Enhancing Cause of Death Prediction: Development and Validation of ML Models Using Multimodal Data Across Multiple Healthcare Sites Article Swipe
Mohammed Ali Al-Garadi
,
Rishi Desai
,
Kerry Ngan
,
Michele L. Lenoue-Newton
,
Ruth Reeves
,
Daniel Park
,
José J. Hernández‐Muñoz
,
Shirley Wang
,
Judith C. Maro
,
Candace C. Fuller
,
Joshua Lin Kueiyu
,
Aida Kuzucan
,
Kevin Coughlin
,
Haritha S. Pillai
,
Melissa L McPheeters
,
John Whitaker
,
Jeff Deere
,
Michael F McLemore
,
David Westerman
,
Thomas Morrow
,
Margaret A. Adgent
,
Michael E. Matheny
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.06.24.25330213
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.06.24.25330213
Importance Timely and accurate determination of causes of death (CoD) is essential for public health surveillance, epidemiological research, and healthcare policy development. However, obtaining up-to-date and detailed CoD information is challenging due to delays in official death records and inconsistencies in data reporting across institutions. Objective To develop and validate machine learning (ML) models capable of predicting probable CoD by integrating comprehensive features from structured electronic health record (EHR) data, unstructured clinical notes, and publicly available data. Design, Setting, and Participants This multi-institutional retrospective cohort study was conducted at Vanderbilt University Medical Center (VUMC) and Massachusetts General Brigham (MGB). Deceased patients were included if they had at least one inpatient or outpatient encounter between October 1, 2015, and January 1, 2021, with corresponding death records from state health departments and the National Death Index. The study was comprised of 13,708 deceased patients from VUMC and 34,839 from MGB. Exposures Integration of structured EHR data, unstructured clinical notes processed using advanced language models, and publicly available data into machine learning models to predict CoD. Main Outcomes and Measures The primary outcome was the underlying CoD, classified into one of the top 15 National Center for Health Statistics (NCHS) rankable CoD categories, with all other causes grouped into an “Other” category. Model performance was evaluated using weighted area under the receiver operating characteristic curve (AUC) and weighted F-measure. Results The XGBoost model using structured EHR data alone achieved weighted AUCs of 0.86 (95% CI, 0.84–0.88) at VUMC and 0.80 (95% CI, 0.79-0.80) at MGB. Adding unstructured notes improved performance, with weighted AUCs of 0.90 (95% CI, 0.88–0.93) at VUMC and 0.92(95% CI, 0.91–0.92) at MGB. Adding publicly available data did not further improve performance. Cross-institutional validation revealed significant performance degradation. Conclusions and Relevance ML models integrating EHR structured and unstructured data to predict underlying CoD at the time of the most recent encounter among deceased patients achieved excellent performance within individual institutions. The inclusion of publicly available data did not improve performance, and all versions had poor portability between institutions. Healthcare institutions may benefit from adopting robust processes for locally tailored models, and future research should focus on enhancing model generalizability while addressing unique institutional data environments.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.06.24.25330213
- https://www.medrxiv.org/content/medrxiv/early/2025/06/24/2025.06.24.25330213.full.pdf
- OA Status
- green
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411661630
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411661630Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.06.24.25330213Digital Object Identifier
- Title
-
Enhancing Cause of Death Prediction: Development and Validation of ML Models Using Multimodal Data Across Multiple Healthcare SitesWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-24Full publication date if available
- Authors
-
Mohammed Ali Al-Garadi, Rishi Desai, Kerry Ngan, Michele L. Lenoue-Newton, Ruth Reeves, Daniel Park, José J. Hernández‐Muñoz, Shirley Wang, Judith C. Maro, Candace C. Fuller, Joshua Lin Kueiyu, Aida Kuzucan, Kevin Coughlin, Haritha S. Pillai, Melissa L McPheeters, John Whitaker, Jeff Deere, Michael F McLemore, David Westerman, Thomas Morrow, Margaret A. Adgent, Michael E. MathenyList of authors in order
- Landing page
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https://doi.org/10.1101/2025.06.24.25330213Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2025/06/24/2025.06.24.25330213.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2025/06/24/2025.06.24.25330213.full.pdfDirect OA link when available
- Concepts
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Health care, Computer science, Data mining, Economics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2099906653, https://openalex.org/W2158226607, https://openalex.org/W1998451170, https://openalex.org/W2163756067, https://openalex.org/W1979851080, https://openalex.org/W2035677778, https://openalex.org/W2473046424, https://openalex.org/W2927032858, https://openalex.org/W3102003975, https://openalex.org/W4297313072, https://openalex.org/W4400952839, https://openalex.org/W3094211658, https://openalex.org/W3018601113, https://openalex.org/W3160596727, https://openalex.org/W4394786597, https://openalex.org/W2967056613, https://openalex.org/W2345195116, https://openalex.org/W2317417584, https://openalex.org/W2566727313, https://openalex.org/W2550759377, https://openalex.org/W4384561707, https://openalex.org/W4384071683, https://openalex.org/W4379769651, https://openalex.org/W4387500346, https://openalex.org/W2614852961, https://openalex.org/W4407218184, https://openalex.org/W2991379615, https://openalex.org/W2109019042, https://openalex.org/W1934602670, https://openalex.org/W4310568840, https://openalex.org/W4403973718, https://openalex.org/W4406867165, https://openalex.org/W2562162676, https://openalex.org/W4252208101, https://openalex.org/W2911964244, https://openalex.org/W3164199608, https://openalex.org/W2295598076, https://openalex.org/W2342603028, https://openalex.org/W2128728535, https://openalex.org/W6683161245, https://openalex.org/W2155653793, https://openalex.org/W1992223383, https://openalex.org/W1981796233, https://openalex.org/W4280634468, https://openalex.org/W3009505149, https://openalex.org/W3014080659, https://openalex.org/W2996764908, https://openalex.org/W2966724412, https://openalex.org/W4214758645, https://openalex.org/W4309294011, https://openalex.org/W3197203470, https://openalex.org/W4400098873, https://openalex.org/W3120293480 |
| referenced_works_count | 53 |
| abstract_inverted_index.1, | 116, 120 |
| abstract_inverted_index.15 | 191 |
| abstract_inverted_index.ML | 292 |
| abstract_inverted_index.To | 47 |
| abstract_inverted_index.an | 207 |
| abstract_inverted_index.at | 89, 107, 244, 251, 266, 272, 304 |
| abstract_inverted_index.by | 60 |
| abstract_inverted_index.if | 104 |
| abstract_inverted_index.in | 35, 41 |
| abstract_inverted_index.is | 11, 30 |
| abstract_inverted_index.of | 6, 8, 56, 139, 151, 188, 239, 261, 307, 323 |
| abstract_inverted_index.on | 356 |
| abstract_inverted_index.or | 111 |
| abstract_inverted_index.to | 33, 171, 300 |
| abstract_inverted_index.CI, | 242, 249, 264, 270 |
| abstract_inverted_index.CoD | 28, 59, 199, 303 |
| abstract_inverted_index.EHR | 153, 233, 295 |
| abstract_inverted_index.The | 135, 178, 228, 321 |
| abstract_inverted_index.all | 202, 332 |
| abstract_inverted_index.and | 3, 19, 26, 39, 49, 74, 80, 95, 118, 130, 145, 163, 176, 224, 246, 268, 290, 297, 331, 351 |
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| abstract_inverted_index.due | 32 |
| abstract_inverted_index.for | 13, 194, 347 |
| abstract_inverted_index.had | 106, 334 |
| abstract_inverted_index.may | 341 |
| abstract_inverted_index.not | 279, 328 |
| abstract_inverted_index.one | 109, 187 |
| abstract_inverted_index.the | 131, 182, 189, 218, 305, 308 |
| abstract_inverted_index.top | 190 |
| abstract_inverted_index.was | 87, 137, 181, 212 |
| abstract_inverted_index.(95% | 241, 248, 263 |
| abstract_inverted_index.(ML) | 53 |
| abstract_inverted_index.0.80 | 247 |
| abstract_inverted_index.0.86 | 240 |
| abstract_inverted_index.0.90 | 262 |
| abstract_inverted_index.AUCs | 238, 260 |
| abstract_inverted_index.CoD, | 184 |
| abstract_inverted_index.CoD. | 173 |
| abstract_inverted_index.MGB. | 148, 252, 273 |
| abstract_inverted_index.Main | 174 |
| abstract_inverted_index.This | 82 |
| abstract_inverted_index.VUMC | 144, 245, 267 |
| abstract_inverted_index.area | 216 |
| abstract_inverted_index.data | 42, 166, 234, 277, 299, 326, 364 |
| abstract_inverted_index.from | 64, 126, 143, 147, 343 |
| abstract_inverted_index.into | 167, 186, 206 |
| abstract_inverted_index.most | 309 |
| abstract_inverted_index.poor | 335 |
| abstract_inverted_index.they | 105 |
| abstract_inverted_index.time | 306 |
| abstract_inverted_index.were | 102 |
| abstract_inverted_index.with | 122, 201, 258 |
| abstract_inverted_index.(AUC) | 223 |
| abstract_inverted_index.(CoD) | 10 |
| abstract_inverted_index.(EHR) | 69 |
| abstract_inverted_index.2015, | 117 |
| abstract_inverted_index.2021, | 121 |
| abstract_inverted_index.Death | 133 |
| abstract_inverted_index.Model | 210 |
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| abstract_inverted_index.among | 312 |
| abstract_inverted_index.curve | 222 |
| abstract_inverted_index.data, | 70, 154 |
| abstract_inverted_index.data. | 77 |
| abstract_inverted_index.death | 9, 37, 124 |
| abstract_inverted_index.focus | 355 |
| abstract_inverted_index.least | 108 |
| abstract_inverted_index.model | 230, 358 |
| abstract_inverted_index.notes | 157, 255 |
| abstract_inverted_index.other | 203 |
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| abstract_inverted_index.study | 86, 136 |
| abstract_inverted_index.under | 217 |
| abstract_inverted_index.using | 159, 214, 231 |
| abstract_inverted_index.while | 360 |
| abstract_inverted_index.(MGB). | 99 |
| abstract_inverted_index.(NCHS) | 197 |
| abstract_inverted_index.(VUMC) | 94 |
| abstract_inverted_index.13,708 | 140 |
| abstract_inverted_index.34,839 | 146 |
| abstract_inverted_index.Adding | 253, 274 |
| abstract_inverted_index.Center | 93, 193 |
| abstract_inverted_index.Health | 195 |
| abstract_inverted_index.Index. | 134 |
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| abstract_inverted_index.health | 15, 67, 128 |
| abstract_inverted_index.models | 54, 170, 293 |
| abstract_inverted_index.notes, | 73 |
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| abstract_inverted_index.unique | 362 |
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| abstract_inverted_index.Brigham | 98 |
| abstract_inverted_index.Design, | 78 |
| abstract_inverted_index.General | 97 |
| abstract_inverted_index.January | 119 |
| abstract_inverted_index.Medical | 92 |
| abstract_inverted_index.October | 115 |
| abstract_inverted_index.Results | 227 |
| abstract_inverted_index.XGBoost | 229 |
| abstract_inverted_index.benefit | 342 |
| abstract_inverted_index.between | 114, 337 |
| abstract_inverted_index.capable | 55 |
| abstract_inverted_index.develop | 48 |
| abstract_inverted_index.further | 280 |
| abstract_inverted_index.grouped | 205 |
| abstract_inverted_index.improve | 281, 329 |
| abstract_inverted_index.locally | 348 |
| abstract_inverted_index.machine | 51, 168 |
| abstract_inverted_index.models, | 162, 350 |
| abstract_inverted_index.outcome | 180 |
| abstract_inverted_index.predict | 172, 301 |
| abstract_inverted_index.primary | 179 |
| abstract_inverted_index.records | 38, 125 |
| abstract_inverted_index.0.92(95% | 269 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Deceased | 100 |
| abstract_inverted_index.However, | 23 |
| abstract_inverted_index.Measures | 177 |
| abstract_inverted_index.National | 132, 192 |
| abstract_inverted_index.Outcomes | 175 |
| abstract_inverted_index.Setting, | 79 |
| abstract_inverted_index.accurate | 4 |
| abstract_inverted_index.achieved | 236, 315 |
| abstract_inverted_index.adopting | 344 |
| abstract_inverted_index.advanced | 160 |
| abstract_inverted_index.clinical | 72, 156 |
| abstract_inverted_index.deceased | 141, 313 |
| abstract_inverted_index.detailed | 27 |
| abstract_inverted_index.features | 63 |
| abstract_inverted_index.improved | 256 |
| abstract_inverted_index.included | 103 |
| abstract_inverted_index.language | 161 |
| abstract_inverted_index.learning | 52, 169 |
| abstract_inverted_index.official | 36 |
| abstract_inverted_index.patients | 101, 142, 314 |
| abstract_inverted_index.probable | 58 |
| abstract_inverted_index.publicly | 75, 164, 275, 324 |
| abstract_inverted_index.rankable | 198 |
| abstract_inverted_index.receiver | 219 |
| abstract_inverted_index.research | 353 |
| abstract_inverted_index.revealed | 285 |
| abstract_inverted_index.tailored | 349 |
| abstract_inverted_index.validate | 50 |
| abstract_inverted_index.versions | 333 |
| abstract_inverted_index.weighted | 215, 225, 237, 259 |
| abstract_inverted_index.Exposures | 149 |
| abstract_inverted_index.Objective | 46 |
| abstract_inverted_index.Relevance | 291 |
| abstract_inverted_index.available | 76, 165, 276, 325 |
| abstract_inverted_index.category. | 209 |
| abstract_inverted_index.comprised | 138 |
| abstract_inverted_index.conducted | 88 |
| abstract_inverted_index.encounter | 113, 311 |
| abstract_inverted_index.enhancing | 357 |
| abstract_inverted_index.essential | 12 |
| abstract_inverted_index.evaluated | 213 |
| abstract_inverted_index.excellent | 316 |
| abstract_inverted_index.inclusion | 322 |
| abstract_inverted_index.inpatient | 110 |
| abstract_inverted_index.obtaining | 24 |
| abstract_inverted_index.operating | 220 |
| abstract_inverted_index.processed | 158 |
| abstract_inverted_index.processes | 346 |
| abstract_inverted_index.reporting | 43 |
| abstract_inverted_index.research, | 18 |
| abstract_inverted_index.0.79-0.80) | 250 |
| abstract_inverted_index.F-measure. | 226 |
| abstract_inverted_index.Healthcare | 339 |
| abstract_inverted_index.Importance | 1 |
| abstract_inverted_index.Statistics | 196 |
| abstract_inverted_index.University | 91 |
| abstract_inverted_index.Vanderbilt | 90 |
| abstract_inverted_index.addressing | 361 |
| abstract_inverted_index.classified | 185 |
| abstract_inverted_index.electronic | 66 |
| abstract_inverted_index.healthcare | 20 |
| abstract_inverted_index.individual | 319 |
| abstract_inverted_index.outpatient | 112 |
| abstract_inverted_index.predicting | 57 |
| abstract_inverted_index.structured | 65, 152, 232, 296 |
| abstract_inverted_index.underlying | 183, 302 |
| abstract_inverted_index.up-to-date | 25 |
| abstract_inverted_index.validation | 284 |
| abstract_inverted_index.Conclusions | 289 |
| abstract_inverted_index.Integration | 150 |
| abstract_inverted_index.categories, | 200 |
| abstract_inverted_index.challenging | 31 |
| abstract_inverted_index.departments | 129 |
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| abstract_inverted_index.integrating | 61, 294 |
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| abstract_inverted_index.significant | 286 |
| abstract_inverted_index.“Other” | 208 |
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| abstract_inverted_index.0.88–0.93) | 265 |
| abstract_inverted_index.0.91–0.92) | 271 |
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| abstract_inverted_index.degradation. | 288 |
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| abstract_inverted_index.institutions | 340 |
| abstract_inverted_index.performance, | 257, 330 |
| abstract_inverted_index.performance. | 282 |
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| abstract_inverted_index.generalizability | 359 |
| abstract_inverted_index.Cross-institutional | 283 |
| abstract_inverted_index.multi-institutional | 83 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5004858670 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 22 |
| corresponding_institution_ids | https://openalex.org/I901861585 |
| citation_normalized_percentile.value | 0.10897357 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |