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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'Metric', 'Value'}) and 24 missing columns ({'How many years of clinical experience do you have?', "Which explanation helps you understand the AI's decision BEST?.1", 'What is your primary work setting?', 'Which aspect of the explanations did you find MOST useful?', 'Have you used AI-based clinical decision support tools before?', 'Rate the clarity of your preferred explanation for clinical decision-making.1', 'Any comments on this case? What information is helpful or missing?', 'Would you trust this explanation in clinical practice?.2', "Which explanation helps you understand the AI's decision BEST?", 'Timestamp', 'Any comments on this case? What information is helpful or missing?.2', 'What is your primary medical specialty?', 'Overall, how valuable would explainable AI be for maternal health in Bangladesh?', 'Rate the clarity of your preferred explanation for clinical decision-making.2', 'How would this AI system be most useful in YOUR specific practice setting?', 'Please create a simple ID (e.g., DOC001, DrAhmed, etc.)', 'What clinical information is MISSING from these AI explanations that you would need for decision-making?', 'Would you trust this explanation in clinical practice?', 'Rate the clarity of your preferred explanation for clinical decision-making', 'Would you trust this explanation in clinical practice?.1', "Which explanation helps you understand the AI's decision BEST?.2", 'Any additional comments, suggestions, or concerns about AI in maternal healthcare?', 'Any comments on this case? What information is helpful or missing?.1', 'What would be the main barriers to using such a system in Bangladesh?'}).
This happened while the csv dataset builder was generating data using
hf://datasets/fairhealth/bangladesh-maternal-health/maternal_upload/final_corrected_results.csv (at revision a97f79f1e0ec22b3d5ee50c2e59139b059934a3c), [/tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/Explainability Evaluation for Maternal Health AI System (Responses) - Form Responses 1.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/Explainability Evaluation for Maternal Health AI System (Responses) - Form Responses 1.csv), /tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/final_corrected_results.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/final_corrected_results.csv), /tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/model_comparison_results.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/model_comparison_results.csv), /tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/results_summary.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/results_summary.csv), /tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/xai_comparison_results.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/xai_comparison_results.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Metric: string
Value: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 482
to
{'Timestamp': Value('string'), 'Please create a simple ID (e.g., DOC001, DrAhmed, etc.)': Value('string'), 'What is your primary medical specialty?': Value('string'), 'How many years of clinical experience do you have?': Value('string'), 'What is your primary work setting?': Value('string'), 'Have you used AI-based clinical decision support tools before?': Value('string'), "Which explanation helps you understand the AI's decision BEST?": Value('string'), 'Rate the clarity of your preferred explanation for clinical decision-making': Value('int64'), 'Would you trust this explanation in clinical practice?': Value('string'), 'Any comments on this case? What information is helpful or missing?': Value('string'), "Which explanation helps you understand the AI's decision BEST?.1": Value('string'), 'Rate the clarity of your preferred explanation for clinical decision-making.1': Value('int64'), 'Would you trust this explanation in clinical practice?.1': Value('string'), 'Any comments on this case? What information is helpful or missing?.1': Value('string'), "Which explanation helps you understand the AI's decision BEST?.2": Value('string'), 'Rate the clarity of your preferred explanation for clinical decision-making.2': Value('int64'), 'Would you trust this explanation in clinical practice?.2': Value('string'), 'Any comments on this case? What information is helpful or missing?.2': Value('string'), 'What clinical information is MISSING from these AI explanations that you would need for decision-making?': Value('string'), 'Which aspect of the explanations did you find MOST useful?': Value('string'), 'How would this AI system be most useful in YOUR specific practice setting?': Value('string'), 'What would be the main barriers to using such a system in Bangladesh?': Value('string'), 'Overall, how valuable would explainable AI be for maternal health in Bangladesh?': Value('int64'), 'Any additional comments, suggestions, or concerns about AI in maternal healthcare?': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'Metric', 'Value'}) and 24 missing columns ({'How many years of clinical experience do you have?', "Which explanation helps you understand the AI's decision BEST?.1", 'What is your primary work setting?', 'Which aspect of the explanations did you find MOST useful?', 'Have you used AI-based clinical decision support tools before?', 'Rate the clarity of your preferred explanation for clinical decision-making.1', 'Any comments on this case? What information is helpful or missing?', 'Would you trust this explanation in clinical practice?.2', "Which explanation helps you understand the AI's decision BEST?", 'Timestamp', 'Any comments on this case? What information is helpful or missing?.2', 'What is your primary medical specialty?', 'Overall, how valuable would explainable AI be for maternal health in Bangladesh?', 'Rate the clarity of your preferred explanation for clinical decision-making.2', 'How would this AI system be most useful in YOUR specific practice setting?', 'Please create a simple ID (e.g., DOC001, DrAhmed, etc.)', 'What clinical information is MISSING from these AI explanations that you would need for decision-making?', 'Would you trust this explanation in clinical practice?', 'Rate the clarity of your preferred explanation for clinical decision-making', 'Would you trust this explanation in clinical practice?.1', "Which explanation helps you understand the AI's decision BEST?.2", 'Any additional comments, suggestions, or concerns about AI in maternal healthcare?', 'Any comments on this case? What information is helpful or missing?.1', 'What would be the main barriers to using such a system in Bangladesh?'}).
This happened while the csv dataset builder was generating data using
hf://datasets/fairhealth/bangladesh-maternal-health/maternal_upload/final_corrected_results.csv (at revision a97f79f1e0ec22b3d5ee50c2e59139b059934a3c), [/tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/Explainability Evaluation for Maternal Health AI System (Responses) - Form Responses 1.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/Explainability Evaluation for Maternal Health AI System (Responses) - Form Responses 1.csv), /tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/final_corrected_results.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/final_corrected_results.csv), /tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/model_comparison_results.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/model_comparison_results.csv), /tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/results_summary.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/results_summary.csv), /tmp/hf-datasets-cache/medium/datasets/68650281045407-config-parquet-and-info-fairhealth-bangladesh-mat-32168f0e/hub/datasets--fairhealth--bangladesh-maternal-health/snapshots/a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/xai_comparison_results.csv (origin=hf://datasets/fairhealth/bangladesh-maternal-health@a97f79f1e0ec22b3d5ee50c2e59139b059934a3c/maternal_upload/xai_comparison_results.csv)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Timestamp string | Please create a simple ID (e.g., DOC001, DrAhmed, etc.) string | What is your primary medical specialty? string | How many years of clinical experience do you have? string | What is your primary work setting? string | Have you used AI-based clinical decision support tools before? string | Which explanation helps you understand the AI's decision BEST? string | Rate the clarity of your preferred explanation for clinical decision-making int64 | Would you trust this explanation in clinical practice? string | Any comments on this case? What information is helpful or missing? string | Which explanation helps you understand the AI's decision BEST?.1 string | Rate the clarity of your preferred explanation for clinical decision-making.1 int64 | Would you trust this explanation in clinical practice?.1 string | Any comments on this case? What information is helpful or missing?.1 string | Which explanation helps you understand the AI's decision BEST?.2 string | Rate the clarity of your preferred explanation for clinical decision-making.2 int64 | Would you trust this explanation in clinical practice?.2 string | Any comments on this case? What information is helpful or missing?.2 string | What clinical information is MISSING from these AI explanations that you would need for decision-making? string | Which aspect of the explanations did you find MOST useful? string | How would this AI system be most useful in YOUR specific practice setting? string | What would be the main barriers to using such a system in Bangladesh? string | Overall, how valuable would explainable AI be for maternal health in Bangladesh? int64 | Any additional comments, suggestions, or concerns about AI in maternal healthcare? string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
12/9/2025 18:00:42 | Doc | General Practice / Family Medicine | Less than 5 years | Medical College/Teaching Hospital | Yes, occasionally | B. (Black-box with SHAP) | 3 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 3 | Yes, I would trust it | null | B. (Black-box with SHAP) | 3 | Yes, I would trust it | null | Age group | Clinical parameters | Need to research more | Integration with existing systems, Regulatory/legal concerns | 3 | null |
12/11/2025 22:31:20 | 0601 | Student | Less than 5 years | Medical College/Teaching Hospital | No, but interested | A. (Hybrid: Fuzzy Rules + SHAP) | 2 | No, I would not trust it | No | B. (Black-box with SHAP) | 1 | No, I would not trust it | No | B. (Black-box with SHAP) | 2 | No, I would not trust it | No | Others investigation | All | No | Trust in AI predictions | 4 | Ni |
12/11/2025 22:44:32 | DrAhmed | Oncology | Less than 5 years | Medical College/Teaching Hospital | No, but interested | A. (Hybrid: Fuzzy Rules + SHAP) | 1 | Maybe, with some reservations | null | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Maybe, with some reservations | null | B. (Black-box with SHAP) | 2 | Maybe, with some reservations | Good | Rural practice | Rural | Very much | Integration with existing systems | 2 | Yes |
12/11/2025 22:47:58 | Prova | General Practice / Family Medicine | Less than 5 years | Medical College/Teaching Hospital | Yes, occasionally | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Maybe, with some reservations | No | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Maybe, with some reservations | No | B. (Black-box with SHAP) | 4 | Yes, I would trust it | null | Nothing | Didn't understand | Very helpful | Lack of internet connectivity, Integration with existing systems, Language barriers (needs Bengali), Regulatory/legal concerns | 5 | It should give info with references |
12/11/2025 22:49:42 | Doc3 | General Practice / Family Medicine | Less than 5 years | Medical College/Teaching Hospital | Yes, occasionally | A. (Hybrid: Fuzzy Rules + SHAP) | 1 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 1 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 1 | Yes, I would trust it | null | Future risk and complications | Risk of the patient | Give me a direction of thinking | Lack of internet connectivity, Training requirements | 4 | null |
12/11/2025 22:59:14 | Masura Islam | Mbbs | Less than 5 years | Medical College/Teaching Hospital | Yes, occasionally | C. (Baseline: Score only) | 5 | No, I would not trust it | Vague symptoms | B. (Black-box with SHAP) | 2 | No, I would not trust it | Other investigation | A. (Hybrid: Fuzzy Rules + SHAP) | 3 | Maybe, with some reservations | Other investigation | Extended explanation | BP | Diagnosis specifically | Training requirements | 5 | No |
12/11/2025 23:32:31 | DOC2002 | Internal Medicine | Less than 5 years | Medical College/Teaching Hospital | Yes, regularly | C. (Baseline: Score only) | 2 | Maybe, with some reservations | null | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Yes, I would trust it | null | Respiratory status | Group B | It will help to gain confidence | Language barriers (needs Bengali) | 3 | null |
12/12/2025 6:31:38 | DrNuman | Internal Medicine | Less than 5 years | Urban Private Hospital/Clinic | No, but interested | A. (Hybrid: Fuzzy Rules + SHAP) | 5 | Maybe, with some reservations | null | A. (Hybrid: Fuzzy Rules + SHAP) | 5 | Maybe, with some reservations | null | A. (Hybrid: Fuzzy Rules + SHAP) | 5 | Maybe, with some reservations | null | Nothing significant | analysis about every point given in the case | I think it will give a accurate result in case of diagnosis a disease easily | Trust in AI predictions, Integration with existing systems | 4 | null |
12/12/2025 11:53:23 | Dr.Muttakin | Internal Medicine | Less than 5 years | Medical College/Teaching Hospital | Yes, regularly | A. (Hybrid: Fuzzy Rules + SHAP) | 1 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 2 | Yes, I would trust it | GDM | B. (Black-box with SHAP) | 3 | Yes, I would trust it | DM with eclapsia | Nothing | History | Time management | Training requirements | 3 | Nutrition and Health Check up |
12/12/2025 15:15:46 | DOC001 | General Practice / Family Medicine | Less than 5 years | Medical College/Teaching Hospital | No, and not interested | A. (Hybrid: Fuzzy Rules + SHAP) | 2 | Yes, I would trust it | Zzz | B. (Black-box with SHAP) | 1 | Yes, I would trust it | Zz | B. (Black-box with SHAP) | 1 | No, I would not trust it | Zz | 12 | Ww | Ww | Lack of internet connectivity | 1 | Zz |
12/16/2025 15:52:57 | DOC05 | Obstetrics & Gynecology | Less than 5 years | Rural Public Hospital | Yes, occasionally | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 3 | Yes, I would trust it | null | null | Explanations | In many ways | Lack of internet connectivity, Integration with existing systems, Language barriers (needs Bengali) | 4 | null |
12/16/2025 15:55:14 | DrAlam | Obstetrics & Gynecology | Less than 5 years | Rural Health Center | Yes, occasionally | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 3 | Maybe, with some reservations | null | Nothing Important | ALL | Decision making | Lack of internet connectivity, Integration with existing systems | 4 | null |
12/16/2025 15:57:39 | DrIslam | General Practice / Family Medicine | Less than 5 years | Medical College/Teaching Hospital | Yes, regularly | A. (Hybrid: Fuzzy Rules + SHAP) | 5 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 4 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 3 | Maybe, with some reservations | null | null | All | Significantly with proper training | Lack of internet connectivity, Training requirements | 4 | null |
12/16/2025 16:01:58 | DOC1 | Obstetrics & Gynecology | Less than 5 years | Urban Private Hospital/Clinic | Yes, regularly | A. (Hybrid: Fuzzy Rules + SHAP) | 5 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 5 | Yes, I would trust it | null | A. (Hybrid: Fuzzy Rules + SHAP) | 3 | Maybe, with some reservations | null | null | All | By implimenting | Lack of internet connectivity, Integration with existing systems | 4 | null |
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Bangladesh Maternal Health — Research Data
Research outputs from the maternal health XAI study by Farjana Yesmin:
Yesmin, F., Shirmin, N. & Bristy, S.S. (2026). Explainable AI for Maternal Health Risk Prediction in Bangladesh: A Hybrid Fuzzy-XGBoost Framework with Clinician Validation. ICAIHE 2026, Waseda University, Tokyo. Preprint
Part of the FairHealth
library — pip install fairhealth
What Is Included
| File | Description |
|---|---|
final_corrected_results.csv |
Final model evaluation results |
model_comparison_results.csv |
7 models compared (accuracy, F1, AUC) |
results_summary.csv |
Summary statistics across experiments |
xai_comparison_results.csv |
SHAP vs Fuzzy vs Hybrid explanation comparison |
Explainability Evaluation...csv |
Clinician validation survey (n=14) |
final_fuzzy_xgboost_model.json |
Trained Fuzzy-XGBoost model weights |
Key Results
| Model | Accuracy | F1 | ROC-AUC |
|---|---|---|---|
| Hybrid Fuzzy-XGBoost (ours) | 88.67% | 0.8869 | 0.9703 |
| Gradient Boosting | 86.21% | 0.8619 | — |
| Random Forest | 85.22% | 0.8521 | — |
Clinician validation (N=14):
- 71.4% preferred hybrid Fuzzy+SHAP explanation
- 54.8% would trust in clinical practice
- Top barrier: internet connectivity (50%)
Data Sources
- UCI Maternal Health Risk Dataset (base clinical data)
- DGHS Dashboard 2024 — Bangladesh regional healthcare indicators
- UNFPA MPDSR 2023 — Maternal mortality ratios by division
- WHO ANC guidelines, ADA 2023, ACC/AHA 2017 (fuzzy rules)
Usage
from fairhealth.explain.fuzzy import get_fired_rules
rules = get_fired_rules(age=42, sbp=145, bs=12, hr=88)
for r in rules:
print(f"Rule {r['id']}: {r['condition']} → {r['outcome']}")
Citation
@dataset{fairhealth_maternal_2026,
author = {Yesmin, Farjana},
title = {Bangladesh Maternal Health Research Dataset},
year = {2026},
publisher = {Hugging Face},
doi = {10.57967/hf/8801},
url = {https://huggingface.co/datasets/fairhealth/bangladesh-maternal-health}
}
Also cite:
@inproceedings{yesmin2026icaihe,
author = {Yesmin, Farjana and Shirmin, Nusrat and Bristy, Suraiya Shabnam},
title = {Explainable AI for Maternal Health Risk Prediction in Bangladesh},
note = {ICAIHE 2026, Waseda University, Tokyo},
year = {2026}
}
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