Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health Records Article Swipe
Yinghao Pan
,
Eric B. Laber
,
Maureen A. Smith
,
Ying‐Qi Zhao
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/01621459.2021.1978467
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1080/01621459.2021.1978467
Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient Electronic Health Records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/01621459.2021.1978467
- OA Status
- green
- Cited By
- 2
- References
- 35
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All OpenAlex metadata
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Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health RecordsWork title
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articleOpenAlex work type
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2021Year of publication
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2021-09-09Full publication date if available
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Yinghao Pan, Eric B. Laber, Maureen A. Smith, Ying‐Qi ZhaoList of authors in order
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https://doi.org/10.1080/01621459.2021.1978467Publisher landing page
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greenOpen access status per OpenAlex
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10274334/pdf/nihms-1761157.pdfDirect OA link when available
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Weighting, Missing data, Medicine, Computer science, Data mining, Health care, Emergency medicine, Machine learning, Radiology, Economics, Economic growthTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2024: 1, 2021: 1Per-year citation counts (last 5 years)
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| abstract_inverted_index.are | 5, 25, 119, 131, 140 |
| abstract_inverted_index.as: | 110 |
| abstract_inverted_index.has | 47 |
| abstract_inverted_index.the | 48, 59, 152 |
| abstract_inverted_index.cost | 199 |
| abstract_inverted_index.data | 106, 180, 212 |
| abstract_inverted_index.deal | 184 |
| abstract_inverted_index.each | 86 |
| abstract_inverted_index.from | 159 |
| abstract_inverted_index.much | 80 |
| abstract_inverted_index.only | 78 |
| abstract_inverted_index.risk | 65 |
| abstract_inverted_index.such | 74 |
| abstract_inverted_index.than | 200 |
| abstract_inverted_index.that | 36, 73, 101 |
| abstract_inverted_index.then | 120 |
| abstract_inverted_index.uses | 102, 172 |
| abstract_inverted_index.with | 7, 27, 162, 185, 216 |
| abstract_inverted_index.(EHR) | 167 |
| abstract_inverted_index.These | 14 |
| abstract_inverted_index.Thus, | 31 |
| abstract_inverted_index.among | 10 |
| abstract_inverted_index.bias. | 189 |
| abstract_inverted_index.care. | 135 |
| abstract_inverted_index.could | 37 |
| abstract_inverted_index.data. | 168 |
| abstract_inverted_index.files | 158 |
| abstract_inverted_index.lower | 198 |
| abstract_inverted_index.made. | 149 |
| abstract_inverted_index.model | 35, 76, 100, 153, 171 |
| abstract_inverted_index.noisy | 178 |
| abstract_inverted_index.risks | 20 |
| abstract_inverted_index.those | 127 |
| abstract_inverted_index.until | 142 |
| abstract_inverted_index.using | 154 |
| abstract_inverted_index.while | 54 |
| abstract_inverted_index.Health | 165 |
| abstract_inverted_index.claims | 155 |
| abstract_inverted_index.costly | 67 |
| abstract_inverted_index.costs. | 30, 57 |
| abstract_inverted_index.events | 9, 16 |
| abstract_inverted_index.health | 19 |
| abstract_inverted_index.higher | 194 |
| abstract_inverted_index.inform | 44 |
| abstract_inverted_index.levels | 4 |
| abstract_inverted_index.linked | 161 |
| abstract_inverted_index.method | 192 |
| abstract_inverted_index.needed | 62, 84 |
| abstract_inverted_index.render | 91 |
| abstract_inverted_index.series | 205 |
| abstract_inverted_index.(HbA1c) | 3 |
| abstract_inverted_index.Because | 58 |
| abstract_inverted_index.Records | 166 |
| abstract_inverted_index.adverse | 8, 15 |
| abstract_inverted_index.collect | 77 |
| abstract_inverted_index.complex | 11, 214 |
| abstract_inverted_index.improve | 51 |
| abstract_inverted_index.methods | 202 |
| abstract_inverted_index.patient | 52, 87, 104, 163 |
| abstract_inverted_index.predict | 64 |
| abstract_inverted_index.present | 17 |
| abstract_inverted_index.propose | 96 |
| abstract_inverted_index.receive | 123 |
| abstract_inverted_index.serious | 18 |
| abstract_inverted_index.Patients | 115, 136 |
| abstract_inverted_index.accuracy | 196 |
| abstract_inverted_index.accurate | 93 |
| abstract_inverted_index.affected | 22 |
| abstract_inverted_index.classify | 108 |
| abstract_inverted_index.diabetic | 12 |
| abstract_inverted_index.glycated | 1 |
| abstract_inverted_index.identify | 38 |
| abstract_inverted_index.low-risk | 130, 146 |
| abstract_inverted_index.outcomes | 53 |
| abstract_inverted_index.patients | 23, 40, 109, 215 |
| abstract_inverted_index.proposed | 170, 191 |
| abstract_inverted_index.reducing | 55 |
| abstract_inverted_index.sampling | 188 |
| abstract_inverted_index.standard | 134 |
| abstract_inverted_index.Medicare, | 160 |
| abstract_inverted_index.biomarker | 60 |
| abstract_inverted_index.competing | 201 |
| abstract_inverted_index.construct | 151 |
| abstract_inverted_index.desirable | 72 |
| abstract_inverted_index.diabetes. | 217 |
| abstract_inverted_index.financial | 29 |
| abstract_inverted_index.high-risk | 39, 118, 144 |
| abstract_inverted_index.low-risk, | 112 |
| abstract_inverted_index.monitored | 141 |
| abstract_inverted_index.patients. | 13 |
| abstract_inverted_index.potential | 49 |
| abstract_inverted_index.principal | 174 |
| abstract_inverted_index.treatment | 46, 125 |
| abstract_inverted_index.uncertain | 139 |
| abstract_inverted_index.weighting | 182 |
| abstract_inverted_index.Electronic | 164 |
| abstract_inverted_index.associated | 6, 26 |
| abstract_inverted_index.classified | 116, 128, 137 |
| abstract_inverted_index.components | 175 |
| abstract_inverted_index.enrollment | 157 |
| abstract_inverted_index.functional | 173 |
| abstract_inverted_index.healthcare | 56 |
| abstract_inverted_index.hemoglobin | 2 |
| abstract_inverted_index.high-risk, | 111 |
| abstract_inverted_index.predictive | 34, 99, 195 |
| abstract_inverted_index.sequential | 98 |
| abstract_inverted_index.simulation | 207 |
| abstract_inverted_index.uncertain. | 114 |
| abstract_inverted_index.accommodate | 177 |
| abstract_inverted_index.application | 210 |
| abstract_inverted_index.burdensome, | 69 |
| abstract_inverted_index.experiments | 208 |
| abstract_inverted_index.information | 61, 81 |
| abstract_inverted_index.missingness | 186 |
| abstract_inverted_index.prediction. | 94 |
| abstract_inverted_index.recommended | 121, 132 |
| abstract_inverted_index.significant | 28 |
| abstract_inverted_index.Uncontrolled | 0 |
| abstract_inverted_index.accumulating | 103 |
| abstract_inverted_index.demonstrates | 193 |
| abstract_inverted_index.high-quality | 33 |
| abstract_inverted_index.longitudinal | 105, 179 |
| abstract_inverted_index.preventative | 45, 124 |
| abstract_inverted_index.determination | 147 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.64223458 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |