diff --git a/examples/length_of_stay/length_of_stay_mimic4_tpc.py b/examples/length_of_stay/length_of_stay_mimic4_tpc.py
new file mode 100644
index 000000000..ce0f4c3f9
--- /dev/null
+++ b/examples/length_of_stay/length_of_stay_mimic4_tpc.py
@@ -0,0 +1,31 @@
+from pyhealth.datasets import MIMIC4EHRDataset
+from pyhealth.tasks import RemainingLOSMIMIC4
+from pyhealth.datasets import  SampleDataset    
+import os
+import numpy as np
+
+MIMIC_ROOT = r"D:\cs598\mimic-iv"
+CACHE_PATH = r"D:\cs598\.cache_dir"
+
+def inspect():
+    mimic4 = MIMIC4EHRDataset( 
+            root=MIMIC_ROOT,
+            tables=["diagnoses_icd", "labevents", "procedures_icd", "prescriptions", "chartevents"],
+            dev=True, cache_dir=CACHE_PATH
+        )
+    
+    mimic4.stats()
+
+    # STEP 2: set task
+    sample_dataset = mimic4.set_task(RemainingLOSMIMIC4())
+    
+    first_sample:SampleDataset = sample_dataset[0]
+    print("Sample keys:", first_sample.keys())
+    for (key, value) in first_sample.items():
+        if key in ["timeseries"]:
+            print(f"Sample '{key}' shape: {value.shape}")
+        else:
+            print(f"Sample '{key}' value: {value}")
+
+if __name__ == "__main__":
+    inspect()
diff --git a/models/tpc.py b/models/tpc.py
new file mode 100644
index 000000000..e2f975cf8
--- /dev/null
+++ b/models/tpc.py
@@ -0,0 +1,815 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Sequence
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from pyhealth.datasets import SampleDataset
+from .base_model import BaseModel
+
+
+class MSLELoss(nn.Module):
+    """Masked mean squared logarithmic error loss used in the TPC paper.
+
+    Computes the mean MSLE over valid (non-zero) timesteps per sequence,
+    then averages across the batch.
+
+    Args:
+        None
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.squared_error = nn.MSELoss(reduction="none")
+
+    def forward(
+        self,
+        y_hat: torch.Tensor,
+        y: torch.Tensor,
+        mask: torch.Tensor,
+        seq_length: torch.Tensor,
+        sum_losses: bool = False,
+    ) -> torch.Tensor:
+        """Compute masked MSLE.
+
+        Args:
+            y_hat: Predicted values of shape (B, T).
+            y: True values of shape (B, T).
+            mask: Boolean mask of shape (B, T); True where loss should be computed.
+            seq_length: Valid sequence lengths of shape (B,).
+            sum_losses: If True, sum per-sequence losses instead of averaging.
+
+        Returns:
+            Scalar loss tensor.
+        """
+        mask = mask.bool()
+        eps = 1e-8
+        log_y_hat = torch.where(mask, torch.log(y_hat.clamp_min(eps)), torch.zeros_like(y_hat))
+        log_y = torch.where(mask, torch.log(y.clamp_min(eps)), torch.zeros_like(y))
+        loss = self.squared_error(log_y_hat, log_y)
+        loss = torch.sum(loss, dim=1)
+        if not sum_losses:
+            loss = loss / seq_length.clamp(min=1).float()
+        return loss.mean()
+
+
+class MaskedMSELoss(nn.Module):
+    """Masked mean squared error loss.
+
+    Args:
+        None
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.squared_error = nn.MSELoss(reduction="none")
+
+    def forward(
+        self,
+        y_hat: torch.Tensor,
+        y: torch.Tensor,
+        mask: torch.Tensor,
+        seq_length: torch.Tensor,
+        sum_losses: bool = False,
+    ) -> torch.Tensor:
+        """Compute masked MSE.
+
+        Args:
+            y_hat: Predicted values of shape (B, T).
+            y: True values of shape (B, T).
+            mask: Boolean mask of shape (B, T); True where loss should be computed.
+            seq_length: Valid sequence lengths of shape (B,).
+            sum_losses: If True, sum per-sequence losses instead of averaging.
+
+        Returns:
+            Scalar loss tensor.
+        """
+        mask = mask.bool()
+        y_hat = torch.where(mask, y_hat, torch.zeros_like(y_hat))
+        y = torch.where(mask, y, torch.zeros_like(y))
+        loss = self.squared_error(y_hat, y)
+        loss = torch.sum(loss, dim=1)
+        if not sum_losses:
+            loss = loss / seq_length.clamp(min=1).float()
+        return loss.mean()
+
+
+class TPC(BaseModel):
+    """Temporal Pointwise Convolution (TPC) model for ICU remaining length-of-stay.
+
+    Adapted from the official TPC-LoS-prediction repository (Rocheteau et al., 2021)
+    and rewritten to fit PyHealth's BaseModel interface and the RemainingLOSMIMIC4 task.
+
+    The model applies grouped temporal convolutions (one group per clinical feature)
+    in parallel with pointwise (1x1) convolutions that look across all features at
+    each timestep. Skip connections are maintained throughout, and the architecture
+    stacks ``n_layers`` of these paired conv blocks before a final linear regression
+    head.
+
+    Expected task input keys
+    ------------------------
+    - ``timeseries``: Float tensor of shape ``(B, 2F+2, T)`` with channel order
+      ``[elapsed, values(F), decay(F), hour_of_day]`` — channels first, as produced
+      by ``RemainingLOSMIMIC4``.
+    - ``static``: Float tensor of shape ``(B, S)`` containing age, sex, etc.
+    - ``conditions``: Long tensor of shape ``(B, L)`` containing padded ICD code
+      indices from ``SequenceProcessor`` (PAD index = 0).
+    - ``los``: Float tensor of shape ``(B, T)`` with remaining LoS in hours at each hour.
+
+    Args:
+        dataset (SampleDataset): Dataset used to train the model. Provides feature
+            and label key lists as well as fitted input processors.
+        timeseries_key (str): Key for the timeseries input. Default: ``"timeseries"``.
+        static_key (Optional[str]): Key for static features, or ``None`` to omit.
+            Default: ``"static"``.
+        conditions_key (Optional[str]): Key for diagnosis code sequences, or ``None``
+            to omit. Default: ``"conditions"``.
+        n_layers (int): Number of TPC layers. Default: ``3``.
+        kernel_size (int): Temporal convolution kernel size. Default: ``4``.
+        temp_kernels (Optional[Sequence[int]]): Number of temporal kernels per layer.
+            Length must equal ``n_layers``. Default: ``[8] * n_layers``.
+        point_sizes (Optional[Sequence[int]]): Pointwise output size per layer.
+            Length must equal ``n_layers``. Default: ``[14] * n_layers``.
+        diagnosis_size (int): Output dimension of the diagnosis encoder. Default: ``64``.
+        last_linear_size (int): Hidden size of the final regression MLP. Default: ``64``.
+        main_dropout_rate (float): Dropout applied to pointwise outputs and the final
+            head. Default: ``0.3``.
+        temp_dropout_rate (float): Dropout applied to temporal conv outputs.
+            Default: ``0.3``.
+        time_before_pred (int): Minimum history hours before predictions are made.
+            Predictions are generated for timesteps ``[time_before_pred, T)``.
+            Default: ``5``.
+        use_msle (bool): Use MSLE loss if ``True``, MSE otherwise. Default: ``True``.
+        sum_losses (bool): Sum per-sequence losses instead of averaging. Default: ``False``.
+        apply_exp (bool): Exponentiate raw predictions before clamping. Since the model
+            learns log-space targets this recovers hours. Default: ``True``.
+
+    Examples:
+        >>> from pyhealth.datasets import create_sample_dataset
+        >>> import torch
+        >>> F = 4  # number of clinical features
+        >>> T = 20
+        >>> samples = [
+        ...     {
+        ...         "patient_id": "p0",
+        ...         "visit_id": "v0",
+        ...         "timeseries": torch.randn(2 * F + 2, T),
+        ...         "static": torch.tensor([65.0, 1.0]),
+        ...         "conditions": ["icd_A01", "icd_B02"],
+        ...         "los": torch.rand(T) * 48,
+        ...     },
+        ...     {
+        ...         "patient_id": "p1",
+        ...         "visit_id": "v1",
+        ...         "timeseries": torch.randn(2 * F + 2, T),
+        ...         "static": torch.tensor([72.0, 0.0]),
+        ...         "conditions": ["icd_A01"],
+        ...         "los": torch.rand(T) * 24,
+        ...     },
+        ... ]
+        >>> dataset = create_sample_dataset(
+        ...     samples=samples,
+        ...     input_schema={
+        ...         "timeseries": "tensor",
+        ...         "static": "tensor",
+        ...         "conditions": "sequence",
+        ...     },
+        ...     output_schema={"los": "tensor"},
+        ...     dataset_name="tpc_demo",
+        ... )
+        >>> model = TPC(dataset=dataset, n_layers=2, temp_kernels=[4, 4], point_sizes=[8, 8])
+        >>> batch = next(iter(torch.utils.data.DataLoader(dataset, batch_size=2)))
+        >>> out = model(**batch)
+        >>> out.keys()
+        dict_keys(['logit', 'y_prob', 'loss', 'y_true'])
+    """
+
+    def __init__(
+        self,
+        dataset: SampleDataset,
+        timeseries_key: str = "timeseries",
+        static_key: Optional[str] = "static",
+        conditions_key: Optional[str] = "conditions",
+        n_layers: int = 3,
+        kernel_size: int = 4,
+        temp_kernels: Optional[Sequence[int]] = None,
+        point_sizes: Optional[Sequence[int]] = None,
+        diagnosis_size: int = 64,
+        last_linear_size: int = 64,
+        main_dropout_rate: float = 0.3,
+        temp_dropout_rate: float = 0.3,
+        time_before_pred: int = 5,
+        use_msle: bool = True,
+        sum_losses: bool = False,
+        apply_exp: bool = True,
+    ) -> None:
+        super().__init__(dataset=dataset)
+        # TPC manages its own loss; prevent BaseModel from resolving mode
+        # from the "tensor" output schema entry (which it cannot map to a mode string).
+        self.mode = "regression"
+
+        if len(self.label_keys) != 1:
+            raise ValueError("TPC supports exactly one label key.")
+        if n_layers < 1:
+            raise ValueError("n_layers must be >= 1.")
+
+        self.label_key = self.label_keys[0]
+        self.timeseries_key = timeseries_key
+        # Only register static/conditions keys if they are actually in the dataset
+        self.static_key = static_key if static_key in self.feature_keys else None
+        self.conditions_key = (
+            conditions_key if conditions_key in self.feature_keys else None
+        )
+
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.temp_kernels: List[int] = (
+            list(temp_kernels) if temp_kernels is not None else [8] * n_layers
+        )
+        self.point_sizes: List[int] = (
+            list(point_sizes) if point_sizes is not None else [14] * n_layers
+        )
+        if len(self.temp_kernels) != n_layers:
+            raise ValueError("temp_kernels must have exactly n_layers entries.")
+        if len(self.point_sizes) != n_layers:
+            raise ValueError("point_sizes must have exactly n_layers entries.")
+
+        self.diagnosis_size = diagnosis_size
+        self.last_linear_size = last_linear_size
+        self.main_dropout_rate = main_dropout_rate
+        self.temp_dropout_rate = temp_dropout_rate
+        self.time_before_pred = time_before_pred
+        self.use_msle = use_msle
+        self.sum_losses = sum_losses
+        self.apply_exp = apply_exp
+
+        self.relu = nn.ReLU()
+        # Clamp predictions to a clinically plausible range (30 min – 100 hours)
+        self.hardtanh = nn.Hardtanh(min_val=1.0 / 48.0, max_val=100.0)
+        self.main_dropout = nn.Dropout(p=self.main_dropout_rate)
+        self.temp_dropout = nn.Dropout(p=self.temp_dropout_rate)
+
+        self.loss_fn: nn.Module = MSLELoss() if use_msle else MaskedMSELoss()
+
+        # ------------------------------------------------------------------ #
+        # Infer channel counts from the first dataset sample
+        # ------------------------------------------------------------------ #
+        sample = dataset[0]
+        if self.timeseries_key not in sample:
+            raise KeyError(
+                f"timeseries_key '{self.timeseries_key}' not found in dataset sample. "
+                f"Available keys: {list(sample.keys())}"
+            )
+        ts_sample: torch.Tensor = sample[self.timeseries_key]
+        if ts_sample.dim() != 2:
+            raise ValueError(
+                f"Each timeseries sample must be 2-D (channels, time) or (time, channels), "
+                f"got shape {tuple(ts_sample.shape)}."
+            )
+
+        # The RemainingLOSMIMIC4 task outputs (2F+2, T) — channels first.
+        # We treat the smaller dimension as the channel axis when it equals 2F+2.
+        # This is safe because the number of channels (70 for the default feature
+        # set) is always much smaller than typical ICU stay lengths.
+        num_channels = min(ts_sample.shape)
+        if (num_channels - 2) % 2 != 0 or num_channels < 4:
+            raise ValueError(
+                "timeseries channel dimension must equal 2F+2 with F >= 1. "
+                f"Detected smallest dim = {num_channels}."
+            )
+        self.F: int = (num_channels - 2) // 2
+
+        # ------------------------------------------------------------------ #
+        # Static feature size
+        # ------------------------------------------------------------------ #
+        self.no_flat_features: int = 0
+        if self.static_key is not None:
+            static_sample: torch.Tensor = sample[self.static_key]
+            self.no_flat_features = (
+                1 if static_sample.dim() == 0 else int(static_sample.shape[-1])
+            )
+
+        # ------------------------------------------------------------------ #
+        # Diagnosis encoder (optional)
+        # ------------------------------------------------------------------ #
+        # D=0 means conditions are not used; we skip the encoder entirely and
+        # substitute a zero vector at forward time to avoid Linear(0, ...).
+        self.D: int = 0
+        self.diagnosis_encoder: Optional[nn.Linear] = None
+        self.bn_diagnosis_encoder: Optional[nn.BatchNorm1d] = None
+
+        if self.conditions_key is not None:
+            self.D = self.dataset.input_processors[self.conditions_key].size()
+            self.diagnosis_encoder = nn.Linear(self.D, self.diagnosis_size)
+            self.bn_diagnosis_encoder = nn.BatchNorm1d(self.diagnosis_size)
+
+        # ------------------------------------------------------------------ #
+        # Build TPC layers and final regression head
+        # ------------------------------------------------------------------ #
+        self.bn_point_last_los = nn.BatchNorm1d(self.last_linear_size)
+        self._init_tpc()
+        self.point_final_los = nn.Linear(self.last_linear_size, 1)
+
+    # ---------------------------------------------------------------------- #
+    # Architecture construction helpers
+    # ---------------------------------------------------------------------- #
+
+    def _init_tpc(self) -> None:
+        """Build per-layer metadata and instantiate all conv/linear modules."""
+        self._layer_info: List[Dict[str, Any]] = []
+        for i in range(self.n_layers):
+            dilation = i * (self.kernel_size - 1) if i > 0 else 1
+            padding = [(self.kernel_size - 1) * dilation, 0]
+            self._layer_info.append(
+                {
+                    "temp_kernels": self.temp_kernels[i],
+                    "point_size": self.point_sizes[i],
+                    "dilation": dilation,
+                    "padding": padding,
+                    "stride": 1,
+                }
+            )
+
+        self._create_temp_pointwise_layers()
+
+        # Final projection input: concatenation of [static, diagnosis_enc, last next_X slice]
+        input_size = (
+            (self.F + self._Zt) * (1 + self._Y)
+            + self.diagnosis_size
+            + self.no_flat_features
+        )
+        self.point_last_los = nn.Linear(input_size, self.last_linear_size)
+
+    def _create_temp_pointwise_layers(self) -> None:
+        """Instantiate grouped temporal conv and pointwise linear for each layer.
+
+        After this call the following attributes are set:
+            _Y  : number of temporal kernels in the last layer (needed by _init_tpc)
+            _Zt : total accumulated pointwise output channels (needed by _init_tpc)
+        """
+        self.layer_modules = nn.ModuleDict()
+        Y = 0   # temporal kernels from the previous layer
+        Z = 0   # pointwise output size of the previous layer
+        Zt = 0  # cumulative sum of all pointwise output sizes so far
+
+        for i in range(self.n_layers):
+            # Input channels to the temporal conv:
+            #   layer 0 : 2*F  (values + decay, interleaved by feature)
+            #   layer i : (F + Zt) * (1 + Y)  (feature groups × kernel fan-out)
+            temp_in = (self.F + Zt) * (1 + Y) if i > 0 else 2 * self.F
+
+            # Output channels: one group of temp_kernels[i] filters per feature group
+            temp_out = (self.F + Zt) * self.temp_kernels[i]
+
+            # Input to pointwise linear:
+            #   prev_temp  : (F + Zt - Z) * Y  (all-but-last-layer skip streams)
+            #   prev_point :  Z                 (previous pointwise output)
+            #   X_orig     : 2*F + 2            (raw channels from input)
+            #   flat       : no_flat_features
+            point_in = (
+                (self.F + Zt - Z) * Y
+                + Z
+                + 2 * self.F
+                + 2
+                + self.no_flat_features
+            )
+            point_out = self.point_sizes[i]
+
+            self.layer_modules[str(i)] = nn.ModuleDict(
+                {
+                    "temp": nn.Conv1d(
+                        in_channels=temp_in,
+                        out_channels=temp_out,
+                        kernel_size=self.kernel_size,
+                        stride=self._layer_info[i]["stride"],
+                        dilation=self._layer_info[i]["dilation"],
+                        groups=self.F + Zt,
+                    ),
+                    "bn_temp": nn.BatchNorm1d(temp_out),
+                    "point": nn.Linear(point_in, point_out),
+                    "bn_point": nn.BatchNorm1d(point_out),
+                }
+            )
+
+            Y = self.temp_kernels[i]
+            Z = point_out
+            Zt += Z
+
+        self._Y = Y
+        self._Zt = Zt
+
+    # ---------------------------------------------------------------------- #
+    # Input preparation helpers
+    # ---------------------------------------------------------------------- #
+
+    def _normalize_timeseries(self, x: torch.Tensor) -> torch.Tensor:
+        """Return timeseries in channels-first layout ``(B, 2F+2, T)``.
+
+        The task always produces ``(2F+2, T)`` per sample, so after batching
+        the input is ``(B, 2F+2, T)``. We identify the channel axis as the
+        dimension whose size equals ``2F+2``; if neither does we raise.
+
+        Args:
+            x: Raw batched timeseries tensor.
+
+        Returns:
+            Tensor of shape ``(B, 2F+2, T)``.
+
+        Raises:
+            ValueError: If the tensor is not 3-D or the channel dimension cannot
+                be identified.
+        """
+        x = x.to(self.device, dtype=torch.float32)
+        if x.dim() != 3:
+            raise ValueError(
+                f"Expected a 3-D batched timeseries (B, C, T) or (B, T, C), "
+                f"got shape {tuple(x.shape)}."
+            )
+        expected_c = 2 * self.F + 2
+        if x.shape[1] == expected_c:
+            return x  # already (B, C, T)
+        if x.shape[2] == expected_c:
+            return x.transpose(1, 2)  # (B, T, C) -> (B, C, T)
+        raise ValueError(
+            f"Cannot identify channel dimension of size {expected_c} in "
+            f"timeseries shape {tuple(x.shape)}."
+        )
+
+    def _prepare_static(self, batch_size: int, kwargs: Dict[str, Any]) -> torch.Tensor:
+        """Return static features as ``(B, no_flat_features)`` or empty tensor.
+
+        Args:
+            batch_size: Number of samples in the batch.
+            kwargs: Full forward-pass keyword arguments.
+
+        Returns:
+            Float tensor of shape ``(B, no_flat_features)``.
+        """
+        if self.static_key is None or self.no_flat_features == 0:
+            return torch.zeros(batch_size, 0, device=self.device)
+        flat = kwargs[self.static_key].to(self.device, dtype=torch.float32)
+        if flat.dim() == 1:
+            # Single feature scalar per sample — reshape to (B, 1)
+            flat = flat.unsqueeze(-1)
+        return flat
+
+    def _prepare_diagnoses(self, batch_size: int, kwargs: Dict[str, Any]) -> torch.Tensor:
+        """Encode diagnosis codes into a multi-hot vector, then project.
+
+        PAD index (0) contributions are zeroed out after scatter.
+        If no conditions key is configured, returns a zero vector of size
+        ``diagnosis_size``.
+
+        Args:
+            batch_size: Number of samples in the batch.
+            kwargs: Full forward-pass keyword arguments.
+
+        Returns:
+            Float tensor of shape ``(B, diagnosis_size)``.
+        """
+        if self.conditions_key is None or self.D == 0 or self.diagnosis_encoder is None:
+            return torch.zeros(batch_size, self.diagnosis_size, device=self.device)
+
+        codes = kwargs[self.conditions_key].to(self.device)  # (B, L)
+        if codes.dim() == 1:
+            codes = codes.unsqueeze(0)  # handle degenerate single-sample edge case
+
+        multi_hot = torch.zeros(batch_size, self.D, device=self.device)
+        valid = codes >= 0  # mask out any -1 padding (PAD=0 handled below)
+        safe_codes = codes.masked_fill(~valid, 0)
+        multi_hot.scatter_add_(1, safe_codes, valid.float())
+        multi_hot[:, 0] = 0.0  # zero the PAD index contribution
+
+        diag_enc = self.relu(
+            self.main_dropout(
+                self.bn_diagnosis_encoder(self.diagnosis_encoder(multi_hot > 0).float())
+            )
+        )
+        return diag_enc
+
+    # ---------------------------------------------------------------------- #
+    # Core TPC block
+    # ---------------------------------------------------------------------- #
+
+    def _temp_pointwise(
+        self,
+        B: int,
+        T: int,
+        X: torch.Tensor,
+        X_orig: torch.Tensor,
+        repeat_flat: torch.Tensor,
+        temp: nn.Conv1d,
+        bn_temp: nn.BatchNorm1d,
+        point: nn.Linear,
+        bn_point: nn.BatchNorm1d,
+        temp_kernels: int,
+        padding: List[int],
+        prev_temp: Optional[torch.Tensor],
+        prev_point: Optional[torch.Tensor],
+        point_skip: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """One TPC layer: grouped temporal conv + pointwise linear + skip connections.
+
+        The function mirrors Algorithm 1 in the paper. All tensors live on the
+        model's device.
+
+        Args:
+            B: Batch size.
+            T: Sequence length.
+            X: Current feature tensor ``(B, C_in, T)`` fed to the temporal conv.
+            X_orig: Flattened raw input ``(B*T, 2F+2)`` for pointwise concat.
+            repeat_flat: Static features tiled over time ``(B*T, S)``.
+            temp: Grouped ``Conv1d`` for this layer.
+            bn_temp: ``BatchNorm1d`` applied after the temporal conv.
+            point: ``Linear`` for the pointwise path.
+            bn_point: ``BatchNorm1d`` applied after the pointwise linear.
+            temp_kernels: Number of temporal kernels for this layer.
+            padding: ``[left_pad, right_pad]`` for causal convolution.
+            prev_temp: Temporal output from the previous layer ``(B*T, C_t)``
+                or ``None`` for layer 0.
+            prev_point: Pointwise output from the previous layer ``(B*T, Z)``
+                or ``None`` for layer 0.
+            point_skip: Accumulated skip-connection tensor ``(B, C_skip, T)``.
+
+        Returns:
+            A 4-tuple:
+                - ``temp_out``: Temporal features for the next layer ``(B*T, C_skip * temp_kernels)``.
+                - ``point_out``: Pointwise features for the next layer ``(B*T, point_size)``.
+                - ``next_X``: Combined feature tensor for the next layer ``(B, C_next, T)``.
+                - ``point_skip``: Updated skip-connection tensor ``(B, C_skip + point_size, T)``.
+        """
+        # ---- Temporal path ------------------------------------------------
+        X_padded = F.pad(X, padding, "constant", 0)
+        X_temp = self.temp_dropout(bn_temp(temp(X_padded)))  # (B, C_out, T)
+        # C_out = (F + Zt) * temp_kernels for this layer; derive feature-group
+        # count directly from the conv output rather than from point_skip so
+        # the reshape is always consistent regardless of update order.
+        C_feat_groups = X_temp.shape[1] // temp_kernels  # = F + Zt for this layer
+
+        # ---- Pointwise path -----------------------------------------------
+        # Flatten to (B*T, ...) so Linear can process every timestep jointly.
+        concat_parts: List[torch.Tensor] = []
+        if prev_temp is not None:
+            concat_parts.append(prev_temp)
+        if prev_point is not None:
+            concat_parts.append(prev_point)
+        concat_parts.append(X_orig)
+        if repeat_flat.shape[1] > 0:
+            concat_parts.append(repeat_flat)
+        X_concat = torch.cat(concat_parts, dim=1)  # (B*T, point_in)
+        point_out = self.main_dropout(bn_point(point(X_concat)))  # (B*T, point_size)
+
+        # ---- Update skip accumulator --------------------------------------
+        # Must happen BEFORE the reshape below so point_skip matches C_feat_groups.
+        if prev_point is not None:
+            Z_prev = prev_point.shape[1]
+            point_skip = torch.cat(
+                [point_skip, prev_point.view(B, T, Z_prev).permute(0, 2, 1)],
+                dim=1,
+            )  # (B, C_feat_groups, T)
+
+        # ---- Combine temporal and pointwise for next layer ----------------
+        # Reshape temporal output to (B, C_feat_groups, temp_kernels, T) and
+        # stack with the skip channel (B, C_feat_groups, 1, T) along kernel axis.
+        temp_4d = X_temp.view(B, C_feat_groups, temp_kernels, T)  # (B, G, K, T)
+        skip_4d = point_skip.unsqueeze(2)                          # (B, G, 1, T)
+        temp_stack = torch.cat([skip_4d, temp_4d], dim=2)  # (B, G, 1+K, T)
+
+        # Broadcast pointwise output over the kernel axis
+        point_size = point_out.shape[1]
+        point_4d = (
+            point_out.view(B, T, point_size)
+            .permute(0, 2, 1)          # (B, point_size, T)
+            .unsqueeze(2)              # (B, point_size, 1, T)
+            .expand(-1, -1, 1 + temp_kernels, -1)  # (B, point_size, 1+K, T)
+        )
+
+        combined = self.relu(
+            torch.cat([temp_stack, point_4d], dim=1)
+        )  # (B, G + point_size, 1+K, T)
+
+        next_X = combined.view(B, (C_feat_groups + point_size) * (1 + temp_kernels), T)
+
+        # Temporal output flattened for next-layer pointwise concat
+        temp_out = (
+            X_temp.permute(0, 2, 1)                   # (B, T, G*K)
+            .contiguous()
+            .view(B * T, C_feat_groups * temp_kernels) # (B*T, G*K)
+        )
+
+        return temp_out, point_out, next_X, point_skip
+
+    # ---------------------------------------------------------------------- #
+    # Forward pass
+    # ---------------------------------------------------------------------- #
+
+    def forward(
+        self,
+        return_full_sequence: bool = False,
+        **kwargs: Any,
+    ) -> Dict[str, torch.Tensor]:
+        """Forward pass of the TPC model.
+
+        Predictions are produced for timesteps ``[time_before_pred, T)``.
+
+        Args:
+            return_full_sequence: If ``True``, return the full ``(B, post_hist)``
+                prediction tensor in ``y_prob`` / ``logit`` instead of the
+                flat masked subset. Useful for sequence-level evaluation.
+            **kwargs: Batch dictionary containing all feature and label tensors.
+
+        Returns:
+            Dictionary with the following keys:
+
+            - ``loss``: Scalar training loss (present only when label is in kwargs).
+            - ``y_prob``: Predicted LoS values.
+            - ``y_true``: Ground-truth LoS values (present only when label is in kwargs).
+            - ``logit``: Same as ``y_prob`` (regression task).
+
+        Raises:
+            ValueError: If the sequence length ``T <= time_before_pred``.
+        """
+        # ---- Prepare inputs -----------------------------------------------
+        X = self._normalize_timeseries(kwargs[self.timeseries_key])  # (B, 2F+2, T)
+        B, _, T = X.shape
+
+        if T <= self.time_before_pred:
+            raise ValueError(
+                f"Sequence length T={T} must be greater than "
+                f"time_before_pred={self.time_before_pred}."
+            )
+
+        flat = self._prepare_static(B, kwargs)            # (B, S)
+        diagnoses_enc = self._prepare_diagnoses(B, kwargs)  # (B, diagnosis_size)
+
+        # Raw per-timestep features, flattened for the pointwise path
+        X_orig = X.permute(0, 2, 1).contiguous().view(B * T, 2 * self.F + 2)
+        repeat_flat = flat.repeat_interleave(T, dim=0)   # (B*T, S)
+
+        # Split elapsed and hour_of_day channels; keep only values + decay
+        # X[:, 0, :] = elapsed,  X[:, 1:F+1, :] = values,
+        # X[:, F+1:2F+1, :] = decay,  X[:, 2F+1, :] = hour_of_day
+        values = X[:, 1 : self.F + 1, :]      # (B, F, T)
+        decay = X[:, self.F + 1 : 2 * self.F + 1, :]  # (B, F, T)
+
+        # Layer 0 temporal conv input: interleave values and decay per feature
+        # to give each feature group its 2 input channels [value, decay]
+        next_X = torch.stack([values, decay], dim=2).reshape(B, 2 * self.F, T)
+
+        # Skip accumulator starts with the values channel (one channel per feature)
+        point_skip = values  # (B, F, T)
+
+        prev_temp: Optional[torch.Tensor] = None
+        prev_point: Optional[torch.Tensor] = None
+
+        # ---- TPC layers ---------------------------------------------------
+        for i in range(self.n_layers):
+            mods = self.layer_modules[str(i)]
+            prev_temp, prev_point, next_X, point_skip = self._temp_pointwise(
+                B=B,
+                T=T,
+                X=next_X,
+                X_orig=X_orig,
+                repeat_flat=repeat_flat,
+                temp=mods["temp"],
+                bn_temp=mods["bn_temp"],
+                point=mods["point"],
+                bn_point=mods["bn_point"],
+                temp_kernels=self._layer_info[i]["temp_kernels"],
+                padding=self._layer_info[i]["padding"],
+                prev_temp=prev_temp,
+                prev_point=prev_point,
+                point_skip=point_skip,
+            )
+
+        # ---- Final regression head ----------------------------------------
+        post_hist = T - self.time_before_pred  # number of prediction timesteps
+
+        # Slice next_X to post-history timesteps and flatten for the linear
+        ts_features = (
+            next_X[:, :, self.time_before_pred :]   # (B, C, post_hist)
+            .permute(0, 2, 1)                        # (B, post_hist, C)
+            .contiguous()
+            .view(B * post_hist, -1)                 # (B*post_hist, C)
+        )
+
+        combined_features = torch.cat(
+            [
+                flat.repeat_interleave(post_hist, dim=0),             # (B*post_hist, S)
+                diagnoses_enc.repeat_interleave(post_hist, dim=0),    # (B*post_hist, diag_size)
+                ts_features,                                           # (B*post_hist, C)
+            ],
+            dim=1,
+        )
+
+        last_hidden = self.relu(
+            self.main_dropout(
+                self.bn_point_last_los(self.point_last_los(combined_features))
+            )
+        )  # (B*post_hist, last_linear_size)
+
+        raw_pred = self.point_final_los(last_hidden).view(B, post_hist)  # (B, post_hist)
+        if self.apply_exp:
+            raw_pred = torch.exp(raw_pred)
+        los_pred = self.hardtanh(raw_pred)  # clamp to [1/48, 100] hours
+
+        # ---- Build output dict --------------------------------------------
+        output: Dict[str, torch.Tensor] = {
+            "logit": los_pred if return_full_sequence else los_pred.reshape(-1),
+            "y_prob": los_pred if return_full_sequence else los_pred.reshape(-1),
+        }
+
+        if self.label_key in kwargs:
+            y_true = kwargs[self.label_key].to(self.device, dtype=torch.float32)
+            if y_true.dim() == 3 and y_true.shape[-1] == 1:
+                y_true = y_true.squeeze(-1)
+            y_true_post = y_true[:, self.time_before_pred :]  # (B, post_hist)
+
+            mask = y_true_post > 0
+            seq_lengths = mask.sum(dim=1)
+            loss = self.loss_fn(los_pred, y_true_post, mask, seq_lengths, self.sum_losses)
+
+            output["loss"] = loss
+            if return_full_sequence:
+                output["y_true"] = y_true_post
+                output["mask"] = mask
+            else:
+                flat_mask = mask.reshape(-1)
+                output["y_true"] = los_pred.reshape(-1)[flat_mask]
+                output["y_prob"] = los_pred.reshape(-1)[flat_mask]
+                output["logit"] = los_pred.reshape(-1)[flat_mask]
+                output["y_true"] = y_true_post.reshape(-1)[flat_mask]
+
+        if kwargs.get("embed", False):
+            # Return the mean-pooled combined feature as a patient embedding
+            output["embed"] = combined_features.view(B, post_hist, -1).mean(dim=1)
+
+        return output
+
+    # ---------------------------------------------------------------------- #
+    # Monte Carlo Dropout uncertainty estimation (ablation)
+    # ---------------------------------------------------------------------- #
+
+    def predict_with_uncertainty(
+        self,
+        mc_samples: int = 30,
+        **kwargs: Any,
+    ) -> Dict[str, torch.Tensor]:
+        """Estimate predictive uncertainty via Monte Carlo Dropout.
+
+        Dropout layers are kept active during inference to obtain a distribution
+        over predictions. This implements the Bayesian approximation proposed by
+        Gal & Ghahramani (2016) and constitutes the ablation extension in the
+        project proposal.
+
+        Args:
+            mc_samples: Number of stochastic forward passes. Default: ``30``.
+            **kwargs: Same keyword arguments as ``forward()``.
+
+        Returns:
+            Dictionary with keys:
+
+            - ``mean``: Mean prediction ``(B, post_hist)``.
+            - ``std``: Standard deviation ``(B, post_hist)``.
+            - ``samples``: All sampled predictions ``(mc_samples, B, post_hist)``.
+            - ``mask``: Valid-timestep mask (when label is present).
+            - ``y_true``: Ground-truth labels (when label is present).
+
+        Raises:
+            ValueError: If ``mc_samples < 1``.
+        """
+        if mc_samples < 1:
+            raise ValueError("mc_samples must be >= 1.")
+
+        was_training = self.training
+        self.train()  # keep dropout active
+
+        samples: List[torch.Tensor] = []
+        mask: Optional[torch.Tensor] = None
+        y_true: Optional[torch.Tensor] = None
+
+        with torch.no_grad():
+            for _ in range(mc_samples):
+                out = self.forward(return_full_sequence=True, **kwargs)
+                samples.append(out["y_prob"])
+                if mask is None:
+                    mask = out.get("mask")
+                if y_true is None:
+                    y_true = out.get("y_true")
+
+        if not was_training:
+            self.eval()
+
+        stacked = torch.stack(samples, dim=0)  # (mc_samples, B, post_hist)
+        result: Dict[str, torch.Tensor] = {
+            "mean": stacked.mean(dim=0),
+            "std": stacked.std(dim=0),
+            "samples": stacked,
+        }
+        if mask is not None:
+            result["mask"] = mask
+        if y_true is not None:
+            result["y_true"] = y_true
+        return result
\ No newline at end of file
diff --git a/pyhealth/datasets/configs/mimic4_ehr.yaml b/pyhealth/datasets/configs/mimic4_ehr.yaml
index 84c570bb9..4f6def983 100644
--- a/pyhealth/datasets/configs/mimic4_ehr.yaml
+++ b/pyhealth/datasets/configs/mimic4_ehr.yaml
@@ -117,3 +117,25 @@ tables:
       - "hcpcs_cd"
       - "seq_num"
       - "short_description"
+
+  chartevents:
+    file_path: "icu/chartevents.csv.gz"
+    patient_id: "subject_id"
+    join:
+      - file_path: "icu/d_items.csv.gz"
+        "on": "itemid"
+        how: "inner"
+        columns:
+          - "label"
+          - "category"
+    timestamp: "charttime"
+    attributes:
+      - "hadm_id"
+      - "stay_id"
+      - "itemid"
+      - "label"
+      - "category"
+      - "value"
+      - "valuenum"
+      - "valueuom"
+      - "storetime"
diff --git a/pyhealth/tasks/__init__.py b/pyhealth/tasks/__init__.py
index 797988377..796fbdfab 100644
--- a/pyhealth/tasks/__init__.py
+++ b/pyhealth/tasks/__init__.py
@@ -29,6 +29,7 @@
     LengthOfStayPredictionOMOP,
 )
 from .length_of_stay_stagenet_mimic4 import LengthOfStayStageNetMIMIC4
+from .length_of_stay_tpc_mimic4 import RemainingLOSMIMIC4
 from .medical_coding import MIMIC3ICD9Coding
 from .medical_transcriptions_classification import MedicalTranscriptionsClassification
 from .mortality_prediction import (
diff --git a/pyhealth/tasks/length_of_stay_tpc_mimic4.py b/pyhealth/tasks/length_of_stay_tpc_mimic4.py
new file mode 100644
index 000000000..969a2d329
--- /dev/null
+++ b/pyhealth/tasks/length_of_stay_tpc_mimic4.py
@@ -0,0 +1,195 @@
+from __future__ import annotations
+
+from pyhealth.tasks import BaseTask
+from pyhealth.data import Event, Patient
+from typing import List, Dict, Any, Type, Union, cast
+
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Sequence, Tuple
+
+import math
+import numpy as np
+
+from pyhealth.data.data import Patient
+from pyhealth.tasks.base_task import BaseTask
+
+from pyhealth.processors import TemporalTimeseriesProcessor, TensorProcessor, SequenceProcessor
+from pyhealth.processors.base_processor import Processor
+import polars as pl
+
+@dataclass
+class RemainingLOSConfig:
+    prediction_step_size: int = 1
+    min_history_hours: int = 5
+    min_remaining_hours: int = 1
+    max_history_hours: int = 366
+
+# Formally, our task is to predict the remaining LoS at regular
+# timepoints 𝑦1, . . . , 𝑦𝑇 ∈ R>0 in the patient’s ICU stay, up to the
+# discharge time 𝑇 , using the diagnoses (d ∈ R𝐷×1), static features
+# (s ∈ R𝑆×1), and time series (x1, . . . , x𝑇 ∈ R𝐹 ×2). Initially, for every
+# timepoint 𝑡, there are two ‘channels’ per time series feature: 𝐹 fea-
+# ture values (x′𝑡 ∈ R𝐹 ×1), and their corresponding decay indicators
+# (x′′𝑡 ∈ R𝐹 ×1). The decay indicators tell the model how recently
+# the observation x′𝑡 was recorded.
+
+class RemainingLOSMIMIC4(BaseTask):
+    """
+    Custom remaining length-of-stay regression task for MIMIC-IV.
+
+    Each sample corresponds to one prediction cutoff time within a stay.
+    Input:
+        - ts: (timestamps, values) where values is shape (T, F)
+        - optionally static / code features
+    Target:
+        - remaining_los_hours: float
+    """
+
+    task_name: str = "RemainingLOSMIMIC4"
+
+    # Keep this conceptual unless you already know the exact schema names
+    # your installed PyHealth version expects.
+    input_schema: Dict[str, Any]  = {
+        "timeseries": TensorProcessor(),
+        "static": TensorProcessor(),
+        "conditions": SequenceProcessor(),
+    }
+
+    output_schema: Dict[str, Any] = {"los": "tensor"}
+
+    def __init__(self, config: Optional[RemainingLOSConfig] = None):
+        self.config = config or RemainingLOSConfig()
+
+        # TODO: These need to be fixed
+        # 17 vitals from chartevents
+        self.chart_itemids = [
+            "220045", "220210", "220277", "220179", "220180", "220181", 
+            "220050", "220051", "220052", "223761", "220739", "223900", 
+            "223901", "226253", "220235", "224690", "220339"
+        ]
+        # 17 lab items from labevents
+        self.lab_itemids = [
+            "51006", "50912", "50931", "50902", "50882", "50868", 
+            "50960", "50970", "51265", "51301", "50811", "51222", 
+            "50813", "50820", "50818", "50821", "50825"
+        ]
+        self.all_itemids = self.chart_itemids + self.lab_itemids
+        self.F = len(self.all_itemids)
+
+
+    def __call__(self, patient: Patient) -> List[Dict]:
+        samples: List[Dict] = []
+
+        admissions_result = patient.get_events(event_type="icustays")
+        # Handle both DataFrame and List[Event] return types
+        if isinstance(admissions_result, list):
+            admissions = admissions_result
+        else:
+            return []
+        
+        if len(admissions) == 0:
+            return []
+        
+        patient_static_attributes = patient.get_events("patients")[0]
+        
+        static = np.array([patient_static_attributes['anchor_age'], 1. if patient_static_attributes['gender'] == 'F' else 0.], dtype=np.float32)
+
+        for admission in admissions:
+
+            admit_time = admission.timestamp
+            # outtime is usually a string in attributes
+            outtime_raw = admission.outtime
+            discharge_time = datetime.strptime(outtime_raw, "%Y-%m-%d %H:%M:%S")
+
+            los_hours = (discharge_time - admit_time).total_seconds() / 3600.0
+            T = min(int(math.ceil(los_hours)), self.config.max_history_hours)
+
+            if admit_time is None or discharge_time is None:
+                continue
+            if discharge_time <= admit_time:
+                continue
+            if los_hours < self.config.min_history_hours + self.config.min_remaining_hours:
+                continue
+
+            labevents = patient.get_events(
+                event_type="labevents",
+                start=admission.timestamp,
+                end=discharge_time,
+            )
+            chartevents = patient.get_events(
+                event_type="chartevents",
+                start=admission.timestamp,
+                end=discharge_time,
+            )
+            diagnoses_events = patient.get_events(
+                event_type="diagnoses_icd",
+                filters=[("hadm_id", "==", admission.hadm_id)],
+            )
+            conditions = [
+                f"{getattr(event, 'icd_version', '10')}_{event.icd_code}" 
+                for event in diagnoses_events if hasattr(event, "icd_code")
+            ]
+
+            all_events = labevents + chartevents
+
+            if len(all_events) == 0:
+                continue
+
+            values_mat = np.zeros((self.F, T), dtype=np.float32)
+            masks_mat = np.zeros((self.F, T), dtype=np.float32)
+            
+            id_to_idx = {id: i for i, id in enumerate(self.all_itemids)}
+            # Pivot events into matrix
+            for row in all_events:
+                if row["itemid"] not in id_to_idx:
+                    continue
+                f_idx = id_to_idx.get(row["itemid"])
+                h_idx = int((row["timestamp"].timestamp() - admit_time.timestamp() ) // 3600)
+                if f_idx is not None and 0 <= h_idx < T:
+                    values_mat[f_idx, h_idx] = row["valuenum"]
+                    masks_mat[f_idx, h_idx] = 1.0
+
+            # Forward-fill and calculate decay indicators
+            # Decay = 0.75 ** (hours since last measurement)
+            decay_mat = np.zeros((self.F, T), dtype=np.float32)
+            for f in range(self.F):
+                last_val = 0.0
+                hours_since = math.inf # Initial large value for decay
+                for t in range(T):
+                    if masks_mat[f, t] > 0:
+                        last_val = values_mat[f, t]
+                        hours_since = 0.0
+                    else:
+                        values_mat[f, t] = last_val
+                        hours_since += 1.0
+                    decay_mat[f, t] = 0.75 ** hours_since
+
+            # Elapsed time channel
+            elapsed = np.arange(T, dtype=np.float32).reshape(1, T)
+
+            # hour_of_day channel
+            hour_of_day = np.array([
+                (admit_time + timedelta(hours=t)).hour
+                for t in range(T)
+            ], dtype=np.float32).reshape(1, T)
+
+            # Concatenate all channels: [elapsed (1), values (F), decays (F), hour_of_day (1)] -> (2F+2, T)
+            timeseries = np.concatenate([elapsed, values_mat, decay_mat, hour_of_day], axis=0)
+            
+            # Label sequence: remaining LoS in hours at each hour
+            labels = np.array([
+                max(0.0, (discharge_time - (admit_time + timedelta(hours=t))).total_seconds() / (3600.0))
+                for t in range(T)
+            ], dtype=np.float32)
+
+            samples.append({
+                "patient_id": patient.patient_id,
+                "visit_id": admission.stay_id ,
+                "timeseries": timeseries,
+                "static": static,
+                "conditions": conditions,
+                "los": labels,
+            })
+
+        return samples
diff --git a/test-resources/core/mimic4demo/icu/chartevents.csv b/test-resources/core/mimic4demo/icu/chartevents.csv
new file mode 100644
index 000000000..cbca898ca
--- /dev/null
+++ b/test-resources/core/mimic4demo/icu/chartevents.csv
@@ -0,0 +1,20 @@
+subject_id,hadm_id,stay_id,caregiver_id,charttime,storetime,itemid,value,valuenum,valueuom,warning
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:45:00,225054,On ,,,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:43:00,223769,100,100,%,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:47:00,223956,Atrial demand,,,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:47:00,224866,Yes,,,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:45:00,227341,No,0,,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:47:00,224751,52,52,bpm,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:44:00,227969,"Quiet, calm space",,,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:46:00,223935,Doppler,,,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:48:00,223782,Intermittent,,,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:47:00,224773,Cool,,,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:48:00,223784,Cough/Deep Breath,,,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:41:00,220047,55,55,bpm,0
+10005817,20626031,32604416,6770,2132-12-16 00:00:00,2132-12-15 23:42:00,220073,0,0,mmHg,0
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