugKey, indicationkey) %>% #, intphaseendyear, strtherapeuticarea) %>%
mutate(number_of_trials=n(), # Number of trials
total_pats=sum(patno)) %>% # Total number of patients
mutate(total_pats=ifelse(total_pats==0, 1, total_pats)) %>%
ungroup() %>%
dplyr::select(DrugKey, indicationkey, intphaseendyear, strtherapeuticarea, number_of_trials, total_pats) %>%
mutate(sta = str_split(strtherapeuticarea, pattern="\\|"), # Therapeutic areas
years_since_1999=ifelse(intphaseendyear>2000, # Years since 2000 (below 2000 also = 1)
ifelse(intphaseendyear>2018, 2018-1999, # Truncate to 2018 (should not have data after)
intphaseendyear-1999), 1)) %>%
unnest(cols="sta") %>%
group_by(sta) %>%
mutate(
intsta = case_when(
sta=="Autoimmune/Inflammation" ~ 1,
sta=="Cardiovascular" ~ 2,
sta=="CNS" ~ 3,
sta=="Genitourinary" ~ 4,
sta=="Infectious Disease" ~ 5,
sta=="Metabolic/Endocrinology" ~ 6,
sta=="Oncology" ~ 7,
sta=="Ophthalmology" ~ 8,
sta=="Vaccines (Infectious Disease)" ~ 9,
TRUE ~ 10),
#mutate(intsta=group_indices(), # Create group incdices - may want to redo this to avoid issues with new groups
flg=1) %>% #group_by(sta, sta) %>%summarize(n=n())
ungroup() %>%
dplyr::select(-strtherapeuticarea, -sta, -intphaseendyear) %>%
distinct() %>%
group_by(DrugKey, indicationkey, years_since_1999, number_of_trials, total_pats) %>%
pivot_wider(names_from=intsta, names_prefix="ta", values_from = flg,
values_fill=list(flg=0)) %>%
ungroup() %>%
fncols(paste0("ta", 1:9))
if ( length(unique(fulldat$intpriorapproval)) == 2 ) {
if ( min(sort(unique(fulldat$intpriorapproval))==c("['0']","[]"))!=1 ) {
warning("Help! They've changed the data on intpriorapproval - populated values don't match")
}
} else {
warning("Help! They've changed the data on intpriorapproval - more than two possible values now")
}
# Determine prior approval status overall y/n, by number and TA
print("lcm1")
lcm1 <- fulldat %>%
mutate(prior_approval = 1*(intpriorapproval=='[]') + 0*(intpriorapproval=='[0]')) %>% # Prior approvals?
group_by(DrugKey, indicationkey, outcome, intphaseendyear, strtherapeuticarea) %>%
summarize(prior_approval=max(prior_approval)) %>%
ungroup() %>%
dplyr::select(DrugKey, indicationkey, outcome, intphaseendyear, prior_approval, strtherapeuticarea) %>%
distinct()
# Number of prior approvals
print("lcm2")
lcm2 <- lcm1 %>%
dplyr::select(DrugKey, intphaseendyear, prior_approval, outcome) %>%
distinct() %>%
group_by(DrugKey, intphaseendyear) %>%
summarize(approvals_in_year=sum(outcome, na.rm=T),
prior_approval=min(prior_approval)) %>%
ungroup() %>%
group_by(DrugKey) %>%
arrange(DrugKey, intphaseendyear) %>%
mutate(#totalapprovals=mean(approvals_in_year, na.rm=T),
fpa = first(prior_approval),
dcum= replace_na( lag(cumsum(approvals_in_year), 1), 0),
past_approvals = pmax( 1*(cumsum(prior_approval)>0), fpa+dcum)) %>%
dplyr::select(DrugKey, intphaseendyear, past_approvals) %>%
distinct()
# Number of prior approvals within TA
print("lcm3")
lcm3 <- lcm1 %>%
group_by(DrugKey) %>%
mutate(number_of_tas=length(unique(strtherapeuticarea))) %>%
ungroup() %>% #dplyr::select(DrugKey, strtherapeuticarea, intphaseendyear) %>% distinct() %>% dim()
group_by(DrugKey, strtherapeuticarea, intphaseendyear, number_of_tas) %>%
summarize(approvals_in_year=sum(outcome, na.rm=T),
prior_approval = min(prior_approval)) %>%
ungroup() %>%
group_by(DrugKey, strtherapeuticarea) %>%
arrange(DrugKey, strtherapeuticarea, intphaseendyear) %>%
mutate(#totalapprovals=mean(approvals_in_year, na.rm=T),
fpa = ifelse(number_of_tas==1, first(prior_approval),0),
dcum= replace_na( lag(cumsum(approvals_in_year), 1), 0),
past_approvals_ta = pmax( 1*((number_of_tas==1)*cumsum(prior_approval)>0), fpa+dcum)) %>%
ungroup() %>% #dplyr::select(DrugKey, strtherapeuticarea, intphaseendyear) %>%
#arrange(desc(totalapprovals), DrugKey, intphaseendyear) %>%
dplyr::select(DrugKey, strtherapeuticarea, intphaseendyear, past_approvals_ta)
print("lcm-merge")
lcm <- distinct(dplyr::select(lcm1, DrugKey, indicationkey, strtherapeuticarea, intphaseendyear, prior_approval)) %>%
left_join(lcm2, by=c("DrugKey", "intphaseendyear")) %>% # distinct() %>% dim() dplyr::select(DrugKey, indicationkey, intphaseendyear) %>% distinct()
left_join(lcm3, by=c("DrugKey", "strtherapeuticarea", "intphaseendyear")) %>%
dplyr::select(DrugKey, indicationkey, prior_approval, past_approvals, past_approvals_ta) %>%
group_by(DrugKey, indicationkey, prior_approval, past_approvals) %>%
summarize(past_approvals_ta=max(past_approvals_ta)) %>%
distinct()
print("Based on GenericName")
# Things based on the generic name and life-cycle/prior approval:
wiian <- fulldat %>% # INN costs 9000 dollars pre 2014, 12000 afterwards, see: https://extranet.who.int/tools/inn_online_application/
mutate(unwilling_to_pay_12k = 1*( ( str_starts(GenericName,"[A-Z]") & !str_starts(GenericName,"(HPV|MMR|DTP|HIV)")) |
str_detect(GenericName, "[A-Z][A-Z][A-Z][A-Z][A-Z]") | str_detect(GenericName, "[0-9][0-9]") ),
is_a_combination = 1*(str_detect(GenericName, "\\+") | (str_detect(GenericName, "[a-z]/[a-z]") & # Identify combination drugs
!str_detect(GenericName, "alpha/beta") &
!str_detect(GenericName, "beta/gamma") &
!str_detect(GenericName, "gamma/delta"))),
is_mab = 1*(str_detect(GenericName, "mab$") | str_detect(GenericName, "mab ") | # Is the drug a monoclonal antibody (ending in -mab or muromonab, which is the one exception to this rule)
str_detect(GenericName, "mab,") | str_detect(GenericName, "mab-") | str_detect(GenericName, "muromonab"))) %>%
dplyr::select(DrugKey, indicationkey, unwilling_to_pay_12k, is_a_combination, is_mab) %>%
group_by(DrugKey, indicationkey) %>%
summarize(unwilling_to_pay_12k=max(unwilling_to_pay_12k),
is_a_combination=max(is_a_combination),
is_mab=max(is_mab)) # ,prior_approval=max(prior_approval)
print("Disease type")
# Using Disease Type variable
dis1 <- fulldat %>%
dplyr::select(DrugKey, indicationkey, strDiseaseType) %>% #strtherapeuticarea, outcome
mutate(strDiseaseType=str_remove_all(str_remove_all(str_remove_all(str_remove_all(strDiseaseType, "\\(N/A\\)\\|"),"\\|\\(N/A\\)"),"\\(\\)\\|"),"\\|\\(\\)")) %>%
mutate(distyp = str_split(strDiseaseType, pattern="\\|")) %>%
unnest(cols="distyp") %>%
distinct() %>%
mutate(distyp=str_remove_all(distyp, "[\\[\\]]"),