Predicting Social Determinants of Health in Patient Navigation: Case Study.
Francisco IacobelliAnna YangLaura TomIvy S LeungJohn CrissmanRufino SalgadoMelissa A SimonPublished in: JMIR formative research (2023)
To our knowledge, this study is the first approach to applying PN encounter data and multiclass learning algorithms to predict SDoHs. The experiments discussed yielded valuable lessons, including the awareness of model limitations and bias, planning for standardization of data sources and measurement, and the need to identify and anticipate the intersectionality and clustering of SDoHs. Although our focus was on predicting patients' SDoHs, machine learning can have a broad range of applications in the field of PN, from tailoring intervention delivery (eg, supporting PN decision-making) to informing resource allocation for measurement, and PN supervision.
Keyphrases
- machine learning
- healthcare
- big data
- end stage renal disease
- electronic health record
- ejection fraction
- decision making
- randomized controlled trial
- public health
- newly diagnosed
- chronic kidney disease
- deep learning
- prognostic factors
- mental health
- drinking water
- case report
- climate change
- rna seq
- risk assessment
- health information