| 1 | Type 2 Diabetes Screening Test by Means of a Pulse Oximeter | E. M. Moreno
M. J. A. Luján
M. T. Rusiñol
P. J. Fernández
P. N. Manrique
C. A. Triviño
M. P. Miquel
M. A. Rodr\xadguez
M. J. G. Burguillos | Spain | 2017 |
| 2 | ProPath - A guideline based software for the implementation into the medical environment
| S. Klausner
K. Entacher
S. Kranzer
A. Sönnichsen
M. Flamm
G. Fritsch | Austria | 2014 |
| 3 | Tackling Missing Data in Community Health Studies Using Additive LS-SVM Classifier | G. Wang
Z. Deng
K. S. Choi | China | 2018 |
| 4 | Rapid identification of familial hypercholesterolemia from electronic health records: the SEARCH study | Safarova, Ms
Liu, H
Kullo, Ij | USA | 2016 |
| 5 | A bioinformatics approach to identify patients with symptomatic peanut allergy using peptide microarray immunoassay | Lin, J
Bruni, Fm
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Boner, Al
Gimenez, G
Sampson, Ha | USA | 2012 |
| 6 | Medicine in words and numbers: a cross-sectional survey comparing probability assessment scales | Witteman, Cl
Renooij, S
Koele, P | Netherlands | 2007 |
| 7 | Development of an Automatic Diagnostic Algorithm for Pediatric Otitis Media | Tran, T. T.
Fang, T. Y.
Pham, V. T.
Lin, C.
Wang, P. C.
Lo, M. T. | Taiwan | 2018 |
| 8 | Using natural language processing for identification of herpes zoster ophthalmicus cases to support population-based study | Zheng, C.
Luo, Y.
Mercado, C.
Sy, L.
Jacobsen, S. J.
Ackerson, B.
Lewin, B.
Tseng, H. F. | USA | 2018 |
| 9 | A Machine Learning Recommender System to Tailor Preference Assessments to Enhance Person-Centered Care Among Nursing Home Residents | Gannod, G. C.
Abbott, K. M.
Van Haitsma, K.
Martindale, N.
Heppner, A. | USA | 2018 |
| 10 | A web-based prediction score for head and neck cancer referrals | Lau, K.
Wilkinson, J.
Moorthy, R. | United Kingdom | 2018 |
| 11 | Long-term outcomes of a large, prospective observational cohort of older adults with back pain | Jarvik, J. G.
Gold, L. S.
Tan, K.
Friedly, J. L.
Nedeljkovic, S. S.
Comstock, B. A.
Deyo, R. A.
Turner, J. A.
Bresnahan, B. W.
Rundell, S. D.
James, K. T.
Nerenz, D. R.
Avins, A. L.
Bauer, Z.
Kessler, L.
Heagerty, P. J. | USA | 2018 |
| 12 | Innovative Informatics Approaches for Peripheral Artery Disease: Current State and Provider Survey of Strategies for Improving Guideline-Based Care | Chaudhry, A. P.
Afzal, N.
Abidian, M. M.
Mallipeddi, V. P.
Elayavilli, R. K.
Scott, C. G.
Kullo, I. J.
Wennberg, P. W.
Pankratz, J. J.
Liu, H.
Chaudhry, R.
Arruda-Olson, A. M. | USA | 2018 |
| 13 | A new computational intelligence approach to detect autistic features for autism screening | Thabtah, F.
Kamalov, F.
Rajab, K. | University of Cambridge United Kingdom | 2018 |
| 14 | Chronic obstructive lung disease "expert system": validation of a predictive tool for assisting diagnosis | Braido, F.
Santus, P.
Corsico, A. G.
Di Marco, F.
Melioli, G.
Scichilone, N.
Solidoro, P. | Italy | 2018 |
| 15 | A machine learning based approach to identify protected health information in Chinese clinical text | Du, L.
Xia, C.
Deng, Z.
Lu, G.
Xia, S.
Ma, J. | China | 2018 |
| 16 | Automatic address validation and health record review to identify homeless Social Security disability applicants | Erickson, J.
Abbott, K.
Susienka, L. | USA | 2018 |
| 17 | Quantifying the incidence and burden of herpes zoster in New Zealand general practice: a retrospective cohort study using a natural language processing software inference algorithm | Turner, N. M.
MacRae, J.
Nowlan, M. L.
McBain, L.
Stubbe, M. H.
Dowell, A. | New Zealand | 2018 |
| 18 | Methods for estimating kidney disease stage transition probabilities using electronic medical records | Luo, L.
Small, D.
Stewart, W. F.
Roy, J. A. | USA | 2013 |
| 19 | Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training | Lee, S. I.
Adans-Dester, C. P.
Grimaldi, M.
Dowling, A. V.
Horak, P. C.
Black-Schaffer, R. M.
Bonato, P.
Gwin, J. T. | USA | 2018 |
| 20 | External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches | Morales, D. R.
Flynn, R.
Zhang, J.
Trucco, E.
Quint, J. K.
Zutis, K. | UK | 2018 |
| 21 | Automatic infection detection based on electronic medical records | Tou, H.
Yao, L.
Wei, Z.
Zhuang, X.
Zhang, B. | China | 2018 |
| 22 | Detecting Motor Impairment in Early Parkinson's Disease via Natural Typing Interaction With Keyboards: Validation of the neuroQWERTY Approach in an Uncontrolled At-Home Setting | Arroyo-Gallego, T.
Ledesma-Carbayo, M. J.
Butterworth, I.
Matarazzo, M.
Montero-Escribano, P.
Puertas-Martin, V.
Gray, M. L.
Giancardo, L.
Sanchez-Ferro, A. | Spain | 2018 |
| 23 | Home Health Care: Nurse-Physician Communication, Patient Severity, and Hospital Readmission | Pesko, M. F.
Gerber, L. M.
Peng, T. R.
Press, M. J. | USA | 2018 |
| 24 | Examining Healthcare Utilization Patterns of Elderly Middle-Aged Adults in the United States | Zayas, C. E.
He, Z.
Yuan, J.
Maldonado-Molina, M.
Hogan, W.
Modave, F.
Guo, Y.
Bian, J. | USA | 2016 |
| 25 | Data-based Decision Rules to Personalize Depression Follow-up | Lin, Y.
Huang, S.
Simon, G. E.
Liu, S. | USA | 2018 |
| 26 | A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes | Hertroijs, D. F. L.
Elissen, A. M. J.
Brouwers, Mcgj
Schaper, N. C.
Kohler, S.
Popa, M. C.
Asteriadis, S.
Hendriks, S. H.
Bilo, H. J.
Ruwaard, D. | Netherlands | 2018 |
| 27 | Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records | Kop, R.
Hoogendoorn, M.
Teije, A. T.
Buchner, F. L.
Slottje, P.
Moons, L. M.
Numans, M. E. | Netherlands | 2016 |
| 28 | Natural language processing improves identification of colorectal cancer testing in the electronic medical record | Denny, J. C.
Choma, N. N.
Peterson, J. F.
Miller, R. A.
Bastarache, L.
Li, M.
Peterson, N. B. | USA | 2012 |
| 29 | Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis | Zhou, S. M.
Fernandez-Gutierrez, F.
Kennedy, J.
Cooksey, R.
Atkinson, M.
Denaxas, S.
Siebert, S.
Dixon, W. G.', "O'Neill, T. W.", 'Choy, E.
Sudlow, C.
U. K. Biobank Follow-up,Outcomes
Group,Brophy | UK | 2016 |
| 30 | Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer | Hoogendoorn, M.
Szolovits, P.
Moons, L. M. G.
Numans, M. E. | Netherlands | 2016 |
| 31 | Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization | Huang, D.
Wu, Z. | China | 2017 |
| 32 | Comparison of the Effectiveness of Interactive Didactic Lecture Versus Online Simulation-Based CME Programs Directed at Improving the Diagnostic Capabilities of Primary Care Practitioners | McFadden, P.
Crim, A. | USA | 2016 |
| 33 | Modelling and extraction of variability in free-text medication prescriptions from an anonymised primary care electronic medical record research database | Karystianis, G.
Sheppard, T.
Dixon, W. G.
Nenadic, G. | UK | 2016 |
| 34 | Identifying acute kidney injury in the community - a novel informatics approach | Xu, G.
Player, P.
Shepherd, D.
Brunskill, N. J. | UK | 2016 |
| 35 | Identifying influenza-like illness presentation from unstructured general practice clinical narrative using a text classifier rule-based expert system versus a clinical expert | MacRae, J.
Love, T.
Baker, M. G.
Dowell, A.
Carnachan, M.
Stubbe, M.
McBain, L. | New Zealand | 2015 |
| 36 | Negative symptoms in schizophrenia: a study in a large clinical sample of patients using a novel automated method | Patel, R.
Jayatilleke, N.
Broadbent, M.
Chang, C. K.
Foskett, N.
Gorrell, G.
Hayes, R. D.
Jackson, R.
Johnston, C.
Shetty, H.
Roberts, A.
McGuire, P.
Stewart, R. | UK | 2015 |
| 37 | Accessing primary care Big Data: the development of a software algorithm to explore the rich content of consultation records | MacRae, J.
Darlow, B.
McBain, L.
Jones, O.
Stubbe, M.
Turner, N.
Dowell, A. | New Zealand | 2015 |
| 38 | Automatic Detection of Skin and Subcutaneous Tissue Infections from Primary Care Electronic Medical Records | Gu, Y.
Kennelly, J.
Warren, J.
Nathani, P.
Boyce, T. | New Zealand | 2015 |
| 39 | Communication Between Home Health Nurses and Physicians: Measurement, Quality, and Outcomes | Press, M. J.
Gerber, L. M.
Peng, T. R.
Pesko, M. F.
Feldman, P. H.
Ouchida, K.
Sridharan, S.
Bao, Y.
Barron, Y.
Casalino, L. P. | USA | 2015 |
| 40 | Regular expression-based learning to extract bodyweight values from clinical notes | Murtaugh, M. A.
Gibson, B. S.
Redd, D.
Zeng-Treitler, Q. | USA | 2015 |
| 41 | Monitoring suicidal patients in primary care using electronic health records | Anderson, H. D.
Pace, W. D.
Brandt, E.
Nielsen, R. D.
Allen, R. R.
Libby, A. M.
West, D. R.
Valuck, R. J. | USA | 2015 |
| 42 | Measuring physician adherence with gout quality indicators: a role for natural language processing | Kerr, G. S.
Richards, J. S.
Nunziato, C. A.
Patterson, O. V.
DuVall, S. L.
Aujero, M.
Maron, D.
Amdur, R. | USA | 2015 |
| 43 | Prevalence of heart failure signs and symptoms in a large primary care population identified through the use of text and data mining of the electronic health record | Vijayakrishnan, R.
Steinhubl, S. R.
Ng, K.
Sun, J.
Byrd, R. J.
Daar, Z.
Williams, B. A.
eFilippi, C.
Ebadollahi, S.
Stewart, W. F. | USA | 2014 |
| 44 | Multilevel temporal Bayesian networks can model longitudinal change in multimorbidity | Lappenschaar, M.
Hommersom, A.
Lucas, P. J.
Lagro, J.
Visscher, S.
Korevaar, J. C.
Schellevis, F. G. | Netherlands | 2013 |
| 45 | Patient-tailored prioritization for a pediatric care decision support system through machine learning | Klann, J. G.
Anand, V.
Downs, S. M. | USA | 2013 |
| 46 | Automatic generation of case-detection algorithms to identify children with asthma from large electronic health record databases | Afzal, Z.
Engelkes, M.
Verhamme, K. M.
Janssens, H. M.
Sturkenboom, M. C.
Kors, J. A.
Schuemie, M. J. | Netherlands | 2013 |
| 47 | The use of data-mining to identify indicators of health-related quality of life in patients with irritable bowel syndrome | Penny, K. I.
Smith, G. D. | UK | 2012 |
| 48 | Using Medical Text Extraction, Reasoning and Mapping System (MTERMS) to process medication information in outpatient clinical notes | Zhou, L.
Plasek, J. M.
Mahoney, L. M.
Karipineni, N.
Chang, F.
Yan, X.
Chang, F.
Dimaggio, D.
Goldman, D. S.
Rocha, R. A. | USA | 2011 |
| 49 | Application of artificial neural networks to a study of nursing burnout | Ladstatter, F.
Garrosa, E.
Badea, C.
Moreno, B. | Spain | 2010 |
| 50 | Data mining of tuberculosis patient data using multiple correspondence analysis | Rennie, T. W.
Roberts, W. | UK | 2019 |
| 51 | Agreement between patient-reported symptoms and their documentation in the medical record | Pakhomov, S. V.
Jacobsen, S. J.
Chute, C. G.
Roger, V. L. | USA | 2008 |
| 52 | An expert system for headache diagnosis: the Computerized Headache Assessment tool (CHAT) | Maizels, M.
Wolfe, W. J. | USA | 2008 |
| 53 | The K-nearest neighbor algorithm predicted rehabilitation potential better than current Clinical Assessment Protocol | Zhu, M.
Chen, W.
Hirdes, J. P.
Stolee, P. | Canada | 2007 |
| 54 | Neural networks for longitudinal studies in Alzheimer's disease | Tandon, R.
Adak, S.
Kaye, J. A. | India | 2006 |
| 55 | Intention to adopt a smoking cessation expert system within a self-selected sample of Dutch general practitioners | Hoving, C.
Mudde, A. N.
e Vries, H. | Netherlands | 2006 |
| 56 | Translating research into practice: organizational issues in implementing automated decision support for hypertension in three medical centers | Goldstein, M. K.
Coleman, R. W.
Tu, S. W.
Shankar, R. D.', "O'Connor, M. J.", 'Musen, M. A.
Martins, S. B.
Lavori, P. W.
Shlipak, M. G.
Oddone, E.
Advani, A. A.
Gholami, P.
Hoffman, B. B. | USA | 2004 |
| 57 | Using an artificial neural network to predict healing times and risk factors for venous leg ulcers | Taylor, R. J.
Taylor, A. D.
Smyth, J. V. | UK | 2002 |
| 58 | Validation of a knowledge based reminder system for diagnostic test ordering in general practice | Bindels, R.
Winkens, R. A.
Pop, P.
van Wersch, J. W.
Talmon, J.
Hasman, A. | Netherlands | 2001 |
| 59 | The use of a computer-based decision support system facilitates primary care physicians' management of chronic pain | Knab, J. H.
Wallace, M. S.
Wagner, R. L.
Tsoukatos, J.
Weinger, M. B. | USA | 2001 |
| 60 | Initial use of a computer system for assisting dermatological diagnosis in general practice | Smith, H. R.
Ashton, R. E.
Brooks, G. J. | UK | 2000 |
| 61 | Electronic surveillance of disease states: a preliminary study in electronic detection of respiratory diseases in a primary care setting | Hung, J.
Posey, J.
Freedman, R.
Thorton, T. | USA | 1998 |
| 62 | Modeling obesity using abductive networks | Abdel-Aal, R. E.
Mangoud, A. M. | UAE | 1997 |
| 63 | A diagnostic support system in general practice: is it feasible? | Ridderikhoff, J.
van Herk, E. | Netherlands | 1997 |
| 64 | Comparison of an expert system with other clinical scores for the evaluation of severity of asthma | Gautier, V.
Redier, H.
Pujol, J. L.
Bousquet, J.
Proudhon, H.
Michel, C.
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Michel, F. B.
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| 65 | An expert system for performance-based direct delivery of published clinical evidence | Balas, E. A.
Li, Z. R.
Spencer, D. C.
Jaffrey, F.
Brent, E.
Mitchell, J. A. | USA | 1996 |
| 66 | Categorization of major depression in an outpatient sample | Haslam, N.
Beck, A. T. | USA | 1993 |
| 67 | Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables | Jordan, P.
Shedden-Mora, M. C.
Löwe, B. | Germany | 2018 |
| 68 | Predicting out-of-office blood pressure in the clinic for the diagnosis of hypertension in primary care: An economic evaluation | Monahan, M.
Jowett, S.
Lovibond, K.
Gill, P.
Godwin, M.
Greenfield, S.
Hanley, J.
Hobbs, F. D. R.
Martin, U.
Mant, J.
McKinstry, B.
Williams, B.
Sheppard, J. P.
McManus, R. J. | UK | 2018 |
| 69 | Artificial neural network based prediction of malaria abundances using big data: A knowledge capturing approach | Thakur, S.
Dharavath, R. | India | 2018 |
| 70 | On an algorithm for decision-making for the optimization of disease prediction at the primary health care level using neural network clustering | Selskyy, P.
Vakulenko, D.
Televiak, A.
Veresiuk, T. | Ukraine | 2018 |
| 71 | Machine Learning Detection of Cognitive Impairment in Primary Care | Levy, B.
Hogan, J.
Hess, C.
Greenspan, S.
Hogan, M.
Gable, S.
Falcon, K.
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Hashmi, A. | USA | 2018 |
| 72 | Development and validation of clinical prediction models: Marginal differences between logistic regression, penalized maximum likelihood estimation, and genetic programming | Janssen, K. J. M.
Siccama, I.
Vergouwe, Y.
Koffijberg, H.
Debray, T. P. A.
Keijzer, M.
Grobbee, D. E.
Moons, K. G. M. | Netherlands | 2012 |
| 73 | A hybrid knowledge-based approach to supporting the medical prescription for general practitioners: Real case in a Hong Kong medical center | Ting, S. L.
Kwok, S. K.
Tsang, A. H. C.
Lee, W. B. | China | 2011 |
| 74 | RACER: Rule-Associated CasE-based Reasoning for supporting General Practitioners in prescription making | Ting, S. L.
Wang, W. M.
Kwok, S. K.
Tsang, A. H. C.
Lee, W. B. | Hong kong | 2010 |
| 75 | THE POTENTIAL FOR COMPUTER-AIDED DIAGNOSIS OF TROPICAL DISEASES IN DEVELOPING-COUNTRIES - AN EXPERT SYSTEM CASE-STUDY | Doukidis, G. I.
Forster, D. | UK | 1990 |
| 76 | Applying a human-factors approach to improve usability of a decision support system in tele-nursing
| Tariq, Amina
Westbrook, Johanna
Byrne, Mary
Robinson, Maureen
Baysari, Melissa T. | Australia | 2017 |
| 77 | Simple Prediction of Type 2 Diabetes Mellitus via Decision Tree Modeling | Sayadi, Mehrab
Zibaeenezhad, Mohammadjavad
Taghi Ayatollahi, Seyyed Mohammad | Iran | 2017 |
| 78 | An annotation and modeling schema for prescription regimens | Aberdeen, J.
Bayer, S.
Clark, C.
Keybl, M.
Tresner-Kirsch, D. | USA | 2019 |
| 79 | Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices | Abramoff, M. D.
Lavin, P. T.
Birch, M.
Shah, N.
Folk, J. C. | USA | 2018 |
| 80 | TOWARD EVIDENCE-BASED PRACTICE. Predicting Common Maternal Postpartum Complications: Leveraging Health Administrative Data and Machine Learning | Adams, Ellise | Australia | 2019 |
| 81 | Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach | Ben-Sasson, Ayelet
Robins, Diana L.
Yom-Tov, Elad | Israel | 2018 |
| 82 | AUTOMATIC DIAGNOSIS OF RHEUMATOID ARTHRITIS FROM HAND RADIOGRAPHS USING CONVOLUTIONAL NEURAL NETWORKS | Betancourt-Hernandez, M.
Viera-Lopez, G.
Serrano-Munoz, A. | Cuba | 2018 |
| 83 | Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections | Burton, R. J.
Albur, M.
Eberl, M.
Cuff, S. M. | UK | 2019 |
| 84 | Design of a Clinical Decision Support System for Predicting Erectile Dysfunction in Men Using NHIRD Dataset | Chen, Y. F.
Lin, C. S.
Hong, C. F.
Lee, D. J.
Sun, C.
Lin, H. H. | China | 2019 |
| 85 | Predicting atrial fibrillation in primary care using machine learning | Hill, N. R.
Ayoubkhani, D.
McEwan, P.
Sugrue, D. M.
Farooqui, U.
Lister, S.
Lumley, M.
Bakhai, A.
Cohen, A. T.', "O'Neill, M.", 'Clifton, D.
Gordon, J. | UK | 2019 |
| 86 | Evaluation of Artificial Intelligence-Based Grading of Diabetic Retinopathy in Primary Care | Kanagasingam, Y.
Xiao, D.
Vignarajan, J.
Preetham, A.
Tay-Kearney, M. L.
Mehrotra, A. | Australia | 2018 |
| 87 | Prognostic Modeling and Prevention of Diabetes Using Machine Learning Techniques | Perveen, S.
Shahbaz, M.
Keshavjee, K.
Guergachi, A. | Canada | 2019 |
| 88 | Scoring algorithms for a computer-based cognitive screening tool: An illustrative example of overfitting machine learning approaches and the impact on estimates of classification accuracy | Ursenbach, J.', "O'Connell, M. E.", 'Neiser, J.
Tierney, M. C.
Morgan, D.
Kosteniuk, J.
Spiteri, R. J. | Canada | 2019 |
| 89 | Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting | Verbraak, F. D.
Abramoff, M. D.
Bausch, G. C. F.
Klaver, C.
Nijpels, G.
Schlingemann, R. O.
van der Heijden, A. A. | Netherlands | 2019 |
| 90 | Does machine learning improve prediction of VA primary care reliance? | Wong, E. S.
Schuttner, L.
Reddy, A. | USA | 2020 |
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