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Cohen AM, Kaner J, Miller R, Kopesky JW, Hersh W. Automatically pre-screening patients for the rare disease aromatic l-amino acid decarboxylase deficiency using knowledge engineering, natural language processing, and machine learning on a large EHR population. J Am Med Inform Assoc 2024; 31:692-704. [PMID: 38134953 PMCID: PMC10873832 DOI: 10.1093/jamia/ocad244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/28/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
OBJECTIVES Electronic health record (EHR) data may facilitate the identification of rare diseases in patients, such as aromatic l-amino acid decarboxylase deficiency (AADCd), an autosomal recessive disease caused by pathogenic variants in the dopa decarboxylase gene. Deficiency of the AADC enzyme results in combined severe reductions in monoamine neurotransmitters: dopamine, serotonin, epinephrine, and norepinephrine. This leads to widespread neurological complications affecting motor, behavioral, and autonomic function. The goal of this study was to use EHR data to identify previously undiagnosed patients who may have AADCd without available training cases for the disease. MATERIALS AND METHODS A multiple symptom and related disease annotated dataset was created and used to train individual concept classifiers on annotated sentence data. A multistep algorithm was then used to combine concept predictions into a single patient rank value. RESULTS Using an 8000-patient dataset that the algorithms had not seen before ranking, the top and bottom 200 ranked patients were manually reviewed for clinical indications of performing an AADCd diagnostic screening test. The top-ranked patients were 22.5% positively assessed for diagnostic screening, with 0% for the bottom-ranked patients. This result is statistically significant at P < .0001. CONCLUSION This work validates the approach that large-scale rare-disease screening can be accomplished by combining predictions for relevant individual symptoms and related conditions which are much more common and for which training data is easier to create.
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Affiliation(s)
- Aaron M Cohen
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
| | - Jolie Kaner
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
| | - Ryan Miller
- PTC Therapeutics, South Plainfield, NJ 07080, United States
| | | | - William Hersh
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR 97239, United States
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Šafran V, Lin S, Nateqi J, Martin AG, Smrke U, Ariöz U, Plohl N, Rojc M, Bēma D, Chávez M, Horvat M, Mlakar I. Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST). SENSORS (BASEL, SWITZERLAND) 2024; 24:1101. [PMID: 38400259 PMCID: PMC10892413 DOI: 10.3390/s24041101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024]
Abstract
The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app's usability as above satisfactory (i.e., 7.9 on 1-10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1-10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.
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Affiliation(s)
- Valentino Šafran
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
| | - Simon Lin
- Science Department, Symptoma GmbH, 1030 Vienna, Austria (A.G.M.)
- Department of Internal Medicine, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Jama Nateqi
- Science Department, Symptoma GmbH, 1030 Vienna, Austria (A.G.M.)
- Department of Internal Medicine, Paracelsus Medical University, 5020 Salzburg, Austria
| | | | - Urška Smrke
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
| | - Umut Ariöz
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
| | - Nejc Plohl
- Department of Psychology, Faculty of Arts, University of Maribor, 2000 Maribor, Slovenia;
| | - Matej Rojc
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
| | - Dina Bēma
- Institute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia;
| | - Marcela Chávez
- Department of Information System Management, Centre Hospitalier Universitaire de Liège, 4000 Liège, Belgium;
| | - Matej Horvat
- Department of Oncology, University Medical Centre Maribor, 2000 Maribor, Slovenia;
| | - Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia; (V.Š.); (U.S.); (U.A.); (M.R.)
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Molnar MJ, Molnar V. AI-based tools for the diagnosis and treatment of rare neurological disorders. Nat Rev Neurol 2023:10.1038/s41582-023-00841-y. [PMID: 37400549 DOI: 10.1038/s41582-023-00841-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Affiliation(s)
- Maria J Molnar
- Institute of Genomic Medicine and Rare Disorders Semmelweis University, Budapest, Hungary.
- ELKH-SE Multiomic Neurodegenerative Research Group, Budapest, Hungary.
| | - Viktor Molnar
- Institute of Genomic Medicine and Rare Disorders Semmelweis University, Budapest, Hungary
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