NPJ Digital Medicine最新文献_第4页

A machine-learned model for predicting weight loss success using weight change features early in treatment 利用治疗早期体重变化特征预测减肥成功的机器学习模型

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-29 DOI: 10.1038/s41746-024-01299-y

Farzad Shahabi, Samuel L. Battalio, Angela Fidler Pfammatter, Donald Hedeker, Bonnie Spring, Nabil Alshurafa

{"title":"A machine-learned model for predicting weight loss success using weight change features early in treatment","authors":"Farzad Shahabi, Samuel L. Battalio, Angela Fidler Pfammatter, Donald Hedeker, Bonnie Spring, Nabil Alshurafa","doi":"10.1038/s41746-024-01299-y","DOIUrl":"10.1038/s41746-024-01299-y","url":null,"abstract":"Stepped-care obesity treatments aim to improve efficiency by early identification of non-responders and adjusting interventions but lack validated models. We trained a random forest classifier to improve the predictive utility of a clinical decision rule (>0.5 lb weight loss/week) that identifies non-responders in the first 2 weeks of a stepped-care weight loss trial (SMART). From 2009 to 2021, 1058 individuals with obesity participated in three studies: SMART, Opt-IN, and ENGAGED. The model was trained on 80% of the SMART data (224 participants), and its in-distribution generalizability was tested on the remaining 20% (remaining 57 participants). The out-of-distribution generalizability was tested on the ENGAGED and Opt-IN studies (472 participants). The model predicted weight loss at month 6 with an 84.5% AUROC and an 86.3% AUPRC. SHAP identified predictive features: weight loss at week 2, ranges/means and ranges of weight loss, slope, and age. The SMART-trained model showed generalizable performance with no substantial difference across studies.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-10"},"PeriodicalIF":12.4,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01299-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimal antibiotic selection framework for Sepsis patients using Artificial Intelligence 利用人工智能为败血症患者提供最佳抗生素选择框架

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-29 DOI: 10.1038/s41746-024-01350-y

Philipp Wendland, Christof Schenkel-Häger, Ingobert Wenningmann, Maik Kschischo

{"title":"An optimal antibiotic selection framework for Sepsis patients using Artificial Intelligence","authors":"Philipp Wendland, Christof Schenkel-Häger, Ingobert Wenningmann, Maik Kschischo","doi":"10.1038/s41746-024-01350-y","DOIUrl":"10.1038/s41746-024-01350-y","url":null,"abstract":"In this work we present OptAB, the first completely data-driven online-updateable antibiotic selection model based on Artificial Intelligence for Sepsis patients accounting for side-effects. OptAB performs an iterative optimal antibiotic selection for real-world Sepsis patients focussing on minimizing the Sepsis-related organ failure score (SOFA-Score) as treatment success while accounting for nephrotoxicity and hepatotoxicity as serious antibiotic side-effects. OptAB provides disease progression forecasts for (combinations of) the antibiotics Vancomycin, Ceftriaxone and Piperacillin/Tazobactam and learns realistic treatment influences on the SOFA-Score and the laboratory values creatinine, bilirubin total and alanine-transaminase indicating possible side-effects. OptAB is based on a hybrid neural network differential equation algorithm and can handle the special characteristics of patient data including irregular measurements, a large amount of missing values and time-dependent confounding. OptAB’s selected optimal antibiotics exhibit faster efficacy than the administered antibiotics.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-13"},"PeriodicalIF":12.4,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01350-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semi automatic quantification of REM sleep without atonia in natural sleep environment 在自然睡眠环境中对无失眠的快速眼动睡眠进行半自动量化

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-28 DOI: 10.1038/s41746-024-01354-8

Daniel Possti, Shani Oz, Aaron Gerston, Danielle Wasserman, Iain Duncan, Matteo Cesari, Andrew Dagay, Riva Tauman, Anat Mirelman, Yael Hanein

{"title":"Semi automatic quantification of REM sleep without atonia in natural sleep environment","authors":"Daniel Possti, Shani Oz, Aaron Gerston, Danielle Wasserman, Iain Duncan, Matteo Cesari, Andrew Dagay, Riva Tauman, Anat Mirelman, Yael Hanein","doi":"10.1038/s41746-024-01354-8","DOIUrl":"10.1038/s41746-024-01354-8","url":null,"abstract":"Polysomnography, the gold standard diagnostic tool in sleep medicine, is performed in an artificial environment. This might alter sleep and may not accurately reflect typical sleep patterns. While macro-structures are sensitive to environmental effects, micro-structures remain more stable. In this study we applied semi-automated algorithms to capture REM sleep without atonia (RSWA) and sleep spindles, comparing lab and home measurements. We analyzed 107 full-night recordings from 55 subjects: 24 healthy adults, 28 Parkinson’s disease patients (15 RBD), and three with isolated Rem sleep behavior disorder (RBD). Sessions were manually scored. An automatic algorithm for quantifying RSWA was developed and tested against manual scoring. RSWAi showed a 60% correlation between home and lab. RBD detection achieved 83% sensitivity, 79% specificity, and 81% balanced accuracy. The algorithm accurately quantified RSWA, enabling the detection of RBD patients. These findings could facilitate more accessible sleep testing, and provide a possible alternative for screening RBD.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-11"},"PeriodicalIF":12.4,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01354-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A virtual scalable model of the Hepatic Lobule for acetaminophen hepatotoxicity prediction 用于对乙酰氨基酚肝毒性预测的虚拟可扩展肝叶模型

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-28 DOI: 10.1038/s41746-024-01349-5

Stelian Camara Dit Pinto, Jalal Cherkaoui, Debarshi Ghosh, Valentine Cazaubon, Kenza E. Benzeroual, Steven M. Levine, Mohammed Cherkaoui, Gagan K. Sood, Sharmila Anandasabapathy, Sadhna Dhingra, John M. Vierling, Nicolas R. Gallo

引用次数: 0

The path forward for large language models in medicine is open 大型语言模型在医学中的应用前景广阔

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-27 DOI: 10.1038/s41746-024-01344-w

Lars Riedemann, Maxime Labonne, Stephen Gilbert

引用次数: 0

A Primer on Reinforcement Learning in Medicine for Clinicians 临床医生医学强化学习入门指南

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-26 DOI: 10.1038/s41746-024-01316-0

Pushkala Jayaraman, Jacob Desman, Moein Sabounchi, Girish N. Nadkarni, Ankit Sakhuja

引用次数: 0

Artificial Intelligence awarded two Nobel Prizes for innovations that will shape the future of medicine 人工智能荣获两项诺贝尔奖，其创新成果将塑造医学的未来

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-25 DOI: 10.1038/s41746-024-01345-9

Ben Li, Stephen Gilbert

引用次数: 0

Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT 利用深度学习提高空间分辨率，改进基于厚切片 CT 的胸部疾病诊断

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-23 DOI: 10.1038/s41746-024-01338-8

Pengxin Yu, Haoyue Zhang, Dawei Wang, Rongguo Zhang, Mei Deng, Haoyu Yang, Lijun Wu, Xiaoxu Liu, Andrea S. Oh, Fereidoun G. Abtin, Ashley E. Prosper, Kathleen Ruchalski, Nana Wang, Huairong Zhang, Ye Li, Xinna Lv, Min Liu, Shaohong Zhao, Dasheng Li, John M. Hoffman, Denise R. Aberle, Chaoyang Liang, Shouliang Qi, Corey Arnold

{"title":"Spatial resolution enhancement using deep learning improves chest disease diagnosis based on thick slice CT","authors":"Pengxin Yu, Haoyue Zhang, Dawei Wang, Rongguo Zhang, Mei Deng, Haoyu Yang, Lijun Wu, Xiaoxu Liu, Andrea S. Oh, Fereidoun G. Abtin, Ashley E. Prosper, Kathleen Ruchalski, Nana Wang, Huairong Zhang, Ye Li, Xinna Lv, Min Liu, Shaohong Zhao, Dasheng Li, John M. Hoffman, Denise R. Aberle, Chaoyang Liang, Shouliang Qi, Corey Arnold","doi":"10.1038/s41746-024-01338-8","DOIUrl":"10.1038/s41746-024-01338-8","url":null,"abstract":"CT is crucial for diagnosing chest diseases, with image quality affected by spatial resolution. Thick-slice CT remains prevalent in practice due to cost considerations, yet its coarse spatial resolution may hinder accurate diagnoses. Our multicenter study develops a deep learning synthetic model with Convolutional-Transformer hybrid encoder-decoder architecture for generating thin-slice CT from thick-slice CT on a single center (1576 participants) and access the synthetic CT on three cross-regional centers (1228 participants). The qualitative image quality of synthetic and real thin-slice CT is comparable (p = 0.16). Four radiologists’ accuracy in diagnosing community-acquired pneumonia using synthetic thin-slice CT surpasses thick-slice CT (p < 0.05), and matches real thin-slice CT (p > 0.99). For lung nodule detection, sensitivity with thin-slice CT outperforms thick-slice CT (p < 0.001) and comparable to real thin-slice CT (p > 0.05). These findings indicate the potential of our model to generate high-quality synthetic thin-slice CT as a practical alternative when real thin-slice CT is preferred but unavailable.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-14"},"PeriodicalIF":12.4,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01338-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform 用于识别人工智能/机器学习模型可能表现不佳的患者亚群的数据驱动框架

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-21 DOI: 10.1038/s41746-024-01275-6

Adarsh Subbaswamy, Berkman Sahiner, Nicholas Petrick, Vinay Pai, Roy Adams, Matthew C. Diamond, Suchi Saria

{"title":"A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform","authors":"Adarsh Subbaswamy, Berkman Sahiner, Nicholas Petrick, Vinay Pai, Roy Adams, Matthew C. Diamond, Suchi Saria","doi":"10.1038/s41746-024-01275-6","DOIUrl":"10.1038/s41746-024-01275-6","url":null,"abstract":"A fundamental goal of evaluating the performance of a clinical model is to ensure it performs well across a diverse intended patient population. A primary challenge is that the data used in model development and testing often consist of many overlapping, heterogeneous patient subgroups that may not be explicitly defined or labeled. While a model’s average performance on a dataset may be high, the model can have significantly lower performance for certain subgroups, which may be hard to detect. We describe an algorithmic framework for identifying subgroups with potential performance disparities (AFISP), which produces a set of interpretable phenotypes corresponding to subgroups for which the model’s performance may be relatively lower. This could allow model evaluators, including developers and users, to identify possible failure modes prior to wide-scale deployment. We illustrate the application of AFISP by applying it to a patient deterioration model to detect significant subgroup performance disparities, and show that AFISP is significantly more scalable than existing algorithmic approaches.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-11"},"PeriodicalIF":12.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01275-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders 表型驱动的分子遗传测试建议用于诊断儿科罕见疾病

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-11-21 DOI: 10.1038/s41746-024-01331-1

Fangyi Chen, Priyanka Ahimaz, Quan M. Nguyen, Rachel Lewis, Wendy K. Chung, Casey N. Ta, Katherine M. Szigety, Sarah E. Sheppard, Ian M. Campbell, Kai Wang, Chunhua Weng, Cong Liu

{"title":"Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders","authors":"Fangyi Chen, Priyanka Ahimaz, Quan M. Nguyen, Rachel Lewis, Wendy K. Chung, Casey N. Ta, Katherine M. Szigety, Sarah E. Sheppard, Ian M. Campbell, Kai Wang, Chunhua Weng, Cong Liu","doi":"10.1038/s41746-024-01331-1","DOIUrl":"10.1038/s41746-024-01331-1","url":null,"abstract":"Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early use of exome sequencing (ES) or genome sequencing (GS) for conditions like congenital anomalies or developmental delays while still recommend gene panels for patients exhibiting strong manifestations of a specific disease. Recognizing the difficulty in navigating these options, we developed a machine learning model trained on 1005 patient records from Columbia University Irving Medical Center to recommend appropriate genetic tests based on the phenotype information. The model achieved a remarkable performance with an AUROC of 0.823 and AUPRC of 0.918, aligning closely with decisions made by genetic specialists, and demonstrated strong generalizability (AUROC:0.77, AUPRC: 0.816) in an external cohort, indicating its potential value for general pediatricians to expedite rare disease diagnosis by enhancing genetic test ordering.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-12"},"PeriodicalIF":12.4,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01331-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0