Datasets
TumorGrowth.patient_data — Functionpatient_data()Return, in row table form, the lesion measurement data collected in Laleh et al. (2022) "Classical mathematical models for prediction of response to chemotherapy and immunotherapy", PLOS Computational Biology.
Each row represents all measurements for a single lesion for a unique patient.
record = first(patient_data())
julia> record.Pt_hashID # patient identifier
"0218075314855e6ceacca856fcd4c737-S1"
julia> record.T_weeks # measure times, in weeks
7-element Vector{Float64}:
0.1
6.0
12.0
17.0
23.0
29.0
35.0
julia> record.Lesion_normvol # all volumes measured, normalised by dataset max
7-element Vector{Float64}:
0.000185364052636979
0.00011229838600811
8.4371439525252e-5
8.4371439525252e-5
1.05464299406565e-5
2.89394037571615e-5
8.4371439525252e-5See also flat_patient_data.
TumorGrowth.flat_patient_data — Functionflat_patient_data()Return, in row table form, the lesion measurement data collected in Laleh et al. (2022) "Classical mathematical models for prediction of response to chemotherapy and immunotherapy", PLOS Computational Biology.
Each row represents a single measurement of a single lesion on some day.
See also patient_data, in which each row represents all measurements of a single lesion.