PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases
Neurodegenerative diseases such as Alzheimer's and Parkinson's impact millions of people worldwide. Early diagnosis has proven to greatly increase the chances of slowing down the diseases' progression. Correct diagnosis often relies on the analysis of large amounts of patient data, an...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/article/10.3389/frai.2020.00023/full |
| _version_ | 1819052015933718528 |
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| author | Tamara T. Müller Pietro Lio |
| author_facet | Tamara T. Müller Pietro Lio |
| author_sort | Tamara T. Müller |
| collection | DOAJ |
| description | Neurodegenerative diseases such as Alzheimer's and Parkinson's impact millions of people worldwide. Early diagnosis has proven to greatly increase the chances of slowing down the diseases' progression. Correct diagnosis often relies on the analysis of large amounts of patient data, and thus lends itself well to support from machine learning algorithms, which are able to learn from past diagnosis and see clearly through the complex interactions of a patient's symptoms and data. Unfortunately, many contemporary machine learning techniques fail to reveal details about how they reach their conclusions, a property considered fundamental when providing a diagnosis. Here we introduce our Personalisable Clinical Decision Support System (PECLIDES), an algorithmic process formulated to address this specific fault in diagnosis detection. PECLIDES provides a clear insight into the decision-making process leading to a diagnosis, making it a gray box model. Our algorithm enriches the fundamental work of Masheyekhi and Gras in data integration, personal medicine, usability, visualization, and interactivity.Our decision support system is an operation of translational medicine. It is based on random forests, is personalisable and allows a clear insight into the decision-making process. A well-structured rule set is created and every rule of the decision-making process can be observed by the user (physician). Furthermore, the user has an impact on the creation of the final rule set and the algorithm allows the comparison of different diseases as well as regional differences in the same disease. The algorithm is applicable to various decision problems. In this paper we will evaluate it on diagnosing neurological diseases and therefore refer to the algorithm as PECLIDES Neuro1. |
| first_indexed | 2024-12-21T12:13:07Z |
| format | Article |
| id | doaj.art-770869f3bcb1406197e2f79970ebf93a |
| institution | Directory Open Access Journal |
| issn | 2624-8212 |
| language | English |
| last_indexed | 2024-12-21T12:13:07Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj.art-770869f3bcb1406197e2f79970ebf93a2022-12-21T19:04:31ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122020-04-01310.3389/frai.2020.00023526217PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological DiseasesTamara T. MüllerPietro LioNeurodegenerative diseases such as Alzheimer's and Parkinson's impact millions of people worldwide. Early diagnosis has proven to greatly increase the chances of slowing down the diseases' progression. Correct diagnosis often relies on the analysis of large amounts of patient data, and thus lends itself well to support from machine learning algorithms, which are able to learn from past diagnosis and see clearly through the complex interactions of a patient's symptoms and data. Unfortunately, many contemporary machine learning techniques fail to reveal details about how they reach their conclusions, a property considered fundamental when providing a diagnosis. Here we introduce our Personalisable Clinical Decision Support System (PECLIDES), an algorithmic process formulated to address this specific fault in diagnosis detection. PECLIDES provides a clear insight into the decision-making process leading to a diagnosis, making it a gray box model. Our algorithm enriches the fundamental work of Masheyekhi and Gras in data integration, personal medicine, usability, visualization, and interactivity.Our decision support system is an operation of translational medicine. It is based on random forests, is personalisable and allows a clear insight into the decision-making process. A well-structured rule set is created and every rule of the decision-making process can be observed by the user (physician). Furthermore, the user has an impact on the creation of the final rule set and the algorithm allows the comparison of different diseases as well as regional differences in the same disease. The algorithm is applicable to various decision problems. In this paper we will evaluate it on diagnosing neurological diseases and therefore refer to the algorithm as PECLIDES Neuro1.https://www.frontiersin.org/article/10.3389/frai.2020.00023/fulldecision supportrandom forestprecision medicineneurological diseasespersonalisable medicinemachine learning |
| spellingShingle | Tamara T. Müller Pietro Lio PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases Frontiers in Artificial Intelligence decision support random forest precision medicine neurological diseases personalisable medicine machine learning |
| title | PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases |
| title_full | PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases |
| title_fullStr | PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases |
| title_full_unstemmed | PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases |
| title_short | PECLIDES Neuro: A Personalisable Clinical Decision Support System for Neurological Diseases |
| title_sort | peclides neuro a personalisable clinical decision support system for neurological diseases |
| topic | decision support random forest precision medicine neurological diseases personalisable medicine machine learning |
| url | https://www.frontiersin.org/article/10.3389/frai.2020.00023/full |
| work_keys_str_mv | AT tamaratmuller peclidesneuroapersonalisableclinicaldecisionsupportsystemforneurologicaldiseases AT pietrolio peclidesneuroapersonalisableclinicaldecisionsupportsystemforneurologicaldiseases |