This paper contributes to interpretable machine learning via visual knowledge discovery in general line coordinates (GLC). The concepts of hyperblocks as interpretable dataset units and general line coordinates are combined to create a visual self-service machine learning model. The DSC1 and DSC2 lossless multidimensional coordinate systems are proposed. DSC1 and DSC2 can map multiple dataset attributes to a single two-dimensional (X, Y) Cartesian plane using a graph construction algorithm. The hyperblock analysis was used to determine visually appealing dataset attribute orders and to reduce line occlusion. It is shown that hyperblocks can generalize decision tree rules and a series of DSC1 or DSC2 plots can visualize a decision tree. The DSC1 and DSC2 plots were tested on benchmark datasets from the UCI ML repository. They allowed for visual classification of data. Additionally, areas of hyperblock impurity were discovered and used to establish dataset splits that highlight the upper estimate of worst-case model accuracy to guide model selection for high-risk decision-making. Major benefits of DSC1 and DSC2 is their highly interpretable nature. They allow domain experts to control or establish new machine learning models through visual pattern discovery.
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore Health Services（新加坡保健服务集团）；Singapore General Hospital（新加坡中央医院）；National University of Singapore（新加坡国立大学）；Duke University（杜克大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）
AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University（杜克大学）；National University of Singapore（新加坡国立大学）；Erasmus MC University Medical Center Rotterdam（鹿特丹伊拉斯谟医学中心大学医院）；Erasmus University（伊拉斯谟大学）；Singapore General Hospital（新加坡中央医院）；Singapore Health Services（新加坡保健服务集团）
Interpretable Survival Analysis for Heart Failure Risk Prediction
Stanford University（斯坦福大学）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人医疗保健系统）；Kaiser Permanente Northern California Division of Research（凯撒医疗北加州研究部）；Stanford Medicine（斯坦福医学院）
An Interpretable AI Tool for SAVR vs TAVR in Low to Intermediate Risk Patients with Severe Aortic Stenosis
Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运营研究中心）；Section of Cardiovascular Medicine, Yale School of Medicine（耶鲁医学院心血管医学部门）；Heart & Vascular Institute, Hartford HealthCare（哈佛法院医疗中心心脏与血管研究院）；Hartford HealthCare Research Institute, Hartford HealthCare（哈佛法院医疗中心研究 institute）；Novant Health Heart & Vascular Institute（诺文特健康心脏与血管研究院）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
LIFT: Interpretable truck driving risk prediction with literature-informed fine-tuned LLMs
Department of Civil Engineering, Tsinghua University, Beijing, China（清华大学土木工程系）；College of Traffic Management, People's Public Security University of China, Beijing, China（中国人民公安大学交通管理学院）；Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China（北京理工大学交通工程重点实验室）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China（清华大学智能绿色车辆与移动国家重点实验室）
Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦大学电子工程与计算机科学学院）；Institute for Data Science and Artificial Intelligence, Boğaziçi University（博雅大学数据科学与人工智能研究所）；College of Science Computer Science Department, Kuwait University（科威特大学科学学院计算机科学系）
Joint Score-Threshold Optimization for Interpretable Risk Assessment
Center for Health Systems and Policy Modeling, Department of Health Policy and Management（健康系统与政策建模中心，健康政策与管理系）；Malone Center for Engineering in Healthcare, Johns Hopkins University（医疗工程中心，约翰霍普金斯大学）；Center for Systems Science and Engineering, Department of Civil and Systems Engineering（系统科学与工程中心，土木与系统工程系）；Department of Physical Medicine and Rehabilitation, School of Medicine, Johns Hopkins University（康复医学系，医学学院，约翰霍普金斯大学）；Johns Hopkins Hospital（约翰霍普金斯医院）；Department of Physical Therapy, University of Nevada, Las Vegas（物理治疗系，内华达大学拉斯维加斯分校）
TA-RNN-Medical-Hybrid: A Time-Aware and Interpretable Framework for Mortality Risk Prediction
Department of Computer Engineering, SRC.,Islamic Azad university（计算机工程系，伊斯兰教Azad大学）；School of Computer Engineering, Iran university of Science and technology（计算机工程学院，伊朗科学技术大学）
How to integrate cloud service, data analytic and machine learning technique to reduce cyber risks associated with the modern cloud based infrastructure
Learning Interpretable Point-Based Clinical Risk Scores via Direct Optimization
Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Decatur High School（德凯高中）；Department of Pathology, Stanford University School of Medicine（斯坦福大学医学院病理学系）；Division of Computational Medicine, Department of Medicine, Stanford University（斯坦福大学医学系计算医学分会）
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
Mederrata（梅德拉塔）；National Institutes of Health（美国国立卫生研究院）；University of Vermont（佛蒙特大学）
