{ "_id": "6a17fbc7acfb0bcc41daa18c", "Package": "AutoScore", "Type": "Package", "Title": "An Interpretable Machine Learning-Based Automatic Clinical Score\nGenerator", "Version": "1.1.0", "Date": "2022-08-27", "Authors@R": "c(person(\"Feng\", \"Xie\", role = c(\"aut\",\"cre\"), comment = c(ORCID = \"0000-0002-0215-667X\"),\nemail= \"xief@u.duke.nus.edu\"),\nperson(\"Yilin\", \"Ning\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-6758-4472\"),\nemail= \"yilin.ning@duke-nus.edu.sg\"),\nperson(\"Han\", \"Yuan\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-2674-6068\"),\nemail= \"yuan.han@u.duke.nus.edu\"),\nperson(\"Mingxuan\", \"Liu\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-4274-9613\"),\nemail= \"e0572499@u.nus.edu\"),\nperson(\"Siqi\", \"Li\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-1660-105X\"),\nemail= \"siqili@u.duke.nus.edu\"),\nperson(\"Ehsan\", \"Saffari\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-6473-4375\"),\nemail = \"ehsan.saffari@duke-nus.edu.sg\"),\nperson(\"Bibhas\", \"Chakraborty\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-7366-0478\"),\nemail = \"bibhas.chakraborty@duke-nus.edu.sg\"),\nperson(\"Nan\", \"Liu\", role = c(\"aut\"), comment = c(ORCID = \"0000-0003-3610-4883\"),\nemail = \"liu.nan@duke-nus.edu.sg\"))", "URL": "https://github.com/nliulab/AutoScore", "BugReports": "https://github.com/nliulab/AutoScore/issues", "Description": "A novel interpretable machine learning-based framework to\nautomate the development of a clinical scoring model for\npredefined outcomes. Our novel framework consists of six\nmodules: variable ranking with machine learning, variable\ntransformation, score derivation, model selection, domain\nknowledge-based score fine-tuning, and performance\nevaluation.The details are described in our research\npaper. Users or clinicians could seamlessly\ngenerate parsimonious sparse-score risk models (i.e., risk\nscores), which can be easily implemented and validated in\nclinical practice. We hope to see its application in various\nmedical case studies.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libjpeg-dev\nlibpng-dev libuv1-dev libxml2-dev libssl-dev libx11-dev\nzlib1g-dev", "Repository": "https://nliulab.r-universe.dev", "Date/Publication": "2025-11-29 06:34:05 UTC", "RemoteUrl": "https://github.com/nliulab/autoscore", "RemoteRef": "HEAD", "RemoteSha": "eb5a2d9e822bc07b34c5f0150c37fa6c8812d15e", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-28 08:19:32 UTC", "User": "root" }, "Author": "Feng Xie [aut, cre] (ORCID: ),\nYilin Ning [aut] (ORCID: ),\nHan Yuan [aut] (ORCID: ),\nMingxuan Liu [aut] (ORCID: ),\nSiqi Li [aut] (ORCID: ),\nEhsan Saffari [aut] (ORCID: ),\nBibhas Chakraborty [aut] (ORCID:\n),\nNan Liu [aut] (ORCID: )", "Maintainer": "Feng Xie ", "MD5sum": "4932c63c7fd31eb7d93a67cefdd04a2c", "_user": "nliulab", "_type": 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Module 1)", "topics": [ "AutoScore_rank_Ordinal" ] }, { "page": "AutoScore_rank_Survival", "title": "AutoScore STEP (1) for survival outcomes: Generate variable ranking List by machine learning (Random Survival Forest) (AutoScore Module 1)", "topics": [ "AutoScore_rank_Survival" ] }, { "page": "AutoScore_testing", "title": "AutoScore STEP(v): Evaluate the final score with ROC analysis (AutoScore Module 6)", "topics": [ "AutoScore_testing" ] }, { "page": "AutoScore_testing_Ordinal", "title": "AutoScore STEP(v) for ordinal outcomes: Evaluate the final score (AutoScore Module 6)", "topics": [ "AutoScore_testing_Ordinal" ] }, { "page": "AutoScore_testing_Survival", "title": "AutoScore STEP(v) for survival outcomes: Evaluate the final score with ROC analysis (AutoScore Module 6)", "topics": [ "AutoScore_testing_Survival" ] }, { "page": "AutoScore_weighting", "title": "AutoScore STEP(iii): Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)", "topics": [ "AutoScore_weighting" ] }, { "page": "AutoScore_weighting_Ordinal", "title": "AutoScore STEP(iii) for ordinal outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)", "topics": [ "AutoScore_weighting_Ordinal" ] }, { "page": "AutoScore_weighting_Survival", "title": "AutoScore STEP(iii) for survival outcomes: Generate the initial score with the final list of variables (Re-run AutoScore Modules 2+3)", "topics": [ "AutoScore_weighting_Survival" ] }, { "page": "bca", "title": "Bias-corrected and accelerated confidence intervals", "topics": [ "bca" ] }, { "page": "change_reference", "title": "Internal Function: Change Reference category after first-step logistic regression (part of AutoScore Module 3)", "topics": [ "change_reference" ] }, { "page": "check_data", "title": "AutoScore function for datasets with binary outcomes: Check whether the input dataset fulfill the requirement of the AutoScore", "topics": [ "check_data" ] }, { "page": "check_data_ordinal", "title": "AutoScore function for ordinal outcomes: Check whether the input dataset fulfil the requirement of the AutoScore", "topics": [ "check_data_ordinal" ] }, { "page": "check_data_survival", "title": "AutoScore function for survival data: Check whether the input dataset fulfill the requirement of the AutoScore", "topics": [ "check_data_survival" ] }, { "page": "check_link", "title": "Internal function: Check link function", "topics": [ "check_link" ] }, { "page": "check_predictor", "title": "Internal function: Check predictors", "topics": [ "check_predictor" ] }, { "page": "compute_auc_val", "title": "Internal function: Compute AUC based on validation set for plotting parsimony (AutoScore Module 4)", "topics": [ "compute_auc_val" ] }, { "page": "compute_auc_val_ord", "title": "Internal function: Compute mean AUC for ordinal outcomes based on validation set for plotting parsimony", "topics": [ "compute_auc_val_ord" ] }, { "page": "compute_auc_val_survival", "title": "Internal function for survival outcomes: Compute AUC based on validation set for plotting parsimony", "topics": [ "compute_auc_val_survival" ] }, { "page": "compute_descriptive_table", "title": "AutoScore function: Descriptive Analysis", "topics": [ "compute_descriptive_table" ] }, { "page": "compute_final_score_ord", "title": "Internal function: Compute risk scores for ordinal data given variables selected, cut-off values and scoring table", "topics": [ "compute_final_score_ord" ] }, { "page": "compute_mauc_ord", "title": "Internal function: Compute mAUC for ordinal predictions", "topics": [ "compute_mauc_ord" ] }, { "page": "compute_multi_variable_table", "title": "AutoScore function: Multivariate Analysis", "topics": [ "compute_multi_variable_table" ] }, { "page": "compute_multi_variable_table_ordinal", "title": "AutoScore-Ordinal function: Multivariate Analysis", "topics": [ "compute_multi_variable_table_ordinal" ] }, { "page": "compute_multi_variable_table_survival", "title": "AutoScore function for survival outcomes: Multivariate Analysis", "topics": [ "compute_multi_variable_table_survival" ] }, { "page": "compute_prob_observed", "title": "Internal function: Based on given labels and scores, compute proportion of subjects observed in each outcome category in given score intervals.", "topics": [ "compute_prob_observed" ] }, { "page": "compute_prob_predicted", "title": "Internal function: Based on given labels and scores, compute average predicted risks in given score intervals.", "topics": [ "compute_prob_predicted" ] }, { "page": "compute_score_table", "title": "Internal function: Compute scoring table based on training dataset (AutoScore Module 3)", "topics": [ "compute_score_table" ] }, { "page": "compute_score_table_ord", "title": "Internal function: Compute scoring table for ordinal outcomes based on training dataset", "topics": [ "compute_score_table_ord" ] }, { "page": "compute_score_table_survival", "title": "Internal function: Compute scoring table for survival outcomes based on training dataset", "topics": [ "compute_score_table_survival" ] }, { "page": "compute_uni_variable_table", "title": "AutoScore function: Univariable Analysis", "topics": [ "compute_uni_variable_table" ] }, { "page": "compute_uni_variable_table_ordinal", "title": "AutoScore-Ordinal function: Univariable Analysis", "topics": [ "compute_uni_variable_table_ordinal" ] }, { "page": "compute_uni_variable_table_survival", "title": "AutoScore function for survival outcomes: Univariate Analysis", "topics": [ "compute_uni_variable_table_survival" ] }, { "page": "conversion_table", "title": "AutoScore function: Print conversion table based on final performance evaluation", "topics": [ "conversion_table" ] }, { "page": "conversion_table_ordinal", "title": "AutoScore function: Print conversion table for ordinal outcomes to map score to risk", "topics": [ "conversion_table_ordinal" ] }, { "page": "conversion_table_survival", "title": "AutoScore function for survival outcomes: Print conversion table", "topics": [ "conversion_table_survival" ] }, { "page": "estimate_p_mat", "title": "Internal function: generate probability matrix for ordinal outcomes given thresholds, linear predictor and link function", "topics": [ "estimate_p_mat" ] }, { "page": "eva_performance_iauc", "title": "Internal function survival outcome: Calculate iAUC for validation set", "topics": [ "eva_performance_iauc" ] }, { "page": "evaluate_model_ord", "title": "Internal function: Evaluate model performance on ordinal data", "topics": [ "evaluate_model_ord" ] }, { "page": "extract_or_ci_ord", "title": "Extract OR, CI and p-value from a proportional odds model", "topics": [ "extract_or_ci_ord" ] }, { "page": "find_one_inds", "title": "Internal function: Find column indices in design matrix that should be 1", "topics": [ "find_one_inds" ] }, { "page": "find_possible_scores", "title": "Internal function: Compute all scores attainable.", "topics": [ "find_possible_scores" ] }, { "page": "get_cut_vec", "title": "Internal function: Calculate cut_vec from the training set (AutoScore Module 2)", "topics": [ "get_cut_vec" ] }, { "page": "group_score", "title": "Internal function: Group scores based on given score breaks, and use friendly names for first and last intervals.", "topics": [ "group_score" ] }, { "page": "induce_informative_missing", "title": "Internal function: induce informative missing to sample data in the package to demonstrate how AutoScore handles missing as a separate category", "topics": [ "induce_informative_missing" ] }, { "page": "induce_median_missing", "title": "Internal function: induce informative missing in a single variable", "topics": [ "induce_median_missing" ] }, { "page": "inv_cloglog", "title": "Internal function: Inverse cloglog link", "topics": [ "inv_cloglog" ] }, { "page": "inv_logit", "title": "Internal function: Inverse logit link", "topics": [ "inv_logit" ] }, { "page": "inv_probit", "title": 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