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JOURNAL ARTICLE
Leeran Talker, Cihan Dogan, Daniel Neville, Rui Hen Lim, Henry Broomfield, Gabriel Lambert, Ahmed Selim, Thomas Brown, Laura Wiffen, Julian Carter, Helen F Ashdown, Gail Hayward, Elango Vijaykumar, Scott T Weiss, Anoop Chauhan, Ameera X Patel
INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study's aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2 ) breath records captured with TidalSense's N-TidalTM capnometer. METHOD: For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1-4) and 705 non-COPD participants...
December 2024: COPD
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JOURNAL ARTICLE
Shengling Ma, Jennifer La, Kaitlin N Swinnerton, Danielle Guffey, Raka Bandyo, Giordana De Las Pozas, Katy Hanzelka, Xiangjun Xiao, Cristhiam M Rojas-Hernandez, Christopher I Amos, Vipul Chitalia, Katya Ravid, Kelly W Merriman, Christopher R Flowers, Nathanael Fillmore, Ang Li
Venous thromboembolism (VTE) poses a significant risk to cancer patients receiving systemic therapy. The generalizability of pan-cancer models to lymphomas is limited. Currently, there are no reliable risk prediction models for thrombosis in patients with lymphoma. Our objective was to create a risk assessment model (RAM) specifically for lymphomas. We performed a retrospective cohort study to develop Fine and Gray sub-distribution hazard model for VTE and pulmonary embolism (PE)/ lower extremity deep vein thrombosis (LE-DVT) respectively in adult lymphoma patients from the Veterans Affairs national healthcare system (VA)...
April 23, 2024: American Journal of Hematology
#3
JOURNAL ARTICLE
Astrid Helene Deleuran Naesager, Sofie Norgil Damgaard, Maarten Pieter Rozing, Volkert Siersma, Anne Møller, Katrine Tranberg
INTRODUCTION: People with severe mental illness (SMI) face a higher risk of premature mortality due to physical morbidity compared to the general population. Establishing regular contact with a general practitioner (GP) can mitigate this risk, yet barriers to healthcare access persist. Population initiatives to overcome these barriers require efficient identification of those persons in need. OBJECTIVE: To develop a predictive model to identify persons with SMI not attending a GP regularly...
April 23, 2024: BMC Psychiatry
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JOURNAL ARTICLE
Barzany Ridha, Nigel Hey, Lauren Ritchie, Ryan Toews, Zachary Turcotte, Brad Jamison
BACKGROUND: Lower gastrointestinal bleeding (LGIB) is a common reason for emergency department visits and subsequent hospitalizations. Recent data suggests that low-risk patients may be safely evaluated as an outpatient. Recommendations for healthcare systems to identify low-risk patients who can be safely discharged with timely outpatient follow-up have yet to be established. The primary objective of this study was to determine the role of patient predictors for the patients with LGIB to receive urgent endoscopic intervention...
April 23, 2024: BMC Emergency Medicine
#5
JOURNAL ARTICLE
Alexander S Young, Jessica Skela, Prabha Siddarth
Many individuals with serious mental illness are at high risk for hospitalization or death due to inadequate treatment of medical conditions or unhealthy behaviors. The authors describe demographic and clinical characteristics associated with increased risk in this population. Electronic data were obtained for individuals in treatment at a large Veterans' healthcare system who were at high risk according to a validated model. A random sample of these individuals was assessed in person. Multivariable regressions estimated the effect of numerous demographic, health, and clinical characteristics on risk...
April 23, 2024: Community Mental Health Journal
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JOURNAL ARTICLE
Jie Chen, Nanxiao Ren, Aifeng Meng, Tiantian Wang, Yamei Bai, Ying Xu, Xiaoli Li, Xiaoxu Zhi
OBJECTIVE: To investigate the trajectories and potential categories of changes in the sense of coherence (SOC) in patients after colorectal cancer surgery and to analyze predictive factors. METHODS: From January to July 2023, 175 patients with colorectal cancer treated at a tertiary Grade A oncology hospital in Jiangsu Province were selected as the study subjects. Prior to surgery, SOC-13 scale, Patient-Generated Subjective Global Assessment (PG-SGA), Brief Illness Perception Questionnaire (BIPQ), and Social Support Rating Scale (SSRS) were used to survey the patients...
April 23, 2024: Supportive Care in Cancer
#7
JOURNAL ARTICLE
Mouhamed Yazan Abou-Ismail, Chong Zhang, Angela P Presson, Shruti Chaturvedi, Ana G Antun, Andrew M Farland, Ryan Woods, Ara Metjian, Yara A Park, Gustaaf de Ridder, Briana Gibson, Raj S Kasthuri, Darla K Liles, Frank Akwaa, Todd Clover, Lisa Baumann Kreuziger, Meera Sridharan, Ronald S Go, Keith R McCrae, Harsh Vardhan Upreti, Radhika Gangaraju, Nicole K Kocher, X Long Zheng, Jay S Raval, Camila Masias, Spero R Cataland, Andrew D Johnson, Elizabeth Davis, Michael D Evans, Marshall Mazepa, Ming Y Lim
BACKGROUND: Mortality due to immune-mediated thrombotic thrombocytopenic purpura (iTTP) remains significant. Predicting mortality risk may potentially help individualize treatment. The French Thrombotic Microangiopathy (TMA) Reference Score has not been externally validated in the United States. Recent advances in machine learning technology can help analyze large numbers of variables with complex interactions for the development of prediction models. OBJECTIVES: To validate the French TMA Reference Score in the United States Thrombotic Microangiopathy (USTMA) iTTP database and subsequently develop a novel mortality prediction tool, the USTMA TTP Mortality Index...
March 2024: Research and Practice in Thrombosis and Haemostasis
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JOURNAL ARTICLE
Vijay Ravi, Jinhan Wang, Jonathan Flint, Abeer Alwan
The proposed method focuses on speaker disentanglement in the context of depression detection from speech signals. Previous approaches require patient/speaker labels, encounter instability due to loss maximization, and introduce unnecessary parameters for adversarial domain prediction. In contrast, the proposed unsupervised approach reduces cosine similarity between latent spaces of depression and pre-trained speaker classification models. This method outperforms baseline models, matches or exceeds adversarial methods in performance, and does so without relying on speaker labels or introducing additional model parameters, leading to a reduction in model complexity...
February 2024: CEUR Workshop Proceedings
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JOURNAL ARTICLE
Kuang-Ming Kuo, Yih-Lon Lin, Chao Sheng Chang, Tin Ju Kuo
OBJECTIVE: The healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning's potential to predict ED dispositions, thus leading to promising substantial benefits. This study's objective is to create a predictive model for ED patient dispositions by employing ensemble learning. It harnesses diverse data types, including structured and unstructured information gathered during ED visits to address the evolving needs of localized healthcare systems...
April 22, 2024: BMC Medical Informatics and Decision Making
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JOURNAL ARTICLE
Rebekah L Bowman, Jan Taylor, Deborah L Davis
BACKGROUND: Raspberry leaf use during pregnancy in Australia is widespread. There has been little research exploring the potential beneficial or harmful effects of raspberry leaf on pregnancy, labour, and birth. More research is needed to appropriately inform childbearing women and maternity healthcare professionals on the effects of raspberry leaf so that women can make informed choices. METHODS: This study aimed to determine associations between raspberry leaf use in pregnancy and augmentation of labour and other secondary outcomes...
April 22, 2024: BMC complementary medicine and therapies
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JOURNAL ARTICLE
Michal Kasher Meron, Sapir Eizenstein, Tali Cukierman-Yaffe, Dan Oieru
OBJECTIVE: To investigate whether individuals with an elevated BMI measurement, for whom a diagnosis of overweight or obesity (OW/OB) is not recorded, are less likely to be offered clinical care for obesity compared to those with a recorded diagnosis. SUBJECTS: A retrospective cohort study using the electronic medical record database of Maccabi Healthcare Services (MHS) in Israel. Included were 200,000 adults with BMI ≥ 25 kg/m2 measurement recorded during a primary care visit between 2014 and 2020, and no prior diagnosis of OW/OB or related co-morbidities...
April 22, 2024: International Journal of Obesity
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JOURNAL ARTICLE
Macarena Peña-Y-Lillo
Fruit and vegetable intake is essential for health, but global adherence to recommended levels remains insufficient. Health information exposure positively influences consumption, yet the underlying mechanisms are not fully understood. This study aims to explore the relationships between information seeking and scanning, attitudes, norms, perceived behavioral control (PBC), intentions, and fruit and vegetable intake, following the main tenets of the Integrative Model of Behavioral Prediction (IM). Data were collected through face-to-face surveys in Santiago, Chile, with a representative sample of individuals aged 25 and older in two waves...
April 22, 2024: Journal of Health Communication
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JOURNAL ARTICLE
Ioannis Prassas, Blaise Clarke, Timothy Youssef, Juliana Phlamon, Lampros Dimitrakopoulos, Andrew Rofaeil, George M Yousef
The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health...
April 23, 2024: Clinical Chemistry and Laboratory Medicine: CCLM
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Alexander J Didier, Anthony Nigro, Zaid Noori, Mohamed A Omballi, Scott M Pappada, Danae M Hamouda
INTRODUCTION: Machine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer. METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement...
2024: Frontiers in artificial intelligence
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EDITORIAL
Zukhruf Zain, Mohammed Khaleel I Kh Almadhoun, Lara Alsadoun, Syed Faqeer Hussain Bokhari
Enhanced recovery after surgery (ERAS) protocols have transformed perioperative care by implementing evidence-based strategies to hasten patient recovery, decrease complications, and shorten hospital stays. However, challenges such as inconsistent adherence and the need for personalized adjustments persist, prompting exploration into innovative solutions. The emergence of artificial intelligence (AI) and machine learning (ML) offers a promising avenue for optimizing ERAS protocols. While ERAS emphasizes preoperative optimization, minimally invasive surgery (MIS), and standardized postoperative care, challenges such as adherence variability and resource constraints impede its effectiveness...
March 2024: Curēus
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JOURNAL ARTICLE
Valdeoso Patterson, Anna Olsavsky, Dana Garcia, Malcolm Sutherland-Foggio, Kathryn Vannatta, Kemar V Prussien, Heather Bemis, Bruce E Compas, Cynthia A Gerhardt
BACKGROUND: While most research has largely focused on medical risks associated with reduced health-related quality of life (HRQOL) in survivors, sociodemographic and family factors may also play a role. Thus, we longitudinally examined sociodemographic factors and family factors associated with survivor HRQOL, including adolescent's cancer-specific stress, mother's general stress, and mother-adolescent communication. METHODS: Mothers (N = 80) and survivors (ages 10-23, N = 50) were assessed 5 years following initial diagnosis...
April 21, 2024: Pediatric Blood & Cancer
#17
JOURNAL ARTICLE
Yue Xiao, Yanfei Chen, Ruijian Huang, Feng Jiang, Jifang Zhou, Tianchi Yang
BACKGROUND: The objective of this research was to create and validate an interpretable prediction model for drug-induced liver injury (DILI) during tuberculosis (TB) treatment. METHODS: A dataset of TB patients from Ningbo City was used to develop models employing the eXtreme Gradient Boosting (XGBoost), random forest (RF), and the least absolute shrinkage and selection operator (LASSO) logistic algorithms. The model's performance was evaluated through various metrics, including the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPR) alongside the decision curve...
April 20, 2024: BMC Medical Research Methodology
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JOURNAL ARTICLE
Martin C Tammemägi, Gail E Darling, Heidi Schmidt, Meghan J Walker, Deanna Langer, Yvonne W Leung, Kathy Nguyen, Beth Miller, Diego Llovet, William K Evans, Daniel N Buchanan, Gabriela Espino-Hernandez, Usman Aslam, Amanda Sheppard, Aisha Lofters, Micheal McInnis, Julian Dobranowski, Steven Habbous, Christian Finley, Marianne Luettschwager, Erin Cameron, Caroline Bravo, Anna Banaszewska, Katherin Creighton-Taylor, Brenda Fernandes, Julia Gao, Alex Lee, Van Lee, Bogdan Pylypenko, Monica Yu, Erin Svara, Shivali Kaushal, Lynda MacNiven, Caitlin McGarry, Lauren Della Mora, Liz Koen, Jessica Moffatt, Michelle Rey, Marta Yurcan, Laurie Bourne, Gillian Bromfield, Melissa Coulson, Rebecca Truscott, Linda Rabeneck
Globally, lung cancer is the leading cause of cancer death. Previous trials demonstrated that low-dose computed tomography lung cancer screening of high-risk individuals can reduce lung cancer mortality by 20% or more. Lung cancer screening has been approved by major guidelines in the United States, and over 4,000 sites offer screening. Adoption of lung screening outside the United States has, until recently, been slow. Between June 2017 and May 2019, the Ontario Lung Cancer Screening Pilot successfully recruited 7,768 individuals at high risk identified by using the PLCOm2012noRace lung cancer risk prediction model...
April 2024: Nature Medicine
#19
JOURNAL ARTICLE
Qinghua Zhang, Jinyu Yin, Yujie Wang, Li Song, Tongtong Liu, Shengguang Cheng, Siyi Shang
PURPOSE: To identify the predictors of infectious disease-specific health literacy (IDSHL), and establish an easy-to-apply nomogram to predict the IDSHL of older adults. METHODS: This cross-sectional study included 380 older adults who completed the IDSHL, self-rated health, sociodemographic and other questionnaires. Logistic regression was used to identify the IDSHL predictors. Nomogram was used to construct a predictive model. RESULTS: Up to 70...
April 17, 2024: Asian Nursing Research
#20
JOURNAL ARTICLE
Gerardo Loza, Pietro Valdastri, Sharib Ali
Real-time detection of surgical tools in laparoscopic data plays a vital role in understanding surgical procedures, evaluating the performance of trainees, facilitating learning, and ultimately supporting the autonomy of robotic systems. Existing detection methods for surgical data need to improve processing speed and high prediction accuracy. Most methods rely on anchors or region proposals, limiting their adaptability to variations in tool appearance and leading to sub-optimal detection results. Moreover, using non-anchor-based detectors to alleviate this problem has been partially explored without remarkable results...
2024: Healthcare Technology Letters
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