Search Results - "BMC medical informatics and decision making"

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective by Amann, Julia, Blasimme, Alessandro, Vayena, Effy, Frey, Dietmar, Madai, Vince I

“…Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven…”
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Comparing different supervised machine learning algorithms for disease prediction by Uddin, Shahadat, Khan, Arif, Hossain, Md Ekramul, Moni, Mohammad Ali

“…Supervised machine learning algorithms have been a dominant method in the data mining field. Disease prediction using health data has recently shown a…”
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The role of artificial intelligence in healthcare: a structured literature review by Secinaro, Silvana, Calandra, Davide, Secinaro, Aurelio, Muthurangu, Vivek, Biancone, Paolo

“…Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated…”
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Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone by Chicco, Davide, Jurman, Giuseppe

“…Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart…”
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ICD-11: an international classification of diseases for the twenty-first century by Harrison, James E, Weber, Stefanie, Jakob, Robert, Chute, Christopher G

“…The International Classification of Diseases (ICD) has long been the main basis for comparability of statistics on causes of mortality and morbidity between…”
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Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives by Esmaeilzadeh, Pouyan

“…Several studies highlight the effects of artificial intelligence (AI) systems on healthcare delivery. AI-based tools may improve prognosis, diagnostics, and…”
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A data-driven approach to predicting diabetes and cardiovascular disease with machine learning by Dinh, An, Miertschin, Stacey, Young, Amber, Mohanty, Somya D

“…Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the…”
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Cognitive biases associated with medical decisions: a systematic review by Saposnik, Gustavo, Redelmeier, Donald, Ruff, Christian C, Tobler, Philippe N

“…Cognitive biases and personality traits (aversion to risk or ambiguity) may lead to diagnostic inaccuracies and medical errors resulting in mismanagement or…”
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Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system by Ancker, Jessica S, Edwards, Alison, Nosal, Sarah, Hauser, Diane, Mauer, Elizabeth, Kaushal, Rainu

“…Although alert fatigue is blamed for high override rates in contemporary clinical decision support systems, the concept of alert fatigue is poorly defined. We…”
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Nearest neighbor imputation algorithms: a critical evaluation by Beretta, Lorenzo, Santaniello, Alessandro

“…Nearest neighbor (NN) imputation algorithms are efficient methods to fill in missing data where each missing value on some records is replaced by a value…”
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Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies by O'Connor, Siobhan, Hanlon, Peter, O'Donnell, Catherine A, Garcia, Sonia, Glanville, Julie, Mair, Frances S

“…Numerous types of digital health interventions (DHIs) are available to patients and the public but many factors affect their ability to engage and enrol in…”
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Predicting factors for survival of breast cancer patients using machine learning techniques by Ganggayah, Mogana Darshini, Taib, Nur Aishah, Har, Yip Cheng, Lio, Pietro, Dhillon, Sarinder Kaur

“…Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these…”
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Improving palliative care with deep learning by Avati, Anand, Jung, Kenneth, Harman, Stephanie, Downing, Lance, Ng, Andrew, Shah, Nigam H

“…Access to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care…”
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On the interpretability of machine learning-based model for predicting hypertension by Elshawi, Radwa, Al-Mallah, Mouaz H, Sakr, Sherif

“…Although complex machine learning models are commonly outperforming the traditional simple interpretable models, clinicians find it hard to understand and…”
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Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis by Li, Wei Tse, Ma, Jiayan, Shende, Neil, Castaneda, Grant, Chakladar, Jaideep, Tsai, Joseph C, Apostol, Lauren, Honda, Christine O, Xu, Jingyue, Wong, Lindsay M, Zhang, Tianyi, Lee, Abby, Gnanasekar, Aditi, Honda, Thomas K, Kuo, Selena Z, Yu, Michael Andrew, Chang, Eric Y, Rajasekaran, Mahadevan Raj, Ongkeko, Weg M

“…The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage…”
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Challenges of Telemedicine during the COVID-19 pandemic: a systematic review by Ftouni, Racha, AlJardali, Baraa, Hamdanieh, Maya, Ftouni, Louna, Salem, Nariman

“…The COVID-19 pandemic has prompted the decrease of in-person visits to reduce the risk of virus transmission. Telemedicine is an efficient communication tool…”
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Combining structured and unstructured data for predictive models: a deep learning approach by Zhang, Dongdong, Yin, Changchang, Zeng, Jucheng, Yuan, Xiaohui, Zhang, Ping

“…The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in…”
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How the public uses social media wechat to obtain health information in china: a survey study by Zhang, Xingting, Wen, Dong, Liang, Jun, Lei, Jianbo

“…On average, 570 million users, 93% in China's first-tier cities, log on to WeChat every day. WeChat has become the most widely and frequently used social media…”
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Ethics and governance of trustworthy medical artificial intelligence by Zhang, Jie, Zhang, Zong-Ming

“…The growing application of artificial intelligence (AI) in healthcare has brought technological breakthroughs to traditional diagnosis and treatment, but it is…”
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A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data by Golas, Sara Bersche, Shibahara, Takuma, Agboola, Stephen, Otaki, Hiroko, Sato, Jumpei, Nakae, Tatsuya, Hisamitsu, Toru, Kojima, Go, Felsted, Jennifer, Kakarmath, Sujay, Kvedar, Joseph, Jethwani, Kamal

“…Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of…”
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