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In this respect, statistical methods offer the \u201cbig picture,\u201d but they have a weak predictive ability at an individual level. This research proposes a new personalized modeling method based on computational spiking neural networks (SNN) for the identification of causal associations between clinical and environmental time series data that can be used to predict individual stroke events. The method is tested on 804 stroke patients. Given a clinical data set of patients who experienced a stroke in the past and the corresponding environmental time-series data for a selected time-window before the stroke event, the method identifies the clusters of individuals with a high risk for stroke under similar conditions. The methodology involves a pipeline of processes when creating a personalized model for an individual <jats:inline-formula><jats:alternatives><jats:tex-math>$$x$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>x<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>: (1) selecting a group of individuals <jats:inline-formula><jats:alternatives><jats:tex-math>$$Gx$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>Gx<\/mml:mi>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> with similar personal records to <jats:inline-formula><jats:alternatives><jats:tex-math>$$x$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>x<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>; (2) training a personalized SNN <jats:inline-formula><jats:alternatives><jats:tex-math>$$x$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>x<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> model of several days of environmental data related to the <jats:inline-formula><jats:alternatives><jats:tex-math>$$Gx$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>Gx<\/mml:mi>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> group to predict the risk of stroke for <jats:inline-formula><jats:alternatives><jats:tex-math>$$x$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>x<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> at least one day earlier; (3) model interpretability through 3D visualization; (4) discovery of personalized predictive markers. The results are twofold, first proposing a new computational methodology and second presenting new findings. It is found that certain environmental factors, such as SO<jats:sub>2<\/jats:sub>, PM<jats:sub>10<\/jats:sub>, CO, and PM<jats:sub>2.5<\/jats:sub>, increase the risk of stroke if an individual <jats:inline-formula><jats:alternatives><jats:tex-math>$$x$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mi>x<\/mml:mi>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> belongs to a certain cluster of people, characterized by a combination of family history of stroke and diabetes, overweight, vascular\/heart disease, age, and other. For the used population data, the proposed method can predict accurately individual risk of stroke before the day of the stroke. The paper presents a new methodology for personalized machine learning methods to define subgroups of the population with a high risk of stroke and to predict early individual risk of the stroke event. This makes the proposed cognitive computation method useful to reduce morbidity and mortality in society. The method is broadly applicable for predicting individual risk of other diseases and mental health conditions.<\/jats:p>","DOI":"10.1007\/s12559-021-09975-x","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T01:02:13Z","timestamp":1660093333000},"page":"2187-2202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Personalized Spiking Neural Network Models of Clinical and Environmental Factors to Predict Stroke"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4953-0662","authenticated-orcid":false,"given":"Maryam","family":"Doborjeh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zohreh","family":"Doborjeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Merkin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rita","family":"Krishnamurthi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Enayatollahi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valery","family":"Feigin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikola","family":"Kasabov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"issue":"2","key":"9975_CR1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1159\/000506396","volume":"54","author":"RV Krishnamurthi","year":"2020","unstructured":"Krishnamurthi RV, Ikeda T, Feigin VL. 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