DataSheet_1_Association between immune-mediated adverse events and efficacy in metastatic non-small-cell lung cancer patients treated with durvalumab and tremelimumab.zip by Agnish Dey (14052210)

“…Using machine learning, we built a predictive model utilizing baseline clinical and laboratory features to identify patients at risk of developing imAEs and further evaluated patient survival based on a threshold index extracted from the model.…”

DataSheet_2_Association between immune-mediated adverse events and efficacy in metastatic non-small-cell lung cancer patients treated with durvalumab and tremelimumab.docx by Agnish Dey (14052210)

“…Using machine learning, we built a predictive model utilizing baseline clinical and laboratory features to identify patients at risk of developing imAEs and further evaluated patient survival based on a threshold index extracted from the model.…”

Data Sheet 1_Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical tri... by Caio César Souza Conceição (21232238)

“…LASSO and CombiROC were used to select optimal predictive variables. The Youden criteria identified the best threshold for different variable combinations, which were then compared based on the highest area under the curve (AUC) and accuracy. …”

Table 1_Predicting clinical outcomes at hospital admission of patients with COVID-19 pneumonia using artificial intelligence: a secondary analysis of a randomized clinical trial.xl... by Caio César Souza Conceição (21232238)

“…LASSO and CombiROC were used to select optimal predictive variables. The Youden criteria identified the best threshold for different variable combinations, which were then compared based on the highest area under the curve (AUC) and accuracy. …”

Supplementary data by Joon Young Kim (22352152)

“…</i> The study retrospectively analyzed clinical and laboratory data from three Korean centers to develop and validate machine learning models predicting optimal methimazole dosing in children and adolescents. …”

Image_4_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”

Image_5_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”

Image_3_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”

Image_1_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”

DataSheet_1_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal chola... by Di Wang (329735)

“…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”

Image_2_Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangio... by Di Wang (329735)

“…A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. …”

Data Sheet 1_Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy.pdf by Xiaote Zhang (21570542)

“…</p>Objective<p>This study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.…”

DataSheet_1_Analytical validation of the 7-gene biosignature for prediction of recurrence risk and radiation therapy benefit for breast ductal carcinoma in situ.docx by David Dabbs (15663155)

“…In this study, we present results from analytical validity, performance assessment, and clinical performance validation and clinical utility for the DCISionRT test comprised of multianalyte assays with algorithmic analysis.</p>Methods<p>The analytical validation of each molecular assay was performed based on the Clinical and Laboratory Standards Institute (CLSI) guidelines Quality Assurance for Design Control and Implementation of Immunohistochemistry Assays and the College of American Pathologists/American Society of Clinical Oncology (CAP/ASCO) recommendations for analytic validation of immunohistochemical assays.…”

Data Sheet 1_Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients—Machine learning models for clinical decision support.do... by Yuanguo Xiong (20135991)

“…Thirteen distinct machine learning (ML) algorithms were trained and benchmarked to construct an optimal risk stratification framework. …”

Data_Sheet_1_Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.docx by Elham Jamshidi (11013993)

“…<p>Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.</p><p>Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission.…”

Data_Sheet_1_Rapid Identification of Mycobacterium tuberculosis Complex Using Mass Spectrometry: A Proof of Concept.docx by Simon Robinne (12324872)

“…In a second step, 70/80 (88%) other isolates were correctly classified by an algorithm based on specific peaks. This study is the first to report a MALDI-TOF-MS method for discriminating M. tuberculosis complex mycobacteria that is easily implemented in clinical microbiology laboratories.…”

Table_1_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.DOCX by Carles Rubio Maturana (17448078)

“…</p>Conclusion<p>The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.…”

Image_1_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.TIFF by Carles Rubio Maturana (17448078)

“…</p>Conclusion<p>The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.…”

Image_2_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.TIFF by Carles Rubio Maturana (17448078)

“…</p>Conclusion<p>The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.…”

Table_1_Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study.DOCX by Wei Liu (20030)

“…Seven ML algorithms, namely logistic regression, random forest, adaptive boosting, gradient boosting decision tree, histogram-based gradient boosting, extreme gradient boosting, and category boosting, were developed to predict END in PAI patients using these critical features. …”